Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2020

• 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.
• 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.
• Baris Yalin Uzunlu & Syed Muzammil Hussain, 2020, "Employing Machine Learning Algorithms to build Trading Strategies with higher than Risk-Free Returns," International Econometric Review (IER), Economic Research Association, volume 12, issue 2, pages 112-138, September.
• 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.

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.