Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2020

• 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-Étienne (GATE Lyon St-Étienne), 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-Étienne (GATE Lyon St-Étienne), 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 1, pages 1-4, 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, Institute of Labor Economics (IZA), 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, Institute of Labor Economics (IZA), number 13776, Oct.
• Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020, "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," JRC Working Papers in Economics and Finance, Joint Research Centre, European Commission, number 2020-04, Sep.
• Berta, Paolo & Lovaglio, Pietro Giorgio & Paruolo, Paolo & Verzillo, Stefano, 2020, "Real Time Forecasting of Covid-19 Intensive Care Units demand," JRC Working Papers in Economics and Finance, Joint Research Centre, European Commission, number 2020-08, Sep.
• Mawuli Segnon & Stelios Bekiros, 2020, "Forecasting volatility in bitcoin market," Annals of Finance, Springer, volume 16, issue 3, pages 435-462, September, DOI: 10.1007/s10436-020-00368-y.
• Murat Midiliç, 2020, "Estimation of STAR–GARCH Models with Iteratively Weighted Least Squares," Computational Economics, Springer;Society for Computational Economics, volume 55, issue 1, pages 87-117, January, DOI: 10.1007/s10614-018-9876-8.

2019

• Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019, "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, volume 52, issue C, pages 201-219, DOI: 10.1016/j.jempfin.2019.03.006.
• Díaz-Hernández, Adán & Constantinou, Nick, 2019, "A multiple regime extension to the Heston–Nandi GARCH(1,1) model," Journal of Empirical Finance, Elsevier, volume 53, issue C, pages 162-180, DOI: 10.1016/j.jempfin.2019.05.004.
• Panopoulou, Ekaterini & Souropanis, Ioannis, 2019, "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, volume 53, issue C, pages 197-221, DOI: 10.1016/j.jempfin.2019.07.004.
• Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019, "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, volume 54, issue C, pages 97-117, DOI: 10.1016/j.jempfin.2019.08.007.
• Westerlund, Joakim & Sharma, Susan Sunila, 2019, "Panel evidence on the ability of oil returns to predict stock returns in the G7 area," Energy Economics, Elsevier, volume 77, issue C, pages 3-12, DOI: 10.1016/j.eneco.2018.05.007.
• Manner, Hans & Alavi Fard, Farzad & Pourkhanali, Armin & Tafakori, Laleh, 2019, "Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae," Energy Economics, Elsevier, volume 78, issue C, pages 143-164, DOI: 10.1016/j.eneco.2018.10.034.
• Chen, Rongda & Xu, Jianjun, 2019, "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, volume 78, issue C, pages 379-391, DOI: 10.1016/j.eneco.2018.11.011.
• Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019, "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, volume 78, issue C, pages 656-667, DOI: 10.1016/j.eneco.2017.12.035.
• Jiao, Ying & Ma, Chunhua & Scotti, Simone & Sgarra, Carlo, 2019, "A branching process approach to power markets," Energy Economics, Elsevier, volume 79, issue C, pages 144-156, DOI: 10.1016/j.eneco.2018.03.002.
• Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019, "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, volume 79, issue C, pages 171-182, DOI: 10.1016/j.eneco.2018.02.007.
• Maryniak, Paweł & Trück, Stefan & Weron, Rafał, 2019, "Carbon pricing and electricity markets — The case of the Australian Clean Energy Bill," Energy Economics, Elsevier, volume 79, issue C, pages 45-58, DOI: 10.1016/j.eneco.2018.06.003.
• Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019, "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, volume 80, issue C, pages 423-433, DOI: 10.1016/j.eneco.2019.01.010.
• Kostrzewski, Maciej & Kostrzewska, Jadwiga, 2019, "Probabilistic electricity price forecasting with Bayesian stochastic volatility models," Energy Economics, Elsevier, volume 80, issue C, pages 610-620, DOI: 10.1016/j.eneco.2019.02.004.
• Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019, "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, volume 80, issue C, pages 937-949, DOI: 10.1016/j.eneco.2019.03.006.
• Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019, "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, volume 81, issue C, pages 1109-1120, DOI: 10.1016/j.eneco.2019.05.018.
• Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019, "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, volume 81, issue C, pages 639-649, DOI: 10.1016/j.eneco.2019.04.030.
• Liu, Jingzhen & Kemp, Alexander, 2019, "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, volume 81, issue C, pages 672-686, DOI: 10.1016/j.eneco.2019.04.023.
• Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019, "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, volume 84, issue C, DOI: 10.1016/j.eneco.2019.104520.
• Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2019, "How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study," Energy Economics, Elsevier, volume 84, issue C, DOI: 10.1016/j.eneco.2019.104536.
• Khalifa, Ahmed & Caporin, Massimiliano & Di Fonzo, Tommaso, 2019, "Scenario-based forecast for the electricity demand in Qatar and the role of energy efficiency improvements," Energy Policy, Elsevier, volume 127, issue C, pages 155-164, DOI: 10.1016/j.enpol.2018.11.047.
• Baldoni, Edoardo & Coderoni, Silvia & D'Orazio, Marco & Di Giuseppe, Elisa & Esposti, Roberto, 2019, "The role of economic and policy variables in energy-efficient retrofitting assessment. A stochastic Life Cycle Costing methodology," Energy Policy, Elsevier, volume 129, issue C, pages 1207-1219, DOI: 10.1016/j.enpol.2019.03.018.
• Maluf de Lima, Lilian & Piedade Bacchi, Mirian Rumenos, 2019, "Assessing the impact of Brazilian economic growth on demand for electricity," Energy, Elsevier, volume 172, issue C, pages 861-873, DOI: 10.1016/j.energy.2019.01.154.
• Moral-Carcedo, Julián & Pérez-García, Julián, 2019, "Time of day effects of temperature and daylight on short term electricity load," Energy, Elsevier, volume 174, issue C, pages 169-183, DOI: 10.1016/j.energy.2019.02.158.
• Zhang, Yaojie & Wei, Yu & Ma, Feng & Yi, Yongsheng, 2019, "Economic constraints and stock return predictability: A new approach," International Review of Financial Analysis, Elsevier, volume 63, issue C, pages 1-9, DOI: 10.1016/j.irfa.2019.02.007.
• Bleher, Johannes & Dimpfl, Thomas, 2019, "Today I got a million, tomorrow, I don't know: On the predictability of cryptocurrencies by means of Google search volume," International Review of Financial Analysis, Elsevier, volume 63, issue C, pages 147-159, DOI: 10.1016/j.irfa.2019.03.003.
• Tissaoui, Kais, 2019, "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, volume 64, issue C, pages 232-249, DOI: 10.1016/j.irfa.2019.06.001.
• Yin, Anwen, 2019, "Out-of-sample equity premium prediction in the presence of structural breaks," International Review of Financial Analysis, Elsevier, volume 65, issue C, DOI: 10.1016/j.irfa.2019.101385.
• Sensoy, Ahmet, 2019, "The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies," Finance Research Letters, Elsevier, volume 28, issue C, pages 68-73, DOI: 10.1016/j.frl.2018.04.002.
• Aslanidis, Nektarios & Christiansen, Charlotte & Cipollini, Andrea, 2019, "Predicting bond betas using macro-finance variables," Finance Research Letters, Elsevier, volume 29, issue C, pages 193-199, DOI: 10.1016/j.frl.2018.07.007.
• Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019, "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, volume 29, issue C, pages 315-322, DOI: 10.1016/j.frl.2018.08.013.
• Troster, Victor & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Macedo, Demian Nicolás, 2019, "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, Elsevier, volume 30, issue C, pages 187-193, DOI: 10.1016/j.frl.2018.09.014.
• Lei, Likun & Shang, Yue & Chen, Yongfei & Wei, Yu, 2019, "Does the financial crisis change the economic risk perception of crude oil traders? A MIDAS quantile regression approach," Finance Research Letters, Elsevier, volume 30, issue C, pages 341-351, DOI: 10.1016/j.frl.2018.10.016.
• Chevapatrakul, Thanaset & Mascia, Danilo V., 2019, "Detecting overreaction in the Bitcoin market: A quantile autoregression approach," Finance Research Letters, Elsevier, volume 30, issue C, pages 371-377, DOI: 10.1016/j.frl.2018.11.004.
• Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019, "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, volume 45, issue C, DOI: 10.1016/j.jfs.2019.100693.
• Inoue, Atsushi & Rossi, Barbara, 2019, "The effects of conventional and unconventional monetary policy on exchange rates," Journal of International Economics, Elsevier, volume 118, issue C, pages 419-447, DOI: 10.1016/j.jinteco.2019.01.015.
• Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019, "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, volume 88, issue C, pages 255-272, DOI: 10.1016/j.insmatheco.2019.07.004.
• Ponomareva, Natalia & Sheen, Jeffrey & Wang, Ben Zhe, 2019, "Forecasting exchange rates using principal components," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 63, issue C, DOI: 10.1016/j.intfin.2019.08.003.
• Götz, Thomas B. & Knetsch, Thomas A., 2019, "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, volume 35, issue 1, pages 45-66, DOI: 10.1016/j.ijforecast.2018.08.001.
• Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019, "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, volume 35, issue 1, pages 80-99, DOI: 10.1016/j.ijforecast.2018.07.013.
• Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019, "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, volume 35, issue 2, pages 521-539, DOI: 10.1016/j.ijforecast.2018.08.003.
• McAdam, Peter & Warne, Anders, 2019, "Euro area real-time density forecasting with financial or labor market frictions," International Journal of Forecasting, Elsevier, volume 35, issue 2, pages 580-600, DOI: 10.1016/j.ijforecast.2018.10.013.
• Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019, "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, volume 35, issue 2, pages 616-633, DOI: 10.1016/j.ijforecast.2018.12.002.
• Szafranek, Karol, 2019, "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, volume 35, issue 3, pages 1042-1059, DOI: 10.1016/j.ijforecast.2019.04.007.
• Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019, "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1263-1272, DOI: 10.1016/j.ijforecast.2019.03.021.
• Naimoli, Antonio & Storti, Giuseppe, 2019, "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1332-1355, DOI: 10.1016/j.ijforecast.2019.06.002.
• Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019, "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1533-1547, DOI: 10.1016/j.ijforecast.2019.02.001.
• Chiu, Ching-Wai (Jeremy) & Hayes, Simon & Kapetanios, George & Theodoridis, Konstantinos, 2019, "A new approach for detecting shifts in forecast accuracy," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1596-1612, DOI: 10.1016/j.ijforecast.2019.01.008.
• Berge, Travis J. & Chang, Andrew C. & Sinha, Nitish R., 2019, "Evaluating the conditionality of judgmental forecasts," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1627-1635, DOI: 10.1016/j.ijforecast.2019.03.026.
• Granziera, Eleonora & Sekhposyan, Tatevik, 2019, "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1636-1657, DOI: 10.1016/j.ijforecast.2019.01.010.
• Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019, "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1658-1668, DOI: 10.1016/j.ijforecast.2018.12.004.
• Diebold, Francis X. & Shin, Minchul, 2019, "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1679-1691, DOI: 10.1016/j.ijforecast.2018.09.006.
• Knotek, Edward S. & Zaman, Saeed, 2019, "Financial nowcasts and their usefulness in macroeconomic forecasting," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1708-1724, DOI: 10.1016/j.ijforecast.2018.10.012.
• Knüppel, Malte & Schultefrankenfeld, Guido, 2019, "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1748-1769, DOI: 10.1016/j.ijforecast.2019.03.014.
• Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019, "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, volume 35, issue 4, pages 1790-1799, DOI: 10.1016/j.ijforecast.2018.09.008.
• Thiele, Stephen, 2019, "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, volume 101, issue C, pages 12-20, DOI: 10.1016/j.jbankfin.2019.01.018.
• Lazar, Emese & Zhang, Ning, 2019, "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, volume 105, issue C, pages 74-93, DOI: 10.1016/j.jbankfin.2019.05.017.
• Gupta, Jairaj & Chaudhry, Sajid, 2019, "Mind the tail, or risk to fail," Journal of Business Research, Elsevier, volume 99, issue C, pages 167-185, DOI: 10.1016/j.jbusres.2019.02.037.
• Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019, "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, volume 132, issue 1, pages 126-149, DOI: 10.1016/j.jfineco.2018.10.001.
• Fan, Ying & Yang, Zan & Yavas, Abdullah, 2019, "Understanding real estate price dynamics: The case of housing prices in five major cities of China✰," Journal of Housing Economics, Elsevier, volume 43, issue C, pages 37-55, DOI: 10.1016/j.jhe.2018.09.003.
• Auer, Simone, 2019, "Monetary policy shocks and foreign investment income: Evidence from a large Bayesian VAR," Journal of International Money and Finance, Elsevier, volume 93, issue C, pages 142-166, DOI: 10.1016/j.jimonfin.2018.12.013.
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• Andrey A. Kozlov & Andrey V. Vlasov, 2019, "Cryptoeconomics: Pilot Study on Investments in ICO Startups Using Neural Networks," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 76-87, February, DOI: 10.31107/2075-1990-2019-1-76-87.
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