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John W. Galbraith

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. John W. Galbraith & Greg Tkacz, 2011. "Analyzing Economic Effects of Extreme Events using Debit and Payments System Data," CIRANO Working Papers 2011s-70, CIRANO.

    Mentioned in:

    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Policy responses > Macroeconomic
  2. David Bounie & Youssouf Camara & John Galbraith, 2020. "Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data," Cahiers de recherche 14-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.

    Mentioned in:

    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Economic consequences > Consumption > E-commerce
  3. Landais, Camille & Bounie, David & Camara, Youssouf & Fize, Etienne & Galbraith, John W. & Lavest, Chloe & Pazem, Tatiana & Savatier, Baptiste, 2020. "Consumption Dynamics in the COVID Crisis: Real Time Insights from French Transaction & Bank Data," CEPR Discussion Papers 15474, C.E.P.R. Discussion Papers.

    Mentioned in:

    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Economic consequences > Consumption

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Galbraith, John W, 1996. "Credit Rationing and Threshold Effects in the Relation between Money and Output," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(4), pages 419-429, July-Aug..

    Mentioned in:

    1. Credit rationing and threshold effects in the relation between money and output (Journal of Applied Econometrics 1996) in ReplicationWiki ()

Working papers

  1. David Bounie & Youssouf Camara & John Galbraith, 2020. "The COVID-19 containment seen through French consumer transaction data: Expenditures, mobility and online substitution," Post-Print hal-02640751, HAL.

    Cited by:

    1. Sakouvogui Kekoura & Guilavogui Mama Genevieve, 2022. "How are the United States Banks faring during the COVID-19 Pandemic? Evidence of Economic Efficiency Measures," Open Economics, De Gruyter, vol. 5(1), pages 11-29, January.

  2. David Bounie & Youssouf Camara & Etienne Fize & John Galbraith & Camille Landais & Chloé Lavest & Tatiana Pazem & Baptiste Savatier, 2020. "Dynamiques de consommation dans la crise : les enseignements en temps réel des données bancaires," Working Papers hal-02972885, HAL.

    Cited by:

    1. Zeynep Or & Coralie Gandré & Isabelle Durand-Zaleski & Monika Steffen, 2021. "France's Response to the Covid-19 Pandemic: between a Rock and a Hard Place," Working Papers DT83, IRDES institut for research and information in health economics, revised Feb 2021.
    2. Baddou Saïda & Jérôme Coffinet & Cécile Fraysse & Stéphane Jarrijon, 2021. "The French life insurance market during the health crisis [Le marché de l’assurance-vie en France pendant la crise sanitaire]," Bulletin de la Banque de France, Banque de France, issue 238.
    3. Julien Albertini & Xavier Fairise & Arthur Poirier & Anthony Terriau, 2022. "Short-time work policies during the COVID-19 pandemic," Working Papers 2204, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.

  3. David Bounie & Youssouf Camara & John W. Galbraith, 2020. "Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data," CIRANO Working Papers 2020s-28, CIRANO.

    Cited by:

    1. Agnieszka Huterska & Anna Iwona Piotrowska & Joanna Szalacha-Jarmużek, 2021. "Fear of the COVID-19 Pandemic and Social Distancing as Factors Determining the Change in Consumer Payment Behavior at Retail and Service Outlets," Energies, MDPI, vol. 14(14), pages 1-18, July.
    2. Perera K.J.T. & Fernando P.I.N. & Ratnayake R.M.C.S. & Udawaththa U.D.I.C., 2021. "Consumer Behavior within the Covid-19 Pandemic A Systematic Review," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(12), pages 806-812, December.
    3. Arceo-Gomez, Eva O. & Campos-Vazquez, Raymundo M. & Esquivel, Gerardo & Alcaraz, Eduardo & Martinez, Luis A. & Lopez, Norma G., 2023. "The impact of COVID-19 infection on labor outcomes of Mexican formal workers," World Development Perspectives, Elsevier, vol. 29(C).
    4. Martin O'Connell & Áureo de Paula & Kate Smith, 2021. "Preparing for a pandemic: spending dynamics and panic buying during the COVID‐19 first wave," Fiscal Studies, John Wiley & Sons, vol. 42(2), pages 249-264, June.
    5. Guerino Ardizzi & Andrea Nobili & Giorgia Rocco, 2020. "A game changer in payment habits: evidence from daily data during a pandemic," Questioni di Economia e Finanza (Occasional Papers) 591, Bank of Italy, Economic Research and International Relations Area.
    6. Surico, Paolo & Bracke, Philippe & Croxson, Karen & Fakhri, Daoud & Valletti, Tommaso, 2020. "Mortgage Market Disruptions," CEPR Discussion Papers 15381, C.E.P.R. Discussion Papers.
    7. Timiryanova, Venera, 2022. "Высокочастотные Данные, Характеризующие Розничную Торговлю: Интересы Государства, Предприятий И Научных Организаций [High-frequency retail data: the interests of the state, enterprises and scientif," MPRA Paper 115681, University Library of Munich, Germany.
    8. Tsutomu Watanabe & Yuki Omori, 2021. "Online Consumption During and After the COVID-19 Pandemic: Evidence from Japan," Working Papers on Central Bank Communication 035, University of Tokyo, Graduate School of Economics.
    9. Scott R. Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2023. "Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments," Review of Finance, European Finance Association, vol. 27(6), pages 2271-2304.
    10. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    11. David Bounie & Youssouf Camara & Etienne Fize & John Galbraith & Camille Landais & Chloé Lavest & Tatiana Pazem & Baptiste Savatier, 2020. "Dynamiques de consommation dans la crise : les enseignements en temps réel des données bancaires," Working Papers hal-02972885, HAL.
    12. Stantcheva, Stefanie, 2022. "Inequalities in the Times of a Pandemic," CEPR Discussion Papers 16856, C.E.P.R. Discussion Papers.
    13. John Gathergood & Fabian Gunzinger & Benedict Guttman-Kenney & Edika Quispe-Torreblanca & Neil Stewart, 2020. "Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending," Papers 2012.09336, arXiv.org, revised Dec 2020.
    14. Tomohiro Okubo & Koji Takahashi & Haruhiko Inatsugu & Masato Takahashi, "undated". "Development of "Alternative Data Consumption Index":Nowcasting Private Consumption Using Alternative Data," Bank of Japan Working Paper Series 22-E-8, Bank of Japan.
    15. Tomoyuki Yagi & Kakuho Furukawa & Jouchi Nakajima, 2022. "Productivity Trends in Japan - Reviewing Recent Facts and the Prospects for the Post-COVID-19 Era -," Bank of Japan Working Paper Series 22-E-10, Bank of Japan.
    16. Raymundo M. Campos-Vazquez & Gerardo Esquivel, 2021. "Consumption and geographic mobility in pandemic times. Evidence from Mexico," Review of Economics of the Household, Springer, vol. 19(2), pages 353-371, June.
    17. Xianjin Tu & Victor Shi & Ming Zhang & Gangwu Lv, 2021. "The Impact of Residents’ Online Consumption on Offline Consumption—An Ordered Probit Semi-Parametric Estimation Method," Sustainability, MDPI, vol. 13(18), pages 1-16, September.
    18. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    19. Francesco Scotti & Andrea Flori & Giovanni Bonaccorsi & Fabio Pammolli, 2023. "Do We Learn From Errors? The Economic Impact of Differentiated Policy Restrictions in Italy," International Regional Science Review, , vol. 46(5-6), pages 613-648, September.
    20. Anete Brinke & Ludmila Fadejeva & Boriss Siliverstovs & Kārlis Vilerts, 2023. "Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 566-577, April.
    21. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
    22. Jonker, Nicole & van der Cruijsen, Carin & Bijlsma, Michiel & Bolt, Wilko, 2022. "Pandemic payment patterns," Journal of Banking & Finance, Elsevier, vol. 143(C).
    23. Brown, Martin & Fengler, Matthias & Huwyler, Jonas & Koeniger, Winfried & Lalive, Rafael & Rohrkemper, Robert, 2023. "Monitoring Consumption Switzerland: Data, Background, and Use Cases," Economics Working Paper Series 2301, University of St. Gallen, School of Economics and Political Science.
    24. Boto-García, David, 2023. "Investigating the two-way relationship between mobility flows and COVID-19 cases," Economic Modelling, Elsevier, vol. 118(C).
    25. Maximiliano Gómez Aguirre & Ariel David Krysa, 2023. "Consumer Loans Dynamics in 2020 in Argentina: An Approach Using Error Correction Models," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(81), pages 111-158, May.
    26. Jouchi Nakajima & Masato Takahashi & Tomoyuki Yagi, "undated". "An Assessment of Online Consumption Trends in Japan during the COVID-19 Pandemic," Bank of Japan Working Paper Series 22-E-11, Bank of Japan.
    27. Hacioglu Sinem & Diego R Känzig & Paolo Surico, 2020. "The Distributional Impact of the Pandemic," Working Papers halshs-03028702, HAL.
    28. Ludmila Fadejeva & Boriss Siliverstovs & Karlis Vilerts & Anete Brinke, 2022. "Consumer Spending in the Covid-19 Pandemic: Evidence from Card Transactions in Latvia," Discussion Papers 2022/01, Latvijas Banka.
    29. Sarbast Moslem & Tiziana Campisi & Agnieszka Szmelter-Jarosz & Szabolcs Duleba & Kh Md Nahiduzzaman & Giovanni Tesoriere, 2020. "Best–Worst Method for Modelling Mobility Choice after COVID-19: Evidence from Italy," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    30. David Bounie & Youssouf Camara & John W. Galbraith, 2021. "Consumer Mobility, Online and On-site Commerce and the Geographic Concentration of Economic Activity: Evidence from 20 Billion Transactions," CIRANO Working Papers 2021s-17, CIRANO.
    31. Pascal Seiler, 2020. "Weighting bias and inflation in the time of COVID-19: evidence from Swiss transaction data," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-11, December.
    32. Mohammad Hoseini & Abolmohsen Valizadeh, 2021. "The effect of COVID-19 lockdown and the subsequent reopening on consumption in Iran," Review of Economics of the Household, Springer, vol. 19(2), pages 373-397, June.
    33. Braut, Beatrice & Migheli, Matteo & Truant, Elisa, 2022. "Food consumption changes during 2020 lockdown in Italy," Research in Economics, Elsevier, vol. 76(2), pages 107-119.
    34. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    35. Kotkowski, Radoslaw & Polasik, Michal, 2021. "COVID-19 pandemic increases the divide between cash and cashless payment users in Europe," Economics Letters, Elsevier, vol. 209(C).
    36. Anna Iwańczuk-Kaliska & Mirosława Kaczmarek & Grzegorz Kotliński, 2023. "Non-cash retail payments in selected banks during the COVID-19 pandemic – the case of Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 54(3), pages 309-334.
    37. Borkowski, Przemysław & Jażdżewska-Gutta, Magdalena & Szmelter-Jarosz, Agnieszka, 2021. "Lockdowned: Everyday mobility changes in response to COVID-19," Journal of Transport Geography, Elsevier, vol. 90(C).
    38. Batiz-Lazo, Bernardo & Bautista-González, Manuel A & González-Correa, Ignacio, 2021. "La transformación en el uso de efectivo y pagos digitales durante la pandemia de Covid-19 [Thye transformation in the use of cash and digital payments during the Covid-19 pandemioc]," MPRA Paper 109943, University Library of Munich, Germany.
    39. Hakan Yilmazkuday, 2021. "Changes in Consumption in the Early COVID-19 Era: Zip-Code Level Evidence from the U.S," JRFM, MDPI, vol. 14(10), pages 1-10, October.
    40. Christelis, Dimitris & Georgarakos, Dimitris & Jappelli, Tullio & Kenny, Geoff, 2021. "How has the COVID-19 crisis affected different households’ consumption in the euro area?," Research Bulletin, European Central Bank, vol. 84.
    41. Hackethal, Andreas & Weber, Annika, 2020. "Fiscal policies and household consumption during the COVID-19 pandemic: A review of early evidence," SAFE White Paper Series 76, Leibniz Institute for Financial Research SAFE.
    42. Valeria Borsellino & Sina Ahmadi Kaliji & Emanuele Schimmenti, 2020. "COVID-19 Drives Consumer Behaviour and Agro-Food Markets towards Healthier and More Sustainable Patterns," Sustainability, MDPI, vol. 12(20), pages 1-26, October.
    43. Moti Zwilling, 2022. "The Impact of Nomophobia, Stress, and Loneliness on Smartphone Addiction among Young Adults during and after the COVID-19 Pandemic: An Israeli Case Analysis," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    44. Hansen, Stephen & Carvalho, Vasco & García, Juan Ramón & Ortiz, Alvaro & Rodrigo, Tomasa & Rodríguez Mora, José V & Ruiz, Pep, 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," CEPR Discussion Papers 14642, C.E.P.R. Discussion Papers.
    45. Gerhard Fenz & Helmut Stix, 2021. "Monitoring the economy in real time with the weekly OeNB GDP indicator: background, experience and outlook," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20-Q1/, pages 17-40.
    46. Tsutomu Watanabe & Yuki Omori, 2021. "Online Consumption During and After the COVID-19 Pandemic: Evidence from Japan," CARF F-Series CARF-F-524, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    47. Horvath, Akos & Kay, Benjamin & Wix, Carlo, 2023. "The COVID-19 shock and consumer credit: Evidence from credit card data," Journal of Banking & Finance, Elsevier, vol. 152(C).
    48. Barbara Gawior & Michal Polasik & Josep Lluís del Olmo, 2022. "Credit Card Use, Hedonic Motivations, and Impulse Buying Behavior in Fast Fashion Physical Stores during COVID-19: The Sustainability Paradox," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
    49. Federica Daniele & Mariona Segu & David Bounie & Youssouf Camara, 2022. "Bike-friendly cities: an opportunity for local businesses? Evidence from the city of Paris," THEMA Working Papers 2022-09, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    50. Akos Horvath & Benjamin S. Kay & Carlo Wix, 2021. "The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data," Finance and Economics Discussion Series 2021-008, Board of Governors of the Federal Reserve System (U.S.).
    51. Michał Suchanek & Agnieszka Szmelter-Jarosz, 2023. "Car enthusiasm during the second and fourth waves of COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.

  4. Landais, Camille & Bounie, David & Camara, Youssouf & Fize, Etienne & Galbraith, John W. & Lavest, Chloe & Pazem, Tatiana & Savatier, Baptiste, 2020. "Consumption Dynamics in the COVID Crisis: Real Time Insights from French Transaction & Bank Data," CEPR Discussion Papers 15474, C.E.P.R. Discussion Papers.

    Cited by:

    1. Emanuel Kohlscheen & Marco Jacopo Lombardi & Egon Zakrajšek, 2021. "Income inequality and the depth of economic downturns," BIS Working Papers 943, Bank for International Settlements.
    2. Michiru Kaneda & So Kubota & Satoshi Tanaka, 2021. "Who spent their COVID-19 stimulus payment? Evidence from personal finance software in Japan," The Japanese Economic Review, Springer, vol. 72(3), pages 409-437, July.
    3. Bounie, David & Camara, Youssouf & Galbraith, John W., 2023. "Consumer mobility and expenditure during the COVID-19 containments: Evidence from French transaction data," European Economic Review, Elsevier, vol. 151(C).
    4. Jung, Haeil & Kim, Jun Hyung & Hong, Gihyeon, 2023. "Impacts of the COVID-19 crisis on single-person households in South Korea," Journal of Asian Economics, Elsevier, vol. 84(C).
    5. Alexander Hodbod & Cars Hommes & Stefanie J. Huber & Isabelle Salle, 2021. "The COVID-19 Consumption Game-Changer: Evidence from a Large-Scale Multi-Country Survey," Staff Working Papers 21-57, Bank of Canada.
    6. Anete Brinke & Ludmila Fadejeva & Boriss Siliverstovs & Kārlis Vilerts, 2023. "Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 566-577, April.
    7. Piluca Alvargonzález & Marina Gómez & Carmen Martínez-Carrascal & Myroslav Pidkuyko & Ernesto Villanueva, 2022. "Analysis of labor flows and consumption in Spain during COVID-19," Occasional Papers 2202, Banco de España.
    8. Ludmila Fadejeva & Boriss Siliverstovs & Karlis Vilerts & Anete Brinke, 2022. "Consumer Spending in the Covid-19 Pandemic: Evidence from Card Transactions in Latvia," Discussion Papers 2022/01, Latvijas Banka.
    9. Gonzalez Pampillon, Nicolas & Nunez Chaim, Gonzalo & Overman, Henry G., 2022. "The economic impacts of the UK's Eat Out to Help Out scheme," LSE Research Online Documents on Economics 117979, London School of Economics and Political Science, LSE Library.
    10. Kubota, So & Onishi, Koichiro & Toyama, Yuta, 2021. "Consumption responses to COVID-19 payments: Evidence from a natural experiment and bank account data," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 1-17.
    11. Zimpelmann, Christian & Gaudecker, Hans-Martin von & Holler, Radost & Janys, Lena & Siflinger, Bettina, 2021. "Hours and income dynamics during the Covid-19 pandemic: The case of the Netherlands," Labour Economics, Elsevier, vol. 73(C).
    12. Ricardo Muñoz-Cancino & Sebastian A. Rios & Marcel Goic & Manuel Graña, 2021. "Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    13. Nicolás González-Pampillón & Gonzalo Nunez-Chaim & Katharina Ziegler, 2021. "Recovering from the first Covid-19 lockdown: Economic impacts of the UK's Eat Out to Help Out scheme," CEP Covid-19 Analyses cepcovid-19-018, Centre for Economic Performance, LSE.
    14. Simone Emiliozzi & Concetta Rondinelli & Stefania Villa, 2023. "Consumption during the Covid-19 pandemic: evidence from Italian credit cards," Questioni di Economia e Finanza (Occasional Papers) 769, Bank of Italy, Economic Research and International Relations Area.
    15. Goodhart, Charles, 2022. "Ben S. Bernanke: 21st century monetary policy: the federal reserve from the great inflation to COVID-19. W.W. Norton & Company, 2022," LSE Research Online Documents on Economics 115779, London School of Economics and Political Science, LSE Library.
    16. Bruno Carvalho & Susana Peralta & Joao Pereira dos Santos, 2020. "Regional and Sectorial Impacts of the Covid-19 Crisis: Evidence from Electronic Payments," Working Papers ECARES 2020-48, ULB -- Universite Libre de Bruxelles.
    17. Melanie Koch & Thomas Scheiber, 2022. "Mitigating the impact of the pandemic on personal finances in CESEE: descriptive evidence for 2020," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q2/22, pages 63-96.
    18. Sandri, Damiano & Grigoli, Francesco, 2022. "Monetary Policy and Credit Card Spending," CEPR Discussion Papers 17751, C.E.P.R. Discussion Papers.
    19. Johannesen, Niels & Andersen, Asger Lau & Toft Hansen, Emil & Sheridan, Adam, 2020. "Consumer Responses to the COVID-19 Crisis: Evidence from Bank Account Transaction Data," CEPR Discussion Papers 14809, C.E.P.R. Discussion Papers.
    20. Stefanie Huber, 2022. "SHE canÕt afford it and HE doesnÕt want it: The gender gap in the COVID-19 consumption response," Tinbergen Institute Discussion Papers 22-029/II, Tinbergen Institute.
    21. Bańnkowska, Katarzyna & Borlescu, Ana Maria & Charalambakis, Evangelos & Da Silva, António Dias & Di Laurea, Davide & Dossche, Maarten & Georgarakos, Dimitris & Honkkila, Juha & Kennedy, Neale & Kenny, 2021. "ECB Consumer Expectations Survey: an overview and first evaluation," Occasional Paper Series 287, European Central Bank.
    22. Archanskaia, Elizaveta & Canton, Erik & Hobza, Alexandr & Nikolov, Plamen & Simons, Wouter, 2023. "The asymmetric impact of COVID-19: A novel approach to quantifying financial distress across industries," European Economic Review, Elsevier, vol. 158(C).
    23. Martin, Philippe & Cros, Mathieu & Epaulard, Anne, 2021. "Will Schumpeter Catch Covid-19?," CEPR Discussion Papers 15834, C.E.P.R. Discussion Papers.
    24. George Kapetanios & Nora Neuteboom & Feiko Ritsema & Alexia Ventouri, 2022. "How did consumers react to the COVID‐19 pandemic over time?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 961-993, October.
    25. Goldfayn-Frank, Olga & Lewis, Vivien & Wehrhöfer, Nils, 2022. "Spending effects of child-related fiscal transfers," Discussion Papers 26/2022, Deutsche Bundesbank.

  5. Galbraith, John W. & Tkacz, Greg, 2015. "Nowcasting GDP with electronic payments data," Statistics Paper Series 10, European Central Bank.

    Cited by:

    1. Pete Richardson, 2018. "Nowcasting and the Use of Big Data in Short-Term Macroeconomic Forecasting: A Critical Review," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 65-87.
    2. Kaustubh & Soumya Bhadury & Saurabh Ghosh, 2024. "Reinvigorating Gva Nowcasting In The Postpandemic Period: A Case Study For India," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 27(Spesial I), pages 95-130, Februari.
    3. Ksenia Yakovleva, 2018. "Text Mining-based Economic Activity Estimation," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 26-41, December.
    4. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
    5. Tut, Daniel, 2023. "FinTech and the COVID-19 pandemic: Evidence from electronic payment systems," Emerging Markets Review, Elsevier, vol. 54(C).
    6. Mantas Lukauskas & Vaida Pilinkienė & Jurgita Bruneckienė & Alina Stundžienė & Andrius Grybauskas & Tomas Ruzgas, 2022. "Economic Activity Forecasting Based on the Sentiment Analysis of News," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    7. Diego Bodas & Juan R. García López & Tomasa Rodrigo López & Pep Ruiz de Aguirre & Camilo A. Ulloa & Juan Murillo Arias & Juan de Dios Romero Palop & Heribert Valero Lapaz & Matías J. Pacce, 2019. "Measuring retail trade using card transactional data," Working Papers 1921, Banco de España.
    8. Irving Fisher Committee, 2023. "Data science in central banking: applications and tools," IFC Bulletins, Bank for International Settlements, number 59.
    9. Mushkudiani Nino, 2018. "Development of Electronic Payments in Georgia," Economics and Culture, Sciendo, vol. 15(2), pages 64-74, December.
    10. Kakuho Furukawa & Ryohei Hisano, 2022. "A Nowcasting Model of Exports Using Maritime Big Data," Bank of Japan Working Paper Series 22-E-19, Bank of Japan.

  6. Hui Jun Zhang & Jean-Marie Dufour & John W. Galbraith, 2013. "Exchange rates and commodity prices: measuring causality at multiple horizons," CIRANO Working Papers 2013s-39, CIRANO.

    Cited by:

    1. Su, Chi-Wei & Wang, Kai-Hua & Chang, Hsu-Ling & Dumitrescu–Peculea, Adelina, 2017. "Do iron ore price bubbles occur?," Resources Policy, Elsevier, vol. 53(C), pages 340-346.
    2. Bakas, Dimitrios & Ioakimidis, Marilou & Triantafyllou, Athanasios, 2020. "Commodity Price Uncertainty as a Leading Indicator of Economic Activity," Essex Finance Centre Working Papers 27361, University of Essex, Essex Business School.
    3. Ye, Wuyi & Guo, Ranran & Jiang, Ying & Liu, Xiaoquan & Deschamps, Bruno, 2019. "Professional macroeconomic forecasts and Chinese commodity futures prices," Finance Research Letters, Elsevier, vol. 28(C), pages 130-136.
    4. Sangram Keshari Jena & Aviral Kumar Tiwari & David Roubaud & Muhammad Shahbaz, 2018. "Index futures volatility and trading activity: Measuring causality at a multiple horizon," Post-Print hal-02061357, HAL.
    5. Yıldırım, Durmuş Çağrı & Erdoğan, Fatma & Tarı, Elif Nur, 2022. "Time-varying volatility spillovers between real exchange rate and real commodity prices for emerging market economies," Resources Policy, Elsevier, vol. 76(C).
    6. Akram, Q. Farooq, 2019. "Oil price drivers, geopolitical uncertainty and oil exporters’ currencies," Working Paper 2019/15, Norges Bank.
    7. Hui Jun ZHANG & Jean-Marie DUFOUR & John W. GALBRAITH, 2013. "Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons," Cahiers de recherche 14-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    8. Wen, Shaobo & An, Haizhong & Chen, Zhihua & Liu, Xueyong, 2017. "Driving factors of interactions between the exchange rate market and the commodity market: A wavelet-based complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 299-308.
    9. Xiangyu Chen & Jittima Tongurai, 2021. "The Relationship Between China’s Real Estate Market and Industrial Metals Futures Market: Evidence from Non-price Measures of the Real Estate Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 527-561, December.
    10. Charles Olivier Mao Takongmo & Laetitia Lebihan, 2021. "Government Spending, GDP and Exchange Rate in Zero Lower Bound: Measuring Causality at Multiple Horizons," Post-Print hal-04288372, HAL.
    11. MAO TAKONGMO, Charles Olivier, 2016. "Government spending, GDP and exchange rate in Zero Lower Bound: measuring causality at multiple horizons," MPRA Paper 79703, University Library of Munich, Germany, revised 02 Jun 2017.
    12. Grossmann, Axel & Kim, Jintae, 2022. "The impact of U.S. dollar movements and U.S. dollar states on non-perishable commodity prices," Research in International Business and Finance, Elsevier, vol. 61(C).
    13. Nekhili, Ramzi & Mensi, Walid & Vo, Xuan Vinh, 2021. "Multiscale spillovers and connectedness between gold, copper, oil, wheat and currency markets," Resources Policy, Elsevier, vol. 74(C).
    14. Bouri, Elie & Lei, Xiaojie & Xu, Yahua & Zhang, Hongwei, 2023. "Connectedness in implied higher-order moments of precious metals and energy markets," Energy, Elsevier, vol. 263(PB).
    15. Mensi, Walid & Hammoudeh, Shawkat & Shahzad, Syed Jawad Hussain & Al-Yahyaee, Khamis Hamed & Shahbaz, Muhammad, 2017. "Oil and foreign exchange market tail dependence and risk spillovers for MENA, emerging and developed countries: VMD decomposition based copulas," Energy Economics, Elsevier, vol. 67(C), pages 476-495.
    16. Chuffart Thomas & Flachaire Emmanuel & Péguin-Feissolle Anne, 2018. "Testing for misspecification in the short-run component of GARCH-type models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-17, December.
    17. Dai, Xingyu & Wang, Qunwei & Zha, Donglan & Zhou, Dequn, 2020. "Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach," Energy Economics, Elsevier, vol. 88(C).
    18. Kassouri, Yacouba & Altıntaş, Halil, 2020. "Commodity terms of trade shocks and real effective exchange rate dynamics in Africa's commodity-exporting countries," Resources Policy, Elsevier, vol. 68(C).
    19. Wang, Wenhao & Cheung, Yin-Wong, 2023. "Commodity price effects on currencies," Journal of International Money and Finance, Elsevier, vol. 130(C).
    20. Michele Patanè & Mattia Tedesco & Stefano Zedda, 2017. "Dynamic Relationship of Commodities prices and EUR/USD exchange rate trends in the recent past," Department of Economics University of Siena 759, Department of Economics, University of Siena.
    21. Rousse, O. & Sévi, B., 2016. "Informed trading in oil-futures market," Working Papers 2016-07, Grenoble Applied Economics Laboratory (GAEL).
    22. Benjamin Mudiangombe Mudiangombe & John Weirstrass Muteba Mwamba, 2023. "Dependence Structure and Time–Frequency Impact of Exchange Rates on Crude Oil and Stock Markets of BRICS Countries: Markov-Switching-Based Wavelet Analysis," JRFM, MDPI, vol. 16(7), pages 1-29, July.
    23. Kamaruddin Kamaruddin & Yusri Hazmi & Raja Masbar & Sofyan Syahnur & M. Shabri Abd. Majid, 2021. "Asymmetric Impact of World Oil Prices on Marketing Margins: Application of NARDL Model for the Indonesian Coffee," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 212-220.
    24. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors," Sustainability, MDPI, vol. 9(2), pages 1-15, January.
    25. Bork, Lasse & Kaltwasser, Pablo Rovira & Sercu, Piet, 2022. "Aggregation bias in tests of the commodity currency hypothesis," Journal of Banking & Finance, Elsevier, vol. 135(C).
    26. Asadi, Mehrad & Roudari, Soheil & Tiwari, Aviral Kumar & Roubaud, David, 2023. "Scrutinizing commodity markets by quantile spillovers: A case study of the Australian economy," Energy Economics, Elsevier, vol. 118(C).
    27. Alam, Md. Samsul & Shahzad, Syed Jawad Hussain & Ferrer, Román, 2019. "Causal flows between oil and forex markets using high-frequency data: Asymmetries from good and bad volatility," Energy Economics, Elsevier, vol. 84(C).
    28. Laurent Ferrara & Aikaterina Karadimitropoulou & Athanasios Triantafyllou & Theodora Bermpei, 2022. "Commodity currencies revisited: The role of global commodity price uncertainty," Working Papers hal-04159791, HAL.
    29. Qureshi, Saba & Rehman, Ijaz Ur & Qureshi, Fiza, 2018. "Does gold act as a safe haven against exchange rate fluctuations? The case of Pakistan rupee," Journal of Policy Modeling, Elsevier, vol. 40(4), pages 685-708.
    30. Tiwari, Aviral Kumar & Trabelsi, Nader & Alqahtani, Faisal & Bachmeier, Lance, 2019. "Modelling systemic risk and dependence structure between the prices of crude oil and exchange rates in BRICS economies: Evidence using quantile coherency and NGCoVaR approaches," Energy Economics, Elsevier, vol. 81(C), pages 1011-1028.
    31. Yip, Pick Schen & Brooks, Robert & Do, Hung Xuan, 2017. "Dynamic spillover between commodities and commodity currencies during United States Q.E," Energy Economics, Elsevier, vol. 66(C), pages 399-410.
    32. Olga Dodd & Adrian Fernandez-Perez & Simon Sosvilla-Rivero, 2022. ""Currency and commodity return relationship under extreme geopolitical risks: Evidence from the invasion of Ukraine"," IREA Working Papers 202204, University of Barcelona, Research Institute of Applied Economics, revised Apr 2022.
    33. José Renato Haas Ornelas & Roberto Baltieri Mauad, 2017. "Volatility Risk Premia and Future Commodity Returns," Working Papers Series 455, Central Bank of Brazil, Research Department.
    34. Werner Kristjanpoller R. & Alejandro Sierra C., 2014. "Relationship between the dollar, the price of copper and the IPSA indifferent time scales: An approach through Wavelet," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 17(3), pages 56-85, December.
    35. Go, You-How & Lau, Wee-Yeap, 2021. "Extreme risk spillovers between crude palm oil prices and exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    36. Feng, Wenjun & Zhang, Zhengjun, 2023. "Currency exchange rate predictability: The new power of Bitcoin prices," Journal of International Money and Finance, Elsevier, vol. 132(C).
    37. Felix Kapfhammer & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "Climate Risk and Commodity Currencies," Working Papers No 10/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    38. Rebeca Jiménez‐Rodríguez & Amalia Morales‐Zumaquero, 2020. "Impact of commodity prices on exchange rates in commodity‐exporting countries," The World Economy, Wiley Blackwell, vol. 43(7), pages 1868-1906, July.
    39. Hong Cheng & Yunqing Wang & Yihong Wang & Tinggan Yang, 2022. "Inferring Causal Interactions in Financial Markets Using Conditional Granger Causality Based on Quantile Regression," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 719-748, February.
    40. Lv, Xin & Lien, Donald & Chen, Qian & Yu, Chang, 2018. "Does exchange rate management affect the causality between exchange rates and oil prices? Evidence from oil-exporting countries," Energy Economics, Elsevier, vol. 76(C), pages 325-343.
    41. Zulfiqar Ali Imran & Muhammad Ahad, 2022. "Safe-haven investments against stock returns in Pakistan: a role of real estate, gold, oil and US dollar," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 16(1), pages 167-189, February.
    42. Imran, Zulfiqar Ali & Ahad, Muhammad, 2021. "Safe Haven or Hedge: Diversification Abilities of Asset Classes in Pakistan," MPRA Paper 107613, University Library of Munich, Germany, revised 02 May 2021.
    43. Belasen, Ariel R. & Demirer, Rıza, 2019. "Commodity-currencies or currency-commodities: Evidence from causality tests," Resources Policy, Elsevier, vol. 60(C), pages 162-168.
    44. Carl-Henrik Dahlqvist, 2018. "Cross-country information transmissions and the role of commodity markets: A multichannel Markov switching approach," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-22, August.
    45. Lu, Xinsheng & Li, Jianfeng & Zhou, Ying & Qian, Yubo, 2017. "Cross-correlations between RMB exchange rate and international commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 168-182.
    46. Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab, 2023. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Papers 2303.16149, arXiv.org.
    47. Zhang, Dayong & Broadstock, David C., 2020. "Global financial crisis and rising connectedness in the international commodity markets," International Review of Financial Analysis, Elsevier, vol. 68(C).
    48. Sokhanvar, Amin & Çiftçioğlu, Serhan & Lee, Chien-Chiang, 2023. "The effect of energy price shocks on commodity currencies during the war in Ukraine," Resources Policy, Elsevier, vol. 82(C).
    49. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2021. "The realized volatility of commodity futures: Interconnectedness and determinants#," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 139-151.
    50. Chaturvedi, Priya & Kumar, Kuldeep, 2022. "Econometric modelling of exchange rate volatility using mixed-frequency data," MPRA Paper 115222, University Library of Munich, Germany.
    51. Liu, Li & Tan, Siming & Wang, Yudong, 2020. "Can commodity prices forecast exchange rates?," Energy Economics, Elsevier, vol. 87(C).
    52. Chen, Pei-Fen & Zeng, Jhih-Hong & Lee, Chien-Chiang, 2018. "Renminbi exchange rate assessment and competitors' exports: New perspective," China Economic Review, Elsevier, vol. 50(C), pages 187-205.
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    56. Changyu Liu & Muhammad Abubakr Naeem & Mobeen Ur Rehman & Saqib Farid & Syed Jawad Hussain Shahzad, 2020. "Oil as Hedge, Safe-Haven, and Diversifier for Conventional Currencies," Energies, MDPI, vol. 13(17), pages 1-19, August.
    57. Evrim Mandaci, Pınar & Azimli, Asil & Mandaci, Nazif, 2023. "The impact of geopolitical risks on connectedness among natural resource commodities: A quantile vector autoregressive approach," Resources Policy, Elsevier, vol. 85(PA).
    58. Yépez, Carlos & Dzikpe, Francis, 2022. "Accounting for real exchange rates in emerging economies: The role of commodity prices," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 476-492.

  7. John W. Galbraith & Greg Tkacz, 2013. "Nowcasting GDP: Electronic Payments, Data Vintages and the Timing of Data Releases," CIRANO Working Papers 2013s-25, CIRANO.

    Cited by:

    1. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    2. Juan de Dios Romero Palop & Juan Murillo Arias & Diego J. Bodas-Sagi & Heribert Valero Lapaz, 2019. "Determining the usual environment of cardholders as a key factor to measure the evolution of domestic tourism," Information Technology & Tourism, Springer, vol. 21(1), pages 23-43, March.
    3. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    4. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).

  8. Victoria Zinde-Walsh & John W. Galbraith, 2011. "A test of singularity for distribution functions," CIRANO Working Papers 2011s-06, CIRANO.

    Cited by:

    1. Victoria Zinde‐Walsh, 2011. "Presidential Address: Mathematics in economics and econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(4), pages 1052-1068, November.

  9. John W. Galbraith & Victoria Zinde-Walsh, 2011. "Partially Dimension-Reduced Regressions with Potentially Infinite-Dimensional Processes," CIRANO Working Papers 2011s-57, CIRANO.

    Cited by:

    1. John Galbraith & Douglas Hodgson, 2015. "Innovation, experience and artists’ age-valuation profiles: evidence from eighteenth-century rococo and neoclassical painters," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 39(3), pages 259-275, August.
    2. John W. Galbraith & Douglas James Hodgson, 2009. "Dimension Reduction and Model Averaging for Estimation of Artists' Age-Valuation Profiles," CIRANO Working Papers 2009s-41, CIRANO.

  10. John W. Galbraith & Douglas James Hodgson, 2009. "Dimension Reduction and Model Averaging for Estimation of Artists' Age-Valuation Profiles," CIRANO Working Papers 2009s-41, CIRANO.

    Cited by:

    1. Galbraith, John W. & Zinde-Walsh, Victoria, 2020. "Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 609-632.
    2. John W. Galbraith & Greg Tkacz, 2013. "Nowcasting GDP: Electronic Payments, Data Vintages and the Timing of Data Releases," CIRANO Working Papers 2013s-25, CIRANO.
    3. Charlin, Ventura & Cifuentes, Arturo, 2013. "A new financial metric for the art market," MPRA Paper 50186, University Library of Munich, Germany.
    4. John Galbraith & Douglas Hodgson, 2015. "Innovation, experience and artists’ age-valuation profiles: evidence from eighteenth-century rococo and neoclassical painters," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 39(3), pages 259-275, August.
    5. Hellmanzik, Christiane, 2016. "Historic art exhibitions and modern - day auction results," Research in Economics, Elsevier, vol. 70(3), pages 421-430.
    6. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    7. John W. Galbraith & Douglas J. Hodgson, 2018. "Econometric Fine Art Valuation by Combining Hedonic and Repeat-Sales Information," Econometrics, MDPI, vol. 6(3), pages 1-15, June.
    8. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    9. Daiva Jurevičienė & Božena Kostecka, 2014. "Peculiarities of selection of investment artworks," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2014(5), pages 71-88.

  11. John W. Galbraith & Simon van Norden, 2009. "Calibration and Resolution Diagnostics for Bank of England Density Forecasts," CIRANO Working Papers 2009s-36, CIRANO.

    Cited by:

    1. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2011. "Scoring rules and survey density forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 379-393, April.

  12. John Galbraith & Dongming Zhu, 2009. "A Generalized Asymmetric Student-T Distribution With Application To Financial Econometrics," Departmental Working Papers 2009-02, McGill University, Department of Economics.

    Cited by:

    1. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
    2. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
    3. James Mitchell & Martin Weale, 2021. "Censored Density Forecasts: Production and Evaluation," Working Papers 21-12R, Federal Reserve Bank of Cleveland, revised 16 Aug 2022.
    4. Palczewski, Andrzej & Palczewski, Jan, 2019. "Black–Litterman model for continuous distributions," European Journal of Operational Research, Elsevier, vol. 273(2), pages 708-720.
    5. Beddock, Arthur, 2021. "Asset pricing with heterogeneous agents and non-normal return distributions," Other publications TiSEM eeaf2925-4cc0-4fe1-8008-6, Tilburg University, School of Economics and Management.
    6. Emmanuel Afuecheta & Idika E. Okorie & Saralees Nadarajah & Geraldine E. Nzeribe, 2024. "Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 271-304, January.
    7. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, vol. 82(1), pages 71-102, January.
    8. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
    9. Saswat Patra & Malay Bhattacharyya, 2020. "How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure," Risks, MDPI, vol. 8(3), pages 1-17, July.
    10. Dejan Živkov & Slavica Manić & Jelena Kovačević & Željana Trbović, 2022. "Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 21(1), pages 67-93, January.
    11. Brenda Castillo-Brais & Ángel León & Juan Mora, 2022. "Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    12. Moosup Kim & Sangyeol Lee, 2019. "Test for tail index constancy of GARCH innovations based on conditional volatility," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 947-981, August.
    13. Peter Reinhard Hansen & Chen Tong, 2024. "Convolution-t Distributions," Papers 2404.00864, arXiv.org.
    14. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    15. Massimiliano Giacalone & Demetrio Panarello, 2022. "A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments," Mathematics, MDPI, vol. 10(5), pages 1-21, February.
    16. Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020. "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, vol. 85(C).
    17. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
    18. Jiang, Chun-Fu & Peng, Hong-Yi & Yang, Yu-Kuan, 2016. "Tail variance of portfolio under generalized Laplace distribution," Applied Mathematics and Computation, Elsevier, vol. 282(C), pages 187-203.
    19. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2010. "Student's t-Distribution Based Option Sensitivities: Greeks for the Gosset Formulae," Papers 1003.1344, arXiv.org, revised Jul 2010.
    20. Francq, Christian & Zakoian, Jean-Michel, 2023. "Local Asymptotic Normality Of General Conditionally Heteroskedastic And Score-Driven Time-Series Models," Econometric Theory, Cambridge University Press, vol. 39(5), pages 1067-1092, October.
    21. Robina Iqbal & Ghulam Sorwar & Rose Baker & Taufiq Choudhry, 2020. "Multiday expected shortfall under generalized t distributions: evidence from global stock market," Review of Quantitative Finance and Accounting, Springer, vol. 55(3), pages 803-825, October.
    22. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    23. Dongming Zhu & John W. Galbraith, 2009. "Forecasting Expected Shortfall with a Generalized Asymmetric Student-t Distribution," CIRANO Working Papers 2009s-24, CIRANO.
    24. Victor Korolev, 2023. "Analytic and Asymptotic Properties of the Generalized Student and Generalized Lomax Distributions," Mathematics, MDPI, vol. 11(13), pages 1-27, June.
    25. Yuancheng Si & Saralees Nadarajah, 2023. "A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting," Annals of Data Science, Springer, vol. 10(1), pages 73-88, February.
    26. J. Miguel Marin & Genaro Sucarrat, 2015. "Financial density selection," The European Journal of Finance, Taylor & Francis Journals, vol. 21(13-14), pages 1195-1213, November.
    27. Mazur Błażej & Pipień Mateusz, 2018. "Time-varying asymmetry and tail thickness in long series of daily financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-21, December.
    28. Patra, Saswat, 2021. "Revisiting value-at-risk and expected shortfall in oil markets under structural breaks: The role of fat-tailed distributions," Energy Economics, Elsevier, vol. 101(C).
    29. Ruijie Guan & Xu Zhao & Weihu Cheng & Yaohua Rong, 2021. "A New Generalized t Distribution Based on a Distribution Construction Method," Mathematics, MDPI, vol. 9(19), pages 1-36, September.
    30. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.
    31. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    32. Samet Gunay & Audil Rashid Khaki, 2018. "Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models," JRFM, MDPI, vol. 11(2), pages 1-19, June.
    33. Yasutomo Murasawa, 2013. "Measuring Inflation Expectations Using Interval-Coded Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(4), pages 602-623, August.
    34. Stephen Chan & Jeffrey Chu & Saralees Nadarajah & Joerg Osterrieder, 2017. "A Statistical Analysis of Cryptocurrencies," JRFM, MDPI, vol. 10(2), pages 1-23, May.
    35. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    36. Mitchell, James & Weale, Martin, 2019. "Forecasting with Unknown Unknowns: Censoring and Fat Tails on the Bank of England's Monetary Policy Committee," EMF Research Papers 27, Economic Modelling and Forecasting Group.
    37. Yang, Lu & Hamori, Shigeyuki, 2021. "The role of the carbon market in relation to the cryptocurrency market: Only diversification or more?," International Review of Financial Analysis, Elsevier, vol. 77(C).
    38. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    39. Dejan Živkov & Marijana Joksimović & Suzana Balaban, 2021. "Measuring parametric and semiparametric downside risks of selected agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(8), pages 305-315.
    40. Blasques, F. & Francq, Christian & Laurent, Sébastien, 2023. "Quasi score-driven models," Journal of Econometrics, Elsevier, vol. 234(1), pages 251-275.
    41. Rui Li & Saralees Nadarajah, 2020. "A review of Student’s t distribution and its generalizations," Empirical Economics, Springer, vol. 58(3), pages 1461-1490, March.
    42. M. C. Jones, 2015. "On Families of Distributions with Shape Parameters," International Statistical Review, International Statistical Institute, vol. 83(2), pages 175-192, August.
    43. Gao, Chun-Ting & Zhou, Xiao-Hua, 2016. "Forecasting VaR and ES using dynamic conditional score models and skew Student distribution," Economic Modelling, Elsevier, vol. 53(C), pages 216-223.
    44. Andrew Harvey & Rutger-Jan Lange, 2015. "Modeling the Interactions between Volatility and Returns," Cambridge Working Papers in Economics 1518, Faculty of Economics, University of Cambridge.
    45. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "GARCH modeling of five popular commodities," Empirical Economics, Springer, vol. 48(4), pages 1691-1712, June.
    46. Nadarajah, Saralees & Chan, Stephen & Afuecheta, Emmanuel, 2013. "On the characteristic function for asymmetric Student t distributions," Economics Letters, Elsevier, vol. 121(2), pages 271-274.
    47. Martin Weale & Paul Labonne, 2022. "Nowcasting in the presence of large measurement errors and revisions," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-05, Economic Statistics Centre of Excellence (ESCoE).
    48. Carol Alexander & Gauss M. Cordeiro & Edwin M. M. Ortega & José María Sarabia, 2011. "Generalized Beta-Generated Distributions," ICMA Centre Discussion Papers in Finance icma-dp2011-05, Henley Business School, University of Reading.
    49. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2013. "Log Student’s t -distribution-based option sensitivities: Greeks for the Gosset formulae," Quantitative Finance, Taylor & Francis Journals, vol. 13(8), pages 1289-1302, July.
    50. Adcock, C J & Meade, N, 2017. "Using parametric classification trees for model selection with applications to financial risk management," European Journal of Operational Research, Elsevier, vol. 259(2), pages 746-765.
    51. Mark F. J. Steel & Francisco J. Rubio, 2015. "Discussion," International Statistical Review, International Statistical Institute, vol. 83(2), pages 218-222, August.
    52. Rubio, Francisco Javier & Steel, Mark F. J., 2014. "Bayesian modelling of skewness and kurtosis with two-piece scale and shape transformations," MPRA Paper 57102, University Library of Munich, Germany.
    53. José María Sarabia & Faustino Prieto & Vanesa Jordá & Stefan Sperlich, 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis," Risks, MDPI, vol. 8(2), pages 1-14, April.
    54. Baker, Rose, 2017. "Creating new distributions by blunting cusps," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 55-63.
    55. Dejan Zivkov & Marina Gajic-Glamoclija & Jelena Kovacevic & Sanja Loncar, 2020. "Inflation Uncertainty and Output Growth - Evidence from the Asia-Pacific Countries Based on the Multiscale Bayesian Quantile Inference," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 70(5), pages 461-486, November.
    56. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    57. Francisco J. Rubio Alvarez, 2020. "Letter to the Editor: ‘On Quantile‐based Asymmetric Family of Distributions: Properties and Inference’," International Statistical Review, International Statistical Institute, vol. 88(3), pages 793-796, December.
    58. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
    59. Ibrahim Ergen, 2015. "Two-step methods in VaR prediction and the importance of fat tails," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1013-1030, June.
    60. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
    61. Gilbert Colletaz & Christophe Hurlin & Christophe Pérignon, 2012. "The Risk Map: A New Tool for Validating Risk Models," Working Papers halshs-00746273, HAL.
    62. Victor Korolev & Alexander Zeifman, 2023. "Mixture Representations for Generalized Burr, Snedecor–Fisher and Generalized Student Distributions with Related Results," Mathematics, MDPI, vol. 11(18), pages 1-25, September.
    63. Mahdi Teimouri & Saralees Nadarajah, 2022. "Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 665-692, August.
    64. Owusu Junior, Peterson & Alagidede, Imhotep, 2020. "Risks in emerging markets equities: Time-varying versus spatial risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    65. Stephen Thiele, 2020. "Modeling the conditional distribution of financial returns with asymmetric tails," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 46-60, January.
    66. Trottier, Denis-Alexandre & Ardia, David, 2016. "Moments of standardized Fernandez–Steel skewed distributions: Applications to the estimation of GARCH-type models," Finance Research Letters, Elsevier, vol. 18(C), pages 311-316.
    67. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    68. Owusu Junior, Peterson & Tiwari, Aviral Kumar & Tweneboah, George & Asafo-Adjei, Emmanuel, 2022. "GAS and GARCH based value-at-risk modeling of precious metals," Resources Policy, Elsevier, vol. 75(C).
    69. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2020. "Loss-based approach to two-piece location-scale distributions with applications to dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 309-333, June.
    70. Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.
    71. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.
    72. Acereda, Beatriz & Leon, Angel & Mora, Juan, 2020. "Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting," Finance Research Letters, Elsevier, vol. 33(C).
    73. Jetro Anttonen & Markku Lanne & Jani Luoto, 2024. "Statistically identified structural VAR model with potentially skewed and fat‐tailed errors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 422-437, April.
    74. Beatriz Vaz de Melo Mendes & André Fluminense Carneiro, 2020. "A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020," JRFM, MDPI, vol. 13(9), pages 1-21, August.
    75. Tianyang Wang & James Dyer & Warren Hahn, 2015. "A copula-based approach for generating lattices," Review of Derivatives Research, Springer, vol. 18(3), pages 263-289, October.
    76. Irène Gijbels & Rezaul Karim & Anneleen Verhasselt, 2020. "Response to the Letter to the Editor on ‘On Quantile‐based Asymmetric Family of Distributions: Properties and Inference’," International Statistical Review, International Statistical Institute, vol. 88(3), pages 797-801, December.
    77. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.
    78. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "A note on "Modelling exchange rate returns: which flexible distribution to use?"," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1777-1785, November.

  13. John Galbraith & Dongming Zhu, 2009. "Forecasting Expected Shortfall With A Generalized Asymmetric Student-T Distribution," Departmental Working Papers 2009-01, McGill University, Department of Economics.

    Cited by:

    1. Saissi Hassani, Samir & Dionne, Georges, 2023. "Using skewed exponential power mixture for VaR and CVaR forecasts to comply with market risk regulation," Working Papers 23-2, HEC Montreal, Canada Research Chair in Risk Management.
    2. Kumiega, Andrew & Neururer, Thaddeus & Van Vliet, Ben, 2011. "Independent component analysis for realized volatility: Analysis of the stock market crash of 2008," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(3), pages 292-302, June.
    3. Chen, Qian & Gerlach, Richard & Lu, Zudi, 2012. "Bayesian Value-at-Risk and expected shortfall forecasting via the asymmetric Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3498-3516.
    4. Dongming Zhu & John W. Galbraith, 2009. "A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics," CIRANO Working Papers 2009s-13, CIRANO.
    5. Richard Gerlach & Zudi Lu & Hai Huang, 2013. "Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 534-550, September.

  14. John W. Galbraith & Greg Tkacz, 2009. "A Note on Monitoring Daily Economic Activity Via Electronic Transaction Data," CIRANO Working Papers 2009s-23, CIRANO.

    Cited by:

    1. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    2. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    3. Tut, Daniel, 2023. "FinTech and the COVID-19 pandemic: Evidence from electronic payment systems," Emerging Markets Review, Elsevier, vol. 54(C).

  15. John W. Galbraith & Simon van Norden, 2008. "The Calibration of Probabilistic Economic Forecasts," CIRANO Working Papers 2008s-28, CIRANO.

    Cited by:

    1. John W. Galbraith & Simon van Norden, 2009. "Calibration and Resolution Diagnostics for Bank of England Density Forecasts," CIRANO Working Papers 2009s-36, CIRANO.

  16. John W. Galbraith & Greg Tkacz, 2007. "Forecast Content And Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2007-01, McGill University, Department of Economics.

    Cited by:

    1. Breitung, Jörg & Knüppel, Malte, 2018. "How far can we forecast? Statistical tests of the predictive content," Discussion Papers 07/2018, Deutsche Bundesbank.
    2. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    3. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    4. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    5. Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
    6. John W. Galbraith & Simon van Norden, 2009. "Calibration and Resolution Diagnostics for Bank of England Density Forecasts," CIRANO Working Papers 2009s-36, CIRANO.
    7. John Galbraith & Simon van Norden, 2008. "The Calibration Of Probabilistic Economic Forecasts," Departmental Working Papers 2008-05, McGill University, Department of Economics.
    8. François-Éric Racicota & David Tessierc, 2023. "On the relationship between Jorda?s IRF local projection and Dufour et al.?s robust (p,h)-autoregression multihorizon causality: a note," Working Papers 2023-001, Department of Research, Ipag Business School.
    9. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    10. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2023. "The accuracy and informativeness of agricultural baselines," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1116-1148, August.
    11. de Bruijn, L.P. & Franses, Ph.H.B.F., 2011. "Evaluating the Rationality of Managers' Sales Forecasts," Econometric Institute Research Papers EI 2011-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.
    13. Galbraith, John W. & Tkacz, Greg, 2015. "Nowcasting GDP with electronic payments data," Statistics Paper Series 10, European Central Bank.
    14. Baggio, Rodolfo, 2015. "Looking into the future of complex dynamic systems," MPRA Paper 65549, University Library of Munich, Germany.

  17. John Galbraith & Greg Tkacz, 2007. "Electronic Transactions as High-Frequency Indicators of Economic Activity," Staff Working Papers 07-58, Bank of Canada.

    Cited by:

    1. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-time measurement of business conditions," International Finance Discussion Papers 901, Board of Governors of the Federal Reserve System (U.S.).
    2. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    3. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    4. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    5. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    6. Tut, Daniel, 2023. "FinTech and the COVID-19 pandemic: Evidence from electronic payment systems," Emerging Markets Review, Elsevier, vol. 54(C).
    7. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    8. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions, Second Version," PIER Working Paper Archive 08-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Apr 2008.
    9. Gurgul, Henryk & Suder, Marcin, 2013. "Modeling of Withdrawals from Selected ATMs of the “Euronet” Network," MPRA Paper 68598, University Library of Munich, Germany, revised 2013.

  18. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.

    Cited by:

    1. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.

  19. John G. Galbraith & Greg Tkacz, 2006. "How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2006-13, McGill University, Department of Economics.

    Cited by:

    1. Michael K. Andersson & Sune Karlsson, 2008. "Bayesian forecast combination for VAR models," Advances in Econometrics, in: Bayesian Econometrics, pages 501-524, Emerald Group Publishing Limited.
    2. Andersson, Michael K. & Karlsson, Gustav & Svensson, Josef, 2007. "The Riksbank’s Forecasting Performance," Working Paper Series 218, Sveriges Riksbank (Central Bank of Sweden).

  20. John Galbraith & Victoria Zinde-Walsh, 2006. "Reduced-Dimension Control Regression," Departmental Working Papers 2006-17, McGill University, Department of Economics.

    Cited by:

    1. Stanislav Anatolyev, 2009. "Inference in Regression Models with Many Regressors," Working Papers w0125, New Economic School (NES).
    2. Stanislav Anatolyev, 2007. "Inference about predictive ability when there are many predictors," Working Papers w0096, New Economic School (NES).

  21. John W. Galbraith & Turgut Kisinbay, 2002. "Information Content of Volatility Forecasts at Medium-term Horizons," CIRANO Working Papers 2002s-21, CIRANO.

    Cited by:

    1. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.

  22. John W. Galbraith & Serguei Zernov, 2002. "Circuit Breakers and the Tail Index of Equity Returns," CIRANO Working Papers 2002s-62, CIRANO.

    Cited by:

    1. Lee, Chien-Chiang & Chiu, Yi-Bin, 2016. "Globalization and insurance activity: Evidence on the industrial and emerging countries," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 328-349.
    2. Bryan Kelly & Hao Jiang, 2013. "Tail Risk and Asset Prices," NBER Working Papers 19375, National Bureau of Economic Research, Inc.
    3. Chen, Zhimin & Ibragimov, Rustam, 2019. "One country, two systems? The heavy-tailedness of Chinese A- and H- share markets," Emerging Markets Review, Elsevier, vol. 38(C), pages 115-141.
    4. Fendel, Ralf & Neumann, Christian, 2021. "Tail risk in the European sovereign bond market during the financial crises: Detecting the influence of the European Central Bank," Global Finance Journal, Elsevier, vol. 50(C).
    5. Daniele Massacci, 2017. "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness," Management Science, INFORMS, vol. 63(9), pages 3072-3089, September.
    6. Wu, Ying, 2019. "Asset pricing with extreme liquidity risk," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 143-165.
    7. Straetmans, Stefan & Candelon, Bertrand, 2013. "Long-term asset tail risks in developed and emerging markets," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 1832-1844.
    8. Ayala Astrid & Blazsek Szabolcs & Escribano Alvaro, 2023. "Anticipating extreme losses using score-driven shape filters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 449-484, September.
    9. Blazsek, Szabolcs & Ayala, Astrid & Escribano, Álvaro, 2019. "Score-driven time series models with dynamic shape : an application to the Standard & Poor's 500 index," UC3M Working papers. Economics 28133, Universidad Carlos III de Madrid. Departamento de Economía.
    10. Ayala, Astrid & Escribano, Álvaro & Blazsek, Szabolcs, 2017. "Dynamic conditional score models with time-varying location, scale and shape parameters," UC3M Working papers. Economics 25043, Universidad Carlos III de Madrid. Departamento de Economía.
    11. Wagner, Niklas, 2005. "Autoregressive conditional tail behavior and results on Government bond yield spreads," International Review of Financial Analysis, Elsevier, vol. 14(2), pages 247-261.
    12. Palumbo, D., 2021. "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics 2111, Faculty of Economics, University of Cambridge.
    13. Bollerslev, Tim & Todorov, Viktor, 2014. "Time-varying jump tails," Journal of Econometrics, Elsevier, vol. 183(2), pages 168-180.
    14. Riedel, Christoph & Wagner, Niklas, 2015. "Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 53-64.
    15. John G. Galbraith & Serguei Zernov, 2006. "Extreme Dependence In The Nasdaq And S&P Composite Indexes," Departmental Working Papers 2006-14, McGill University, Department of Economics.
    16. Blazsek, Szabolcs & Ayala, Astrid & Escribano, Álvaro, 2019. "Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk," UC3M Working papers. Economics 28638, Universidad Carlos III de Madrid. Departamento de Economía.

  23. Marc Brisson & Bryan Campbell & John W. Galbraith, 2001. "Forecasting Some Low-Predictability Time Series Using Diffusion Indices," CIRANO Working Papers 2001s-46, CIRANO.

    Cited by:

    1. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    2. Hyungsik Roger MOON & Benoit PERRON, 2002. "Testing For A Unit Root In Panels With Dynamic Factors," Cahiers de recherche 18-2002, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    4. Bryan Campbell & Steve Murphy, 2006. "The Recent Performance of the Canadian Forecasting Industry," Canadian Public Policy, University of Toronto Press, vol. 32(1), pages 23-40, March.
    5. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    6. Boriss Siliverstovs & Konstantin A. Kholodilin, 2006. "On Selection of Components for a Diffusion Index Model: It's not the Size, It's How You Use It," Discussion Papers of DIW Berlin 598, DIW Berlin, German Institute for Economic Research.
    7. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    8. John W. Galbraith & Greg Tkacz, 2007. "Forecast Content And Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2007-01, McGill University, Department of Economics.
    9. Roberto Tatiwa Ferreira & Herman Bierens & Ivan Castelar, 2005. "Forecasting Quarterly Brazilian GDP Growth Rate With Linear and NonLinear Diffusion Index Models," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 6(3), pages 261-292.
    10. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
    11. Schumacher, Christian, 2009. "Factor forecasting using international targeted predictors: the case of German GDP," Discussion Paper Series 1: Economic Studies 2009,10, Deutsche Bundesbank.
    12. John G. Galbraith & Greg Tkacz, 2006. "How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2006-13, McGill University, Department of Economics.
    13. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
    14. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Staff Working Papers 01-18, Bank of Canada.
    15. Marcel Dagenais & Claude Montmarquette & Nathalie Viennot-Briot, 2001. "Dropout, School Performance and Working while in School : An Econometric Model with Heterogeneous Groups," CIRANO Working Papers 2001s-63, CIRANO.

  24. John W. Galbraith & Victoria Zinde-Walsh, 2001. "Autoregression-Based Estimators for ARFIMA Models," CIRANO Working Papers 2001s-11, CIRANO.

    Cited by:

    1. Emilian DOBRESCU, 2016. "Controversies over the Size of the Public Budget," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-34, December.

  25. John W. Galbraith & Victoria Zinde-Walsh, 2000. "Properties of Estimates of Daily GARCH Parameters Based on Intra-Day Observations," Econometric Society World Congress 2000 Contributed Papers 1800, Econometric Society.

    Cited by:

    1. Meddahi, N., 2001. "A Theoretical Comparison Between Integrated and Realized Volatilies," Cahiers de recherche 2001-26, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    2. Woerner Jeannette H. C., 2003. "Variational sums and power variation: a unifying approach to model selection and estimation in semimartingale models," Statistics & Risk Modeling, De Gruyter, vol. 21(1/2003), pages 47-68, January.
    3. Nour Meddahi, 2003. "ARMA representation of integrated and realized variances," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 335-356, December.
    4. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Realised power variation and stochastic volatility models," Economics Papers 2001-W18, Economics Group, Nuffield College, University of Oxford.

  26. John W. Galbraith & Greg Tkacz, 1999. "Testing For Asymmetry In The Link Between The Yield Spread And Output In The G-7 Countries," Departmental Working Papers 1999-02, McGill University, Department of Economics.

    Cited by:

    1. David Longworth, 2003. "Money in the Bank (of Canada)," Technical Reports 93, Bank of Canada.
    2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    3. Marina Tkalec, 2013. "The Dynamics of Deposit Euroization in European Post-Transition Countries: Evidence from Threshold VAR," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 1, pages 66-83.
    4. Ekaterini Panopoulou, 2006. "The predictive content of financial variables: Evidence from the euro area," The Institute for International Integration Studies Discussion Paper Series iiisdp178, IIIS.
    5. Chris Birchenhall & Denise Osborn & Marianne Sensier, 2001. "Predicting UK Business Cycle Regimes," Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(2), pages 179-195, May.
    6. Hogrefe, Jens, 2007. "The yield spread and GDP growth - Time Varying Leading Properties and the Role of Monetary Policy," Economics Working Papers 2007-12, Christian-Albrechts-University of Kiel, Department of Economics.
    7. Greg Tkacz, 2002. "Inflation Changes, Yield Spreads, and Threshold Effects," Staff Working Papers 02-40, Bank of Canada.
    8. Panopoulou, Ekaterini, 2009. "Financial variables and euro area growth: A non-parametric causality analysis," Economic Modelling, Elsevier, vol. 26(6), pages 1414-1419, November.
    9. Schrimpf, Andreas & Wang, Qingwei, 2010. "A reappraisal of the leading indicator properties of the yield curve under structural instability," International Journal of Forecasting, Elsevier, vol. 26(4), pages 836-857, October.
    10. Heather M. Anderson & George Athanasopoulos & Farshid Vahid, 2006. "Nonlinear autoregressive leading indicator models of output in G-7 countries," CAMA Working Papers 2006-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Ibarra-Ramírez Raúl, 2021. "The Yield Curve as a Predictor of Economic Activity in Mexico: The Role of the Term Premium," Working Papers 2021-07, Banco de México.
    12. Markku Lanne & Henri Nyberg, 2014. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," CREATES Research Papers 2014-17, Department of Economics and Business Economics, Aarhus University.
    13. Gebka, Bartosz & Wohar, Mark E., 2018. "The predictive power of the yield spread for future economic expansions: Evidence from a new approach," Economic Modelling, Elsevier, vol. 75(C), pages 181-195.
    14. Kuosmanen, Petri & Vataja, Juuso, 2019. "Time-varying predictive content of financial variables in forecasting GDP growth in the G-7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 211-222.
    15. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    16. Nakaota, Hiroshi & Fukuta, Yuichi, 2013. "The leading indicator property of the term spread and the monetary policy factors in Japan," Japan and the World Economy, Elsevier, vol. 28(C), pages 85-98.
    17. Lazzarini, S. G. & Madalozzo, R. C & Artes, R. & Siqueira, J. O., 2004. "Measuring trust: An experiment in Brazil," Insper Working Papers wpe_42, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    18. Novella Maugeri, 2014. "Some Pitfalls in Smooth Transition Models Estimation: A Monte Carlo Study," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 339-378, October.
    19. Nakaota, Hiroshi, 2005. "The term structure of interest rates in Japan: the predictability of economic activity," Japan and the World Economy, Elsevier, vol. 17(3), pages 311-326, August.
    20. Hiroshi Nakaota & Yuichi Fukuta, 2013. "The Leading Indicator Property of the Term Spread and the Monetary Policy Factors in Japan," Discussion Papers in Economics and Business 13-09-Rev, Osaka University, Graduate School of Economics.
    21. Mili, Mehdi & Sahut, Jean-Michel & Teulon, Frédéric, 2012. "Non linear and asymmetric linkages between real growth in the Euro area and global financial market conditions: New evidence," Economic Modelling, Elsevier, vol. 29(3), pages 734-741.
    22. Christis Hassapis, 2003. "Financial variables and real activity in Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 36(2), pages 421-442, May.
    23. Shigeyuki Hamori & Naoko Hamori, 2009. "Introduction of the Euro and the Monetary Policy of the European Central Bank," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 7169.
    24. M Sensier & M Artis & C R Birchenhall & D R Osborn, 2002. "Domestic and International Influences on Business Cycle Regimes in Europe," Centre for Growth and Business Cycle Research Discussion Paper Series 11, Economics, The University of Manchester.
    25. Paulo M.M. Rodrigues & Gabriel Zsurkis, 2020. "The expected time to cross a threshold and its determinants: A simple and flexible framework," Working Papers w202006, Banco de Portugal, Economics and Research Department.
    26. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    27. Duarte, Agustin & Venetis, Ioannis A. & Paya, Ivan, 2005. "Predicting real growth and the probability of recession in the Euro area using the yield spread," International Journal of Forecasting, Elsevier, vol. 21(2), pages 261-277.
    28. Paya, Ivan & Matthews, Kent & Peel, David, 2005. "The term spread and real economic activity in the US inter-war period," Journal of Macroeconomics, Elsevier, vol. 27(2), pages 331-343, June.
    29. Knut Lehre Seip & Dan Zhang, 2021. "The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    30. Huseyin Kaya, 2013. "On the Predictive Power of Yield Spread for Future Growth and Recession: The Turkish Case," Working Papers 010, Bahcesehir University, Betam, revised Mar 2013.
    31. Ivan Paya & Kent Matthews, 2004. "Term spread and real economic activity in Korea: was the crisis predictable?," Applied Economics Letters, Taylor & Francis Journals, vol. 11(13), pages 797-801.
    32. Ronald Lange, 2008. "A decomposition of the predictive content of the term structure for output growth in Canada," Applied Economics, Taylor & Francis Journals, vol. 40(12), pages 1537-1545.
    33. Mark J. Holmes & Maghrebi Nabil, 2002. "Non-Linearities, Regime Switching and the Relationship Between Asian Equity and Foreign Exchange Markets," International Economic Journal, Taylor & Francis Journals, vol. 16(4), pages 121-139.
    34. Evgenidis, Anastasios & Papadamou, Stephanos & Siriopoulos, Costas, 2020. "The yield spread's ability to forecast economic activity: What have we learned after 30 years of studies?," Journal of Business Research, Elsevier, vol. 106(C), pages 221-232.
    35. Venetis, Ioannis A. & Paya, Ivan & Peel, David A., 2003. "Re-examination of the predictability of economic activity using the yield spread: a nonlinear approach," International Review of Economics & Finance, Elsevier, vol. 12(2), pages 187-206.
    36. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).
    37. Masashi Hasegawa & Yuichi Fukuta, 2011. "An empirical analysis of information in the yield spread on future recessions in Japan," Applied Economics, Taylor & Francis Journals, vol. 43(15), pages 1865-1881.
    38. Greg Tkacz & Carolyn Wilkins, 2008. "Linear and threshold forecasts of output and inflation using stock and housing prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 131-151.
    39. Galvão, Ana Beatriz C., 2003. "Multivariate Threshold Models: TVARs and TVECMs," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(1), May.
    40. Ana Beatriz C. Galvão, 2006. "Structural break threshold VARs for predicting US recessions using the spread," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 463-487, May.
    41. Jean-michel Sahut & Medhi Mili & Frédéric Teulon, 2012. "What is the linkage between real growth in the Euro area and global financial market conditions?," Economics Bulletin, AccessEcon, vol. 32(3), pages 2464-2480.
    42. Ivan Paya & David A. Peel & Ioannis A. Venetis, 2004. "Asymmetry In The Link Between The Yield Spread And Industrial Production. Threshold Effects And Forecasting," Working Papers. Serie AD 2004-41, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    43. Killins, Robert N. & Egly, Peter V. & Batabyal, Sourav, 2021. "The impact of the yield curve on bank equity returns: Evidence from Canada," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 319-329.
    44. David C. Wheelock & Mark E. Wohar, 2009. "Can the term spread predict output growth and recessions? a survey of the literature," Review, Federal Reserve Bank of St. Louis, vol. 91(Sep), pages 419-440.
    45. Costanza Torricelli & Marianna Brunetti, 2006. "Economic activity and Recession Probabilities: spread predictive power in Italy," Computing in Economics and Finance 2006 350, Society for Computational Economics.
    46. Atanasova Christina, 2003. "Credit Market Imperfections and Business Cycle Dynamics: A Nonlinear Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(4), pages 1-22, December.
    47. Dalu Zhang & Peter Moffatt, 2013. "Time series non-linearity in the real growth / recession-term spread relationship," University of East Anglia Applied and Financial Economics Working Paper Series 047, School of Economics, University of East Anglia, Norwich, UK..
    48. Greg Tkacz & Carolyn A. Wilkins, 2006. "Linear and Threshold Forecasts of Output and Inflation with Stock and Housing Prices," Staff Working Papers 06-25, Bank of Canada.
    49. Kaul, Aditya & Mehrotra, Vikas, 2007. "The role of trades in price convergence: A study of dual-listed Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 196-219, March.
    50. Lange, Ronald H., 2015. "International long-term yields and monetary policy in a small open economy: The case of Canada," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 292-310.
    51. Hiroshi Nakaota & Yuichi Fukuta, 2013. "The Leading Indicator Property of the Term Spread and the Monetary Policy Factors in Japan," Discussion Papers in Economics and Business 13-09, Osaka University, Graduate School of Economics, revised Jul 2013.
    52. Omay, Tolga, 2008. "The Term Structure of Interest Rate as a Predictor of Inflation and Real Economic Activity: Nonlinear Evidence from Turkey," MPRA Paper 28572, University Library of Munich, Germany.

  27. Dolado, J. & Galbraith, J.W. & Banerjee, A., 1991. "Estimating Intertemporal Quadratic Adjustment Cost Models with Integrated Series," Economics Series Working Papers 99111, University of Oxford, Department of Economics.

    Cited by:

    1. Rodrigo Pereira, 2015. "Investment and Uncertainty in a Quadratic Adjustment Cost Model: Evidence from Brazil," Discussion Papers 0085, Instituto de Pesquisa Econômica Aplicada - IPEA.
    2. Jaebeom Kim & Masao Ogaki & Minseok Yang, 2007. "Structural Error Correction Models: A System Method for Linear Rational Expectations Models and an Application to an Exchange Rate Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(8), pages 2057-2075, December.
    3. Pål Boug & Ådne Cappelen & Anders R. Swensen, 2000. "Expectations in Export Price Formation Tests using Cointegrated VAR Models," Discussion Papers 283, Statistics Norway, Research Department.
    4. Croix de la, D. & Urbain, J.R.Y.J. & Palm, F.C., 1996. "Labor market dynamics when effort depends on wage growth comparisons," Research Memorandum 028, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Robert A. Amano & Tony S. Wirjanto, 1994. "A Further Analysis of Exchange Rate Targeting in Canada," Econometrics 9406001, University Library of Munich, Germany, revised 22 Jun 1994.
    6. Amano, Robert A. & Wirjanto, Tony S., 1997. "Adjustment costs and import demand behavior: evidence from Canada and the United States," Journal of International Money and Finance, Elsevier, vol. 16(3), pages 461-476, June.
    7. Robert A. Amano & Tony S. Wirjanto, 1994. "The Dynamic Behaviour of Canadian Imports and the Linear-Quadratic Model: Evidence Based on the Euler Equation," Econometrics 9406002, University Library of Munich, Germany.
    8. Paul Mizen & Anindya Banerjee, 2006. "A re-interpretation of the linear quadratic model when inventories and sales are polynomially cointegrated," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1249-1264.
    9. de la Croix, David & Palm, Franz & Urbain, Jean-Pierre, 1996. "Labor market dynamics when effort depends on wage growth comparisons," LIDAM Discussion Papers IRES 1996019, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES), revised 00 Sep 1996.
    10. Gregory, Allan W. & Nason, James M. & Watt, David G., 1996. "Testing for structural breaks in cointegrated relationships," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 321-341.
    11. Engsted, Tom & Haldrup, Niels, 1997. "Money demand, adjustment costs, and forward-looking behavior," Journal of Policy Modeling, Elsevier, vol. 19(2), pages 153-173, April.
    12. Willman, Alpo, 2003. "Consumption, habit persistence, imperfect information and the lifetime budget constraint," Working Paper Series 251, European Central Bank.
    13. Philippe Jeanfils & Koen Burggraeve, 2005. "Noname - A new quarterly model for Belgium," Working Paper Research 68, National Bank of Belgium.
    14. Pereira, Rodrigo M., 2001. "Investment and Uncertainty in a Quadratic Adjustment Cost Model: Evidence from Brazil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 55(2), April.
    15. Philippe Jeanfils, 2000. "A model with explicit expectations for Belgium," Working Paper Research 04, National Bank of Belgium.
    16. Engsted, Tom & Gonzalo, Jesús & Haldrup, Niels, 1996. "Multicointegration and present value relations," DES - Working Papers. Statistics and Econometrics. WS 4540, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Robert Amano, "undated". "Empirical Evidence on the Cost of Adjustment and Dynamic Labour Demand," Staff Working Papers 95-3, Bank of Canada.
    18. Jaebeom Kim & Masao Ogaki & Minseok Yang, 2003. "Structural Error Correction Models: Instrumental Variables Methods and an application to an exchange rate model," RCER Working Papers 502, University of Rochester - Center for Economic Research (RCER).

  28. Dolado, J. & Galbraith, J.W. & Banerjee, A., 1989. "Estimating Euler Equations With Integrated Series," Economics Series Working Papers 9981, University of Oxford, Department of Economics.

    Cited by:

    1. Escribano, Álvaro & Pfann, Gerard, 1991. "Nonlinear error correction, asymmetric adjusment and cointegration," UC3M Working papers. Economics 2807, Universidad Carlos III de Madrid. Departamento de Economía.

Articles

  1. Galbraith, John W. & Zinde-Walsh, Victoria, 2020. "Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 609-632.

    Cited by:

    1. Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2021. "Inference in high-dimensional regression models without the exact or $L^p$ sparsity," Papers 2108.09520, arXiv.org, revised Dec 2022.
    2. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.

  2. Galbraith, John W. & van Norden, Simon, 2019. "Asymmetry in unemployment rate forecast errors," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1613-1626.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    3. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    4. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    5. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    6. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    7. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Papers 2110.03411, arXiv.org.
    8. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    9. Ovidiu BUZOIANU & Amelia DIACONU & Marcela Antoaneta NICULESCU & Silviu DIACONU, 2020. "Analysis Of The Economic And Social Effects Of Unemployment In Romania," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 705-711, November.
    10. Donayre, Luiggi, 2022. "On the behavior of Okun's law across business cycles," Economic Modelling, Elsevier, vol. 112(C).
    11. Muneeb Ahmad & Yousaf Ali Khan & Chonghui Jiang & Syed Jawad Haider Kazmi & Syed Zaheer Abbas, 2023. "The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 528-543, January.

  3. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.

    Cited by:

    1. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    2. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    3. Ashton de Silva & Maria Yanotti & Sarah Sinclair & Sveta Angelopoulos, 2023. "Place‐Based Policies and Nowcasting," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(3), pages 363-370, September.
    4. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    5. Daniel Aaronson & Scott A. Brave & Michael Fogarty & Ezra Karger & Spencer D. Krane, 2021. "Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade," Working Paper Series WP-2021-05, Federal Reserve Bank of Chicago, revised 18 Jun 2021.
    6. Aditya Aladangady & Shifrah Aron-Dine & Wendy Dunn & Laura Feiveson & Paul Lengermann & Claudia Sahm, 2021. "From Transaction Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 115-145, National Bureau of Economic Research, Inc.
    7. Tatjana Dahlhaus & Angelika Welte, 2021. "Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data," Staff Working Papers 21-43, Bank of Canada.
    8. Tingguo Zheng & Xinyue Fan & Wei Jin & Kuangnan Fang, 2024. "Forecasting CPI with multisource data: The value of media and internet information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 702-753, April.
    9. Christiane Baumeister & Danilo Leiva-León & Eric R. Sims, 2021. "Tracking Weekly State-Level Economic Conditions," CESifo Working Paper Series 9165, CESifo.
    10. Marta Crispino & Vincenzo Mariani, 2023. "A tool to nowcast tourist overnight stays with payment data and complementary indicators," Questioni di Economia e Finanza (Occasional Papers) 746, Bank of Italy, Economic Research and International Relations Area.
    11. Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
    12. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    13. Guerino Ardizzi & Simone Emiliozzi & Juri Marcucci & Libero Monteforte, 2019. "News and consumer card payments," Temi di discussione (Economic working papers) 1233, Bank of Italy, Economic Research and International Relations Area.
    14. Guillermo Carlomagno & Nicolas Eterovic & L. G. Hernández-Román, 2023. "Disentangling Demand and Supply Inflation Shocks from Chilean Electronic Payment Data," Working Papers Central Bank of Chile 986, Central Bank of Chile.
    15. David Bounie & Youssouf Camara & John W. Galbraith, 2021. "Consumer Mobility, Online and On-site Commerce and the Geographic Concentration of Economic Activity: Evidence from 20 Billion Transactions," CIRANO Working Papers 2021s-17, CIRANO.
    16. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    17. García, Juan R. & Pacce, Matías & Rodrigo, Tomasa & Ruiz de Aguirre, Pep & Ulloa, Camilo A., 2021. "Measuring and forecasting retail trade in real time using card transactional data," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1235-1246.
    18. Tomas Adam & Jan Belka & Martin Hluze & Jakub Mateju & Hana Prause & Jiri Schwarz, 2023. "Ace in Hand: The Value of Card Data in the Game of Nowcasting," Working Papers 2023/14, Czech National Bank.
    19. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    20. Ferrara, Laurent & Sheng, Xuguang Simon, 2022. "Guest editorial: Economic forecasting in times of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 527-528.
    21. Simone Emiliozzi & Concetta Rondinelli & Stefania Villa, 2023. "Consumption during the Covid-19 pandemic: evidence from Italian credit cards," Questioni di Economia e Finanza (Occasional Papers) 769, Bank of Italy, Economic Research and International Relations Area.
    22. Paulick, Jan, 2022. "Financial market infrastructures : Essays on liquidity, participant behaviour and information extraction," Other publications TiSEM 004942ed-f68d-40cc-a830-b, Tilburg University, School of Economics and Management.
    23. David Bounie & Youssouf Camara & John Galbraith, 2020. "Online Commerce, Inter-Regional Retail Trade, and the Evolution of Gravity Effects: Evidence from 20 Billion Transactions," Working Papers hal-02864695, HAL.
    24. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.
    25. Kohei Matsumura & Yusuke Oh & Tomohiro Sugo & Koji Takahashi, "undated". "Nowcasting Economic Activity with Mobility Data," Bank of Japan Working Paper Series 21-E-2, Bank of Japan.

  4. John W. Galbraith & Douglas J. Hodgson, 2018. "Econometric Fine Art Valuation by Combining Hedonic and Repeat-Sales Information," Econometrics, MDPI, vol. 6(3), pages 1-15, June.

    Cited by:

    1. Steven B. Caudill & Franklin G. Mixon, 2020. "Estimating Bargaining Power in Real Estate Pricing Models: Conceptual and Empirical Issues," JRFM, MDPI, vol. 13(5), pages 1-8, May.
    2. Andrey A. Zaytsev & Roman S. Blizkyi & Irina I. Rakhmeeva & Nikolay D. Dmitriev, 2021. "Building a Model for Financial Management of Digital Technologies in the Areas of Combinatorial Effects," Economies, MDPI, vol. 9(2), pages 1-15, April.
    3. Vecco, Marilena & Chang, Simeng & Zanola, Roberto, 2022. "The more you know, the better: A Heckman repeat-sales price index," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 194-199.

  5. Zhang, Hui Jun & Dufour, Jean-Marie & Galbraith, John W., 2016. "Exchange rates and commodity prices: Measuring causality at multiple horizons," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 100-120.
    See citations under working paper version above.
  6. John W. Galbraith & Victoria Zinde-Walsh & Jingmei Zhu, 2015. "GARCH Model Estimation Using Estimated Quadratic Variation," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1172-1192, December.

    Cited by:

    1. John W. Galbraith & Liam Cheung, 2013. "Forecasting financial volatility with combined QML and LAD-ARCH estimators of the GARCH model," CIRANO Working Papers 2013s-19, CIRANO.

  7. John Galbraith & Douglas Hodgson, 2015. "Innovation, experience and artists’ age-valuation profiles: evidence from eighteenth-century rococo and neoclassical painters," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 39(3), pages 259-275, August.

    Cited by:

    1. Douglas J. Hodgson & Christiane Hellmanzik, 2019. "Relationships between artistic movements and careers of modern artists: evidence from hedonic regressions with auction data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 43(2), pages 309-337, June.
    2. Shailendra Gurjar & Usha Ananthakumar, 2023. "The economics of art: price determinants and returns on investment in Indian paintings," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 50(6), pages 839-859, January.

  8. John W. Galbraith & Greg Tkacz, 2013. "Analyzing Economic Effects of September 11 and Other Extreme Events Using Debit and Payments System Data," Canadian Public Policy, University of Toronto Press, vol. 39(1), pages 119-134, March.

    Cited by:

    1. John Bagnall & David Bounie & Kim P. Huynh & Anneke Kosse & Tobias Schmidt & Scott Schuh, 2016. "Consumer Cash Usage: A Cross-Country Comparison with Payment Diary Survey Data," International Journal of Central Banking, International Journal of Central Banking, vol. 12(4), pages 1-61, December.
    2. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    3. Eunae Jung & Hyungun Sung, 2017. "The Influence of the Middle East Respiratory Syndrome Outbreak on Online and Offline Markets for Retail Sales," Sustainability, MDPI, vol. 9(3), pages 1-23, March.
    4. David Bounie & Youssouf Camara & John W. Galbraith, 2020. "Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data," CIRANO Working Papers 2020s-28, CIRANO.
    5. Bounie, David & Camara, Youssouf & Galbraith, John W., 2023. "Consumer mobility and expenditure during the COVID-19 containments: Evidence from French transaction data," European Economic Review, Elsevier, vol. 151(C).
    6. Jie Xu & Ming Gao & Yina Zhang, 2021. "The variations in individual consumption change and the substitution effect under the shock of COVID‐19: Evidence from payment system data in China," Growth and Change, Wiley Blackwell, vol. 52(2), pages 990-1010, June.
    7. John W. Galbraith & Greg Tkacz, 2013. "Nowcasting GDP: Electronic Payments, Data Vintages and the Timing of Data Releases," CIRANO Working Papers 2013s-25, CIRANO.
    8. Leonard Sabetti & David Jacho-Chávez & Robert Petrunia & Marcel Voia, 2018. "Tail Risk in a Retail Payments System," Post-Print hal-03573058, HAL.
    9. Tatjana Dahlhaus & Angelika Welte, 2021. "Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data," Staff Working Papers 21-43, Bank of Canada.
    10. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    11. David Bounie & Youssouf Camara & John W. Galbraith, 2021. "Consumer Mobility, Online and On-site Commerce and the Geographic Concentration of Economic Activity: Evidence from 20 Billion Transactions," CIRANO Working Papers 2021s-17, CIRANO.
    12. David Bounie & Youssouf Camara & John Galbraith, 2020. "Online Commerce, Inter-Regional Retail Trade, and the Evolution of Gravity Effects: Evidence from 20 Billion Transactions," Working Papers hal-02864695, HAL.
    13. Hojin Jung & Minjae Park & Kihoon Hong & Eunjung Hyun, 2016. "The Impact of an Epidemic Outbreak on Consumer Expenditures:An Empirical Assessment for MERS Korea," Sustainability, MDPI, vol. 8(5), pages 1-15, May.

  9. Galbraith, John W. & Hodgson, Douglas J., 2012. "Dimension reduction and model averaging for estimation of artists' age-valuation profiles," European Economic Review, Elsevier, vol. 56(3), pages 422-435.
    See citations under working paper version above.
  10. John W. Galbraith & Simon van Norden, 2012. "Assessing gross domestic product and inflation probability forecasts derived from Bank of England fan charts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(3), pages 713-727, July.

    Cited by:

    1. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Carola Conces Binder & Rodrigo Sekkel, 2023. "Central Bank Forecasting: A Survey," Staff Working Papers 23-18, Bank of Canada.
    3. Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël, 2023. "Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1448-1459.
    4. Christopher McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the economic value of probabilistic forecasts in the presence of an inflation target," CAMA Working Papers 2016-40, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Simon van Norden, 2015. "Estimates of Québec’s Growth Uncertainty," CIRANO Project Reports 2015rp-01, CIRANO.
    6. Carlos Diaz Vela, 2016. "Extracting the Information Shocks from the Bank of England Inflation Density Forecasts," Discussion Papers in Economics 16/13, Division of Economics, School of Business, University of Leicester.
    7. Galvao, Ana Beatriz & Owyang, Michael, 2020. "Forecasting Low Frequency Macroeconomic Events with High Frequency Data," EMF Research Papers 38, Economic Modelling and Forecasting Group.
    8. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    9. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    10. Elena Andreou & Andros Kourtellos, 2018. "Scoring rules for simple forecasting models: The case of Cyprus GDP and its sectors," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(1), pages 59-73, June.
    11. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    12. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    13. Anthony Garratt & Timo Henckel & Shaun P. Vahey, 2019. "Empirically-transformed linear opinion pools," CAMA Working Papers 2019-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Probability Forecasting for Inflation Warnings from the Federal Reserve," EMF Research Papers 07, Economic Modelling and Forecasting Group.
    15. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    16. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    17. Schreiber, Sven & Soldatenkova, Natalia, 2016. "Anticipating business-cycle turning points in real time using density forecasts from a VAR," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 166-187.
    18. Galbraith, John W. & van Norden, Simon, 2019. "Asymmetry in unemployment rate forecast errors," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1613-1626.
    19. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Too many skew normal distributions? The practitioner’s perspective," Discussion Papers in Economics 13/07, Division of Economics, School of Business, University of Leicester.
    20. Luca Brugnolini & Giuseppe Ragusa, 2022. "Euro Area Deflationary Pressure Index," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 883-900, October.
    21. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.

  11. Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.

    Cited by:

    1. Christopher McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the economic value of probabilistic forecasts in the presence of an inflation target," CAMA Working Papers 2016-40, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Simon van Norden, 2015. "Estimates of Québec’s Growth Uncertainty," CIRANO Project Reports 2015rp-01, CIRANO.
    3. Turgut Kisinbay & Chikako Baba, 2011. "Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations," IMF Working Papers 2011/235, International Monetary Fund.
    4. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    5. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    6. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    7. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    8. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    9. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
    10. Tsyplakov, Alexander, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.

  12. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.

    Cited by:

    1. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
    2. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
    3. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
    4. Brenda Castillo-Brais & Ángel León & Juan Mora, 2022. "Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    5. Luca Merlo & Lea Petrella & Valentina Raponi, 2021. "Forecasting VaR and ES using a joint quantile regression and implications in portfolio allocation," Papers 2106.06518, arXiv.org.
    6. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    7. Saissi Hassani, Samir & Dionne, Georges, 2023. "Using skewed exponential power mixture for VaR and CVaR forecasts to comply with market risk regulation," Working Papers 23-2, HEC Montreal, Canada Research Chair in Risk Management.
    8. Benjamin Beckers & Helmut Herwartz & Moritz Seidel, 2017. "Risk forecasting in (T)GARCH models with uncorrelated dependent innovations," Quantitative Finance, Taylor & Francis Journals, vol. 17(1), pages 121-137, January.
    9. Lingbing Feng & Yanlin Shi, 2017. "A simulation study on the distributions of disturbances in the GARCH model," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1355503-135, January.
    10. Shi, Yanlin & Ho, Kin-Yip, 2015. "Modeling high-frequency volatility with three-state FIGARCH models," Economic Modelling, Elsevier, vol. 51(C), pages 473-483.
    11. M. Caivano & A. Harvey, 2013. "Time series models with an EGB2 conditional distribution," Cambridge Working Papers in Economics 1325, Faculty of Economics, University of Cambridge.
    12. Robina Iqbal & Ghulam Sorwar & Rose Baker & Taufiq Choudhry, 2020. "Multiday expected shortfall under generalized t distributions: evidence from global stock market," Review of Quantitative Finance and Accounting, Springer, vol. 55(3), pages 803-825, October.
    13. Victor Korolev, 2023. "Analytic and Asymptotic Properties of the Generalized Student and Generalized Lomax Distributions," Mathematics, MDPI, vol. 11(13), pages 1-27, June.
    14. Mazur Błażej & Pipień Mateusz, 2018. "Time-varying asymmetry and tail thickness in long series of daily financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-21, December.
    15. Sorwar, Ghulam & Pappas, Vasileios & Pereira, John & Nurullah, Mohamed, 2016. "To debt or not to debt: Are Islamic banks less risky than conventional banks?," Journal of Economic Behavior & Organization, Elsevier, vol. 132(S), pages 113-126.
    16. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    17. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    18. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.
    19. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2013. "Pair Copula Construction based Expected Shortfall estimation," Economics Bulletin, AccessEcon, vol. 33(2), pages 1067-1072.
    20. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    21. Andrew J. Patton & Johanna F. Ziegel & Rui Chen, 2017. "Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk)," Papers 1707.05108, arXiv.org.
    22. Gao, Chun-Ting & Zhou, Xiao-Hua, 2016. "Forecasting VaR and ES using dynamic conditional score models and skew Student distribution," Economic Modelling, Elsevier, vol. 53(C), pages 216-223.
    23. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "GARCH modeling of five popular commodities," Empirical Economics, Springer, vol. 48(4), pages 1691-1712, June.
    24. Nadarajah, Saralees & Chan, Stephen & Afuecheta, Emmanuel, 2013. "On the characteristic function for asymmetric Student t distributions," Economics Letters, Elsevier, vol. 121(2), pages 271-274.
    25. Rehman, Mobeen Ur & Owusu Junior, Peterson & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Time-varying risk analysis for commodity futures," Resources Policy, Elsevier, vol. 78(C).
    26. Adcock, C J & Meade, N, 2017. "Using parametric classification trees for model selection with applications to financial risk management," European Journal of Operational Research, Elsevier, vol. 259(2), pages 746-765.
    27. Rubio, Francisco Javier & Steel, Mark F. J., 2014. "Bayesian modelling of skewness and kurtosis with two-piece scale and shape transformations," MPRA Paper 57102, University Library of Munich, Germany.
    28. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    29. Baker, Rose, 2017. "Creating new distributions by blunting cusps," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 55-63.
    30. Michele Caivano & Andrew Harvey & Alessandra Luati, 2016. "Robust time series models with trend and seasonal components," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 99-120, March.
    31. Xu Wang & Xueyan Wu & Yingying Zhou, 2022. "Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model," Energies, MDPI, vol. 15(14), pages 1-21, July.
    32. Owusu Junior, Peterson & Alagidede, Imhotep, 2020. "Risks in emerging markets equities: Time-varying versus spatial risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    33. Stephen Thiele, 2020. "Modeling the conditional distribution of financial returns with asymmetric tails," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 46-60, January.
    34. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    35. Owusu Junior, Peterson & Tiwari, Aviral Kumar & Tweneboah, George & Asafo-Adjei, Emmanuel, 2022. "GAS and GARCH based value-at-risk modeling of precious metals," Resources Policy, Elsevier, vol. 75(C).
    36. León, Ángel & Ñíguez, Trino-Manuel, 2021. "The transformed Gram Charlier distribution: Parametric properties and financial risk applications," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 323-349.
    37. Shi, Yanlin & Feng, Lingbing, 2016. "A discussion on the innovation distribution of the Markov regime-switching GARCH model," Economic Modelling, Elsevier, vol. 53(C), pages 278-288.
    38. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2020. "Loss-based approach to two-piece location-scale distributions with applications to dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 309-333, June.
    39. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    40. Tong Liu & Yanlin Shi, 2022. "Innovation of the Component GARCH Model: Simulation Evidence and Application on the Chinese Stock Market," Mathematics, MDPI, vol. 10(11), pages 1-18, June.
    41. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    42. Acereda, Beatriz & Leon, Angel & Mora, Juan, 2020. "Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting," Finance Research Letters, Elsevier, vol. 33(C).
    43. Merlo, Luca & Petrella, Lea & Raponi, Valentina, 2021. "Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation," Journal of Banking & Finance, Elsevier, vol. 133(C).

  13. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
    See citations under working paper version above.
  14. Zernov, Serguei & Zinde-Walsh, Victoria & Galbraith, John W., 2009. "Asymptotics for estimation of quantile regressions with truncated infinite-dimensional processes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 497-508, March.

    Cited by:

    1. John W. Galbraith & Victoria Zinde-Walsh, 2011. "Partially Dimension-Reduced Regressions with Potentially Infinite-Dimensional Processes," CIRANO Working Papers 2011s-57, CIRANO.
    2. Nicholas C.S. Sim, 2009. "Modeling Quantile Dependence: A New Look at the Money-Output Relationship," School of Economics and Public Policy Working Papers 2009-34, University of Adelaide, School of Economics and Public Policy.
    3. John W. Galbraith & Liam Cheung, 2013. "Forecasting financial volatility with combined QML and LAD-ARCH estimators of the GARCH model," CIRANO Working Papers 2013s-19, CIRANO.

  15. John W. Galbraith & Greg Tkacz, 2007. "Forecast content and content horizons for some important macroeconomic time series," Canadian Journal of Economics, Canadian Economics Association, vol. 40(3), pages 935-953, August.
    See citations under working paper version above.
  16. Galbraith, John W. & KI[#x1e63]Inbay, Turgut, 2005. "Content horizons for conditional variance forecasts," International Journal of Forecasting, Elsevier, vol. 21(2), pages 249-260.

    Cited by:

    1. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    2. Georgios Tsiotas, 2009. "On the use of non-linear transformations in Stochastic Volatility models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(4), pages 555-583, November.
    3. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    4. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.
    5. John W. Galbraith & Greg Tkacz, 2007. "Forecast Content And Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2007-01, McGill University, Department of Economics.
    6. Tsiotas, Georgios, 2012. "On generalised asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 151-172, January.
    7. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    8. John G. Galbraith & Greg Tkacz, 2006. "How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2006-13, McGill University, Department of Economics.
    9. Li, Xingyi & Zakamulin, Valeriy, 2020. "The term structure of volatility predictability," International Journal of Forecasting, Elsevier, vol. 36(2), pages 723-737.
    10. Edoardo Otrano & Umberto Triacca, 2007. "Testing for Equal Predictability of Stationary ARMA Processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1091-1108.
    11. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    12. Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional Variance Forecasts for Long-Term Stock Returns," Risks, MDPI, vol. 7(4), pages 1-22, November.
    13. Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
    14. Raunig, Burkhard, 2008. "The predictability of exchange rate volatility," Economics Letters, Elsevier, vol. 98(2), pages 220-228, February.

  17. John W. Galbraith, 2004. "Circuit Breakers and the Tail Index of Equity Returns," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 109-129.
    See citations under working paper version above.
  18. John Galbraith & Aman Ullah & Victoria Zinde-Walsh, 2002. "Estimation Of The Vector Moving Average Model By Vector Autoregression," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 205-219.

    Cited by:

    1. Sisi Qin & Wee‐Yeap Lau, 2023. "Cross‐border and cross‐commodity volatility spillover effects of Chinese soybean futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1836-1852, December.
    2. Hernandez, Manuel A. & Ibarra, Raul & Trupkin, Danilo R., 2011. "How far do shocks move across borders?: Examining volatility transmission in major agricultural futures markets," IFPRI discussion papers 1109, International Food Policy Research Institute (IFPRI).
    3. Patrick Richard, 2009. "Improving the accuracy of the analytical indirect inference estimator for MA models," Economics Bulletin, AccessEcon, vol. 29(4), pages 2795-2802.
    4. Stefan Bruder, 2014. "Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions," ECON - Working Papers 181, Department of Economics - University of Zurich, revised Dec 2015.
    5. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    6. Ufuk Devrim Demirel, 2015. "Identification of technology shocks using misspecified VARs," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 48(4), pages 1321-1349, November.
    7. Poloni, Federico & Sbrana, Giacomo, 2019. "Closed-form results for vector moving average models with a univariate estimation approach," Econometrics and Statistics, Elsevier, vol. 10(C), pages 27-52.
    8. BenSaïda, Ahmed, 2019. "Good and bad volatility spillovers: An asymmetric connectedness," Journal of Financial Markets, Elsevier, vol. 43(C), pages 78-95.
    9. Conlon, Thomas & Cotter, John & Gençay, Ramazan, 2018. "Long-run wavelet-based correlation for financial time series," European Journal of Operational Research, Elsevier, vol. 271(2), pages 676-696.

  19. Galbraith, John W. & Tkacz, Greg, 2000. "Testing for asymmetry in the link between the yield spread and output in the G-7 countries," Journal of International Money and Finance, Elsevier, vol. 19(5), pages 657-672, October.
    See citations under working paper version above.
  20. Galbraith, JohnW. & Zinde-Walsh, Victoria, 1999. "On the distributions of Augmented Dickey-Fuller statistics in processes with moving average components," Journal of Econometrics, Elsevier, vol. 93(1), pages 25-47, November.

    Cited by:

    1. Gianfreda, Angelica & Maranzano, Paolo & Parisio, Lucia & Pelagatti, Matteo, 2023. "Testing for integration and cointegration when time series are observed with noise," Economic Modelling, Elsevier, vol. 125(C).
    2. Kai Carstensen, 2003. "The finite-sample performance of robust unit root tests," Statistical Papers, Springer, vol. 44(4), pages 469-482, October.
    3. Kung-Sik Chan & Simone Giannerini & Greta Goracci & Howell Tong, 2020. "Testing for threshold regulation in presence of measurement error with an application to the PPP hypothesis," Papers 2002.09968, arXiv.org, revised Nov 2021.
    4. Patrick Richard, 2008. "Modified Fast Double Sieve Bootstraps for ADF Tests," Cahiers de recherche 08-17, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    5. Arghyrou, Michael G. & Gregoriou, Andros & Kontonikas, Alexandros, 2009. "Do real interest rates converge? Evidence from the European union," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(3), pages 447-460, July.
    6. Tomas del Barrio Castro & Denise R. Osborn, 2006. "A Random Walk through Seasonal Adjustment: Noninvertible Moving Averages and Unit Root Tests," Economics Discussion Paper Series 0612, Economics, The University of Manchester.
    7. Politis, Dimitris, 2016. "HEGY test under seasonal heterogeneity," University of California at San Diego, Economics Working Paper Series qt2q4054kf, Department of Economics, UC San Diego.
    8. Xingyu Zhang & Tao Zhang & Jiao Pei & Yuanyuan Liu & Xiaosong Li & Pau Medrano-Gracia, 2016. "Time Series Modelling of Syphilis Incidence in China from 2005 to 2012," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
    9. Jin-Yi Wu & Yi-Biao Zhou & Yue Chen & Song Liang & Lin-Han Li & Sheng-Bang Zheng & Shao-ping Zhu & Guang-Hui Ren & Xiu-Xia Song & Qing-Wu Jiang, 2015. "Three Gorges Dam: Impact of Water Level Changes on the Density of Schistosome-Transmitting Snail Oncomelania hupensis in Dongting Lake Area, China," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(6), pages 1-13, June.
    10. Ismael Sánchez, 2004. "Implementing unit roost tests in ARMA models of unknow order," Statistical Papers, Springer, vol. 45(2), pages 249-266, April.

  21. Campbell, Bryan & Galbraith, John W, 1997. "Non-parametric Regression Models of Deviations from Orthogonality in the Expectations Theory of the Term Structure," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(2), pages 265-284, May.

    Cited by:

    1. Lise Godbout & Paul Storer & Christian Zimmermann, 1999. "The Canadian Treasury Bill Auction and the Term Structure of Interest Rates," Cahiers de recherche CREFE / CREFE Working Papers 75, CREFE, Université du Québec à Montréal.

  22. Galbraith, John W. & Kaiserman, Murray, 1997. "Taxation, smuggling and demand for cigarettes in Canada: Evidence from time-series data," Journal of Health Economics, Elsevier, vol. 16(3), pages 287-301, June.

    Cited by:

    1. Rajeev Goel, 2004. "Cigarette demand in Canada and the US-Canadian cigarette smuggling," Applied Economics Letters, Taylor & Francis Journals, vol. 11(9), pages 537-540.
    2. Anindya Sen, 2017. "Smokes, Smugglers and Lost Tax Revenues: How Governments Should Respond," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 471, February.
    3. Howard Chernick & David Merriman, 2013. "Using Littered Pack Data to Estimate Cigarette Tax Avoidance in Nyc," National Tax Journal, National Tax Association;National Tax Journal, vol. 66(3), pages 635-668, June.
    4. Nikolay Gospodinov & Ian Irvine, 2005. "A ‘long march’ perspective on tobacco use in Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 38(2), pages 366-393, May.
    5. Sen Anindya & Ariizumi Hideki & Driambe Daciana, 2010. "Do Changes In Cigarette Taxes Impact Youth Smoking? Evidence from Canadian Provinces," Forum for Health Economics & Policy, De Gruyter, vol. 13(2), pages 1-25, August.
    6. Luca Macedoni, 2021. "Has the Euro Shrunk the Band? Relative Purchasing Power Parity Convergence in a Currency Union," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(2), pages 593-620, April.
    7. Auld M. Christopher & Zarrabi Mahmood, 2015. "Long-Term Effects of Tobacco Prices Faced by Adolescents," Forum for Health Economics & Policy, De Gruyter, vol. 18(1), pages 1-24, January.
    8. Gruber, Jonathan & Sen, Anindya & Stabile, Mark, 2003. "Estimating price elasticities when there is smuggling: the sensitivity of smoking to price in Canada," Journal of Health Economics, Elsevier, vol. 22(5), pages 821-842, September.
    9. James E. Prieger & Jonathan Kulick, 2018. "Cigarette Taxes And Illicit Trade In Europe," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1706-1723, July.
    10. Isao Yorozu & Yimin Zhou, 2002. "The Demand For Cigarettes In Japan: Impact Of Information Dissemination On Cigarette Consumption," Contemporary Economic Policy, Western Economic Association International, vol. 20(1), pages 72-82, January.
    11. Auld, M. Christopher & Grootendorst, Paul, 2004. "An empirical analysis of milk addiction," Journal of Health Economics, Elsevier, vol. 23(6), pages 1117-1133, November.
    12. Gospodinov, Nikolay & Irvine, Ian, 2009. "Tobacco taxes and regressivity," Journal of Health Economics, Elsevier, vol. 28(2), pages 375-384, March.
    13. Abdulbaki Bilgic & Wojciech Florkowski & Cuma Akbay, 2010. "Demand for cigarettes in Turkey: an application of count data models," Empirical Economics, Springer, vol. 39(3), pages 733-765, December.
    14. Shu Wang & David Merriman & Frank J. Chaloupka, 2016. "Relative Tax Rates, Proximity and Cigarette Tax Noncompliance: Evidence from a National Sample of Littered Cigarette Packs," NBER Working Papers 22577, National Bureau of Economic Research, Inc.
    15. Martyn Duffy, 2006. "Tobacco consumption and policy in the United Kingdom," Applied Economics, Taylor & Francis Journals, vol. 38(11), pages 1235-1257.
    16. Michael Chaiton & Jolene Dubray & G. Emmanuel Guindon & Robert Schwartz, 2021. "Tobacco Endgame Simulation Modelling: Assessing the Impact of Policy Changes on Smoking Prevalence in 2035," Forecasting, MDPI, vol. 3(2), pages 1-9, April.
    17. Stehr, Mark, 2005. "Cigarette tax avoidance and evasion," Journal of Health Economics, Elsevier, vol. 24(2), pages 277-297, March.
    18. Charles A.M. De Bartolome, 2007. "Tax competition and the creation of redundant products," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(4), pages 1213-1236, November.
    19. Austan Goolsbee, 1998. "In a World Without Borders: The Impact of Taxes on Internet Commerce," NBER Working Papers 6863, National Bureau of Economic Research, Inc.
    20. Songjune Kim & Barry J. Seldon, 2004. "The Demand for Cigarettes in the Republic of Korea and Implications for Government Policy to Lower Cigarette Consumption," Contemporary Economic Policy, Western Economic Association International, vol. 22(2), pages 299-308, April.

  23. Galbraith, John W, 1996. "Credit Rationing and Threshold Effects in the Relation between Money and Output," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(4), pages 419-429, July-Aug..

    Cited by:

    1. Mehmet Caner & Bruce E. Hansen, 1998. "Threshold Autoregressions with a Near Unit Root," Working Papers 9821, Department of Economics, Bilkent University.
    2. John W. Galbraith & Greg Tkacz, 1999. "Testing For Asymmetry In The Link Between The Yield Spread And Output In The G-7 Countries," Departmental Working Papers 1999-02, McGill University, Department of Economics.
    3. Régis Barnichon & Christian Matthes & Alexander Ziegenbein, 2016. "Theory Ahead of Measurement? Assessing the Nonlinear Effects of Financial Market Disruptions," Working Paper 16-15, Federal Reserve Bank of Richmond.
    4. Bazán, Walter, 2011. "No-linealidades y asimetrías en el crédito peruano," Working Papers 2011-015, Banco Central de Reserva del Perú.
    5. Rozina Shaheen, 2020. "Credit market conditions and impact of monetary policy in a developing economy context," International Economics and Economic Policy, Springer, vol. 17(2), pages 409-425, May.
    6. Marcin Owczarczuk, 2009. "Maximum Score Type Estimators," Working Papers 30, Department of Applied Econometrics, Warsaw School of Economics.
    7. Barnichon, Regis & Matthes, Christian & Ziegenbein, Alexander, 2016. "Assessing the Non-Linear Effects of Credit Market Shocks," CEPR Discussion Papers 11410, C.E.P.R. Discussion Papers.
    8. Miyakoshi, Tatsuyoshi & Jalolov, Mirzosharif, 2005. "Money-income causality revisited in EGARCH: Spillovers of monetary policy to Asia from the US," Journal of Asian Economics, Elsevier, vol. 16(2), pages 299-313, April.
    9. Li Jing, 2016. "Effects of filtering data on testing asymmetry in threshold autoregressive models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 549-565, December.
    10. Ahmad Yamin & Donayre Luiggi, 2016. "Outliers and persistence in threshold autoregressive processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 37-56, February.
    11. Paramsothy Silvapulle & Imad A. Moosa & Mervyn J. Silvapulle, 2004. "Asymmetry in Okun's law," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 37(2), pages 353-374, May.
    12. Baum, Anja & Koester, Gerrit B., 2011. "The impact of fiscal policy on economic activity over the business cycle - evidence from a threshold VAR analysis," Discussion Paper Series 1: Economic Studies 2011,03, Deutsche Bundesbank.
    13. Gilles DUFRENOT & Val=E9rie MIGNON & Anne PEGUIN-FEISSOLE, 2003. "Business cycles asymmetry and monetary policy: a further investigatio= n=20 using MRSTAR models," Macroeconomics 0309002, University Library of Munich, Germany.
    14. Hansen,B.E., 1999. "Testing for linearity," Working papers 7, Wisconsin Madison - Social Systems.
    15. Saten Kumar, 2016. "Is the US Consumer Credit Asymmetric?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(2), pages 194-215, May.
    16. Mehmet Caner & Bruce E. Hansen, 2001. "Threshold Autoregression with a Unit Root," Econometrica, Econometric Society, vol. 69(6), pages 1555-1596, November.
    17. Dufrenot, Gilles & Mignon, Valerie & Peguin-Feissolle, Anne, 2004. "Business cycles asymmetry and monetary policy: a further investigation using MRSTAR models," Economic Modelling, Elsevier, vol. 21(1), pages 37-71, January.
    18. Alessio Anzuini & Francesca Brusa, 2016. "Carry trades and exchange rate volatility: a TVAR approach," Temi di discussione (Economic working papers) 1046, Bank of Italy, Economic Research and International Relations Area.
    19. Calza Alessandro & Sousa João, 2006. "Output and Inflation Responses to Credit Shocks: Are There Threshold Effects in the Euro Area?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-21, May.

  24. Galbraith, John W. & Zinde-Walsh, Victoria, 1995. "Transforming the error-components model for estimation with general ARMA disturbances," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 349-355.

    Cited by:

    1. Badi Baltagi & Long Liu, 2020. "Forecasting with Unbalanced Panel Data," Center for Policy Research Working Papers 221, Center for Policy Research, Maxwell School, Syracuse University.
    2. Qiu, Jin & Ma, Qing & Wu, Lang, 2019. "A moving blocks empirical likelihood method for panel linear fixed effects models with serial correlations and cross-sectional dependences," Economic Modelling, Elsevier, vol. 83(C), pages 394-405.
    3. Badi H. Baltagi & Byoung Cheol Jung & Seuck Heun Song, 2008. "Testing for Heteroskedasticity and Serial Correlation in a Random Effects Panel Data Model," Center for Policy Research Working Papers 111, Center for Policy Research, Maxwell School, Syracuse University.
    4. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    5. Yuichi Goto & Koichi Arakaki & Yan Liu & Masanobu Taniguchi, 2023. "Homogeneity tests for one-way models with dependent errors under correlated groups," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 163-183, March.
    6. Paolo, Foschi, 2005. "Estimating regressions and seemingly unrelated regressions with error component disturbances," MPRA Paper 1424, University Library of Munich, Germany, revised 07 Sep 2006.

  25. Campbell, Bryan & Galbraith, John W, 1993. "Inference in Expectations Models of the Term Structure: A Non-parametric Approach," Empirical Economics, Springer, vol. 18(4), pages 623-638.

    Cited by:

    1. Lise Godbout & Paul Storer & Christian Zimmermann, 1999. "The Canadian Treasury Bill Auction and the Term Structure of Interest Rates," Cahiers de recherche CREFE / CREFE Working Papers 75, CREFE, Université du Québec à Montréal.
    2. Breitung, Jörg, 1998. "Rank tests for nonlinear cointegration," SFB 373 Discussion Papers 1998,65, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Pierre Jaillard, 1993. "Le coût du crédit aux entreprises : une gestion plus rationnelle du risque," Économie et Statistique, Programme National Persée, vol. 268(1), pages 77-86.

  26. Galbraith, John W. & Zinde-Walsh, Victoria, 1992. "The GLS Transformation Matrix and a Semi-recursive Estimator for the Linear Regression Model with ARMA Errors," Econometric Theory, Cambridge University Press, vol. 8(1), pages 95-111, March.

    Cited by:

    1. Patrick Richard, 2009. "Improving the accuracy of the analytical indirect inference estimator for MA models," Economics Bulletin, AccessEcon, vol. 29(4), pages 2795-2802.
    2. Galbraith, John W. & Zinde-Walsh, Victoria, 1995. "Transforming the error-components model for estimation with general ARMA disturbances," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 349-355.
    3. Choudhury, Askar H. & Power, Simon, 1995. "A new approximate GLS estimator for the linear regression model with ARMA(p, q) disturbances," Economics Letters, Elsevier, vol. 48(2), pages 119-127, May.
    4. Galbraith, JohnW. & Zinde-Walsh, Victoria, 1999. "On the distributions of Augmented Dickey-Fuller statistics in processes with moving average components," Journal of Econometrics, Elsevier, vol. 93(1), pages 25-47, November.

  27. Zinde-Walsh, Victoria & Galbraith, John W., 1991. "Estimation of a linear regression model with stationary ARMA(p, q) errors," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 333-357, February.

    Cited by:

    1. Galbraith, John W. & Zinde-Walsh, Victoria, 1995. "Transforming the error-components model for estimation with general ARMA disturbances," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 349-355.
    2. Peide Shi & Chih‐Ling Tsai, 2004. "A Joint Regression Variable and Autoregressive Order Selection Criterion," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 923-941, November.
    3. Paolo Maranzano & Matteo Maria Pelagatti, 2022. "Spatio-temporal Event Studies for Air Quality Assessment under Cross-sectional Dependence," Papers 2210.17529, arXiv.org.
    4. Galbraith, JohnW. & Zinde-Walsh, Victoria, 1999. "On the distributions of Augmented Dickey-Fuller statistics in processes with moving average components," Journal of Econometrics, Elsevier, vol. 93(1), pages 25-47, November.
    5. vdr Leeuw, J.L., 1997. "Maximum Likelihood Estimation of Exact ARMA Models," Other publications TiSEM a1cdd9b8-93d9-460c-a0c9-1, Tilburg University, School of Economics and Management.
    6. David Mandy & Sandor Fridli, 2004. "Exact FGLS Asymptotics for MA Errors," Working Papers 0405, Department of Economics, University of Missouri, revised 16 Dec 2004.
    7. Galbraith, John W. & Kaiserman, Murray, 1997. "Taxation, smuggling and demand for cigarettes in Canada: Evidence from time-series data," Journal of Health Economics, Elsevier, vol. 16(3), pages 287-301, June.
    8. Greg Tkacz, 2007. "Gold Prices and Inflation," Staff Working Papers 07-35, Bank of Canada.

  28. Dolado, Juan & Galbraith, John W & Banerjee, Anindya, 1991. "Estimating Intertemporal Quadratic Adjustment Cost Models with Integrated Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(4), pages 919-936, November.
    See citations under working paper version above.
  29. Robert D. Cairns & John W. Galbraith, 1990. "Artificial Compatibility, Barriers to Entry, and Frequent-Flyer Programs," Canadian Journal of Economics, Canadian Economics Association, vol. 23(4), pages 807-816, November.

    Cited by:

    1. Ramon Caminal, 2009. "The design and efficiency of loyalty rewards," UFAE and IAE Working Papers 789.09, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    2. Austan Goolsbee & Chad Syverson, 2005. "How do Incumbents Respond to the Threat of Entry? Evidence from the Major Airlines," NBER Working Papers 11072, National Bureau of Economic Research, Inc.
    3. John Kwoka & Birzhan Batkeyev, 2019. "Strategic Responses to Competitive Threats: Airlines in Action," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(1), pages 83-109, February.
    4. Fredrik Carlsson & Åsa Lofgren, 2006. "Airline choice, switching costs and frequent flyer programmes," Applied Economics, Taylor & Francis Journals, vol. 38(13), pages 1469-1475.
    5. Luttmann, Alexander & Ladd, Daniel, 2023. "Loyalty rewards and redemption behavior: Stylized facts for the U.S. airline industry," MPRA Paper 119214, University Library of Munich, Germany.
    6. Lam, Wing Man Wynne & Liu, Xingyi, 2020. "Does data portability facilitate entry?," International Journal of Industrial Organization, Elsevier, vol. 69(C).
    7. Agostini, Claudio A. & Inostroza, Diego & Willington, Manuel, 2015. "Price effects of airlines frequent flyer programs: The case of the dominant firm in Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 283-297.
    8. Yuk‐fai Fong & Qihong Liu, 2011. "Loyalty Rewards Facilitate Tacit Collusion," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 20(3), pages 739-775, September.
    9. Hartmann, Wesley R. & Viard, V. Brian, 2007. "Do Frequency Reward Programs Create Switching Costs? A Dynamic Structural Analysis of Demand in a Reward Program," Research Papers 1941r, Stanford University, Graduate School of Business.
    10. Caminal, Ramon & Claici, Adina, 2007. "Are loyalty-rewarding pricing schemes anti-competitive?," International Journal of Industrial Organization, Elsevier, vol. 25(4), pages 657-674, August.
    11. Mara Lederman, 2007. "Do enhancements to loyalty programs affect demand? The impact of international frequent flyer partnerships on domestic airline demand," RAND Journal of Economics, RAND Corporation, vol. 38(4), pages 1134-1158, December.
    12. Mara Lederman, 2008. "Are Frequent‐Flyer Programs a Cause of the “Hub Premium”?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 17(1), pages 35-66, March.
    13. Castillo-Manzano, José I. & López-Valpuesta, Lourdes, 2014. "Living “up in the air†: Meeting the frequent flyer passenger," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 48-55.
    14. Huric Larsen, Jesper Fredborg, 2012. "Pricing behaviour at capacity constrained facilities," MPRA Paper 39362, University Library of Munich, Germany.
    15. Byung-Do Kim & Mengze Shi & Kannan Srinivasan, 2004. "Managing Capacity Through Reward Programs," Management Science, INFORMS, vol. 50(4), pages 503-520, April.
    16. Kakuya Matsushima & Kiyoshi Kobayashi, 2012. "Economic Evaluation of Pre- and Post-Discounting Fee Systems," Chapters, in: Charlie Karlsson & Börje Johansson & Roger R. Stough (ed.), The Regional Economics of Knowledge and Talent, chapter 10, Edward Elgar Publishing.
    17. Claudio Agostini, 2005. "El Mercado de Transporte Aéreo: Lecciones para Chile de una Revisión de la Literatura," ILADES-UAH Working Papers inv163, Universidad Alberto Hurtado/School of Economics and Business.

  30. Banerjee, Anindya & Galbraith, John W & Dolado, Juan, 1990. "Dynamic Specification and Linear Transformations of the Autoregressive-Distributed Lag Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(1), pages 95-104, February.

    Cited by:

    1. Holton, Sarah & Rodriguez d’Acri, Costanza, 2015. "Jagged Cliffs and Stumbling Blocks: Interest Rate Pass-through Fragmentation during the Euro Area Crisis," Research Technical Papers 01/RT/15, Central Bank of Ireland.
    2. Jose Sanchez-fung, 2005. "Estimating a monetary policy reaction function for the dominican republic," International Economic Journal, Taylor & Francis Journals, vol. 19(4), pages 563-577.
    3. Kesavan, T. & Hassan, Zuhair A. & Jensen, Helen H. & Johnson, Stanley R., 1993. "Dynamics and Long-run Structure in U.S. Meat Demand," ISU General Staff Papers 199307010700001059, Iowa State University, Department of Economics.
    4. Chowdhury, Subhasish M. & Crede, Carsten J., 2020. "Post-cartel tacit collusion: Determinants, consequences, and prevention," International Journal of Industrial Organization, Elsevier, vol. 70(C).
    5. Byrne, David & Kelly, Robert, 2017. "Bank Asset Quality & Monetary Policy Pass-Through," Research Technical Papers 11/RT/17, Central Bank of Ireland.
    6. Muzafar Shah Habibullah & Peter Smith & W. N. W. Azman-Saini, 2006. "Testing liquidity constraints in 10 Asian developing countries: an error-correction model approach," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2535-2543.
    7. Christopoulos, Dimitris K., 2000. "The demand for energy in Greek manufacturing," Energy Economics, Elsevier, vol. 22(5), pages 569-586, October.
    8. Kurt Kratena & Michael Wüger, 2012. "Technological Change and Energy Demand in Europe," WIFO Working Papers 427, WIFO.
    9. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    10. Allanson, Paul & Petrie, Dennis, 2013. "Longitudinal methods to investigate the role of health determinants in the dynamics of income-related health inequality," Journal of Health Economics, Elsevier, vol. 32(5), pages 922-937.
    11. Menzie David Chinn, 1991. "Beware of econometricians bearing estimates: Policy analysis in a “unit root” world," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 10(4), pages 546-567.
    12. Kesavan, T. & Aradhyula, Satheesh V. & Johnson, Stanley R., 1992. "Dynamics And Price Volatility In Farm-Retail Livestock Price Relationships," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 17(2), pages 1-14, December.
    13. Nzuma, Jonathan Makau & Kirui, Patrick Kipruto, 2021. "Transmission of global wheat prices to domestic markets in Kenya: A cointegration approach," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 16(1), March.
    14. Hee Soo Lee & Tae Yoon Kim, 2022. "A new analytical approach for identifying market contagion," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    15. Hee Soo Lee & Tae Yoon Kim, 2018. "Hedge Fund Styles and their Contagion from the Equity Market," International Review of Finance, International Review of Finance Ltd., vol. 18(1), pages 91-112, March.
    16. Holton, Sarah & d’Acri, Costanza Rodriguez, 2015. "Jagged cliffs and stumbling blocks: interest rate pass-through fragmentation during the Euro area crisis," Working Paper Series 1850, European Central Bank.
    17. Kelly, Robert & Byrne, David, 2019. "Bank asset quality and monetary policy pass-through," ESRB Working Paper Series 98, European Systemic Risk Board.
    18. Holton, Sarah & Rodriguez d’Acri, Costanza, 2018. "Interest rate pass-through since the euro area crisis," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 277-291.
    19. Islami, Mevlud & Kurz-Kim, Jeong-Ryeol, 2013. "A single composite financial stress indicator and its real impact in the euro area," Discussion Papers 31/2013, Deutsche Bundesbank.

  31. Galbraith, John W. & Rae, Nicol C., 1989. "A Test of the Importance of Tactical Voting: Great Britain, 1987," British Journal of Political Science, Cambridge University Press, vol. 19(1), pages 126-136, January.

    Cited by:

    1. Ricardo Troncoso Sepúlveda, 2019. "Estimación del voto estratégico en elecciones parlamentarias chilenas 2013," Revista Facultad de Ciencias Económicas, Universidad Militar Nueva Granada, vol. 27(1), pages 169-184, February.
    2. Artabe, Alaitz & Gardeazabal, Javier, 2014. "Strategic Votes and Sincere Counterfactuals," Political Analysis, Cambridge University Press, vol. 22(2), pages 243-257, April.
    3. HeeMin Kim & Tatiana Kostadinova, 2011. "Does Tactical Voting Matter? The Political Impact of Tactical Voting in Canadian Elections," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 14(1), pages 49-72, March.
    4. Myoung-jae Lee & Sung-jin Kang, 2009. "Strategic Voting and Multinomial Choice In US Presidential Elections," Discussion Paper Series 0907, Institute of Economic Research, Korea University.

  32. Galbraith, John W, 1988. "Modelling Expectations Formation with Measurement Errors," Economic Journal, Royal Economic Society, vol. 98(391), pages 412-428, June.

    Cited by:

    1. Silva Lopes, Artur, 1994. "A "hipótese das expectativas racionais": teoria e realidade (uma visita guiada à literatura até 1992) [The "rational expectations hypothesis": theory and reality (a guided tour ," MPRA Paper 9699, University Library of Munich, Germany, revised 23 Jul 2008.
    2. Demery, David & Duck, Nigel W., 2007. "The theory of rational expectations and the interpretation of macroeconomic data," Journal of Macroeconomics, Elsevier, vol. 29(1), pages 1-18, March.
    3. David Demery & Nigel Duck, 2003. "Inflation Dynamics and Inflation Regimes," Bristol Economics Discussion Papers 03/549, School of Economics, University of Bristol, UK.
    4. David Demery & Nigel Duck, 2002. "Optimally Rational Expectations and Macroeconomics," Bristol Economics Discussion Papers 02/533, School of Economics, University of Bristol, UK.

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    5. Ahumada, Hildegart & Cavallo, Eduardo A. & Espina-Mairal, Santos & Navajas, Fernando, 2021. "Sectoral Productivity Growth, COVID-19 Shocks, and Infrastructure," IDB Publications (Working Papers) 11404, Inter-American Development Bank.
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    35. Omar A Mendoza Lugo, 2008. "The differential impact of real interest rates and credit availability on private investment: evidence from Venezuela," BIS Papers chapters, in: Bank for International Settlements (ed.), Transmission mechanisms for monetary policy in emerging market economies, volume 35, pages 501-537, Bank for International Settlements.
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