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Frédéric Karamé
(Frederic Karame)

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.

Working papers

  1. Adjemian, Stéphane & Bastani, Houtan & Juillard, Michel & Karamé, Fréderic & Mihoubi, Ferhat & Mutschler, Willi & Pfeifer, Johannes & Ratto, Marco & Rion, Normann & Villemot, Sébastien, 2022. "Dynare: Reference Manual Version 5," Dynare Working Papers 72, CEPREMAP, revised Mar 2023.
    • Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," PSE Working Papers hal-04219920, HAL.
    • Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," Working Papers hal-04219920, HAL.

    Cited by:

    1. Guljanov, Gaygysyz & Mutschler, Willi & Trede, Mark, 2022. "Pruned Skewed Kalman Filter and Smoother: With Application to the Yield Curve," Dynare Working Papers 78, CEPREMAP.
    2. Gantert, Konstantin, 2022. "The Impact of Active Aggregate Demand on Utilization-Adjusted TFP," VfS Annual Conference 2022 (Basel): Big Data in Economics 264103, Verein für Socialpolitik / German Economic Association.
    3. Gantert, Konstantin, 2022. "The impact of active aggregate demand on utilisation-adjusted TFP," IWH Discussion Papers 9/2022, Halle Institute for Economic Research (IWH).
    4. del Río, Fernando & Lores, Francisco-Xavier, 2023. "Accounting for the role of investment frictions in recessions," MPRA Paper 116024, University Library of Munich, Germany.
    5. William Gatt, 2022. "MEDSEA-FIN: an estimated DSGE model with housing and financial frictions for Malta," CBM Working Papers WP/05/2022, Central Bank of Malta.

  2. Adjemian, Stéphane & Karamé, Frédéric & Langot, François, 2021. "Nonlinearities and Workers’ Heterogeneity in Unemployment Dynamics," Dynare Working Papers 71, CEPREMAP.

    Cited by:

    1. Kandoussi, Malak & Langot, François, 2020. "Uncertainty Shocks and Unemployment Dynamics," IZA Discussion Papers 13438, Institute of Labor Economics (IZA).

  3. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.

    Cited by:

    1. Maddalena Cavicchioli, 2021. "OLS Estimation of Markov switching VAR models: asymptotics and application to energy use," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 431-449, September.
    2. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    3. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.

  4. Frédéric Karamé, 2012. "An Algorithm for Generalized Impulse-Response Functions in Markov-Switching Structural VAR," Documents de recherche 12-04, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    2. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    3. Huang, Shupei & An, Haizhong & Wen, Shaobo & An, Feng, 2017. "Revisiting driving factors of oil price shocks across time scales," Energy, Elsevier, vol. 139(C), pages 617-629.
    4. Thomas St�rdal Gundersen & Even Soltvedt Hvinden, 2021. "OPEC's crude game: Strategic Competition and Regime-switching in Global Oil Markets," Working Papers No 01/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    6. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.
    7. Cavicchioli, Maddalena, 2023. "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, vol. 232(C).

  5. Frédéric Karamé & Yannick Fondeur, 2012. "Can Google Data Help Predict French Youth Unemployment?," Documents de recherche 12-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
    2. Pietro Giorgio Lovaglio & Mario Mezzanzanica & Emilio Colombo, 2020. "Comparing time series characteristics of official and web job vacancy data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 85-98, February.
    3. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    4. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    5. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    6. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading Newspapers," Post-Print hal-03205161, HAL.
    7. Ronald MacDonald & Xuxin Mao, 2015. "An Alternative way of Predicting the Outcome of the Scottish Independence Referendum: The Information in the Ether," SIRE Discussion Papers 2015-69, Scottish Institute for Research in Economics (SIRE).
    8. Zhongchen Song & Tom Coupé, 2022. "Predicting Chinese consumption series with Baidu," Working Papers in Economics 22/19, University of Canterbury, Department of Economics and Finance.
    9. Gupta, Kartick & Banerjee, Rajabrata, 2019. "Does OPEC news sentiment influence stock returns of energy firms in the United States?," Energy Economics, Elsevier, vol. 77(C), pages 34-45.
    10. Branislav Saxa, 2014. "Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand," Working Papers 2014/14, Czech National Bank.
    11. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.
    12. Marcos González-Fernández & Carmen González-Velasco, 2019. "An approach to predict Spanish mortgage market activity using Google data," Economics and Business Letters, Oviedo University Press, vol. 8(4), pages 209-214.
    13. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving: Evidence from SMS Donations," CAGE Online Working Paper Series 536, Competitive Advantage in the Global Economy (CAGE).
    14. Anastasiou, Dimitrios & Drakos, Konstantinos, 2021. "European depositors’ behavior and crisis sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 117-136.
    15. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    16. González-Fernández, Marcos & González-Velasco, Carmen, 2018. "Can Google econometrics predict unemployment? Evidence from Spain," Economics Letters, Elsevier, vol. 170(C), pages 42-45.
    17. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
    18. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    19. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    20. Jakub Szymkowiak & Lechosław Kuczyński, 2015. "Avoiding predators in a fluctuating environment: responses of the wood warbler to pulsed resources," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(2), pages 601-608.
    21. Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
    22. Botezat, Alina, 2017. "Austerity plan announcements and the impact on the employees’ wellbeing," Journal of Economic Psychology, Elsevier, vol. 63(C), pages 1-16.
    23. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    24. Siliverstovs, Boriss & Wochner, Daniel S., 2018. "Google Trends and reality: Do the proportions match?," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 1-23.
    25. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    26. Mahalia Jackman & Simon Naitram, 2015. "Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it!," Tourism Economics, , vol. 21(6), pages 1309-1313, December.
    27. Pavlicek, Jaroslav & Kristoufek, Ladislav, 2015. "Nowcasting unemployment rates with Google searches: Evidence from the Visegrad Group countries," FinMaP-Working Papers 34, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    28. Juan Camilo Anzoátegui-Zapata & Juan Camilo Galvis-Ciro, 2020. "Disagreements in Consumer Inflation Expectations: Empirical Evidence for a Latin American Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 99-122, November.
    29. Afkhami, Mohamad & Ghoddusi, Hamed & Rafizadeh, Nima, 2021. "Google Search Explains Your Gasoline Consumption!," Energy Economics, Elsevier, vol. 99(C).
    30. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    31. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    32. Gulsah Senturk, 2022. "Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 229-244, July.
    33. Alberto Urtasun & Mara Gil & Javier J. Perez, 2017. "Nowcasting private consumption: traditional indicators, uncertainty measures, and the role of internet search query data," EcoMod2017 10745, EcoMod.
    34. Askitas, Nikos & Zimmermann, Klaus F., 2015. "The Internet as a Data Source for Advancement in Social Sciences," IZA Discussion Papers 8899, Institute of Labor Economics (IZA).
    35. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
    36. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
    37. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    38. VAN DER WIELEN Wouter & BARRIOS Salvador, 2020. "Fear and Employment During the COVID Pandemic: Evidence from Search Behaviour in the EU," JRC Working Papers on Taxation & Structural Reforms 2020-08, Joint Research Centre.
    39. Voraprapa Nakavachara & Nuarpear Lekfuangfu, 2017. "Predicting the Present Revisited: The Case of Thailand," PIER Discussion Papers 70, Puey Ungphakorn Institute for Economic Research.
    40. Carlo Perroni & Kimberley Ann Scharf & Oleksandr Talavera & Linh Vi, 2021. "Does Online Salience Predict Charitable Giving? Evidence from SMS Text Donations," CESifo Working Paper Series 9436, CESifo.
    41. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
    42. KOCAK, Necmettin Alpay, 2021. "The Impacts Of Speeches On Nowcasting Gdp: A Case Study On Euro Area Markets," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 25(1), pages 6-29, March.
    43. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    44. Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
    45. Palma Lampreia Dos Santos, Maria José, 2018. "Nowcasting and forecasting aquaponics by Google Trends in European countries," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 178-185.
    46. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
    47. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
    48. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    49. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    50. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.
    51. Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
    52. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    53. Dimitrios Anastasiou & Zacharias Bragoudakis & Stelios Giannoulakis, 2020. "Perceived vs actual financial crisis and bank credit standards: is there any indication of self-fulfilling prophecy?," Working Papers 277, Bank of Greece.
    54. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.
    55. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    56. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    57. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
    58. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
    59. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
    60. Nakamura, Nobuyuki & Suzuki, Aya, 2021. "COVID-19 and the intentions to migrate from developing countries: Evidence from online search activities in Southeast Asia," Journal of Asian Economics, Elsevier, vol. 76(C).
    61. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "An alternative approach to predicting bank credit risk in Europe with Google data," Finance Research Letters, Elsevier, vol. 35(C).
    62. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
    63. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    64. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    65. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    66. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    67. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    68. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    69. Erik Christian Montes Schütte, 2018. "In Search of a Job: Forecasting Employment Growth in the US using Google Trends," CREATES Research Papers 2018-25, Department of Economics and Business Economics, Aarhus University.
    70. Dimitrios Anastasiou & Konstantinos Drakos, 2021. "Nowcasting the Greek (semi‐) deposit run: Hidden uncertainty about the future currency in a Google search," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1133-1150, January.
    71. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
    72. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).

  6. Adjemian, Stéphane & Bastani, Houtan & Juillard, Michel & Karamé, Fréderic & Maih, Junior & Mihoubi, Ferhat & Mutschler, Willi & Perendia, George & Pfeifer, Johannes & Ratto, Marco & Villemot, Sébasti, 2011. "Dynare: Reference Manual Version 4," Dynare Working Papers 1, CEPREMAP, revised Mar 2021.

    Cited by:

    1. James M. Nason & Ellis W. Tallman, 2012. "Business cycles and financial crises: the roles of credit supply and demand shocks," Working Papers 12-24, Federal Reserve Bank of Philadelphia.
    2. Andrés González & Sergio Ocampo & Diego Rodríguez & Norberto Rodríguez, 2011. "Asimetrías del empleo y el producto, una aproximación de equilibrio general," Borradores de Economia 663, Banco de la Republica de Colombia.
    3. Igor Kotlán & Daniel Němec & Eva Kotlánová & Petr Skalka & Rudolf Macek & Zuzana Machová, 2021. "European Green Deal: Environmental Taxation and Its Sustainability in Conditions of High Levels of Corruption," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    4. Michael T. Kiley, 2014. "Policy Paradoxes in the New Keynesian Model," Finance and Economics Discussion Series 2014-29, Board of Governors of the Federal Reserve System (U.S.).
    5. Naohisa Hirakata & Takushi Kurozumi, 2013. "The International Finance Multiplier in Business Cycle Fluctuations," IMES Discussion Paper Series 13-E-12, Institute for Monetary and Economic Studies, Bank of Japan.
    6. Claire Loupias & Bertrand Wigniolle, 2013. "Population, land, and growth," Post-Print halshs-00823255, HAL.
    7. Kollmann, Robert & Roeger, Werner & in t Veld, Jan & Ratto, Marco & Pataracchia, Beatrice, 2014. "International Capital Flows and the Boom-Bust Cycle in Spain," CEPR Discussion Papers 9957, C.E.P.R. Discussion Papers.
    8. Holden, Tom D. & Levine, Paul & Swarbrick, Jonathan M., 2017. "Credit crunches from occasionally binding bank borrowing constraints," EconStor Preprints 168441, ZBW - Leibniz Information Centre for Economics.
    9. Konstantinos Angelopoulos & Andrea Benecchi & James Malley, 2017. "Can subsidising job-related training reduce inequality?," Working Papers 2017_10, Business School - Economics, University of Glasgow.
    10. Ginters Buss, 2016. "Financial frictions in Latvia," Empirical Economics, Springer, vol. 51(2), pages 547-575, September.
    11. Bjørnar Karlsen Kivedal, 2013. "A New Keynesian Framework and Wage and Price Dynamics in the US," Working Paper Series 15113, Department of Economics, Norwegian University of Science and Technology.
    12. Yannick Kalantzis & Kenza Benhima & Philippe Bacchetta, 2012. "Capital Controls with International Reserve Accumulation: Can this Be Optimal?," 2012 Meeting Papers 448, Society for Economic Dynamics.
    13. Marek Jarocinski & Albert Marcet, 2013. "Priors about Observables in Vector Autoregressions," UFAE and IAE Working Papers 929.13, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    14. Michael T. Kiley, 2015. "What Can the Data Tell Us About the Equilibrium Real Interest Rate?," Finance and Economics Discussion Series 2015-77, Board of Governors of the Federal Reserve System (U.S.).
    15. Jaccard, Ivan, 2022. "The trade-off between public health and the economy in the early stage of the COVID-19 pandemic," Working Paper Series 2690, European Central Bank.
    16. Marc Pourroy & Benjamin Carton & Dramane Coulibaly, 2012. "Food Prices and Inflation Targeting in Emerging Economies," Working Papers 2012-33, CEPII research center.
    17. Clancy, Daragh & Jacquinot, Pascal & Lozej, Matija, 2014. "The Effects of Government Spending in a Small Open Economy within a Monetary Union," Research Technical Papers 12/RT/14, Central Bank of Ireland.
    18. Christoph Görtz & John D. Tsoukalas, 2013. "Sector Specific News Shocks in Aggregate and Sectoral Fluctuations," CESifo Working Paper Series 4269, CESifo.
    19. Jan in't Veld & Robert Kollmann & Marco Ratto & Werner Roeger & Lukas Vogel, 2014. "What drives the German current account? and how does it affect other EU member states?," Globalization Institute Working Papers 176, Federal Reserve Bank of Dallas.
    20. Jean-Christophe Poutineau & Gauthier Vermandel, 2015. "Financial Frictions and the Extensive Margin of Activity," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 201510, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
    21. Epstein, Brendan & Finkelstein Shapiro, Alan, 2017. "Employment and firm heterogeneity, capital allocation, and countercyclical labor market policies," Journal of Development Economics, Elsevier, vol. 127(C), pages 25-41.
    22. Giri, Federico, 2014. "Does interbank market matter for business cycle fluctuation? An estimated DSGE model with financial frictions for the Euro area," FinMaP-Working Papers 27, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    23. Di Pace, Federico & Hertweck, Matthias S., 2012. "Labour Market Frictions, Monetary Policy and Durable Goods," Dynare Working Papers 20, CEPREMAP.
    24. Christoph Görtz & John D. Tsoukalas, 2013. "News shocks and business cycles: bridging the gap from different methodologies," Working Papers 2013_25, Business School - Economics, University of Glasgow.
    25. Sumru Altug & Serdar Kabaca, 2017. "Search Frictions, Financial Frictions, and Labor Market Fluctuations in Emerging Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(1), pages 128-149, January.
    26. Paul Kitney, 2015. "Does the central bank respond to credit market factors? A Bayesian DSGE approach," CAMA Working Papers 2015-21, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    27. Hess Chung & Edward Herbst & Michael T. Kiley, 2014. "Effective Monetary Policy Strategies in New Keynesian Models: A Reexamination," NBER Chapters, in: NBER Macroeconomics Annual 2014, Volume 29, pages 289-344, National Bureau of Economic Research, Inc.
    28. Robert Kollmann, 2014. "Exchange rates dynamics with long-run risk and recursive preferences," Globalization Institute Working Papers 212, Federal Reserve Bank of Dallas.
    29. Cheng, Gong, 2011. "A Growth Perspective on Foreign Reserve Accumulation," MPRA Paper 46668, University Library of Munich, Germany, revised 01 Mar 2013.
    30. Burgess, Stephen & Fernandez-Corugedo, Emilio & Groth, Charlotta & Harrison, Richard & Monti, Francesca & Theodoridis, Konstantinos & Waldron, Matt, 2013. "The Bank of England's forecasting platform: COMPASS, MAPS, EASE and the suite of models," Bank of England working papers 471, Bank of England.
    31. Jae Sim & Raphael Schoenle & Egon Zakrajsek & Simon Gilchrist, 2014. "Inflation Dynamics During the Financial Crisis," 2014 Meeting Papers 206, Society for Economic Dynamics.
    32. Ivashchenko, S., 2013. "Dynamic Stochastic General Equilibrium Model with Banks and Endogenous Defaults of Firms," Journal of the New Economic Association, New Economic Association, vol. 19(3), pages 27-50.
    33. Pawel Baranowski & Mariusz Gorajski & Maciej Malaczewski & Grzegorz Szafranski, 2013. "Inflation in Poland under state-dependent pricing," Discussion Papers 83, Aboa Centre for Economics.
    34. Gelain, Paolo & Guerrazzi, Marco, 2014. "A demand-driven search model with self-fulfilling expectations: The new `Farmerian' framework under scrutiny," MPRA Paper 55773, University Library of Munich, Germany.
    35. Stefan Hohberger & Romanos Priftis & Lukas Vogel, 2018. "The Macroeconomic Effects of Quantitative Easing in the Euro Area: Evidence from an Estimated DSGE Model," Staff Working Papers 18-11, Bank of Canada.
    36. Ginters Buss, 2014. "Financial Frictions in a DSGE Model for Latvia," Working Papers 2014/02, Latvijas Banka.
    37. Viktors Ajevskis, 2015. "Nonlocal Solutions to Dynamic Equilibrium Models: The Approximate Stable Manifolds Approach," Papers 1506.02521, arXiv.org.
    38. Anella Munro, 2014. "Exchange rates, expected returns and risk: UIP unbound," CAMA Working Papers 2014-73, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
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    75. Miguel Casares Polo & Hashmat Khan & Jean-Christophe Poutineau, 2018. "A structural analysis of US entry and exit dynamics," Documentos de Trabajo - Lan Gaiak Departamento de Economía - Universidad Pública de Navarra 1801, Departamento de Economía - Universidad Pública de Navarra.
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    80. Daniel Němec, 2013. "Investigating Differences Between the Czech and Slovak Labour Market Using a Small DSGE Model with Search and Matching Frictions," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 7(1), pages 021-041, March.
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    84. Hashmat Khan & Abeer Reza, 2017. "House Prices and Government Spending Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(6), pages 1247-1271, September.
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    87. Ahmed, Shahzad & Pasha, Farooq, 2014. "The Role of Money in Explaining Business Cycles for a Developing Economy: The Case of Pakistan," MPRA Paper 55262, University Library of Munich, Germany, revised 11 Apr 2014.
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    89. Joris de Wind, 2017. "SMOOTHIES: A Toolbox for the Exact Nonlinear and Non-Gaussian Kalman Smoother," CPB Discussion Paper 360, CPB Netherlands Bureau for Economic Policy Analysis.
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    91. Lan, Hong & Meyer-Gohde, Alexander, 2014. "Decomposing Risk in Dynamic Stochastic General Equilibrium," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100523, Verein für Socialpolitik / German Economic Association.
    92. Engin Kara & Jasmin Sin, 2013. "Liquidity, Quantitative Easing and Optimal Monetary Policy," Bristol Economics Discussion Papers 13/635, School of Economics, University of Bristol, UK.
    93. Wieland, Volker & Afanasyeva, Elena & Kuete, Meguy & Yoo, Jinhyuk, 2016. "New methods for macro-financial model comparison and policy analysis," IMFS Working Paper Series 107, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
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  7. Frédéric Karamé, 2010. "Impulse-Response Functions in Markov-Switching Structural Vector AutoRegressions: a Step Further," Documents de recherche 10-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. James D. Hamilton, 2016. "Macroeconomic Regimes and Regime Shifts," NBER Working Papers 21863, National Bureau of Economic Research, Inc.
    2. Maximo Camacho & Gabriel Perez-Quiros, 2013. "Commodity prices and the business cycle in Latin America: Living and dying by commodities," Working Papers 1304, Banco de España.
    3. Frédéric Karamé, 2012. "An Algorithm for Generalized Impulse-Response Functions in Markov-Switching Structural VAR," Documents de recherche 12-04, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    4. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    5. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    6. Cavicchioli, Maddalena, 2023. "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, vol. 232(C).

  8. Frédéric Karamé & Alexandra Olmedo, 2010. "Asymmetric Properties of Impulse Response Functions in Markov-Switching Structural Vector AutoRegressions," Documents de recherche 10-04, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. James D. Hamilton, 2016. "Macroeconomic Regimes and Regime Shifts," NBER Working Papers 21863, National Bureau of Economic Research, Inc.
    2. Maximo Camacho & Gabriel Perez-Quiros, 2013. "Commodity prices and the business cycle in Latin America: Living and dying by commodities," Working Papers 1304, Banco de España.
    3. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    5. Aydanur GACENER-ATIŞ & Deniz ERER, 2019. "Effects of Capital Flows on Carry Trade Activities: The Case of TurkeyAbstract: Carry trade is described as the capital flow coming into a country based on interest rate differential. A negative chang," Sosyoekonomi Journal, Sosyoekonomi Society, issue 27(42).

  9. Frédéric Karamé & Lise Patureau & Thepthida Sopraseuth, 2003. "Limited Participation and Exchange Rate Dynamics: Does Theory Meet the Data?," Documents de recherche 03-15, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. Sacht, Stephen & Franke, Reiner & Jang, Tae-Seok, 2013. "Moment Matching versus Bayesian Estimation: Backward-Looking Behaviour in a New-Keynesian Baseline Model," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79694, Verein für Socialpolitik / German Economic Association.
    2. Julien Albertini & Hong Lan, 2016. "The importance of time-varying parameters in new Keynesian models with zero lower bound," SFB 649 Discussion Papers SFB649DP2016-013, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Diego E. Vacaflores, 2011. "Monetary Stimulus: Through Wall Street or Main Street?," Revista de Análisis del BCB, Banco Central de Bolivia, vol. 14(1), pages 9-40, June.
    4. de Blas, Beatriz, 2010. "Exchange rate dynamics in economies with portfolio rigidities," International Review of Economics & Finance, Elsevier, vol. 19(3), pages 366-382, June.
    5. Franke, Reiner, 2013. "Competitive Moment Matching of a New-Keynesian and an Old-Keynesian Model," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79988, Verein für Socialpolitik / German Economic Association.
    6. Diego E. Vacaflores, 2013. "Monetary Transfers in the U.S.: How Efficient Are Tax Rebates?," Economies, MDPI, vol. 1(3), pages 1-23, November.
    7. Diego E. Vacaflores, 2012. "Remittances, Monetary Policy, and Partial Sterilization," Southern Economic Journal, John Wiley & Sons, vol. 79(2), pages 367-387, October.
    8. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2011. "Moment matching versus Bayesian estimation: Backward-looking behaviour in the new-Keynesian three-equations model," Economics Working Papers 2011-10, Christian-Albrechts-University of Kiel, Department of Economics.
    9. Reiner Franke, 2018. "Competitive moment matching of a New-Keynesian and an Old-Keynesian model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 201-239, July.

  10. Frédéric Karamé & Lise Patureau & Thepthida Sopraseuth, 2002. "Can We Beat the Random Walk Forecasts of Out-of-Sample Exchange Rates? A Structural Approach," Computing in Economics and Finance 2002 233, Society for Computational Economics.

    Cited by:

    1. Otavio De Medeiros, 2005. "Order Flow and Exchange Rate Dynamics in Brazil," Finance 0503019, University Library of Munich, Germany.

Articles

  1. Karamé, Frédéric, 2015. "Asymmetries and Markov-switching structural VAR," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 85-102.
    See citations under working paper version above.
  2. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    See citations under working paper version above.
  3. Karamé, F., 2012. "An algorithm for generalized impulse-response functions in Markov-switching structural VAR," Economics Letters, Elsevier, vol. 117(1), pages 230-234.
    See citations under working paper version above.
  4. Karamé, F., 2010. "Impulse-response functions in Markov-switching structural vector autoregressions: A step further," Economics Letters, Elsevier, vol. 106(3), pages 162-165, March.
    See citations under working paper version above.
  5. Karamé, Frédéric & Patureau, Lise & Sopraseuth, Thepthida, 2008. "Limited participation and exchange rate dynamics: Does theory meet the data?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(4), pages 1041-1087, April.
    See citations under working paper version above.Sorry, no citations of articles recorded.
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