Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2020

• Salisu, Afees A. & Gupta, Rangan & Bouri, Elie & Ji, Qiang, 2020, "The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach," Research in International Business and Finance, Elsevier, volume 54, issue C, DOI: 10.1016/j.ribaf.2020.101308.
• Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020, "Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, volume 89, issue C, DOI: 10.1016/j.socec.2019.101502.
• Mihaela, Simionescu, 2020, "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, volume 155, issue C, DOI: 10.1016/j.techfore.2020.120026.
• Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020, "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, volume 158, issue C, DOI: 10.1016/j.techfore.2020.120126.
• Nasir, Muhammad Ali, 2020, "Forecasting inflation under uncertainty: The forgotten dog and the frisbee," Technological Forecasting and Social Change, Elsevier, volume 158, issue C, DOI: 10.1016/j.techfore.2020.120172.
• Weshah Razzak, 2020, "The Dynamic of COVID-19 New Infections under Different Stringent Policies," EERI Research Paper Series, Economics and Econometrics Research Institute (EERI), Brussels, number EERI RP 2020/07, Jul.
• Patrick J. Coe & Shaun P. Vahey, 2020, "Financial conditions and the risks to economic growth in the United States since 1875," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2020-36, Apr.
• Laurent Pauwels & Peter Radchenko & Andrey L. Vasnev, 2020, "High Moment Constraints for Predictive Density Combination," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2020-45, May, revised Jun 2023.
• Marek Kwas & Alessia Paccagnini & Michal Rubaszek, 2020, "Common Factors and the Dynamics of Cereal Prices: A Forecasting Perspective," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2020-47, May.
• Laurent Ferrara & Joseph Yapi, 2020, "Measuring Exchange Rate Risks During Periods of Uncertainty," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2020-60, Jun.
• William D. Larson & Tara M. Sinclair, 2020, "Nowcasting Unemployment Insurance Claims in the Time of COVID-19," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2020-63, Jul.
• Bo Zhang & Bao H. Nguyen, 2020, "Real-Time Forecasting of the Australian Macroeconomy Using Flexible Bayesian VARs," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2020-91, Oct.
• Gergo Toth & Zoltan Elekes & Adam Whittle & Changjun Lee & Dieter F. Kogler, 2020, "Technology network structure conditions the economic resilience of regions," Papers in Evolutionary Economic Geography (PEEG), Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, number 2048, Sep, revised Sep 2020.
• Gandy, Axel & Veraart, Luitgard A. M., 2021, "Compound poisson models for weighted networks with applications in finance," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 104185, Jan.
• J Reade & C Singleton & L Vaughan Williams, 2020, "Betting Markets for English Premier League Results and Scorelines: Evaluating a Simple Forecasting Model," Economic Issues Journal Articles, Economic Issues, volume 25, issue 1, pages 87-106, March.
• Cindy S. H. Wang & Shui Ki Wan, 2020, "A VAR Approach to Forecasting Multivariate Long Memory Processes Subject to Structural Breaks," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of Cheng Hsiao", DOI: 10.1108/S0731-905320200000041004.
• Pierre Rostan & Alexandra Rostan, 2020, "Where is Saudi Arabia's economy heading?," International Journal of Emerging Markets, Emerald Group Publishing Limited, volume 16, issue 8, pages 2009-2033, July, DOI: 10.1108/IJOEM-08-2018-0447.
• Hardik Marfatia, 2020, "Evaluating the forecasting power of foreign Country's income growth: a global analysis," Journal of Economic Studies, Emerald Group Publishing Limited, volume 47, issue 5, pages 1071-1092, April, DOI: 10.1108/JES-06-2019-0261.
• Rahul Roy & Santhakumar Shijin, 2020, "The nexus of asset pricing, volatility and the business cycle," Journal of Economic Studies, Emerald Group Publishing Limited, volume 48, issue 1, pages 79-101, May, DOI: 10.1108/JES-08-2019-0357.
• Zidong An & Joao Tovar Jalles, 2020, "On the performance of US fiscal forecasts: government vs. private information," Journal of Economic Studies, Emerald Group Publishing Limited, volume 48, issue 2, pages 367-391, June, DOI: 10.1108/JES-08-2019-0388.
• Franses, Ph.H.B.F. & Welz, M., 2020, "Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI-1687, Jan.
• Franses, Ph.H.B.F., 2020, "An introduction to time-varying lag autoregression," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2020-05, Apr.
• Raymundo M. Campos Vázquez & Sergio E. López-Araiza B., 2020, "Grandes datos, Google y desempleo/Big Data, Google and Unemployment," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, volume 35, issue 1, pages 125-151.
• Francisco Corona & Jesús López-Pérez, 2020, "Una evaluación econométrica de la retropolación de la actividad económica estatal de México," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, volume 35, issue 2, pages 193-212.
• Oscar de J. Gálvez-Soriano, 2020, "Nowcasting Mexico's quarterly GDP using factor models and bridge equations," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, volume 35, issue 2, pages 213-265.
• My-Linh Thi Nguyen, 2020, "The Hedonic Pricing Model Applied to the Housing Market," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), volume 0, issue 3, pages 416-428.
• Ioannis N. Kallianiotis & Karen Bianchi & Augustine C. Arize & John Malindretos & Ikechukwu Ndu, 2020, "Financial Assets, Expected Return and Risk, Speculation, Uncertainty, and Exchange Rate Determination," European Research Studies Journal, European Research Studies Journal, volume 0, issue 3, pages 3-30.
• Mariusz Doszyn, 2020, "Accuracy of Intermittent Demand Forecasting Systems in the Enterprise," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 912-930.
• Bartlomiej H. Toszek, 2020, "Innovative Arrangements of Waste Management Environment Strategy: The Case of London," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 1, pages 1024-1032.
• Mariusz Doszyn, 2020, "Biasedness of Forecasts Errors for Intermittent Demand Data," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 1, pages 1113-1127.
• Anna Warchlewska & Krzysztof Waliszewski, 2020, "Who uses Robo-Advisors? The Polish Case," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 1, pages 97-114.
• Tomasz Zawadzki & Tomasz Walecki & Halina Swieboda & Ryszard Szpyra & M.Kuczabski & P.Stobiecki, 2020, "Introduction to Methods of Modelling Information Wars as a 21st Century Threat," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 2, pages 1011-1026.
• Katarzyna Witczynska, 2020, "The Impact of the Electronic Commerce Market in the Supply Chain during COVID-19 Pandemic in Poland," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 2, pages 648-658.
• Krzysztof Waliszewski & Anna Warchlewska, 2020, "Socio-Demographic Factors Determining Expectation Experienced while Using Modern Technologies in Personal Financial Management (PFM and robo-advice): A Polish Case," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 2, pages 893-904.
• Evzen Kocenda & Karen Poghosyan, 2020, "Nowcasting Real GDP Growth: Comparison between Old and New EU Countries," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2020/5, Feb, revised Feb 2020.
• Bruno Ducoudré & Paul Hubert & Guilhem Tabarly, 2020, "The state-dependence of output revisions," Documents de Travail de l'OFCE, Observatoire Francais des Conjonctures Economiques (OFCE), number 2020-04, Jan.
• Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020, "Financial Variables as Predictors of Real Growth Vulnerability," Documents de Travail de l'OFCE, Observatoire Francais des Conjonctures Economiques (OFCE), number 2020-06, Feb.
• Bo Zhao, 2020, "Forecasting the New England States’ Tax Revenues in the Time of the COVID-19 Pandemic," Current Policy Perspectives, Federal Reserve Bank of Boston, number 88356, Jul.
• Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020, "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers, Federal Reserve Bank of Cleveland, number 20-02R, Jan, revised 22 Sep 2020, DOI: 10.26509/frbc-wp-202002r.
• Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020, "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers, Federal Reserve Bank of Cleveland, number 20-13R2, May, revised 22 Sep 2020, DOI: 10.26509/frbc-wp-202013r2.
• Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020, "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers, Federal Reserve Bank of Cleveland, number 20-27, Sep, DOI: 10.26509/frbc-wp-202027.
• Edward S. Knotek & Saeed Zaman, 2020, "Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach," Working Papers, Federal Reserve Bank of Cleveland, number 20-31, Oct, DOI: 10.26509/frbc-wp-202031.
• Aitor Erce & Xu Jiang & Diana Zigraiova, 2020, "Quantifying Risks to Sovereign Market Access: Methods and Challenges," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 377, Feb, DOI: 10.24149/gwp377.
• Enrique Martínez García & Efthymios Pavlidis & Kostas Vasilopoulos, 2020, "exuber: Recursive Right-Tailed Unit Root Testing with R," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 383, May, revised 19 Oct 2021, DOI: 10.24149/gwp383r1.
• Jonathan Benchimol & Makram El-Shagi, 2020, "Forecast Performance in Times of Terrorism," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 390, Jun, DOI: 10.24149/gwp390.
• Ayse Dur & Enrique Martínez García, 2020, "Mind the Gap!—A Monetarist View of the Open-Economy Phillips Curve," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 392, Jun, DOI: 10.24149/gwp392.
• Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020, "Variable Selection in High Dimensional Linear Regressions with Parameter Instability," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 394, Aug, revised 05 Aug 2024, DOI: 10.24149/gwp394r3.
• Francis X. Diebold & Glenn D. Rudebusch, 2020, "Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections," Working Paper Series, Federal Reserve Bank of San Francisco, number 2020-02, Jan, DOI: 10.24148/wp2020-02.
• Lukas Hoesch & Barbara Rossi & Tatevik Sekhposyan, 2020, "Has the Information Channel of Monetary Policy Disappeared? Revisiting the Empirical Evidence," Working Paper Series, Federal Reserve Bank of San Francisco, number 2020-08, Feb, DOI: 10.24148/wp2020-08.
• Christopher A. Hollrah & Steven A. Sharpe & Nitish R. Sinha, 2020, "The Power of Narratives in Economic Forecasts," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-001, Jan, DOI: 10.17016/FEDS.2020.001.
• Travis J. Berge, 2020, "Time-varying Uncertainty of the Federal Reserve’s Output Gap Estimate," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-012r1, Feb, revised 14 Apr 2021, DOI: 10.17016/FEDS.2020.012r1.
• Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2020, "Online Estimation of DSGE Models," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-023, Feb, DOI: 10.17016/FEDS.2020.023.
• Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2020, "Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-030, Apr, DOI: 10.17016/FEDS.2020.030.
• Michael Puglia & Adam Tucker, 2020, "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-038, May, DOI: 10.17016/FEDS.2020.038.
• Elena Afanasyeva, 2020, "Can Forecast Errors Predict Financial Crises? Exploring the Properties of a New Multivariate Credit Gap," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-045, Jun, DOI: 10.17016/FEDS.2020.045.
• Hie Joo Ahn & Yun Liu & Yeonwoo Rho, 2020, "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-082, Sep, DOI: 10.17016/FEDS.2020.082.
• Andrew C. Chang & Trace J. Levinson, 2020, "Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-090, Oct, DOI: 10.17016/FEDS.2020.090.
• Scott A. Brave & R. Andrew Butters & Michael Fogarty, 2020, "Looking down the road with ALEX: Forecasting U.S. GDP," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue 447, pages 1-5, October.
• Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel W. Sacks & Boyoung Seo, 2020, "Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2020-10, Apr, DOI: 10.21033/wp-2020-10.
• Scott A. Brave & R. Andrew Butters & Michael Fogarty, 2020, "The perils of working with Big Data and a SMALL framework you can use to avoid them," Working Paper Series, Federal Reserve Bank of Chicago, number WP-2020-35, Dec, revised 02 Mar 2020, DOI: 10.21033/wp-2020-35.
• Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel Sacks & Boyoung Seo, 2020, "Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims," Working Paper Series, Federal Reserve Bank of Chicago, number WP-2020-10, Apr, revised 16 Apr 2020, DOI: 10.21033/wp-2020-10.
• Yifei Lyu & Jun Nie & Shu-Kuei X. Yang, 2020, "Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 20-09, Aug, DOI: 10.18651/RWP2020-09.
• Ana B. Galvão & Michael T. Owyang, 2020, "Forecasting Low Frequency Macroeconomic Events with High Frequency Data," Working Papers, Federal Reserve Bank of St. Louis, number 2020-028, Sep, revised Apr 2022, DOI: 10.20955/wp.2020.028.
• Michael W. McCracken, 2020, "Tests of Conditional Predictive Ability: Existence, Size, and Power," Working Papers, Federal Reserve Bank of St. Louis, number 2020-050, Dec, DOI: 10.20955/wp.2020.050.
• Daniel J. Lewis & Karel Mertens & James H. Stock, 2020, "Monitoring Real Activity in Real Time: The Weekly Economic Index," Liberty Street Economics, Federal Reserve Bank of New York, number 20200330b, Mar.
• Dean Croushore & Stephanie M. Wilshusen, 2020, "Forecasting Consumption Spending Using Credit Bureau Data," Working Papers, Federal Reserve Bank of Philadelphia, number 20-22, Jun, DOI: 10.21799/frbp.wp.2020.22.
• Frank Schorfheide & Dongho Song, 2020, "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," Working Papers, Federal Reserve Bank of Philadelphia, number 20-26, Jul, DOI: 10.21799/frbp.wp.2020.26.
• Ryan Cumings-Menon & Minchul Shin, 2020, "Probability Forecast Combination via Entropy Regularized Wasserstein Distance," Working Papers, Federal Reserve Bank of Philadelphia, number 20-31/R, Aug, DOI: 10.21799/frbp.wp.2020.31.
• Fabrizio Cipollini & Giampiero Gallo & Alessandro Palandri, 2020, "A Dynamic Conditional Approach to Portfolio Weights Forecasting," Econometrics Working Papers Archive, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", number 2020_06, May.
• Ludovic Dobbelaere & Igor Lebrun, 2020, "Working Paper 05-20 - Évaluation de la précision des prévisions à court terme et des perspectives à moyen terme du BFP - Une mise à jour des Working Papers 12-17 et 13-17
  [Working Paper 05-20 - Evaluatie van de nauwkeurigheid van de korte- en m," Working Papers, Federal Planning Bureau, Belgium, number 202005, Nov.
• Lucian Liviu Albu & Radu Lupu, 2020, "Anomaly detection in stock market indices with neural networks," Journal of Financial Studies, Institute of Financial Studies, volume 9, issue 5, pages 10-23, November, DOI: 10.6084/m9.figshare.13621304.
• Tsukhlo Sergey, 2020, "Russian industrial sector in 2019: (based on surveys’ findings)," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2020-1047, revised 2020.
• Barinova Vera & Zemtsov Tsepan & Tsareva Yulia, 2020, "Small and medium-sized entrepreneurship in Russia and regions in 2019–2020," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2020-1052, revised 2020.
• Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020, "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, volume 13, issue 14, pages 1-19, July.
• Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020, "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, volume 13, issue 7, pages 1-16, April.
• Dean Fantazzini & Nikita Kolodin, 2020, "Does the Hashrate Affect the Bitcoin Price?," JRFM, MDPI, volume 13, issue 11, pages 1-29, October.
• Philip Hans Franses & Max Welz, 2020, "Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?," JRFM, MDPI, volume 13, issue 3, pages 1-8, March.
• Ruben Fotso, 2020, "Evaluation of indirect effects of place-based science-industry transfer policies: Case of French Technological Research Institutes," Working Papers, Groupe d'Analyse et de Théorie Economique Lyon St-Etienne (GATE Lyon St-Etienne), Université de Lyon, number 2031.
• Ruben Fotso, 2020, "Evaluation of the effectiveness of technological platforms as a technology transfer tool: the impact of French Technological Research Institutes on the socio-economic performance of SMEs," Working Papers, Groupe d'Analyse et de Théorie Economique Lyon St-Etienne (GATE Lyon St-Etienne), Université de Lyon, number 2032.
• Gary Koop & Dimitris Korobilis, 2020, "Bayesian dynamic variable selection in high dimensions," Working Papers, Business School - Economics, University of Glasgow, number 2020_11, May.
• Dimitris Korobilis, 2020, "Sign restrictions in high-dimensional vector autoregressions," Working Papers, Business School - Economics, University of Glasgow, number 2020_21, Sep.
• Adrien LAGARDE & Luc DOYEN & Joachim CLAUDET & Olivier THEBAUD, 2020, "Ecological-economic resilience of a fished coral reef through stochastic multi-species MSY," Bordeaux Economics Working Papers, Bordeaux School of Economics (BSE), number 2020-11.
• Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020, "Short-Term Exuberance and long-term stability: A simultaneous optimization of stock return predictions for short and long horizons," Graz Economics Papers, University of Graz, Department of Economics, number 2020-20, Dec.
• Constantin Bürgi & Tara M. Sinclair, 2020, "What Does Forecaster Disagreement Tell Us about the State of the Economy?," Working Papers, The George Washington University, The Center for Economic Research, number 2020-001, Feb.
• William D. Larson & Tara M. Sinclair, 2020, "Nowcasting Unemployment Insurance Claims in the Time of COVID-19," Working Papers, The George Washington University, The Center for Economic Research, number 2020-004, Jun, revised Aug 2020.
• Constantin Bürgi, 2020, "Expectation Formation and the Persistence of Shocks," Working Papers, The George Washington University, The Center for Economic Research, number 2020-005, Aug, revised Sep 2020.
• Natsuki Arai, 2020, "The FOMC’s New Individual Economic Projections and Macroeconomic Theories," Working Papers, The George Washington University, The Center for Economic Research, number 2020-007, Oct.
• Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2020, "Smooth Robust Multi-Horizon Forecasts," Working Papers, The George Washington University, The Center for Economic Research, number 2020-009, Dec.
• Philip ME Garboden, 2019, "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, number 2019-3, Jul.
• Peter Fuleky, 2020, "Nowcasting the Trajectory of the COVID-19 Recovery," Working Papers, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, number 2020-3, Sep.
• Peter Fuleky, 2020, "Nowcasting the Trajectory of the COVID-19 Recovery," Working Papers, University of Hawaii at Manoa, Department of Economics, number 202022, Sep.
• Michael Brei & Claudio Borio & Leonardo Gambacorta, 2020, "Bank intermediation activity in a low‐interest‐rate environment," Post-Print, HAL, number hal-02985986, Jul, DOI: 10.1111/ecno.12164.
• Matteo Mogliani & Anna Simoni, 2020, "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print, HAL, number hal-03089878, Aug, DOI: 10.1016/j.jeconom.2020.07.022.
• Catherine Doz & Peter Fuleky, 2020, "Dynamic Factor Models," Post-Print, HAL, number halshs-02491811, Nov, DOI: 10.1007/978-3-030-31150-6_2.
• Jonathan Benchimol & Makram El-Shagi, 2020, "Forecast performance in times of terrorism," Post-Print, HAL, number halshs-03248938, Sep, DOI: 10.1016/j.econmod.2020.05.018.
• Catherine Doz & Peter Fuleky, 2020, "Dynamic Factor Models," PSE-Ecole d'économie de Paris (Postprint), HAL, number halshs-02491811, Nov, DOI: 10.1007/978-3-030-31150-6_2.
• Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020, "Financial Variables as Predictors of Real Growth Vulnerability," Sciences Po Economics Publications (main), HAL, number hal-03403077, Jan.
• Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021, "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers, HAL, number hal-02507499, Jan.
• Olivier Damette & Claude Diebolt & Stephane Goutte & Umberto Triacca, 2020, "Cliometrics of Climate Change," Working Papers, HAL, number hal-03215675, Apr.
• Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020, "Financial Variables as Predictors of Real Growth Vulnerability," Working Papers, HAL, number hal-03403077, Jan.
• Laurent Ferrara & Anna Simoni, 2020, "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers, HAL, number hal-04159714.
• Steffen Q. Mueller & Patrick Ring & Maria Fischer, 2020, "Excited and aroused: The predictive importance of simple choice process metrics," Working Papers, Chair for Economic Policy, University of Hamburg, number 067, Dec.
• William D. Larson & Tara M. Sinclair, 2020, "Nowcasting Unemployment Insurance Claims in the Time of COVID-19," FHFA Staff Working Papers, Federal Housing Finance Agency, number 20-02, Jun.
• Gregory Chernov, 2020, "How to Learn to Defeat Noisy Robot in Rock-Paper-Scissors Game: An Exploratory Study," HSE Economic Journal, National Research University Higher School of Economics, volume 24, issue 4, pages 503-538.
• Inna S. Lola & Anton Manukov, 2020, "Forecasting Employment In Small Businesses In Russia: The Relevance Of Business Tendency Surveys," HSE Working papers, National Research University Higher School of Economics, number WP BRP 113/STI/2020.
• Hutter, Christian, 2020, "A new indicator for nowcasting employment subject to social security contributions in Germany," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], volume 54, issue , pages 1-004, DOI: 10.1186/s12651-020-00274-w.
• Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020, "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers, IMT School for Advanced Studies Lucca, number 01/2020, Jun, revised Jun 2020.
• Shahrin Saaid Shaharuddin & Wee-Yeap Lau & Tien-Ming Yip, 2020, "Do Islamic Equity Style Indices Contain Economic Information?," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, volume 6, issue 4, pages 895-918, November, DOI: https://doi.org/10.21098/jimf.v6i4..
• Thomas Lustenberger & Enzo Rossi, 2020, "Does Central Bank Transparency and Communication Affect Financial and Macroeconomic Forecasts?," International Journal of Central Banking, International Journal of Central Banking, volume 16, issue 2, pages 153-201, March.
• Toshiaki Ogawa & Masato Ubukata & Toshiaki Watanabe, 2020, "Stock Return Predictability and Variance Risk Premia around the ZLB," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 20-E-09, Jul.
• Mr. Bas B. Bakker & Mr. Manuk Ghazanchyan & Alex Ho & Vibha Nanda, 2020, "The Lack of Convergence of Latin-America Compared with CESEE: Is Low Investment to Blame?," IMF Working Papers, International Monetary Fund, number 2020/098, Jun.
• Rey Francisco Ayala Castrejon & Christian Bucio Pacheco, 2020, "Modelo ARIMA aplicado al tipo de cambio peso-dólar en el periodo 2016-2017 mediante ventanas temporales deslizantes," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 15, issue 3, pages 331-354, Julio - S.
• Sergio Lagunas Puls & Miguel Angel Oropeza Tagle & Juan Bautista Boggio Vázquez, 2020, "Energy Consumption in North America: Visualization and Pyramidal Perspective," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 15, issue 4, pages 709-723, Octubre -.
• Xavier Estupinan & Mohit Sharma & Sargam Gupta & Bharti Birla, 2020, "Impact of COVID-19 pandemic on labour supply and gross value added in India," Indira Gandhi Institute of Development Research, Mumbai Working Papers, Indira Gandhi Institute of Development Research, Mumbai, India, number 2020-022, Jun.
• Lasse Bork & Stig V. Møller & Thomas Q. Pedersen, 2020, "A New Index of Housing Sentiment," Management Science, INFORMS, volume 66, issue 4, pages 1563-1583, April, DOI: 10.1287/mnsc.2018.3258.
• Laura Liu, 2020, "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," CAEPR Working Papers, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, number 2020-003, Mar.
• Oscar Claveria & Ivana Lolic & Enric Monte & Petar Soric, 2020, "Economic determinants of employment sentiment: A socio-demographic analysis for the euro area," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202001, Jan, revised Jan 2020.
• Marc Burri & Daniel Kaufmann, 2020, "A daily fever curve for the Swiss economy," IRENE Working Papers, IRENE Institute of Economic Research, number 20-05, May.
• Zidong An & João Tovar Jalles, 2020, "On the Performance of US Fiscal Forecasts: Government vs. Private Information," Working Papers REM, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa, number 2020/0130, May.
• Kaiser, Ulrich & Kuhn, Johan Moritz, 2020, "Value of Publicly Available, Textual and Non-textuThe al Information for Startup Performance Prediction," IZA Discussion Papers, IZA Network @ LISER, number 13029, Mar.
• Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020, "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," IZA Discussion Papers, IZA Network @ LISER, number 13776, Oct.
• Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020, "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," JRC Working Papers in Economics and Finance, Joint Research Centre, European Commission, number 2020-04, Sep.
• Berta, Paolo & Lovaglio, Pietro Giorgio & Paruolo, Paolo & Verzillo, Stefano, 2020, "Real Time Forecasting of Covid-19 Intensive Care Units demand," JRC Working Papers in Economics and Finance, Joint Research Centre, European Commission, number 2020-08, Sep.
• Mawuli Segnon & Stelios Bekiros, 2020, "Forecasting volatility in bitcoin market," Annals of Finance, Springer, volume 16, issue 3, pages 435-462, September, DOI: 10.1007/s10436-020-00368-y.

2019

• Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019, "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, volume 52, issue C, pages 201-219, DOI: 10.1016/j.jempfin.2019.03.006.
• Díaz-Hernández, Adán & Constantinou, Nick, 2019, "A multiple regime extension to the Heston–Nandi GARCH(1,1) model," Journal of Empirical Finance, Elsevier, volume 53, issue C, pages 162-180, DOI: 10.1016/j.jempfin.2019.05.004.
• Panopoulou, Ekaterini & Souropanis, Ioannis, 2019, "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, volume 53, issue C, pages 197-221, DOI: 10.1016/j.jempfin.2019.07.004.
• Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019, "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, volume 54, issue C, pages 97-117, DOI: 10.1016/j.jempfin.2019.08.007.
• Westerlund, Joakim & Sharma, Susan Sunila, 2019, "Panel evidence on the ability of oil returns to predict stock returns in the G7 area," Energy Economics, Elsevier, volume 77, issue C, pages 3-12, DOI: 10.1016/j.eneco.2018.05.007.
• Manner, Hans & Alavi Fard, Farzad & Pourkhanali, Armin & Tafakori, Laleh, 2019, "Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae," Energy Economics, Elsevier, volume 78, issue C, pages 143-164, DOI: 10.1016/j.eneco.2018.10.034.
• Chen, Rongda & Xu, Jianjun, 2019, "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, volume 78, issue C, pages 379-391, DOI: 10.1016/j.eneco.2018.11.011.
• Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019, "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, volume 78, issue C, pages 656-667, DOI: 10.1016/j.eneco.2017.12.035.
• Jiao, Ying & Ma, Chunhua & Scotti, Simone & Sgarra, Carlo, 2019, "A branching process approach to power markets," Energy Economics, Elsevier, volume 79, issue C, pages 144-156, DOI: 10.1016/j.eneco.2018.03.002.
• Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019, "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, volume 79, issue C, pages 171-182, DOI: 10.1016/j.eneco.2018.02.007.
• Maryniak, Paweł & Trück, Stefan & Weron, Rafał, 2019, "Carbon pricing and electricity markets — The case of the Australian Clean Energy Bill," Energy Economics, Elsevier, volume 79, issue C, pages 45-58, DOI: 10.1016/j.eneco.2018.06.003.
• Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019, "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, volume 80, issue C, pages 423-433, DOI: 10.1016/j.eneco.2019.01.010.
• Kostrzewski, Maciej & Kostrzewska, Jadwiga, 2019, "Probabilistic electricity price forecasting with Bayesian stochastic volatility models," Energy Economics, Elsevier, volume 80, issue C, pages 610-620, DOI: 10.1016/j.eneco.2019.02.004.
• Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019, "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, volume 80, issue C, pages 937-949, DOI: 10.1016/j.eneco.2019.03.006.
• Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019, "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, volume 81, issue C, pages 1109-1120, DOI: 10.1016/j.eneco.2019.05.018.
• Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019, "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, volume 81, issue C, pages 639-649, DOI: 10.1016/j.eneco.2019.04.030.
• Liu, Jingzhen & Kemp, Alexander, 2019, "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, volume 81, issue C, pages 672-686, DOI: 10.1016/j.eneco.2019.04.023.
• Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019, "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, volume 84, issue C, DOI: 10.1016/j.eneco.2019.104520.
• Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2019, "How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study," Energy Economics, Elsevier, volume 84, issue C, DOI: 10.1016/j.eneco.2019.104536.
• Khalifa, Ahmed & Caporin, Massimiliano & Di Fonzo, Tommaso, 2019, "Scenario-based forecast for the electricity demand in Qatar and the role of energy efficiency improvements," Energy Policy, Elsevier, volume 127, issue C, pages 155-164, DOI: 10.1016/j.enpol.2018.11.047.
• Baldoni, Edoardo & Coderoni, Silvia & D'Orazio, Marco & Di Giuseppe, Elisa & Esposti, Roberto, 2019, "The role of economic and policy variables in energy-efficient retrofitting assessment. A stochastic Life Cycle Costing methodology," Energy Policy, Elsevier, volume 129, issue C, pages 1207-1219, DOI: 10.1016/j.enpol.2019.03.018.
• Maluf de Lima, Lilian & Piedade Bacchi, Mirian Rumenos, 2019, "Assessing the impact of Brazilian economic growth on demand for electricity," Energy, Elsevier, volume 172, issue C, pages 861-873, DOI: 10.1016/j.energy.2019.01.154.
• Moral-Carcedo, Julián & Pérez-García, Julián, 2019, "Time of day effects of temperature and daylight on short term electricity load," Energy, Elsevier, volume 174, issue C, pages 169-183, DOI: 10.1016/j.energy.2019.02.158.
• Zhang, Yaojie & Wei, Yu & Ma, Feng & Yi, Yongsheng, 2019, "Economic constraints and stock return predictability: A new approach," International Review of Financial Analysis, Elsevier, volume 63, issue C, pages 1-9, DOI: 10.1016/j.irfa.2019.02.007.
• Bleher, Johannes & Dimpfl, Thomas, 2019, "Today I got a million, tomorrow, I don't know: On the predictability of cryptocurrencies by means of Google search volume," International Review of Financial Analysis, Elsevier, volume 63, issue C, pages 147-159, DOI: 10.1016/j.irfa.2019.03.003.
• Tissaoui, Kais, 2019, "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, volume 64, issue C, pages 232-249, DOI: 10.1016/j.irfa.2019.06.001.
• Yin, Anwen, 2019, "Out-of-sample equity premium prediction in the presence of structural breaks," International Review of Financial Analysis, Elsevier, volume 65, issue C, DOI: 10.1016/j.irfa.2019.101385.
• Sensoy, Ahmet, 2019, "The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies," Finance Research Letters, Elsevier, volume 28, issue C, pages 68-73, DOI: 10.1016/j.frl.2018.04.002.
• Aslanidis, Nektarios & Christiansen, Charlotte & Cipollini, Andrea, 2019, "Predicting bond betas using macro-finance variables," Finance Research Letters, Elsevier, volume 29, issue C, pages 193-199, DOI: 10.1016/j.frl.2018.07.007.
• Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019, "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, volume 29, issue C, pages 315-322, DOI: 10.1016/j.frl.2018.08.013.
• Troster, Victor & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Macedo, Demian Nicolás, 2019, "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, Elsevier, volume 30, issue C, pages 187-193, DOI: 10.1016/j.frl.2018.09.014.
• Lei, Likun & Shang, Yue & Chen, Yongfei & Wei, Yu, 2019, "Does the financial crisis change the economic risk perception of crude oil traders? A MIDAS quantile regression approach," Finance Research Letters, Elsevier, volume 30, issue C, pages 341-351, DOI: 10.1016/j.frl.2018.10.016.
• Chevapatrakul, Thanaset & Mascia, Danilo V., 2019, "Detecting overreaction in the Bitcoin market: A quantile autoregression approach," Finance Research Letters, Elsevier, volume 30, issue C, pages 371-377, DOI: 10.1016/j.frl.2018.11.004.
• Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019, "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, volume 45, issue C, DOI: 10.1016/j.jfs.2019.100693.
• Inoue, Atsushi & Rossi, Barbara, 2019, "The effects of conventional and unconventional monetary policy on exchange rates," Journal of International Economics, Elsevier, volume 118, issue C, pages 419-447, DOI: 10.1016/j.jinteco.2019.01.015.
• Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019, "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, volume 88, issue C, pages 255-272, DOI: 10.1016/j.insmatheco.2019.07.004.
• Ponomareva, Natalia & Sheen, Jeffrey & Wang, Ben Zhe, 2019, "Forecasting exchange rates using principal components," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 63, issue C, DOI: 10.1016/j.intfin.2019.08.003.
• Götz, Thomas B. & Knetsch, Thomas A., 2019, "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, volume 35, issue 1, pages 45-66, DOI: 10.1016/j.ijforecast.2018.08.001.
• Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019, "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, volume 35, issue 1, pages 80-99, DOI: 10.1016/j.ijforecast.2018.07.013.
• Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019, "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, volume 35, issue 2, pages 521-539, DOI: 10.1016/j.ijforecast.2018.08.003.
• McAdam, Peter & Warne, Anders, 2019, "Euro area real-time density forecasting with financial or labor market frictions," International Journal of Forecasting, Elsevier, volume 35, issue 2, pages 580-600, DOI: 10.1016/j.ijforecast.2018.10.013.
• Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019, "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, volume 35, issue 2, pages 616-633, DOI: 10.1016/j.ijforecast.2018.12.002.
• Szafranek, Karol, 2019, "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, volume 35, issue 3, pages 1042-1059, DOI: 10.1016/j.ijforecast.2019.04.007.
• Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019, "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1263-1272, DOI: 10.1016/j.ijforecast.2019.03.021.
• Naimoli, Antonio & Storti, Giuseppe, 2019, "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1332-1355, DOI: 10.1016/j.ijforecast.2019.06.002.
• Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019, "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1533-1547, DOI: 10.1016/j.ijforecast.2019.02.001.
• Chiu, Ching-Wai (Jeremy) & Hayes, Simon & Kapetanios, George & Theodoridis, Konstantinos, 2019, "A new approach for detecting shifts in forecast accuracy," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1596-1612, DOI: 10.1016/j.ijforecast.2019.01.008.
• Berge, Travis J. & Chang, Andrew C. & Sinha, Nitish R., 2019, "Evaluating the conditionality of judgmental forecasts," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1627-1635, DOI: 10.1016/j.ijforecast.2019.03.026.
• Granziera, Eleonora & Sekhposyan, Tatevik, 2019, "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1636-1657, DOI: 10.1016/j.ijforecast.2019.01.010.
• Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019, "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1658-1668, DOI: 10.1016/j.ijforecast.2018.12.004.
• Diebold, Francis X. & Shin, Minchul, 2019, "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1679-1691, DOI: 10.1016/j.ijforecast.2018.09.006.
• Knotek, Edward S. & Zaman, Saeed, 2019, "Financial nowcasts and their usefulness in macroeconomic forecasting," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1708-1724, DOI: 10.1016/j.ijforecast.2018.10.012.
• Knüppel, Malte & Schultefrankenfeld, Guido, 2019, "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1748-1769, DOI: 10.1016/j.ijforecast.2019.03.014.
• Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019, "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1790-1799, DOI: 10.1016/j.ijforecast.2018.09.008.
• Thiele, Stephen, 2019, "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, volume 101, issue C, pages 12-20, DOI: 10.1016/j.jbankfin.2019.01.018.
• Lazar, Emese & Zhang, Ning, 2019, "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, volume 105, issue C, pages 74-93, DOI: 10.1016/j.jbankfin.2019.05.017.
• Gupta, Jairaj & Chaudhry, Sajid, 2019, "Mind the tail, or risk to fail," Journal of Business Research, Elsevier, volume 99, issue C, pages 167-185, DOI: 10.1016/j.jbusres.2019.02.037.
• Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019, "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, volume 132, issue 1, pages 126-149, DOI: 10.1016/j.jfineco.2018.10.001.
• Fan, Ying & Yang, Zan & Yavas, Abdullah, 2019, "Understanding real estate price dynamics: The case of housing prices in five major cities of China✰," Journal of Housing Economics, Elsevier, volume 43, issue C, pages 37-55, DOI: 10.1016/j.jhe.2018.09.003.
• Auer, Simone, 2019, "Monetary policy shocks and foreign investment income: Evidence from a large Bayesian VAR," Journal of International Money and Finance, Elsevier, volume 93, issue C, pages 142-166, DOI: 10.1016/j.jimonfin.2018.12.013.
• Chen, Jian & Jiang, Fuwei & Xue, Shuyu & Yao, Jiaquan, 2019, "The world predictive power of U.S. equity market skewness risk," Journal of International Money and Finance, Elsevier, volume 96, issue C, pages 210-227, DOI: 10.1016/j.jimonfin.2019.05.003.
• Xu, Zhongxiang & Chevapatrakul, Thanaset & Li, Xiafei, 2019, "Return asymmetry and the cross section of stock returns," Journal of International Money and Finance, Elsevier, volume 97, issue C, pages 93-110, DOI: 10.1016/j.jimonfin.2019.06.005.
• Brei, Michael & Moreno, Ramon, 2019, "Reserve requirements and capital flows in Latin America," Journal of International Money and Finance, Elsevier, volume 99, issue C, DOI: 10.1016/j.jimonfin.2019.102079.
• Kim, Insu & Kim, Young Se, 2019, "Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach," Journal of Macroeconomics, Elsevier, volume 62, issue C, DOI: 10.1016/j.jmacro.2019.103139.