Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2020

• 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.
• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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 - ," 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.