Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2020

• Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020, "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper, Norges Bank, number 2020/17, Nov.
• Felix Kapfhammer & Vegard H. Larsen & Leif Anders Thorsrud, 2020, "Climate risk and commodity currencies," Working Paper, Norges Bank, number 2020/18, Dec.
• Kristina Bluwstein & Marcus Buckmann & Andreas Joseph & Miao Kang & Sujit Kapadia & Özgür Simsek, 2020, "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers, Bank of England, number 848, Jan.
• Alexandros E. Milionis & Nikolaos G. Galanopoulos, 2020, "A study of the effect of data transformation and «linearization» on time series forecasts. A practical approach," Working Papers, Bank of Greece, number 280, Jun.
• Jonathan Benchimol & Makram El-Shagi & Yossi Saadon, 2020, "Do Expert Experience and Characteristics Affect Inflation Forecasts?," Bank of Israel Working Papers, Bank of Israel, number 2020.11, Oct.
• Hyeongwoo Kim & Soohyon Kim, 2020, "Common Factor Augmented Forecasting Models for the US Dollar-Korean Won Exchange Rate," Working Papers, Economic Research Institute, Bank of Korea, number 2020-5, Feb.
• Nam Gang Lee, 2020, "Vulnerable Growth: A Revisit," Working Papers, Economic Research Institute, Bank of Korea, number 2020-22, Oct.
• Pierdzioch Christian & Gupta Rangan, 2020, "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 24, issue 4, pages 1-20, September, DOI: 10.1515/snde-2018-0083.
• Funke Michael & Loermann Julius & Moessner Richhild, 2020, "The discontinuation of the EUR/CHF minimum exchange rate: information from option-implied break probabilities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 25, issue 3, pages 63-79, June, DOI: 10.1515/snde-2019-0078.
• Rutzer, Christian & Niggli, Matthias & Weder, Rolf, 2020, "Estimating the Green Potential of Occupations: A New Approach Applied to the U.S. Labor Market," Working papers, Faculty of Business and Economics - University of Basel, number 2020/03.
• Lake, A., 2020, "Optimal Feasible Expectations in Economics and Finance," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 20105, Nov.
• Cristea, R. G., 2020, "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 20108, Nov.
• Mueller, H. & Rauh, C., 2020, "The Hard Problem of Prediction for Conflict Prevention," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2015, Mar.
• Ahmed, R. & Pesaran, M. H., 2020, "Regional Heterogeneity and U.S. Presidential Elections," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2092, Oct.
• Ba Chu & Shafiullah Qureshi, 2020, "Predicting the COVID-19 Pandemic in Canada and the US," Carleton Economic Papers, Carleton University, Department of Economics, number 20-05, May, revised 30 Jul 2020.
• Congressional Budget Office, 2020, "The Accuracy of CBO’s Budget Projections for Fiscal Year 2020," Reports, Congressional Budget Office, number 56885, Dec.
• Pizzinelli, Carlo & Theodoridis, Konstantinos & Zanetti, Francesco, 2020, "State Dependence in Labor Market Fluctuations," Cardiff Economics Working Papers, Cardiff University, Cardiff Business School, Economics Section, number E2020/2, Mar.
• Mykola Babiak & Jozef Barunik, 2020, "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp677, Dec.
• Markus Heinrich & Magnus Reif, 2020, "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series, CESifo, number 8054.
• Alexandros Botsis & Christoph Görtz & Plutarchos Sakellaris, 2020, "Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms' Forecasts," CESifo Working Paper Series, CESifo, number 8148.
• Kajal Lahiri & Yongchen Zhao, 2020, "The Nordhaus Test with Many Zeros," CESifo Working Paper Series, CESifo, number 8350.
• Benedikt Janzen & Doina Maria Radulescu, 2020, "Electricity Use as a Real Time Indicator of the Economic Burden of the Covid-19-Related Lockdown: Evidence from Switzerland," CESifo Working Paper Series, CESifo, number 8363.
• Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020, "Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks," CESifo Working Paper Series, CESifo, number 8475.
• Constantin Bürgi & Nisan Gorgulu, 2020, "Social Distancing and the Economic Impact of Covid-19 in the United States," CESifo Working Paper Series, CESifo, number 8577.
• Rashad Ahmed & M. Hashem Pesaran, 2020, "Regional Heterogeneity and U.S. Presidential Elections," CESifo Working Paper Series, CESifo, number 8615.
• Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020, "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series, CESifo, number 8639.
• Felix Kapfhammer & Vegard H. Larsen & Leif Anders Thorsrud, 2020, "Climate Risk and Commodity Currencies," CESifo Working Paper Series, CESifo, number 8788.
• Magnus Reif, 2020, "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
• Mostak Ahamed & Roxana Gutiérrez-Romero, 2020, "COVID-19 response needs to broaden financial inclusion to curb the rise in poverty," Working Papers, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research, number 105, May.
• J-C Gerlach & Dongshuai Zhao, CFA & Didier Sornette, 2020, "Forecasting Financial Crashes: A Dynamic Risk Management Approach," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-103, Dec.
• Roberto Molinari & Gaetan Bakalli & Stéphane Guerrier & Cesare Miglioli & Samuel Orso & O. Scaillet, 2020, "Swag: A Wrapper Method for Sparse Learning," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-49, Jun.
• Rebecca Westphal & Didier Sornette, 2020, "How market intervention can prevent bubbles and crashes," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-74, Aug.
• Marc-Aurèle Divernois, 2020, "A Deep Learning Approach to Estimate Forward Default Intensities," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-79, Jul.
• J-C Gerlach & Jerome L Kreuser & Didier Sornette, 2020, "Crash-sensitive Kelly Strategy built on a modified Kreuser-Sornette bubble model tested over three decades of twenty equity indices," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-85, Oct.
• Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2020, "Forecasting the Covid-19 Recession and Recovery: Lessons from the Financial Crisis," CIRANO Working Papers, CIRANO, number 2020s-32, Jun.
• Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2020, "Macroeconomic Data Transformations Matter," CIRANO Working Papers, CIRANO, number 2020s-42, Aug.
• Kevin Moran & Dalibor Stevanovic & Adam Abdel Kader Touré, 2020, "Macroeconomic Uncertainty and the COVID-19 Pandemic: Measure and Impacts on the Canadian Economy," CIRANO Working Papers, CIRANO, number 2020s-47, Sep.
• Milan Szabo, 2020, "Growth-at-Risk: Bayesian Approach," Working Papers, Czech National Bank, Research and Statistics Department, number 2020/3, Nov.
• Frantisek Brazdik & Tibor Hledik & Zuzana Humplova & Iva Martonosi & Karel Musil & Jakub Rysanek & Tomas Sestorad & Jaromir Tonner & Stanislav Tvrz & Jan Zacek, 2020, "The g3+ Model: An Upgrade of the Czech National Bank's Core Forecasting Framework," Working Papers, Czech National Bank, Research and Statistics Department, number 2020/7, Dec.
• Ramiro Losada & Ricardo Laborda, 2020, "Non-alternative collective investment schemes, connectedness and systemic risk," CNMV Working Papers, CNMV- Spanish Securities Markets Commission - Research and Statistics Department, number CNMV Working Papers no. 7.
• Alfredo Trespalacios & Lina M. Cort�s & Javier Perote, 2020, "Modeling electricity price and quantity uncertainty: An application for hedging with forward contracts," Documentos de Trabajo de Valor Público, Universidad EAFIT, number 18186, Jun.
• Henry Caicedo-Asprilla *, 2020, "La producción del conocimiento de las regiones competitivas: una aproximación basada en modelos de variables latentes," Estudios Gerenciales, Universidad Icesi, volume 36, issue 155, pages 177-192, DOI: 10.18046/j.estger.2020.155.3257.
• Paola Mariell Brens Ortega, 2020, "An Econometric Analysis of a Calibrated Macroeconomic Model for the Dominican Republic: A Closer Look into Monetary Policy," Documentos de Trabajo, The Latin American and Caribbean Economic Association (LACEA), number 18253, Jul.
• Rodríguez, Aldo, 2020, "Estimación Bayesiana de un Modelo de Economía Abierta con Sector Bancario," Dynare Working Papers, CEPREMAP, number 52, Feb.
• Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020, "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14267, Jan.
• Reichlin, Lucrezia & Ricco, Giovanni & Hasenzagl, Thomas, 2020, "Financial Variables as Predictors of Real Growth Vulnerability," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14322, Jan.
• Anderson, Kym & Wittwer, Glyn, 2020, "A Model of Global Beverage Markets," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14387, Feb.
• Schorfheide, Frank & Liu, Laura & Moon, Hyungsik Roger, 2020, "Panel Forecasts of Country-Level Covid-19 Infectionsliu," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14790, May.
• Acharya, Viral & Bhadury, Soumya & Surti, Jay, 2020, "Financial Vulnerability and Risks to Growth in Emerging Markets," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14962, Jun.
• Taylor, Mark & Hsu, Po-Hsuan & Wang, Zigan, 2020, "The Out-of-Sample Performance of Carry Trades," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15052, Jul.
• Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2022, "Modeling and Forecasting Macroeconomic Downside Risk," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15109, Feb.
• Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020, "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15114, Jul.
• Rossi, Barbara & Odendahl, Florens & Sekhposyan, Tatevik, 2020, "Comparing Forecast Performance with State Dependence," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15217, Aug.
• Escribano, Álvaro & Wang, Dandan, 2020, "Forecasting gasoline prices with mixed random forest error correction models," UC3M Working papers. Economics, Universidad Carlos III de Madrid. Departamento de Economía, number 30557, Jun.
• Gonzalo, Jesús & Pitarakis, Jean-Yves, 2020, "Out of sample predictability in predictive regressions with many predictor candidates," UC3M Working papers. Economics, Universidad Carlos III de Madrid. Departamento de Economía, number 31554, Dec.
• Da Silva Neto, Anibal Emiliano & Gonzalo, Jesús & Pitarakis, Jean-Yves, 2020, "Uncovering regimes in out of sample forecast errors from predictive regressions," UC3M Working papers. Economics, Universidad Carlos III de Madrid. Departamento de Economía, number 31555, Dec.
• Nieto Delfin, Maria Rosa & Ruiz Ortega, Esther, 2020, "Direct versus iterated multi-period Value at Risk," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 30349, May.
• Carlomagno Real, Guillermo & Espasa, Antoni, 2020, "Discovering general and sectorial trends in a large set of time series," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 30899, Sep.
• Casas Villalba, Maria Isabel & Mao, Xiuping & Lopes Moreira da Veiga, María Helena, 2020, "Adaptative predictability of stock market returns," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 31648, Dec.
• Bekaert, Geert & Panayotov, George, 2020, "Good Carry, Bad Carry," Journal of Financial and Quantitative Analysis, Cambridge University Press, volume 55, issue 4, pages 1063-1094, June.
• Wittwer, Glyn & Anderson, Kym, 2020, "A Model of Global Beverage Markets," Journal of Wine Economics, Cambridge University Press, volume 15, issue 3, pages 330-354, August.
• Galvão, Ana Beatriz & Lopresto, Marta, 2020, "Real-Time Probabilistic Nowcasts Of Uk Quarterly Gdp Growth Using A Mixed-Frequency Bottom-Up Approach," National Institute Economic Review, National Institute of Economic and Social Research, volume 254, issue , pages 1-11, November.
• Moulay Driss ELBOUSTY & Lahsen OUBDI, 2020, "Volatility stylized facts in the Moroccan stock market: Evidence from both aggregate and disaggregate data," Turkish Economic Review, EconSciences Journals, volume 7, issue 2, pages 111-138, July.
• Nassiba El HAROUS & Taacha El HASSAN, 2020, "Intangible capital: A strategic lever for value creation," Turkish Economic Review, EconSciences Journals, volume 7, issue 3, pages 139-150, October.
• Siméon Maxime BIKOUE, 2020, "The allocation of time in public administrations subject to bribery in developing countries: The basic model of labour supplu revisited," Turkish Economic Review, EconSciences Journals, volume 7, issue 3, pages 151-163, October.
• Jean Luc ERERO & Mangalani Peter MAKANANISA, 2020, "Impact of Covid-19 on the South African economy: A CGE, Holt-Winter and SARIMA model’s analysis," Turkish Economic Review, EconSciences Journals, volume 7, issue 4, pages 193-213, December.
• Todd Henry & Peter C.B. Phillips, 2020, "Forecasting Economic Activity Using the Yield Curve: Quasi-Real-Time Applications for New Zealand, Australia and the US," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2259, Oct.
• Андрей Захариев & Стефан Проданов & Николай Здравков, 2020, "Управленски Финансов Модел На Застрахователен Брокер В България - Методически И Приложни Аспекти," Electronic magazine "Dialogue", D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 2 Year 20, pages 1-31.
• Laurent Ferrara & Anna Simoni, 2020, "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2020-11.
• Niango Ange Joseph Yapi, 2020, "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2020-16.
• Marc Hallin & Carlos Trucíos, 2020, "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES, ULB -- Universite Libre de Bruxelles, number 2020-50, Dec.
• Bańbura, Marta & Saiz, Lorena, 2020, "Short-term forecasting of euro area economic activity at the ECB," Economic Bulletin Articles, European Central Bank, volume 2.
• Delle Monache, Davide & Venditti, Fabrizio & Petrella, Ivan, 2020, "Price dividend ratio and long-run stock returns: a score driven state space model," Working Paper Series, European Central Bank, number 2369, Feb.
• Meyler, Aidan, 2020, "Forecast performance in the ECB SPF: ability or chance?," Working Paper Series, European Central Bank, number 2371, Feb.
• McAdam, Peter & Warne, Anders, 2020, "Density forecast combinations: the real-time dimension," Working Paper Series, European Central Bank, number 2378, Feb.
• Jarmulska, Barbara, 2020, "Random forest versus logit models: which offers better early warning of fiscal stress?," Working Paper Series, European Central Bank, number 2408, May.
• De Santis, Roberto A. & Van der Veken, Wouter, 2020, "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series, European Central Bank, number 2436, Jul.
• Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2020, "Nowcasting with large Bayesian vector autoregressions," Working Paper Series, European Central Bank, number 2453, Aug.
• Quint, Dominic & Venditti, Fabrizio, 2020, "The influence of OPEC+ on oil prices: a quantitative assessment," Working Paper Series, European Central Bank, number 2467, Sep.
• Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2020, "Forecasting the Covid-19 recession and recovery: lessons from the financial crisis," Working Paper Series, European Central Bank, number 2468, Sep.
• Falconio, Andrea & Manganelli, Simone, 2020, "Financial conditions, business cycle fluctuations and growth at risk," Working Paper Series, European Central Bank, number 2470, Sep.
• Bańbura, Marta & Bobeica, Elena, 2020, "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series, European Central Bank, number 2471, Sep.
• Venditti, Fabrizio & Veronese, Giovanni, 2020, "Global financial markets and oil price shocks in real time," Working Paper Series, European Central Bank, number 2472, Sep.
• Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020, "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series, European Central Bank, number 2501, Dec.
• Chia-Cheng Chen & Chun-Hung Chen & Ting-Yin Liu, 2020, "Investment Performance of Machine Learning: Analysis of S&P 500 Index," International Journal of Economics and Financial Issues, Econjournals, volume 10, issue 1, pages 59-66.
• Hatice Erkekoglu & Aweng Peter Majok Garang & Adire Simon Deng, 2020, "Modeling and Forecasting USD/UGX Volatility through GARCH Family Models: Evidence from Gaussian, T and GED Distributions," International Journal of Economics and Financial Issues, Econjournals, volume 10, issue 2, pages 268-281.
• Rim Ammar Lamouchi, 2020, "Long Memory and Stock Market Efficiency: Case of Saudi Arabia," International Journal of Economics and Financial Issues, Econjournals, volume 10, issue 3, pages 29-34.
• Rialdi Azhar & Fajrin Satria Dwi Kesumah & Ambya Ambya & Febryan Kusuma Wisnu & Edwin Russel, 2020, "Application of Short-term Forecasting Models for Energy Entity Stock Price (Study on Indika Energi Tbk, JII)," International Journal of Energy Economics and Policy, Econjournals, volume 10, issue 1, pages 294-301.
• Aderibigbe Israel Adekitan & Odunayo Salau, 2020, "The Significance of Stock Management to Jet Fuel Supply using Partial Least Squares," International Journal of Energy Economics and Policy, Econjournals, volume 10, issue 3, pages 389-395.
• Melina Dritsaki & Chaido Dritsaki, 2020, "Forecasting European Union CO2 Emissions Using Autoregressive Integrated Moving Average-autoregressive Conditional Heteroscedasticity Models," International Journal of Energy Economics and Policy, Econjournals, volume 10, issue 4, pages 411-423.
• Sameer Al-Dahidi & Salah Al-Nazer & Osama Ayadi & Shuruq Shawish & Nahed Omran, 2020, "Analysis of the Effects of Cell Temperature on the Predictability of the Solar Photovoltaic Power Production," International Journal of Energy Economics and Policy, Econjournals, volume 10, issue 5, pages 208-219.
• Toto Gunarto & Rialdi Azhar & Novita Tresiana & Supriyanto Supriyanto & Ayi Ahadiat, 2020, "Accurate Estimated Model of Volatility Crude Oil Price," International Journal of Energy Economics and Policy, Econjournals, volume 10, issue 5, pages 228-233.
• Ambya Ambya & Toto Gunarto & Ernie Hendrawaty & Fajrin Satria Dwi Kesumah & Febryan Kusuma Wisnu, 2020, "Future Natural Gas Price Forecasting Model and Its Policy Implication," International Journal of Energy Economics and Policy, Econjournals, volume 10, issue 5, pages 64-70.
• Heo, Wookjae & Lee, Jae Min & Park, Narang & Grable, John E., 2020, "Using Artificial Neural Network techniques to improve the description and prediction of household financial ratios," Journal of Behavioral and Experimental Finance, Elsevier, volume 25, issue C, DOI: 10.1016/j.jbef.2020.100273.
• Uddin, Ajim & Yu, Dantong, 2020, "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, volume 27, issue C, DOI: 10.1016/j.jbef.2020.100353.
• Li, Jinjing & Wang, Xinmei & Xu, Jing & Yuan, Chang, 2020, "The role of public pensions in income inequality among elderly households in China 1988–2013," China Economic Review, Elsevier, volume 61, issue C, DOI: 10.1016/j.chieco.2020.101422.
• Yan, Ruzhen & Yue, Ding & Chen, Xudong & Wu, Xu, 2020, "Non-linear characterization and trend identification of liquidity in China's new OTC stock market based on multifractal detrended fluctuation analysis," Chaos, Solitons & Fractals, Elsevier, volume 139, issue C, DOI: 10.1016/j.chaos.2020.110063.
• Yuan, Ying & Zhang, Tonghui, 2020, "Forecasting stock market in high and low volatility periods: a modified multifractal volatility approach," Chaos, Solitons & Fractals, Elsevier, volume 140, issue C, DOI: 10.1016/j.chaos.2020.110252.
• Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2020, "Exploiting ergodicity in forecasts of corporate profitability," Journal of Economic Dynamics and Control, Elsevier, volume 111, issue C, DOI: 10.1016/j.jedc.2019.103820.
• Dur, Ayşe & Martínez García, Enrique, 2020, "Mind the gap!—A monetarist view of the open-economy Phillips curve," Journal of Economic Dynamics and Control, Elsevier, volume 117, issue C, DOI: 10.1016/j.jedc.2020.103959.
• Pham, Manh Cuong & Anderson, Heather Margot & Duong, Huu Nhan & Lajbcygier, Paul, 2020, "The effects of trade size and market depth on immediate price impact in a limit order book market," Journal of Economic Dynamics and Control, Elsevier, volume 120, issue C, DOI: 10.1016/j.jedc.2020.103992.
• Li, Hong, 2020, "Volatility spillovers across European stock markets under the uncertainty of Brexit," Economic Modelling, Elsevier, volume 84, issue C, pages 1-12, DOI: 10.1016/j.econmod.2019.03.001.
• Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020, "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, volume 87, issue C, pages 148-157, DOI: 10.1016/j.econmod.2019.07.014.
• McKnight, Stephen & Mihailov, Alexander & Rumler, Fabio, 2020, "Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend," Economic Modelling, Elsevier, volume 87, issue C, pages 383-393, DOI: 10.1016/j.econmod.2019.08.011.
• Kim, Hyeongwoo & Ko, Kyunghwan, 2020, "Improving forecast accuracy of financial vulnerability: PLS factor model approach," Economic Modelling, Elsevier, volume 88, issue C, pages 341-355, DOI: 10.1016/j.econmod.2019.09.046.
• Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020, "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, volume 90, issue C, pages 143-158, DOI: 10.1016/j.econmod.2020.05.008.
• Jahn, Malte, 2020, "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, volume 91, issue C, pages 148-154, DOI: 10.1016/j.econmod.2020.06.008.
• Qiu, Yue, 2020, "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, volume 91, issue C, pages 247-256, DOI: 10.1016/j.econmod.2020.06.003.
• Benchimol, Jonathan & El-Shagi, Makram, 2020, "Forecast performance in times of terrorism," Economic Modelling, Elsevier, volume 91, issue C, pages 386-402, DOI: 10.1016/j.econmod.2020.05.018.
• Xu, Yahua & Xiao, Jun & Zhang, Liguo, 2020, "Global predictive power of the upside and downside variances of the U.S. equity market," Economic Modelling, Elsevier, volume 93, issue C, pages 605-619, DOI: 10.1016/j.econmod.2020.09.006.
• Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020, "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, volume 93, issue C, pages 642-650, DOI: 10.1016/j.econmod.2020.03.022.
• Zhang, Yaojie & Lei, Likun & Wei, Yu, 2020, "Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching," The North American Journal of Economics and Finance, Elsevier, volume 52, issue C, DOI: 10.1016/j.najef.2020.101145.
• Díaz-Mendoza, Ana-Carmen & Pardo, Angel, 2020, "Holidays, weekends and range-based volatility," The North American Journal of Economics and Finance, Elsevier, volume 52, issue C, DOI: 10.1016/j.najef.2019.101124.
• Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020, "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, volume 52, issue C, DOI: 10.1016/j.najef.2020.101163.
• Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2020, "Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators," The North American Journal of Economics and Finance, Elsevier, volume 52, issue C, DOI: 10.1016/j.najef.2020.101165.
• Dai, Zhifeng & Zhou, Huiting & Wen, Fenghua & He, Shaoyi, 2020, "Efficient predictability of stock return volatility: The role of stock market implied volatility," The North American Journal of Economics and Finance, Elsevier, volume 52, issue C, DOI: 10.1016/j.najef.2020.101174.
• Choe, Geon Ho & Choi, So Eun & Jang, Hyun Jin, 2020, "Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness," The North American Journal of Economics and Finance, Elsevier, volume 54, issue C, DOI: 10.1016/j.najef.2019.01.004.
• Yin, Anwen, 2020, "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, volume 54, issue C, DOI: 10.1016/j.najef.2020.101274.
• Gupta, Rangan & Sun, Xiaojin, 2020, "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs," Economics Letters, Elsevier, volume 186, issue C, DOI: 10.1016/j.econlet.2019.108677.
• Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2020, "Computationally efficient inference in large Bayesian mixed frequency VARs," Economics Letters, Elsevier, volume 191, issue C, DOI: 10.1016/j.econlet.2020.109120.
• Lahiri, Kajal & Zhao, Yongchen, 2020, "The Nordhaus test with many zeros," Economics Letters, Elsevier, volume 193, issue C, DOI: 10.1016/j.econlet.2020.109308.
• Huh, Sungjun & Kim, Insu, 2020, "Growth forecast revisions over business cycles: Evidence from the Survey of Professional Forecasters," Economics Letters, Elsevier, volume 196, issue C, DOI: 10.1016/j.econlet.2020.109541.
• Nonejad, Nima, 2020, "An observation regarding Hamilton’s recent criticisms of Kilian’s global real economic activity index," Economics Letters, Elsevier, volume 196, issue C, DOI: 10.1016/j.econlet.2020.109582.
• Tu, Yundong & Wang, Siwei, 2020, "Jackknife model averaging for expectile regressions in increasing dimension," Economics Letters, Elsevier, volume 197, issue C, DOI: 10.1016/j.econlet.2020.109607.
• Holt, Matthew T. & Teräsvirta, Timo, 2020, "Global hemispheric temperatures and co-shifting: A vector shifting-mean autoregressive analysis," Journal of Econometrics, Elsevier, volume 214, issue 1, pages 198-215, DOI: 10.1016/j.jeconom.2019.05.011.
• Boot, Tom & Pick, Andreas, 2020, "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, volume 215, issue 1, pages 35-59, DOI: 10.1016/j.jeconom.2019.07.007.
• Arcidiacono, Peter & Miller, Robert A., 2020, "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, volume 215, issue 2, pages 473-485, DOI: 10.1016/j.jeconom.2018.12.025.
• Borowska, Agnieszka & Hoogerheide, Lennart & Koopman, Siem Jan & van Dijk, Herman K., 2020, "Partially censored posterior for robust and efficient risk evaluation," Journal of Econometrics, Elsevier, volume 217, issue 2, pages 335-355, DOI: 10.1016/j.jeconom.2019.12.007.
• Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020, "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, volume 217, issue 2, pages 411-430, DOI: 10.1016/j.jeconom.2019.12.011.
• Dhaene, Geert & Wu, Jianbin, 2020, "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, volume 217, issue 2, pages 471-495, DOI: 10.1016/j.jeconom.2019.12.013.
• Gonçalves, Sílvia & Perron, Benoit, 2020, "Bootstrapping factor models with cross sectional dependence," Journal of Econometrics, Elsevier, volume 218, issue 2, pages 476-495, DOI: 10.1016/j.jeconom.2020.04.026.
• Nevrla, Matěj, 2020, "Systemic risk in European financial and energy sectors: Dynamic factor copula approach," Economic Systems, Elsevier, volume 44, issue 4, DOI: 10.1016/j.ecosys.2020.100820.
• Chu, Amanda M.Y. & Lv, Zhihui & Wagner, Niklas F. & Wong, Wing-Keung, 2020, "Linear and nonlinear growth determinants: The case of Mongolia and its connection to China," Emerging Markets Review, Elsevier, volume 43, issue C, DOI: 10.1016/j.ememar.2020.100693.
• Nguyen, Linh Hoang & Chevapatrakul, Thanaset & Yao, Kai, 2020, "Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach," Journal of Empirical Finance, Elsevier, volume 58, issue C, pages 333-355, DOI: 10.1016/j.jempfin.2020.06.006.
• Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020, "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, volume 58, issue C, pages 36-49, DOI: 10.1016/j.jempfin.2020.05.007.
• Reschenhofer, Erhard & Mangat, Manveer Kaur & Stark, Thomas, 2020, "Volatility forecasts, proxies and loss functions," Journal of Empirical Finance, Elsevier, volume 59, issue C, pages 133-153, DOI: 10.1016/j.jempfin.2020.09.006.
• Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020, "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, volume 85, issue C, DOI: 10.1016/j.eneco.2019.104567.
• Bissoondoyal-Bheenick, Emawtee & Brooks, Robert & Do, Hung Xuan & Smyth, Russell, 2020, "Exploiting the heteroskedasticity in measurement error to improve volatility predictions in oil and biofuel feedstock markets," Energy Economics, Elsevier, volume 86, issue C, DOI: 10.1016/j.eneco.2020.104689.
• Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020, "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, volume 87, issue C, DOI: 10.1016/j.eneco.2020.104721.
• Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020, "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, volume 91, issue C, DOI: 10.1016/j.eneco.2020.104897.
• Trespalacios, Alfredo & Cortés, Lina M. & Perote, Javier, 2020, "Uncertainty in electricity markets from a semi-nonparametric approach," Energy Policy, Elsevier, volume 137, issue C, DOI: 10.1016/j.enpol.2019.111091.
• Belbute, José M. & Pereira, Alfredo M., 2020, "Reference forecasts for CO2 emissions from fossil-fuel combustion and cement production in Portugal," Energy Policy, Elsevier, volume 144, issue C, DOI: 10.1016/j.enpol.2020.111642.
• Jasiński, Tomasz, 2020, "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, volume 213, issue C, DOI: 10.1016/j.energy.2020.118784.
• Nonejad, Nima, 2020, "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, volume 71, issue C, DOI: 10.1016/j.irfa.2020.101521.
• Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020, "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, volume 71, issue C, DOI: 10.1016/j.irfa.2020.101552.
• Fang, Tong & Su, Zhi & Yin, Libo, 2020, "Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility," International Review of Financial Analysis, Elsevier, volume 71, issue C, DOI: 10.1016/j.irfa.2020.101566.
• Sobreira, Nuno & Louro, Rui, 2020, "Evaluation of volatility models for forecasting Value-at-Risk and Expected Shortfall in the Portuguese stock market," Finance Research Letters, Elsevier, volume 32, issue C, DOI: 10.1016/j.frl.2019.01.010.
• Çepni, Oğguzhan & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2020, "Time-varying risk aversion and the predictability of bond premia," Finance Research Letters, Elsevier, volume 34, issue C, DOI: 10.1016/j.frl.2019.07.014.
• Elaad, Guy & Reade, J. James & Singleton, Carl, 2020, "Information, prices and efficiency in an online betting market," Finance Research Letters, Elsevier, volume 35, issue C, DOI: 10.1016/j.frl.2019.09.006.
• Aslan, Aylin & Sensoy, Ahmet, 2020, "Intraday efficiency-frequency nexus in the cryptocurrency markets," Finance Research Letters, Elsevier, volume 35, issue C, DOI: 10.1016/j.frl.2019.09.013.
• Pincheira-Brown, Pablo & Neumann, Federico, 2020, "Can we beat the Random Walk? The case of survey-based exchange rate forecasts in Chile," Finance Research Letters, Elsevier, volume 37, issue C, DOI: 10.1016/j.frl.2019.101380.
• Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020, "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, volume 51, issue C, DOI: 10.1016/j.finmar.2020.100541.
• Kupiec, Paul H., 2020, "Policy uncertainty and bank stress testing," Journal of Financial Stability, Elsevier, volume 51, issue C, DOI: 10.1016/j.jfs.2020.100761.
• Cupido, Kyran & Jevtić, Petar & Paez, Antonio, 2020, "Spatial patterns of mortality in the United States: A spatial filtering approach," Insurance: Mathematics and Economics, Elsevier, volume 95, issue C, pages 28-38, DOI: 10.1016/j.insmatheco.2020.08.003.
• Andrada-Félix, Julián & Fernandez-Perez, Adrian & Sosvilla-Rivero, Simón, 2020, "Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 67, issue C, DOI: 10.1016/j.intfin.2020.101219.
• Tallman, Ellis W. & Zaman, Saeed, 2020, "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, volume 36, issue 2, pages 373-398, DOI: 10.1016/j.ijforecast.2019.04.024.
• Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020, "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, volume 36, issue 2, pages 466-479, DOI: 10.1016/j.ijforecast.2019.07.002.
• Maheu, John M. & Song, Yong & Yang, Qiao, 2020, "Oil price shocks and economic growth: The volatility link," International Journal of Forecasting, Elsevier, volume 36, issue 2, pages 570-587, DOI: 10.1016/j.ijforecast.2019.07.008.
• Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020, "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, volume 36, issue 3, pages 1163-1172, DOI: 10.1016/j.ijforecast.2018.12.006.
• Monokroussos, George & Zhao, Yongchen, 2020, "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, volume 36, issue 3, pages 1173-1180, DOI: 10.1016/j.ijforecast.2020.03.004.
• Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020, "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, volume 36, issue 3, pages 829-850, DOI: 10.1016/j.ijforecast.2019.09.005.
• Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020, "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, volume 36, issue 3, pages 851-872, DOI: 10.1016/j.ijforecast.2019.09.006.
• Strohsal, Till & Wolf, Elias, 2020, "Data revisions to German national accounts: Are initial releases good nowcasts?," International Journal of Forecasting, Elsevier, volume 36, issue 4, pages 1252-1259, DOI: 10.1016/j.ijforecast.2019.12.006.
• Colombo, Emilio & Pelagatti, Matteo, 2020, "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, volume 36, issue 4, pages 1260-1289, DOI: 10.1016/j.ijforecast.2019.12.007.
• Zhang, Bo & Chan, Joshua C.C. & Cross, Jamie L., 2020, "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," International Journal of Forecasting, Elsevier, volume 36, issue 4, pages 1318-1328, DOI: 10.1016/j.ijforecast.2020.01.004.
• Jones, Jacob T. & Sinclair, Tara M. & Stekler, Herman O., 2020, "A textual analysis of Bank of England growth forecasts," International Journal of Forecasting, Elsevier, volume 36, issue 4, pages 1478-1487, DOI: 10.1016/j.ijforecast.2019.05.015.
• Rombouts, Jeroen V.K. & Stentoft, Lars & Violante, Francesco, 2020, "Pricing individual stock options using both stock and market index information," Journal of Banking & Finance, Elsevier, volume 111, issue C, DOI: 10.1016/j.jbankfin.2019.105727.
• Bengtsson, Elias & Grothe, Magdalena & Lepers, Etienne, 2020, "Home, safe home: Cross-country monitoring framework for vulnerabilities in the residential real estate sector," Journal of Banking & Finance, Elsevier, volume 112, issue C, DOI: 10.1016/j.jbankfin.2017.12.006.
• Das, Prashant & Füss, Roland & Hanle, Benjamin & Russ, Isabel Nina, 2020, "The cross-over effect of irrational sentiments in housing, commercial property, and stock markets," Journal of Banking & Finance, Elsevier, volume 114, issue C, DOI: 10.1016/j.jbankfin.2020.105799.
• Moura, Guilherme V. & Santos, André A.P. & Ruiz, Esther, 2020, "Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, volume 118, issue C, DOI: 10.1016/j.jbankfin.2020.105882.
• Hüttner, Amelie & Scherer, Matthias & Gräler, Benedikt, 2020, "Geostatistical modeling of dependent credit spreads: Estimation of large covariance matrices and imputation of missing data," Journal of Banking & Finance, Elsevier, volume 118, issue C, DOI: 10.1016/j.jbankfin.2020.105897.
• Westphal, Rebecca & Sornette, Didier, 2020, "Market impact and performance of arbitrageurs of financial bubbles in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, volume 171, issue C, pages 1-23, DOI: 10.1016/j.jebo.2020.01.004.
• Delli Gatti, Domenico & Grazzini, Jakob, 2020, "Rising to the challenge: Bayesian estimation and forecasting techniques for macroeconomic Agent Based Models," Journal of Economic Behavior & Organization, Elsevier, volume 178, issue C, pages 875-902, DOI: 10.1016/j.jebo.2020.07.023.
• Maehashi, Kohei & Shintani, Mototsugu, 2020, "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, volume 58, issue C, DOI: 10.1016/j.jjie.2020.101104.
• Kaiser, Ulrich & Kuhn, Johan M., 2020, "The value of publicly available, textual and non-textual information for startup performance prediction," Journal of Business Venturing Insights, Elsevier, volume 14, issue C, DOI: 10.1016/j.jbvi.2020.e00179.
• Arunanondchai, Panit & Sukcharoen, Kunlapath & Leatham, David J., 2020, "Dealing with tail risk in energy commodity markets: Futures contracts versus exchange-traded funds," Journal of Commodity Markets, Elsevier, volume 20, issue C, DOI: 10.1016/j.jcomm.2019.100112.
• Nonejad, Nima, 2020, "A comprehensive empirical analysis of the predictive impact of the price of crude oil on aggregate equity return volatility," Journal of Commodity Markets, Elsevier, volume 20, issue C, DOI: 10.1016/j.jcomm.2019.100121.
• Ogbuabor, Jonathan E. & Anthony-Orji, Onyinye I. & Manasseh, Charles O. & Orji, Anthony, 2020, "Measuring the dynamics of COMESA output connectedness with the global economy," The Journal of Economic Asymmetries, Elsevier, volume 21, issue C, DOI: 10.1016/j.jeca.2019.e00138.
• Nonejad, Nima, 2020, "Crude oil price changes and the United Kingdom real gross domestic product growth rate: An out-of-sample investigation," The Journal of Economic Asymmetries, Elsevier, volume 21, issue C, DOI: 10.1016/j.jeca.2020.e00154.
• Virbickaitė, Audronė & Frey, Christoph & Macedo, Demian N., 2020, "Bayesian sequential stock return prediction through copulas," The Journal of Economic Asymmetries, Elsevier, volume 22, issue C, DOI: 10.1016/j.jeca.2020.e00173.
• Coda Moscarola, Flavia & Colombino, Ugo & Figari, Francesco & Locatelli, Marilena, 2020, "Shifting taxes away from labour enhances equity and fiscal efficiency," Journal of Policy Modeling, Elsevier, volume 42, issue 2, pages 367-384, DOI: 10.1016/j.jpolmod.2019.07.002.
• Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian & Shahzad, Syed Jawad Hussain, 2020, "The predictive power of oil price shocks on realized volatility of oil: A note," Resources Policy, Elsevier, volume 69, issue C, DOI: 10.1016/j.resourpol.2020.101856.
• León, Carlos, 2020, "Detecting anomalous payments networks: A dimensionality-reduction approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100001.
• Rodríguez-Vargas, Adolfo, 2020, "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100012.
• Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2020, "Crisis transmission: Visualizing vulnerability," Pacific-Basin Finance Journal, Elsevier, volume 59, issue C, DOI: 10.1016/j.pacfin.2019.101255.
• Dai, Zhifeng & Zhu, Huan, 2020, "Stock return predictability from a mixed model perspective," Pacific-Basin Finance Journal, Elsevier, volume 60, issue C, DOI: 10.1016/j.pacfin.2020.101267.
• Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2020, "Why are some Chinese firms failing in the US capital markets? A machine learning approach," Pacific-Basin Finance Journal, Elsevier, volume 61, issue C, DOI: 10.1016/j.pacfin.2020.101331.
• Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2020, "Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆," Pacific-Basin Finance Journal, Elsevier, volume 64, issue C, DOI: 10.1016/j.pacfin.2020.101454.
• McGurk, Zachary, 2020, "US real estate inflation prediction: Exchange rates and net foreign assets," The Quarterly Review of Economics and Finance, Elsevier, volume 75, issue C, pages 53-66, DOI: 10.1016/j.qref.2019.04.004.
• Christou, Christina & Gupta, Rangan, 2020, "Forecasting equity premium in a panel of OECD countries: The role of economic policy uncertainty," The Quarterly Review of Economics and Finance, Elsevier, volume 76, issue C, pages 243-248, DOI: 10.1016/j.qref.2019.08.001.
• Zargar, Faisal Nazir & Kumar, Dilip, 2020, "Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis," The Quarterly Review of Economics and Finance, Elsevier, volume 77, issue C, pages 271-285, DOI: 10.1016/j.qref.2019.09.015.
• Indaco, Agustín, 2020, "From twitter to GDP: Estimating economic activity from social media," Regional Science and Urban Economics, Elsevier, volume 85, issue C, DOI: 10.1016/j.regsciurbeco.2020.103591.
• Arvanitopoulos, T. & Agnolucci, P., 2020, "The long-term effect of renewable electricity on employment in the United Kingdom," Renewable and Sustainable Energy Reviews, Elsevier, volume 134, issue C, DOI: 10.1016/j.rser.2020.110322.
• Hicks, Robert L. & Holland, Daniel S. & Kuriyama, Peter T. & Schnier, Kurt E., 2020, "Choice sets for spatial discrete choice models in data rich environments," Resource and Energy Economics, Elsevier, volume 60, issue C, DOI: 10.1016/j.reseneeco.2019.101148.
• Kim, Young Min & Lee, Seojin, 2020, "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, volume 70, issue C, pages 117-134, DOI: 10.1016/j.iref.2020.05.001.
• Bonollo, Michele & Di Persio, Luca & Oliva, Immacolata, 2020, "A quantization approach to the counterparty credit exposure estimation," International Review of Economics & Finance, Elsevier, volume 70, issue C, pages 335-356, DOI: 10.1016/j.iref.2020.08.005.