Research classified by Journal of Economic Literature (JEL) codes

1. Nowcasting
2. Bayesian Vector autoregressions (BVARs)
3. Dynamic Factor Models

2022

• Waweru David & Mose Naftaly, 2022, "Household Fuel Choice in Urban Kenya: A Multinomial Logit Analysis," Financial Internet Quarterly (formerly e-Finanse), Sciendo, volume 18, issue 2, pages 30-41, June, DOI: 10.2478/fiqf-2022-0011.
• Kevin Moran & Dalibor Stevanovic & Adam Kader Touré, 2022, "Macroeconomic uncertainty and the COVID‐19 pandemic: Measure and impacts on the Canadian economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, volume 55, issue S1, pages 379-405, February, DOI: 10.1111/caje.12551.
• Michael P. Clements, 2022, "Individual forecaster perceptions of the persistence of shocks to GDP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 3, pages 640-656, April, DOI: 10.1002/jae.2884.
• Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022, "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 5, pages 896-919, August, DOI: 10.1002/jae.2907.
• Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022, "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 5, pages 920-964, August, DOI: 10.1002/jae.2910.
• Massimo Ferrari Minesso & Frederik Kurcz & Maria Sole Pagliari, 2022, "Do words hurt more than actions? The impact of trade tensions on financial markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 6, pages 1138-1159, September, DOI: 10.1002/jae.2924.
• Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022, "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 37, issue 6, pages 1230-1255, September, DOI: 10.1002/jae.2923.
• Caroline Jardet & Baptiste Meunier, 2022, "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., volume 41, issue 6, pages 1181-1200, September, DOI: 10.1002/for.2858.
• Saskia Ter Ellen & Vegard H. Larsen & Leif Anders Thorsrud, 2022, "Narrative Monetary Policy Surprises and the Media," Journal of Money, Credit and Banking, Blackwell Publishing, volume 54, issue 5, pages 1525-1549, August, DOI: 10.1111/jmcb.12868.
• Joshua C. C. Chan, 2022, "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, volume 13, issue 3, pages 1145-1169, July, DOI: 10.3982/QE1381.
• M. Hashem Pesaran & Yimeng Xie, 2022, "A Bias-Corrected CD Test for Error Cross-Sectional Dependence in Panel Data Models with Latent Factors," Working Papers, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, number 2022-06-10, Jun.
• Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2022, "Economists in the 2008 Financial Crisis: Slow to See, Fast to Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Forthcomi.
• Hauber, Philipp, 2022, "Real-time nowcasting with sparse factor models," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 251551.
• Porras-Arena, M. Sylvina & Martín-Román, Ángel L., 2022, "The heterogeneity of Okun's law: A metaregression analysis," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1069.
• Rossouw, Stephanié & Greyling, Talita, 2022, "Collective emotions and macro-level shocks: COVID-19 vs the Ukrainian war," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1210.
• Bender, Benedikt & Bruinsma, Bastiaan, 2022, "Patterns in the Press Releases of Trade Unions: How to Use Structural Topic Models in the Field of Industrial Relations
  [Muster in Pressemitteilungen von Gewerkschaften. Die Anwendung von Structura," Industrielle Beziehungen. Zeitschrift für Arbeit, Organisation und Management, Verlag Barbara Budrich, volume 29, issue 2, pages 91-116, DOI: 10.3224/indbez.v29i2.02.
• Benner, Niklas & Lange, Kai-Robin & Jentsch, Carsten, 2022, "Named entity narratives," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 962, DOI: 10.4419/96973126.
• Lange, Kai-Robin & Reccius, Matthias & Schmidt, Tobias & Müller, Henrik & Roos, Michael W. M. & Jentsch, Carsten, 2022, "Towards extracting collective economic narratives from texts," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 963, DOI: 10.4419/96973127.
• Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022, "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 964, DOI: 10.4419/96973128.

2021

• Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021, "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2021-02, Jan.
• Gloria González-Rivera & Carlos Vladimir Rodríguez-Caballero & Esther Ruiz Ortega, 2021, "Expecting the unexpected: economic growth under stress," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2021-06, Mar.
• Isaac K. Ofori, 2021, "Catching the Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning," Research Africa Network Working Papers, Research Africa Network (RAN), number 21/044, Jan.
• Isaac K. Ofori & Christopher Quaidoo & Pamela E. Ofori, 2021, "What Drives Financial Sector Development in Africa? Insights from Machine Learning," Research Africa Network Working Papers, Research Africa Network (RAN), number 21/074, Jan.
• Sarthak Behera & Hyeongwoo Kim & Soohyon Kim, 2021, "Superior Predictability of American Factors of the Won/Dollar Real Exchange Rate," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2021-03, Jul.
• Nigel E.N. Chitambo & Darren Lee & Sure Mataramvura, 2021, "A Hybrid Neural Network GARCH Approach to Forecasting Zimbabwean Inflation Volatility," The African Finance Journal, Africagrowth Institute, volume 23, issue 1, pages 56-73.
• Isaac K. Ofori, 2021, "Catching the Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning," Working Papers of the African Governance and Development Institute., African Governance and Development Institute., number 21/044, Jan.
• Isaac K. Ofori & Christopher Quaidoo & Pamela E. Ofori, 2021, "What Drives Financial Sector Development in Africa? Insights from Machine Learning," Working Papers of the African Governance and Development Institute., African Governance and Development Institute., number 21/074, Jan.
• D.V. Firsov & T.C. Chernyshevа, 2021, "Review of Successful Practices of Applying Nowcasting in Socio-Economic Forecasting," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, volume 20, issue 2, pages 269-293, DOI: http://dx.doi.org/10.15826/vestnik..
• Lachlan O'Neill & Nandini Anantharama & Wray Buntine & Simon D Angus, 2021, "Quantitative Discourse Analysis at Scale - AI, NLP and the Transformer Revolution," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2021-12, Dec.
• Nurdaulet Abilov & Aizhan Bolatbayeva, 2021, "Nowcasting GDP growth in Russia with an incomplete dataset: A factor model approach," NAC Analytica Working Paper, NAC Analytica, Nazarbayev University, number 18, Dec, revised Feb 2022.
• Mauricio Gallardo & María Emma Santos & Pablo Villatoro & Vicky Pizarro, 2021, "Measuring vulnerability to multidimensional poverty in Latin America," Working Papers, Red Nacional de Investigadores en Economía (RedNIE), number 36, Mar.
• Oscar Claveria & Enric Monte & Salvador Torra, 2021, "“Nowcasting and forecasting GDP growth with machine-learning sentiment indicators”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 202101, Feb, revised Feb 2021.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," Papers, arXiv.org, number 2103.01201, Mar.
• Mehmet Caner, 2021, "Generalized Linear Models with Structured Sparsity Estimators," Papers, arXiv.org, number 2104.14371, Apr.
• Francesca Micocci & Armando Rungi, 2021, "Predicting Exporters with Machine Learning," Papers, arXiv.org, number 2107.02512, Jul, revised Sep 2022.
• M. Hashem Pesaran & Yimeng Xie, 2021, "How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Test," Papers, arXiv.org, number 2109.00408, Sep, revised Jan 2026.
• Joshua C. C. Chan, 2021, "Asymmetric Conjugate Priors for Large Bayesian VARs," Papers, arXiv.org, number 2111.07170, Nov.
• Minseog Oh & Donggyu Kim, 2021, "Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective," Papers, arXiv.org, number 2111.09655, Nov.
• Emilia VASILE & Danut Octavian SIMION, 2021, "Optimization Of Application Objects Used In The Economic Environments," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 61, issue 1, pages 9-19, March.
• Danut Octavian SIMION & Emilia VASILE, 2021, "Implementing Different Types Of Data In Economic Application," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 62, issue 2, pages 9-18, June.
• Emilia VASILE & Danut Octavian SIMION, 2021, "Methods For Storing And Finding Data In The Business Logic For Economic Applications," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 63, issue 3, pages 9-18, September.
• Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021, "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers, Institute of Economic Growth, number 446, Oct.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 21-01, Mar.
• Ali Batuhan Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021, "Turquía | Big Data y Nowcasting: consumo e inversión de transacciones bancarias
  [Turkey | Big Data and Nowcasting: Consumption and Investment from Bank Transactions]," Working Papers, BBVA Bank, Economic Research Department, number 21/07, Jul.
• James Chapman & Ajit Desai, 2021, "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers, Bank of Canada, number 21-2, Jan, DOI: 10.34989/swp-2021-2.
• Tatjana Dahlhaus & Angelika Welte, 2021, "Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data," Staff Working Papers, Bank of Canada, number 21-43, Sep, DOI: 10.34989/swp-2021-43.
• Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner P. Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Yihao Lin, 2021, "Machine Learning and Oil Price Point and Density Forecasting," Working Papers Series, Central Bank of Brazil, Research Department, number 544, Feb.
• Levent GUNTAY & Mehmet AKTUNA, 2021, "Scenario Based Anomaly Detection in Financial Institutions: A Study on the Turkish Factoring Sector," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 15, issue 1, pages 83-113.
• Christiane Baumeister & Danilo Leiva-León & Eric Sims, 2021, "Tracking weekly state-level economic conditions," Working Papers, Banco de España, number 2134, Aug.
• Cristina Angelico & Juri Marcucci & Marcello Miccoli & Filippo Quarta, 2021, "Can we measure inflation expectations using Twitter?," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1318, Feb.
• Valentina Aprigliano & Simone Emiliozzi & Gabriele Guaitoli & Andrea Luciani & Juri Marcucci & Libero Monteforte, 2021, "The power of text-based indicators in forecasting the Italian economic activity," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1321, Mar.
• Rangel González Erick & Llamosas-Rosas Irving, 2021, "Observing the Evolution of the Informal Sector from Space: A Municipal Approach 2013-2020," Working Papers, Banco de México, number 2021-18, Dec.
• Massimo Ferrari & Frederik Kurcz & Maria Sole Pagliari, 2021, "Do Words Hurt More Than Actions? The Impact of Trade Tensions on Financial Markets," Working papers, Banque de France, number 802.
• Henri Fraisse & Matthias Laporte, 2021, "Return on Investment on AI: The Case of Capital Requirement," Working papers, Banque de France, number 809.
• Marlene Amstad & Leonardo Gambacorta & Chao He & Dora Xia, 2021, "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," BIS Working Papers, Bank for International Settlements, number 917, Jan.
• Vrigazova Borislava, 2021, "The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems," Business Systems Research, Sciendo, volume 12, issue 1, pages 228-242, May, DOI: 10.2478/bsrj-2021-0015.
• Ksenia Mayorova & Nikita Fokin, 2021, "Nowcasting Growth Rates of Russia's Export and Import by Commodity Group," Russian Journal of Money and Finance, Bank of Russia, volume 80, issue 3, pages 34-48, September, DOI: 10.31477/rjmf.202103.34.
• Alexander Plum & Gail Pacheco & Kabir Dasgupta, 2021, "When There is No Way Up: Reconsidering Low‐paid Jobs as Stepping‐stones," The Economic Record, The Economic Society of Australia, volume 97, issue 318, pages 387-409, September, DOI: 10.1111/1475-4932.12609.
• Andreas Joseph & Eleni Kalamara & George Kapetanios & Galina Potjagailo & Chiranjit Chakraborty, 2021, "Forecasting UK inflation bottom up," Bank of England working papers, Bank of England, number 915, Mar.
• Nikoleta Anesti & Eleni Kalamara & George Kapetanios, 2021, "Forecasting UK GDP growth with large survey panels," Bank of England working papers, Bank of England, number 923, May.
• Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021, "Comparing minds and machines: implications for financial stability," Bank of England working papers, Bank of England, number 937, Aug.
• Tim Munday & James Brookes, 2021, "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers, Bank of England, number 944, Oct.
• Jonathan Benchimol & Sophia Kazinnik & Yossi Saadon, 2021, "Federal Reserve Communication and the COVID-19 Pandemic," Bank of Israel Working Papers, Bank of Israel, number 2021.15, Jul.
• Jouchi Nakajima & Hiroaki Yamagata & Tatsushi Okuda & Shinnosuke Katsuki & Takeshi Shinohara, 2021, "Extracting Firms' Short-Term Inflation Expectations from the Economy Watchers Survey Using Text Analysis," Bank of Japan Working Paper Series, Bank of Japan, number 21-E-12, Oct.
• Reif Magnus, 2021, "Macroeconomic uncertainty and forecasting macroeconomic aggregates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 25, issue 2, pages 1-20, April, DOI: 10.1515/snde-2019-0073.
• Komla M. Agudze & Monica Billio & Roberto Casarin & Francesco Ravazzolo, 2021, "Markov Switching Panel with Endogenous Synchronization Effects," BEMPS - Bozen Economics & Management Paper Series, Faculty of Economics and Management at the Free University of Bozen, number BEMPS82, Mar.
• Mueller, H. & Rauh, C., 2021, "The Hard Problem of Prediction for Conflict Prevention," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2103, Jan.
• Kang, J. & Reiner, D., 2021, "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2141, May.
• Kang, J. & Reiner, D., 2021, "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2142, May.
• Kang, J. & Reiner, D., 2021, "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2143, May.
• Pesaran, M. H. & Xie, Y., 2021, "How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Testy," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2158, Aug.
• Ba Chu & Shafiullah Qureshi, 2021, "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers, Carleton University, Department of Economics, number 21-12, Oct.
• Parle, Conor, 2021, "The financial market impact of ECB monetary policy press conferences - a text based approach," Research Technical Papers, Central Bank of Ireland, number 4/RT/21, May.
• Bernadette Power & Gavin C Reid, 2021, "The Impact of Intellectual Property Types on the Performance of Business Start-ups in the USA," Working Papers, Centre for Business Research, University of Cambridge, number wp523, Apr.
• Stanislav Anatolyev & Vladimir Pyrlik, 2021, "Shrinkage for Gaussian and t Copulas in Ultra-High Dimensions," CERGE-EI Working Papers, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp699, Aug.
• Niklas Amberg & Thomas Jansson & Mathias Klein & Anna Rogantini Picco, 2021, "Five Facts about the Distributional Income Effects of Monetary Policy," CESifo Working Paper Series, CESifo, number 9062.
• Christiane Baumeister & Danilo Leiva-León & Eric R. Sims, 2021, "Tracking Weekly State-Level Economic Conditions," CESifo Working Paper Series, CESifo, number 9165.
• M. Hashem Pesaran & Yimeng Xie, 2021, "A Bias-Corrected CD Test for Error Cross-Sectional Dependence in Panel Data Models with Latent Factors," CESifo Working Paper Series, CESifo, number 9234.
• Els Bekaert & Amelie F. Constant & Killian Foubert & Ilse Ruyssen, 2021, "Longing for Which Home: Evidence from Global Aspirations to Stay, Return or Migrate Onwards," CESifo Working Paper Series, CESifo, number 9301.
• Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2021, "A penalized two-pass regression to predict stock returns with time-varying risk premia," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-09, Jan.
• Alexis Marchal, 2021, "Risk & Returns around Fomc Press Conferences: A Novel Perspective from Computer Vision," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-18, Mar.
• Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Scognamiglio, 2021, "Structured Additive Regression and Tree Boosting," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-83, Sep.
• Hugo Couture & Dalibor Stevanovic, 2021, "Analyse du marché du travail à l’aide des données de Google Trends," CIRANO Project Reports, CIRANO, number 2021rp-15, Aug.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," CIRANO Working Papers, CIRANO, number 2021s-09, Mar.
• Jorge Rojas-Vallejos & Carmen Gloria Jim�nez Bucarey & Marcela Espinoza & Luis Araya-Castillo, 2021, "The short-term impact of urban air pollution on student achievement," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, volume 87, issue 2.
• Susana Martínez-Restrepo & Lina Tafur & Juan G. Ocio & Caroline Brethenoux & Patrick Furey & Orlando Rivera, 2021, "Análisis de narrativas en línea sobre el empoderamiento de las mujeres sobrevivientes de las violencias basadas en género en Colombia con procesamiento de lenguaje natural," Informes de Investigación, Fedesarrollo, number 19664, Aug.
• Gambacorta, Leonardo & Amstad, Marlene & He, Chao & XIA, Fan Dora, 2021, "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15682, Jan.
• Marcellino, Massimiliano & Stevanovic, Dalibor & Goulet Coulombe, Philippe, 2021, "Can Machine Learning Catch the COVID-19 Recession?," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15867, Mar.
• Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd & Mertens, Elmar, 2021, "Measuring Uncertainty and Its Effects in the COVID-19 Era," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15965, Mar.
• Moench, Emanuel & Soofi Siavash, Soroosh, 2022, "What Moves Treasury Yields?," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15978, Mar.
• Taylor, Mark & Filippou, Ilias & Wang, Zigan, 2021, "Media Sentiment and Currency Reversals," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16049, Apr.
• Mckibbin, Warwick & Fernando, Roshen & Liu, Weifeng, 2021, "Global Economic Impacts of Climate Shocks, Climate Policy and Changes in Climate Risk Assessment," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16154, May.
• Rao, Justin, 2021, "Demand for Online News under Government Control: Evidence from Russia," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16233, Jun.
• Calvo Pardo, Héctor & Olmo, Jose & Mancini, Tullio, 2021, "Machine Learning the Carbon Footprint of Bitcoin Mining," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16267, Jun.
• Baumeister, Christiane & Leiva-León, Danilo & Sims, Eric, 2021, "Tracking Weekly State-Level Economic Conditions," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16317, Jul.
• Blumenstock, Joshua & Aiken, Emily & Bellue, Suzanne & Udry, Christopher & Karlan, Dean, 2021, "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16385, Jul.
• Mckibbin, Warwick & Jaumotte, Florence & Liu, Weifeng, 2021, "Mitigating Climate Change: Growth-Friendly Policies to Achieve Net Zero Emissions by 2050," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16553, Sep.
• Iaria, Alessandro & Wang, Ao, 2021, "An Empirical Model of Quantity Discounts with Large Choice Sets," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16666, Oct.
• Gonzalez Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2021, "Expecting the unexpected: economic growth under stress," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 32148, Mar.
• Chao, Shih-Kang & Härdle, Wolfgang K. & Yuan, Ming, 2021, "Factorisable Multitask Quantile Regression," Econometric Theory, Cambridge University Press, volume 37, issue 4, pages 794-816, August.
• Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021, "Can Machine Learning Catch The Covid-19 Recession?," National Institute Economic Review, National Institute of Economic and Social Research, volume 256, issue , pages 71-109, May.
• Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021, "Der gesellschaftliche Mehrwert verknüpfter Daten: Algorithmen als Entscheidungshilfen bei Antibiotikaverschreibungen," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, volume 88, issue 13/14, pages 239-246.
• Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021, "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1939.
• Dugast, Jerome & Foucault, Thierry, 2021, "Equilibrium Data Mining and Data Abundance," HEC Research Papers Series, HEC Paris, number 1393, Jan, DOI: 10.2139/ssrn.3710495.
• Hurlin, Christophe & Pérignon, Christophe & Saurin, Sébastien, 2021, "The Fairness of Credit Scoring Models," HEC Research Papers Series, HEC Paris, number 1411, Feb, DOI: 10.2139/ssrn.3785882.
• Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2021, "Using machine learning and big data to analyse the business cycle," Economic Bulletin Articles, European Central Bank, volume 5.
• Alogoskoufis, Spyros & Dunz, Nepomuk & Emambakhsh, Tina & Hennig, Tristan & Kaijser, Michiel & Kouratzoglou, Charalampos & Muñoz, Manuel A. & Parisi, Laura & Salleo, Carmelo, 2021, "ECB’s economy-wide climate stress test," Occasional Paper Series, European Central Bank, number 281, Sep.
• Bobasu, Alina & Geis, André & Quaglietti, Lucia & Ricci, Martino, 2021, "Tracking global economic uncertainty: implications for the euro area," Working Paper Series, European Central Bank, number 2541, Apr.
• Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2021, "Economic predictions with big data: the illusion of sparsity," Working Paper Series, European Central Bank, number 2542, Apr.
• Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021, "The time-varying evolution of inflation risks," Working Paper Series, European Central Bank, number 2600, Oct.
• Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021, "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series, European Central Bank, number 2616, Nov.
• Santiago Gall n & Jorge Barrientos, 2021, "Forecasting the Colombian Electricity Spot Price under a Functional Approach," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 2, pages 67-74.
• Bharat Kumar Meher & Iqbal Thonse Hawaldar & Mathew Thomas Gil & Deebom Zorle Dum, 2021, "Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data," International Journal of Energy Economics and Policy, Econjournals, volume 11, issue 6, pages 489-502.
• 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, volume 76, issue C, DOI: 10.1016/j.asieco.2021.101348.
• Camacho, Maximo & Romeu, Andres & Ruiz-Marin, Manuel, 2021, "Symbolic transfer entropy test for causality in longitudinal data," Economic Modelling, Elsevier, volume 94, issue C, pages 649-661, DOI: 10.1016/j.econmod.2020.02.007.
• Lourenço, Nuno & Gouveia, Carlos Melo & Rua, António, 2021, "Forecasting tourism with targeted predictors in a data-rich environment," Economic Modelling, Elsevier, volume 96, issue C, pages 445-454, DOI: 10.1016/j.econmod.2020.03.030.
• Bastianin, Andrea & Manera, Matteo, 2021, "A test of symmetry based on L-moments with an application to the business cycles of the G7 economies," Economics Letters, Elsevier, volume 198, issue C, DOI: 10.1016/j.econlet.2020.109662.
• Lyu, Yifei & Nie, Jun & Yang, Shu-Kuei X., 2021, "Forecasting US economic growth in downturns using cross-country data," Economics Letters, Elsevier, volume 198, issue C, DOI: 10.1016/j.econlet.2020.109668.
• Biewen, Martin & Kugler, Philipp, 2021, "Two-stage least squares random forests with an application to Angrist and Evans (1998)," Economics Letters, Elsevier, volume 204, issue C, DOI: 10.1016/j.econlet.2021.109893.
• Smeekes, Stephan & Wijler, Etienne, 2021, "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, volume 221, issue 1, pages 247-276, DOI: 10.1016/j.jeconom.2020.07.021.
• Yang, Xinxin & Zheng, Xinghua & Chen, Jiaqi, 2021, "Testing high-dimensional covariance matrices under the elliptical distribution and beyond," Journal of Econometrics, Elsevier, volume 221, issue 2, pages 409-423, DOI: 10.1016/j.jeconom.2020.05.017.
• Kapetanios, G. & Pesaran, M.H. & Reese, S., 2021, "Detection of units with pervasive effects in large panel data models," Journal of Econometrics, Elsevier, volume 221, issue 2, pages 510-541, DOI: 10.1016/j.jeconom.2020.05.001.
• Ding, Yi & Li, Yingying & Zheng, Xinghua, 2021, "High dimensional minimum variance portfolio estimation under statistical factor models," Journal of Econometrics, Elsevier, volume 222, issue 1, pages 502-515, DOI: 10.1016/j.jeconom.2020.07.013.
• Jin, Sainan & Miao, Ke & Su, Liangjun, 2021, "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, volume 222, issue 1, pages 745-777, DOI: 10.1016/j.jeconom.2020.08.002.
• Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021, "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, volume 225, issue 1, pages 2-26, DOI: 10.1016/j.jeconom.2021.03.012.
• Di Iorio, Francesca & Fachin, Stefano, 2021, "Evaluating restricted common factor models for non-stationary data," Econometrics and Statistics, Elsevier, volume 17, issue C, pages 64-75, DOI: 10.1016/j.ecosta.2020.10.004.
• Cavallari, Lilia & Romano, Simone & Naticchioni, Paolo, 2021, "The original sin: Firms’ dynamics and the life-cycle consequences of economic conditions at birth," European Economic Review, Elsevier, volume 138, issue C, DOI: 10.1016/j.euroecorev.2021.103844.
• Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021, "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, volume 63, issue C, pages 252-269, DOI: 10.1016/j.jempfin.2021.07.009.
• Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021, "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, volume 102, issue C, DOI: 10.1016/j.eneco.2021.105494.
• Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021, "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, volume 96, issue C, DOI: 10.1016/j.eneco.2021.105118.
• Nam, Kyungsik, 2021, "Investigating the effect of climate uncertainty on global commodity markets," Energy Economics, Elsevier, volume 96, issue C, DOI: 10.1016/j.eneco.2021.105123.
• Strauss, Ilan & Yang, Jangho, 2021, "Slowing investment rates in developing economies: Evidence from a Bayesian hierarchical model," International Review of Financial Analysis, Elsevier, volume 77, issue C, DOI: 10.1016/j.irfa.2021.101843.
• Shynkevich, Andrei, 2021, "Bitcoin arbitrage," Finance Research Letters, Elsevier, volume 40, issue C, DOI: 10.1016/j.frl.2020.101698.
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• Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021, "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, volume 100, issue C, pages 296-308, DOI: 10.1016/j.insmatheco.2021.06.004.
• Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021, "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, volume 99, issue C, pages 9-24, DOI: 10.1016/j.insmatheco.2021.01.002.
• Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021, "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, volume 37, issue 1, pages 360-377, DOI: 10.1016/j.ijforecast.2020.06.006.
• Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021, "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 941-948, DOI: 10.1016/j.ijforecast.2020.10.005.
• Chan, Joshua C.C., 2021, "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, volume 37, issue 3, pages 1212-1226, DOI: 10.1016/j.ijforecast.2021.01.002.
• Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021, "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, volume 37, issue 4, pages 1338-1354, DOI: 10.1016/j.ijforecast.2021.05.005.
• Hillen, Judith & Fedoseeva, Svetlana, 2021, "E-commerce and the end of price rigidity?," Journal of Business Research, Elsevier, volume 125, issue C, pages 63-73, DOI: 10.1016/j.jbusres.2020.11.052.
• Brettschneider, Julia & Burro, Giovanni & Henderson, Vicky, 2021, "Wide framing disposition effect: An empirical study," Journal of Economic Behavior & Organization, Elsevier, volume 185, issue C, pages 330-347, DOI: 10.1016/j.jebo.2021.03.003.
• Painter, Marcus & Qiu, Tian, 2021, "Political beliefs affect compliance with government mandates," Journal of Economic Behavior & Organization, Elsevier, volume 185, issue C, pages 688-701, DOI: 10.1016/j.jebo.2021.03.019.
• Godzinski, Alexandre & Suarez Castillo, Milena, 2021, "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, volume 109, issue C, DOI: 10.1016/j.jeem.2021.102489.
• Hirche, Martin & Farris, Paul W. & Greenacre, Luke & Quan, Yiran & Wei, Susan, 2021, "Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship," Journal of Retailing, Elsevier, volume 97, issue 4, pages 697-714, DOI: 10.1016/j.jretai.2021.04.002.
• Martins, Pedro S., 2021, "Working to get fired? Unemployment benefits and employment duration," Journal of Policy Modeling, Elsevier, volume 43, issue 5, pages 1016-1030, DOI: 10.1016/j.jpolmod.2021.03.004.
• Turrell, Arthur & Speigner, Bradley & Copple, David & Djumalieva, Jyldyz & Thurgood, James, 2021, "Is the UK’s productivity puzzle mostly driven by occupational mismatch? An analysis using big data on job vacancies," Labour Economics, Elsevier, volume 71, issue C, DOI: 10.1016/j.labeco.2021.102013.
• Brownlees, Christian & Hans, Christina & Nualart, Eulalia, 2021, "Bank credit risk networks: Evidence from the Eurozone," Journal of Monetary Economics, Elsevier, volume 117, issue C, pages 585-599, DOI: 10.1016/j.jmoneco.2020.03.014.
• Tissaoui, Kais & Zaghdoudi, Taha, 2021, "Dynamic connectedness between the U.S. financial market and Euro-Asian financial markets: Testing transmission of uncertainty through spatial regressions models," The Quarterly Review of Economics and Finance, Elsevier, volume 81, issue C, pages 481-492, DOI: 10.1016/j.qref.2020.10.020.
• Garciga, Christian & Verbrugge, Randal, 2021, "Robust covariance matrix estimation and identification of unusual data points: New tools," Research in Economics, Elsevier, volume 75, issue 2, pages 176-202, DOI: 10.1016/j.rie.2021.03.001.
• Roshen Fernando & Weifeng Liu & Warwick J McKibbin, 2021, "Global economic impacts of climate shocks, climate policy and changes in climate risk assessment," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-37, Apr.
• Christiane Baumeister & Danilo Leiva-León & Eric Sims, 2021, "Tracking weekly state-level economic conditions," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-55, Jul.
• Hanol Lee & Jong-Wha Lee, 2021, "Why East Asian students perform better in mathematics than their peers: An investigation using a machine learning approach," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-66, Jul.
• Florence Jaumotte & Weifeng Liu & Warwick J. McKibbin, 2021, "Mitigating climate change: Growth-friendly policies to achieve net zero emissions by 2050," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-75, Aug.
• Jieyi Kang & David Reiner, 2021, "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2112, May.
• Jieyi Kang & David Reiner, 2021, "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2113, May.
• Jieyi Kang & David Reiner, 2021, "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2114, May.
• Krzysztof Marcjan & Lucjan Gucma & Kotkowska Diana, 2021, "The Collision Risk Management Method for Ships Navigating on Coastal Waters Based on Ship Domain and Near-Miss Concept," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4 - Part , pages 127-146.
• Isaac K. Ofori & Christopher Quaidoo & Pamela E. Ofori, 2021, "What Drives Financial Sector Development in Africa? Insights from Machine Learning," Working Papers, European Xtramile Centre of African Studies (EXCAS), number 21/074, Jan.
• Lenka Nechvatalova, 2021, "Multi-Horizon Equity Returns Predictability via Machine Learning," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2021/02, Feb, revised Feb 2021.
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• Hie Joo Ahn & Matteo Luciani, 2021, "Relative prices and pure inflation since the mid-1990s," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2021-069, Oct, DOI: 10.17016/FEDS.2021.069.
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• Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021, "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, volume 3, issue 4, pages 1-30, October.
• Joelle Noailly; Laura Nowzohour; Matthias van den Heuvel, 2021, "Heard the News? Environmental Policy and Clean Investments," CIES Research Paper series, Centre for International Environmental Studies, The Graduate Institute, number 70-2021, Nov.
• Hager Ben Romdhane, 2021, "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 11-2021, Jun.
• Tetiana Yukhymenko, 2021, "Role of the Media in the Inflation Expectation Formation Process," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 13-2021, Jun.
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• Camino González Vasco & María Jesús Delgado Rodríguez & Sonia de Lucas Santos, 2021, "Segmentation of Potential Fraud Taxpayers and Characterization in Personal Income Tax Using Data Mining Techniques," Hacienda Pública Española / Review of Public Economics, IEF, volume 239, issue 4, pages 127-157, November.
• Hutter, Christian, 2021, "Cyclicality of labour market search: a new big data Approach," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], volume 55, pages 1-1, DOI: 10.1186/s12651-020-00283-9.
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• Kadriye Hilal Topal, 2021, "Variable selection via the adaptive elastic net- mathematics success of the students in Singapore and Turkey," Journal of Applied Microeconometrics, Holistence Publications, volume 1, issue 1, pages 41-55, June, DOI: 10.53753/jame.1.1.04.
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• Zhan Gao & Zhentao Shi, 2021, "Implementing Convex Optimization in R: Two Econometric Examples," Computational Economics, Springer;Society for Computational Economics, volume 58, issue 4, pages 1127-1135, December, DOI: 10.1007/s10614-020-09995-z.
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• François Gardes, 2021, "Endogenous Prices in a Riemannian Geometry Framework," Documents de travail du Centre d'Economie de la Sorbonne, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, number 21026, Jul.
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• Sevvandi Kandanaarachchi & Rob J Hyndman, 2021, "Leave-one-out Kernel Density Estimates for Outlier Detection," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 2/21.
• Sayani Gupta & Rob J Hyndman & Dianne Cook, 2021, "Detecting Distributional Differences between Temporal Granularities for Exploratory Time Series Analysis," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 20/21.
• Fan Cheng & Rob J Hyndman & Anastasios Panagiotelis, 2021, "Manifold Learning with Approximate Nearest Neighbors," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 3/21.
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