Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2019

• Hwang, Youngjin, 2019, "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, volume 62, issue C, DOI: 10.1016/j.jmacro.2019.103153.
• Darolles, Serge & Fol, Gaëlle Le & Lu, Yang & Sun, Ran, 2019, "Bivariate integer-autoregressive process with an application to mutual fund flows," Journal of Multivariate Analysis, Elsevier, volume 173, issue C, pages 181-203, DOI: 10.1016/j.jmva.2019.02.015.
• Afik, Zvika & Katz, Hagai, 2019, "Reconsidering the philanthropic foundation minimum payout policy under a “new normal”," Journal of Policy Modeling, Elsevier, volume 41, issue 2, pages 219-233, DOI: 10.1016/j.jpolmod.2018.09.004.
• Álvarez, Luis J. & Sánchez, Isabel, 2019, "Inflation projections for monetary policy decision making," Journal of Policy Modeling, Elsevier, volume 41, issue 4, pages 568-585, DOI: 10.1016/j.jpolmod.2018.09.005.
• Salmanzadeh-Meydani, N. & Fatemi Ghomi, S.M.T., 2019, "The causal relationship among electricity consumption, economic growth and capital stock in Iran," Journal of Policy Modeling, Elsevier, volume 41, issue 6, pages 1230-1256, DOI: 10.1016/j.jpolmod.2019.05.003.
• Pincheira Brown, Pablo & Hardy, Nicolás, 2019, "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, volume 62, issue C, pages 256-281, DOI: 10.1016/j.resourpol.2019.02.019.
• Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019, "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, volume 60, issue C, pages 64-74, DOI: 10.1016/j.labeco.2019.06.002.
• Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019, "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, volume 54, issue C, pages 132-146, DOI: 10.1016/j.pacfin.2019.02.006.
• Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019, "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 514, issue C, pages 156-166, DOI: 10.1016/j.physa.2018.09.027.
• Radivojević, Nikola & Cvijanović, Drago & Sekulic, Dejan & Pavlovic, Dejana & Jovic, Srdjan & Maksimović, Goran, 2019, "Econometric model of non-performing loans determinants," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 520, issue C, pages 481-488, DOI: 10.1016/j.physa.2019.01.015.
• Demos, G. & Sornette, D., 2019, "Comparing nested data sets and objectively determining financial bubbles’ inceptions," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 524, issue C, pages 661-675, DOI: 10.1016/j.physa.2019.04.050.
• Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019, "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 525, issue C, pages 466-477, DOI: 10.1016/j.physa.2019.03.097.
• Xiao, Binqing & Yang, Ye & Peng, Xuerong & Fang, Libing, 2019, "Measuring the connectedness of European electricity markets using the network topology of variance decompositions," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 535, issue C, DOI: 10.1016/j.physa.2019.122279.
• Isah, Kazeem O. & Raheem, Ibrahim D., 2019, "The hidden predictive power of cryptocurrencies and QE: Evidence from US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 536, issue C, DOI: 10.1016/j.physa.2019.04.268.
• Ivo da Rocha Lima Filho, Roberto, 2019, "Does PPI lead CPI IN Brazil?," International Journal of Production Economics, Elsevier, volume 214, issue C, pages 73-79, DOI: 10.1016/j.ijpe.2019.03.007.
• Simlai, Prodosh, 2019, "Subprime credit, idiosyncratic risk, and foreclosures," The Quarterly Review of Economics and Finance, Elsevier, volume 74, issue C, pages 175-189, DOI: 10.1016/j.qref.2019.01.015.
• Fingleton, Bernard & Szumilo, Nikodem, 2019, "Simulating the impact of transport infrastructure investment on wages: A dynamic spatial panel model approach," Regional Science and Urban Economics, Elsevier, volume 75, issue C, pages 148-164, DOI: 10.1016/j.regsciurbeco.2018.12.004.
• Croonenbroeck, Carsten & Stadtmann, Georg, 2019, "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, volume 108, issue C, pages 312-322, DOI: 10.1016/j.rser.2019.03.029.
• Balcilar, Mehmet & Gupta, Rangan & Kim, Won Joong & Kyei, Clement, 2019, "The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea," International Review of Economics & Finance, Elsevier, volume 59, issue C, pages 150-163, DOI: 10.1016/j.iref.2018.08.016.
• Chevapatrakul, Thanaset & Xu, Zhongxiang & Yao, Kai, 2019, "The impact of tail risk on stock market returns: The role of market sentiment," International Review of Economics & Finance, Elsevier, volume 59, issue C, pages 289-301, DOI: 10.1016/j.iref.2018.09.005.
• Gebka, Bartosz & Wohar, Mark E., 2019, "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, volume 60, issue C, pages 1-25, DOI: 10.1016/j.iref.2018.12.002.
• Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & Maasoumi, Esfandiar & McAleer, Michael & Pérez-Amaral, Teodosio, 2019, "Choosing expected shortfall over VaR in Basel III using stochastic dominance," International Review of Economics & Finance, Elsevier, volume 60, issue C, pages 95-113, DOI: 10.1016/j.iref.2018.12.016.
• Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019, "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, volume 64, issue C, pages 476-492, DOI: 10.1016/j.iref.2019.07.002.
• Śmiech, Sławomir & Papież, Monika & Dąbrowski, Marek A., 2019, "How important are different aspects of uncertainty in driving industrial production in the CEE countries?," Research in International Business and Finance, Elsevier, volume 50, issue C, pages 252-266, DOI: 10.1016/j.ribaf.2019.05.008.
• Landini, S. & Uberti, M. & Casellina, S., 2019, "Credit risk migration rates modelling as open systems II: A simulation model and IFRS9-baseline principles," Structural Change and Economic Dynamics, Elsevier, volume 50, issue C, pages 175-189, DOI: 10.1016/j.strueco.2019.06.013.
• Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019, "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, volume 126, issue C, pages 195-214, DOI: 10.1016/j.tra.2019.06.008.
• Leo Krippner, 2019, "Will the Real Eigensystem VAR Please Stand Up? A Univariate Primer," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-01, Jan.
• Deborah Gefang & Gary Koop & Aubrey Poon, 2019, "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-08, Jan.
• Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019, "An Automated Prior Robustness Analysis in Bayesian Model Comparison," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-45, Jun.
• Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019, "Efficient Selection of Hyperparameters in Large Bayesian VARs Using Automatic Differentiation," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-46, Jun.
• Luis Escobar & Miguel Chalup, 2019, "Desempeño Económico Departamental Durante la Crisis del COVID-19 en Bolivia: Un Enfoque de VAR de Frecuencia Mixta con Volatilidad Estocástica," Cuadernos de Investigación Económica Boliviana, Ministerio de Economía y Finanzas Públicas de Bolivia, volume 3, issue 1, pages 89-110, Diciembre.
• Fingleton, Bernard & Szumilo, Nikodem, 2019, "Simulating the impact of transport infrastructure investment on wages: a dynamic spatial panel model approach," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 100014, Mar.
• Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019, "How Sensitive Are VAR Forecasts to Prior Hyperparameters? An Automated Sensitivity Analysis," Advances in Econometrics, Emerald Group Publishing Limited, "Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A", DOI: 10.1108/S0731-90532019000040A010.
• Jianghao Chu & Tae-Hwy Lee & Aman Ullah, 2019, "Variable Selection in Sparse Semiparametric Single Index Models," Advances in Econometrics, Emerald Group Publishing Limited, "Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B", DOI: 10.1108/S0731-90532019000040B005.
• Pierre Rostan & Alexandra Rostan, 2019, "When will European Muslim population be majority and in which country?," PSU Research Review, Emerald Group Publishing Limited, volume 3, issue 2, pages 123-144, August, DOI: 10.1108/PRR-12-2018-0034.
• Franses, Ph.H.B.F. & Welz, M., 2018, "Evaluating heterogeneous forecasts for vintages of macroeconomic variables," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2018-47, Sep.
• Franses, Ph.H.B.F., 2019, "IMA(1,1) as a new benchmark for forecast evaluation," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2019-28, Aug.
• Franses, Ph.H.B.F., 2019, "Professional Forecasters and January," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2019-25, Jul.
• Bhaghoe, S. & Ooft, G. & Franses, Ph.H.B.F., 2019, "Estimates of quarterly GDP growth using MIDAS regressions," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2019-29, Aug.
• Yang Han & Victor OK Li & Jacqueline CK Lam & Michael Pollitt, 2019, "How BLUE is the Sky? Estimating the Air Quality Data in Beijing During the Blue Sky Day Period (2008-2012) by the Bayesian LSTM Approach," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG1912, Mar.
• O.N. Dmitriev & S.V. Novikov, 2019, "Optimizing the Economic Information Transparency Level of High-Tech Enterprises in the Post-Industrial Globalized Economy," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), volume 0, issue 3, pages 25-56.
• Carole Bonnet & Sandrine Juin & Anne Laferrère, 2019, "Private financing of long-term care: income, savings and reverse mortgages," Erudite Working Paper, Erudite, number 2019-14.
• Werner Roeger & Janos Varga & Jan in 't Veld & Lukas Vogel, 2019, "A Model-Based Assessment of the Distributional Impact of Structural Reforms," European Economy - Discussion Papers, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission, number 091, Feb.
• Simplice A. Asongu & Nicholas M. Odhiambo, 2019, "Foreign aid, instability and governance in Africa," Working Papers, European Xtramile Centre of African Studies (EXCAS), number 19/022, Jan.
• Ansgar Belke & Jens Klose, 2019, "Forecasting ECB Policy Rates with Different Monetary Policy Rules," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, volume 69, issue 3, pages 238-252, June.
• Joseph E. Stiglitz, 2019, "An Agenda for Reforming Economic Theory," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, volume 14, issue 2, pages 149-167, June.
• Charles B. Perkins & J. Christina Wang, 2019, "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers, Federal Reserve Bank of Boston, number 19-16, Oct, DOI: 10.29412/res.wp.2019.16.
• Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2019, "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers, Federal Reserve Bank of Dallas, number 1902, Mar, revised 17 Dec 2022, DOI: 10.24149/wp1902r2.
• John G. Fernald & Eric Hsu & Mark M. Spiegel, 2019, "Is China Fudging Its GDP Figures? Evidence from Trading Partner Data," Working Paper Series, Federal Reserve Bank of San Francisco, number 2019-19, Sep, DOI: 10.24148/wp2019-19.
• Travis J. Berge & Andrew C. Chang & Nitish R. Sinha, 2019, "Evaluating the Conditionality of Judgmental Forecasts," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-002, Feb, DOI: 10.17016/FEDS.2019.002.
• Francesca Loria & Christian Matthes & Donghai Zhang, 2019, "Assessing Macroeconomic Tail Risk," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-026, Apr, DOI: 10.17016/FEDS.2019.026.
• Pablo A. Cuba-Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019, "Likelihood Evaluation of Models with Occasionally Binding Constraints," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-028, Apr, DOI: 10.17016/FEDS.2019.028.
• Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2019, "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-065, Sep, DOI: 10.17016/FEDS.2019.065.
• Tyler Pike & Horacio Sapriza & Tom Zimmermann, 2019, "Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-070, Sep, DOI: 10.17016/FEDS.2019.070.
• Gary S. Anderson & Alena Audzeyeva, 2019, "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-074, Oct, DOI: 10.17016/FEDS.2019.074.
• John H. Rogers & Jiawen Xu, 2019, "How Well Does Economic Uncertainty Forecast Economic Activity?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-085, Dec, DOI: 10.17016/FEDS.2019.085.
• Lewis Gaul & Jonathan Jones & Pinar Uysal, 2019, "Forecasting High-Risk Composite CAMELS Ratings," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), number 1252, Jul, DOI: 10.17016/IFDP.2019.1252.
• Scott Brave & Charles S. Gascon & William Kluender & Thomas Walstrum, 2019, "Predicting Benchmarked US State Employment Data in Realtime," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2019-11, Dec, DOI: 10.21033/wp-2019-11.
• Michael W. McCracken, 2019, "Tests of Conditional Predictive Ability: Some Simulation Evidence," Working Papers, Federal Reserve Bank of St. Louis, number 2019-11, Mar, DOI: 10.20955/wp.2019.011.
• Michael W. McCracken, 2019, "Diverging Tests of Equal Predictive Ability," Working Papers, Federal Reserve Bank of St. Louis, number 2019-018, Jul, revised 09 Mar 2020, DOI: 10.20955/wp.2019.018.
• Michael W. McCracken & Joseph McGillicuddy & Michael T. Owyang, 2019, "Binary Conditional Forecasts," Working Papers, Federal Reserve Bank of St. Louis, number 2019-029, Oct, revised Apr 2021, DOI: 10.20955/wp.2019.029.
• Scott A. Brave & Charles S. Gascon & William Kluender & Thomas Walstrum, 2019, "Predicting Benchmarked US State Employment Data in Real Time," Working Papers, Federal Reserve Bank of St. Louis, number 2019-037, Nov, revised 11 Mar 2021, DOI: 10.20955/wp.2019.037.
• Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019, "Online Estimation of DSGE Models," Staff Reports, Federal Reserve Bank of New York, number 893, Aug.
• Francesca Loria & Christian Matthes & Donghai Zhang, 2019, "Assessing Macroeconomic Tail Risk," Working Paper, Federal Reserve Bank of Richmond, number 19-10, Apr.
• Fabrizio Cipollini & Giampiero M. Gallo & Edoardo Otranto, 2019, "Realized Volatility Forecasting: Robustness to Measurement Errors," Econometrics Working Papers Archive, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", number 2019_04, Jul.
• Fabrizio Cipollini & Giampiero M. Gallo & Alessandro Palandri, 2019, "Realized variance modeling: decoupling forecasting from estimation," Econometrics Working Papers Archive, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", number 2019_05, Jul.
• Gijs Dekkers & Ekaterina Tarantchenko & Karel Van den Bosch, 2019, "Working Paper 03-19 - Medium-term projection for Belgium of the at-risk-of-poverty and social exclusion indicators based on EU-SILC
  [Working Paper 03-19 - Prévisions à moyen terme des indicateurs de pauvreté et d’exclusion sociale basés sur l," Working Papers, Federal Planning Bureau, Belgium, number 201903, Feb.
• 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.
• Roman S. Leukhin, 2019, "Short-Term Fiscal Projections Using Forecast Combination Approach," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 9-21, June, DOI: 10.31107/2075-1990-2019-3-9-21.
• Nataliya G. Filatova, 2019, "Improving the Credit Rating of Loan Recipients Implementing Long-Term Investment Projects," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 102-115, August, DOI: 10.31107/2075-1990-2019-4-102-115.
• Bozhechkova Alexandra & Trunin Pavel & Knobel Alexander & Khromov Mikhail & Tsukhlo Sergey & Kaukin Andrey & Abramov Alexander & Miller Evgenia & Lavrischeva A. & Zhemkova A., 2019, "Monitoring of Russia's Economic Outlook. Trends and Challenges of Socio-economic Development," Monitoring of Russia's Economic Outlook. Trends and Challenges of Socio-Economic Development (In Russian), Gaidar Institute for Economic Policy, issue 2, pages 1-26, January.
• Khromov Mikhail & Tsukhlo Sergey & Florinskaya Yulia & Mkrtchian Nikita & Dobrolyubova E. & Yuzhakov V. & Mkrtchyan N., 2019, "Monitoring of Russia`s Economic Outlook. Trends and Challenges of Socio-economic Development," Monitoring of Russia's Economic Outlook. Trends and Challenges of Socio-Economic Development (In Russian), Gaidar Institute for Economic Policy, issue 5, pages 1-18, April.
• Bozhechkova Alexandra & Trunin Pavel & Knobel Alexander & Khromov Mikhail & Tsukhlo Sergey & Kaukin Andrey & Abramov Alexander & Miller Evgenia & Lavrischeva A. & Zhemkova A., 2019, "Monitoring of Russia's Economic Outlook. Trends and Challenges of Socio-economic Development," Monitoring of Russia's Economic Outlook. Trends and Challenges of Socio-Economic Development, Gaidar Institute for Economic Policy, issue 2, pages 1-25, February.
• Khromov Mikhail & Tsukhlo Sergey & Florinskaya Yulia & Mkrtchian Nikita & Dobrolyubova E. & Yuzhakov V., 2019, "Monitoring of Russia`s Economic Outlook. Trends and Challenges of Socio-economic Development," Monitoring of Russia's Economic Outlook. Trends and Challenges of Socio-Economic Development, Gaidar Institute for Economic Policy, issue 6, pages 1-17, April.
• Tsukhlo Sergey, 2019, "Russian industrial sector in 2018: slowdown of exiting from stagnation of 2012–2016 (on business surveys’ findings)," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2019-969, revised 2019.
• Barinova Vera & Zemtsov Tsepan & Tsareva Yulia, 2019, "Government support of small and medium sized entrepreneurship in Russia," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2019-977, revised 2019.
• Tomasz Serafin & Bartosz Uniejewski & Rafał Weron, 2019, "Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, volume 12, issue 13, pages 1-12, July.
• Michael Kostmann & Wolfgang K. Härdle, 2019, "Forecasting in Blockchain-Based Local Energy Markets," Energies, MDPI, volume 12, issue 14, pages 1-27, July.
• Peter G. Dunne, 2019, "Positive Liquidity Spillovers from Sovereign Bond-Backed Securities," JRFM, MDPI, volume 12, issue 2, pages 1-25, April.
• Meri Papavangjeli, 2019, "Forecasting the Albanian short-term inflation through a Bayesian VAR model," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 16-2019, Oct, revised 09 Oct 2019.
• Miriam Steurer & Robert Hill, 2019, "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers, University of Graz, Department of Economics, number 2019-02, Feb.
• Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019, "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers, University of Graz, Department of Economics, number 2019-06, Aug.
• Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019, "Conditional variance forecasts for long-term stock returns," Graz Economics Papers, University of Graz, Department of Economics, number 2019-08, Aug.
• Tara M. Sinclair, 2019, "Continuities and Discontinuities in Economic Forecasting," Working Papers, The George Washington University, The Center for Economic Research, number 2019-003, Aug.
• Catherine Doz & Peter Fuleky, 2019, "Dynamic Factor Models," Working Papers, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, number 2019-4, Jul.
• Roman Matkovskyy & Taoufik Bouraoui, 2019, "Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model," Post-Print, HAL, number hal-02155402, Jun, DOI: 10.1007/s40953-018-0133-8.
• Michael Brei & Ramon Moreno, 2019, "Reserve requirements and capital flows in Latin America," Post-Print, HAL, number hal-02504212, Dec, DOI: 10.1016/j.jimonfin.2019.102079.
• Serge Darolles & Gaëlle Le Fol & Yang Lu & Ran Sun, 2019, "Bivariate integer-autoregressive process with an application to mutual fund flows," Post-Print, HAL, number hal-04582262, Jun.
• Serge Darolles & Gaëlle Le Fol & Yang Lu & Ran Sun, 2019, "Bivariate integer-autoregressive process with an application to mutual fund flows," Post-Print, HAL, number halshs-02418967, DOI: 10.1016/j.jmva.2019.02.015.
• Yao T. Kpegli & Anne Bator, 2019, "Poisson-model Analysis of Power Alternation in Africa," Post-Print, HAL, number halshs-02900779, DOI: 10.32479/ijefi.6335.
• Catherine Doz & Peter Fuleky, 2019, "Dynamic Factor Models," PSE Working Papers, HAL, number halshs-02262202, Jul.
• Catherine Doz & Peter Fuleky, 2019, "Dynamic Factor Models," Working Papers, HAL, number halshs-02262202, Jul.
• Steffen Q. Mueller & Patrick Ring & Maria Schmidt, 2019, "Forecasting economic decisions under risk: The predictive importance of choice-process data," Working Papers, Chair for Economic Policy, University of Hamburg, number 066, Jan.
• Reslow, André, 2019, "Inefficient Use of Competitors'Forecasts?," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden), number 380, Oct.
• Reslow, André, 2019, "Inefficient Use of Competitors’ Forecasts?," Working Paper Series, Uppsala University, Department of Economics, number 2019:9, Oct.
• Alex Borodin & Victoria Pyatanova & Anton Yashin, 2019, "Bankruptcy Predictions for Air Carriers: Global Market," HSE Economic Journal, National Research University Higher School of Economics, volume 23, issue 3, pages 418-443.
• Morita, Hiroshi & 森田, 裕史, 2019, "Forecasting Public Investment Using Daily Stock Returns," Discussion paper series, Hitotsubashi Institute for Advanced Study, Hitotsubashi University, number HIAS-E-88, Aug.
• SYAHRIL & Raja MASBAR & M. Shabri Abd. MAJID & Sofyan SYAHNUR, 2019, "Does Indonesia As The World Largest Palm Oil Producing Country Determine The World Crude Palm Oil Price Volatility?," Regional Science Inquiry, Hellenic Association of Regional Scientists, volume 0, issue 2, pages 93-104, June.
• David Kohns & Arnab Bhattacharjee, 2019, "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series, Centre for Energy Economics Research and Policy, Heriot-Watt University, number 010, Oct.
• Claveria, Oscar, 2019, "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], volume 53, issue , pages 1-003, DOI: 10.1186/s12651-019-0253-4.
• Carole Bonnet & Sandrine Juin & Anne Laferrère, 2019, "Private financing of long-term care: income, savings and reverse mortgages," Working Papers, French Institute for Demographic Studies, number 14.
• Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019, "LASSO-Driven Inference in Time and Space," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP20/19, Apr.
• Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019, "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, volume 15, issue 2, pages 151-178, June.
• Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2019, "Using Payment System Data to Forecast Economic Activity," International Journal of Central Banking, International Journal of Central Banking, volume 15, issue 4, pages 55-80, October.
• Jonathan Benchimol & Lahcen Bounader, 2019, "Optimal Monetary Policy Under Bounded Rationality," IMF Working Papers, International Monetary Fund, number 2019/166, Aug.
• Jonathan O. ONIORE & Uju R. EZENEKWE & Uche C.C NWOGWUGWU, 2019, "Forecasting of Nigeria Manufacturing Sector Growth Rates Using Arima Model," Romanian Journal of Economics, Institute of National Economy, volume 48, issue 1(57), pages 63-70, June.
• Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2019, "Forecasting with a Panel Tobit Model," CAEPR Working Papers, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, number 2019-005, May.
• Julián Andrada-Félix & Adrian Fernandez-Perez & Simón Sosvilla-Rivero, 2019, "“Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities”," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 201912, Jul, revised Jul 2019.
• Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E, 2019, "lassopack: Model Selection and Prediction with Regularized Regression in Stata," IZA Discussion Papers, IZA Network @ LISER, number 12081, Jan.
• Emmanuel Antwi & Emmanuel Numapau Gyamfi & Kwabena A. Kyei, 2019, "Modeling And Forecasting Ghana’s Inflation Rate Under Threshold Models," Journal of Developing Areas, Tennessee State University, College of Business, volume 53, issue 3, pages 93-105, Summer.
• Aytaç PEKMEZCÄ° & Nevin Güler DÄ°NCER & Öznur İŞÇİGÃœNERÄ°, 2019, "Comparison Of The Performance Of Fuzzy Time Series Methods Based On Clustering In The Econometric Time Series Estimation," JOURNAL OF LIFE ECONOMICS, Holistence Publications, volume 6, issue 3, pages 307-320, July, DOI: 10.15637/jlecon.6.019.
• Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2019, "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model," Jena Economics Research Papers, Friedrich-Schiller-University Jena, number 2019-006, Sep.
• Zied Ftiti & Fredj Jawadi, 2019, "Forecasting Inflation Uncertainty in the United States and Euro Area," Computational Economics, Springer;Society for Computational Economics, volume 54, issue 1, pages 455-476, June, DOI: 10.1007/s10614-018-9794-9.
• Elettra Agliardi & Thomas Alexopoulos & Christian Cech, 2019, "On the Relationship Between GHGs and Global Temperature Anomalies: Multi-level Rolling Analysis and Copula Calibration," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, volume 72, issue 1, pages 109-133, January, DOI: 10.1007/s10640-018-0259-3.
• Marzia De Donno & Riccardo Donati & Gino Favero & Paola Modesti, 2019, "Risk estimation for short-term financial data through pooling of stable fits," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, volume 33, issue 4, pages 447-470, December, DOI: 10.1007/s11408-019-00340-5.
• Heather L. R. Tierney, 2019, "Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, volume 25, issue 1, pages 39-63, February, DOI: 10.1007/s11294-019-09726-7.
• Riadh Abed & Amna Zardoub, 2019, "On the co-movements among gold and other financial markets: a multivariate time-varying asymmetric approach," International Economics and Economic Policy, Springer, volume 16, issue 4, pages 701-719, October, DOI: 10.1007/s10368-019-00444-3.
• Sabrina Backs & Markus Günther & Christian Stummer, 2019, "Stimulating academic patenting in a university ecosystem: an agent-based simulation approach," The Journal of Technology Transfer, Springer, volume 44, issue 2, pages 434-461, April, DOI: 10.1007/s10961-018-9697-x.
• Phillip A. Cartwright & Natalija Riabko, 2019, "Do spot food commodity and oil prices predict futures prices?," Review of Quantitative Finance and Accounting, Springer, volume 53, issue 1, pages 153-194, July, DOI: 10.1007/s11156-018-0746-1.
• Mihaela Simionescu & Maria-Simona Naros, 2019, "Sustainable Development and the Insertion of Higher Educated Unemployed People on Romanian Labour Market," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, volume 5, issue 1, pages 12-16, March.
• Mirela Catalina Turkes, 2019, "The Evolution of Fertility over the Life Course. A Comparative Study between Romania and Turkey," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, volume 5, issue 1, pages 95-105, March.
• Florian Eckert & Rob J Hyndman & Anastasios Panagiotelis, 2019, "Forecasting Swiss Exports using Bayesian Forecast Reconciliation," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 19-457, Jul, DOI: 10.3929/ethz-b-000354388.
• Tóth, Tamás & Kulin, Ferenc, 2019, "A megújuló energia részarányának modellezése 2020-as kitekintéssel
  [Modelling the proportion of renewable energy with an outlook up to 2020]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), volume 0, issue 10, pages 1073-1092, DOI: 10.18414/KSZ.2019.10.1073.
• Deborah Gefang & Gary Koop & Aubrey Poon, 2019, "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Discussion Papers in Economics, Division of Economics, School of Business, University of Leicester, number 19/05, May.
• Laurynas Narusevicius & Tomas Ramanauskas & Laura Gudauskaitė & Tomas Reichenbachas, 2019, "Lithuanian house price index: modelling and forecasting," Bank of Lithuania Occasional Paper Series, Bank of Lithuania, number 28, Nov.
• Egle Jakucionyte & Swapnil Singh, 2019, "Mortgage Foreclosure Risk After the Great Recession," Bank of Lithuania Working Paper Series, Bank of Lithuania, number 69, Dec.
• P.N. Brusov & T.V. Filatova & N.P. Orekhova & V.L. Kulik & I. Weil, 2019, "Ratings of The Investment Projects of Arbitrary Durations: New Methodology," Journal of Reviews on Global Economics, Lifescience Global, volume 8, pages 437-448.
• Boriss Siliverstovs, 2019, "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers, Latvijas Banka, number 2019/01, Mar.
• Daniel Gros & Roberto Musmeci, 2019, "Preparing for the next MFF: Where did the money go in the past?," Working Papers LuissLab, Dipartimento di Economia e Finanza, LUISS Guido Carli, number 19147.
• José M. Belbute & Alfredo M. Pereira, 2019, "Reference Forecasts for CO2 Emissions from Fossil-Fuel Combustion and Cement Production in Portugal," GEE Papers, Gabinete de Estratégia e Estudos, Ministério da Economia, number 00126, Aug, revised Aug 2019.
• José M. Belbute & Alfredo Marvão Pereira, 2019, "ARFIMA Reference Forecasts for Worldwide CO2 Emissions and the National Dimension of the Policy Efforts to Meet IPCC Targets," GEE Papers, Gabinete de Estratégia e Estudos, Ministério da Economia, number 0125, Aug, revised Aug 2019.
• Ewert Kleynhans & Clive Coetzee, 2021, "Regional Business Confidence as Early Indicator of Regional Economic Growth," Managing Global Transitions, University of Primorska, Faculty of Management Koper, volume 19, issue 1 (Spring, pages 27-48, DOI: 10.26493/1854-6935.19.27-48.
• Kristen M. Altenburger & Daniel E. Ho, 2019, "When Algorithms Import Private Bias into Public Enforcement: The Promise and Limitations ofStatistical Debiasing Solutions," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, volume 175, issue 1, pages 98-122, DOI: 10.1628/jite-2019-0001.
• Sándor Karajz, 2019, "Multi-Agent-Based Macroeconomic Modelling," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, volume 15, issue 01, pages 19-24.
• Florian Eckert & Rob J Hyndman & Anastasios Panagiotelis, 2019, "Forecasting Swiss Exports Using Bayesian Forecast Reconciliation," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 14/19.
• Li Chen & Jiti Gao & Farshid Vahid, 2019, "Global Temperatures and Greenhouse Gases: A Common Features Approach," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 23/19.
• Hannes Mueller & Christopher Rauh, 2019, "The hard problem of prediction for conflict prevention," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques, number 2019-02, Apr.
• Hannes Mueller & Christopher Rauh, 2019, "The Hard Problem of Prediction for Conflict Prevention," Cahiers de recherche, Centre interuniversitaire de recherche en économie quantitative, CIREQ, number 02-2019, Apr.
• Krystian Jaworski, 2019, "Sentiment-induced regime switching in density forecasts of emerging markets’ exchange rates. Calibrated simulation trumps estimated autoregression," Bank i Kredyt, Narodowy Bank Polski, volume 50, issue 1, pages 83-106.
• Marcin Pełka, 2019, "Symbolic decision stumps in individual credit scoring," Bank i Kredyt, Narodowy Bank Polski, volume 50, issue 6, pages 513-528.
• Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019, "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Chapters, National Bureau of Economic Research, Inc, "Big Data for Twenty-First-Century Economic Statistics".
• Geert Bekaert & George Panayotov, 2019, "Good Carry, Bad Carry," NBER Working Papers, National Bureau of Economic Research, Inc, number 25420, Jan.
• Arash Aloosh & Geert Bekaert, 2019, "Currency Factors," NBER Working Papers, National Bureau of Economic Research, Inc, number 25449, Jan.
• John B. Donaldson & Rajnish Mehra, 2019, "Average Crossing Time: An Alternative Characterization of Mean Aversion and Reversion," NBER Working Papers, National Bureau of Economic Research, Inc, number 25519, Jan.
• Samuel Bazzi & Robert A. Blair & Christopher Blattman & Oeindrila Dube & Matthew Gudgeon & Richard Merton Peck, 2019, "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," NBER Working Papers, National Bureau of Economic Research, Inc, number 25980, Jun.
• Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019, "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Working Papers, National Bureau of Economic Research, Inc, number 26033, Jul.
• Sruthi Davuluri & René García Franceschini & Christopher R. Knittel & Chikara Onda & Kelly Roache, 2019, "Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability," NBER Working Papers, National Bureau of Economic Research, Inc, number 26178, Sep.
• Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019, "Predicting Returns With Text Data," NBER Working Papers, National Bureau of Economic Research, Inc, number 26186, Aug.
• Antoine Arnoud & Fatih Guvenen & Tatjana Kleineberg, 2019, "Benchmarking Global Optimizers," NBER Working Papers, National Bureau of Economic Research, Inc, number 26340, Oct.
• Christopher R. Knittel & Samuel Stolper, 2019, "Using Machine Learning to Target Treatment: The Case of Household Energy Use," NBER Working Papers, National Bureau of Economic Research, Inc, number 26531, Dec.
• Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2019, "Forecasting with a Panel Tobit Model," NBER Working Papers, National Bureau of Economic Research, Inc, number 26569, Dec.
• Andrzej JARYNOWSKI, 2019, "Cost-Effectiveness Analysis For Hpv Mitigation Strategies In The Republic Of Moldova Based On Infectious Disease Modelling," ECONOMY AND SOCIOLOGY: Theoretical and Scientifical Journal, Socionet;Complexul Editorial "INCE", issue 2, pages 50-64.
• Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth, 2019, "Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis," Foundations and Trends(R) in Econometrics, now publishers, volume 10, issue 1-2, pages 1-144, January, DOI: 10.1561/0800000036.
• Shrestha, Ruzel & Chakraborty, Lekha, 2019, "Practising Subnational Public Finance in an Emerging Economy: Fiscal Marksmanship in Kerala," Working Papers, National Institute of Public Finance and Policy, number 19/261, Apr.
• Chakraborty, Lekha & Chakraborty, Pinaki & Shrestha, Ruzel, 2019, "Budget Credibility of Subnational Governments: Analyzing the Fiscal Forecasting Errors of 28 States in India," Working Papers, National Institute of Public Finance and Policy, number 19/280, Sep.
• Carole Bonnet & Sandrine Juin & Anne Laferrère, 2019, "Private Financing of Long‑Term Care: Income, Savings and Reverse Mortgages," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 507-508, pages 5-24, DOI: https://doi.org/10.24187/ecostat.20.
• Jérôme Wittwer, 2019, "Comment – Is Self‑Insurance for Long‑Term Care Risk a Solution?," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 507-508, pages 25-30, DOI: https://doi.org/10.24187/ecostat.20.
• François Legendre, 2019, "The Emergence and Consolidation of Microsimulation Methods in France," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 201-217, DOI: https://doi.org/10.24187/ecostat.20.
• Deborah Gefang & Gary Koop & Aubrey Poon, 2019, "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2019-07, Mar.
• Ana Beatriz Galvão & James Mitchell, 2019, "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2019-08, May.
• Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019, "Nowcasting GDP using machine learning algorithms: A real-time assessment," Reserve Bank of New Zealand Discussion Paper Series, Reserve Bank of New Zealand, number DP2019/03, Nov.
• Luca Lorenzoni & Alberto Marino & David Morgan & Chris James, 2019, "Health Spending Projections to 2030: New results based on a revised OECD methodology," OECD Health Working Papers, OECD Publishing, number 110, May, DOI: 10.1787/5667f23d-en.
• Martin Feldkircher & Nico Hauzenberger, 2019, "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
• Nosakhare Liberty Arodoye & John Norense Izevbigie, 2019, "Sectoral Composition And Tax Revenue Performance In Ecowas Countries," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, volume 4, issue 2, pages 45-55, September.
• Bazzi, Samuel & Blair, Robert & Blattman, Chris & Dube, Oeindrila & Gudgeon, Matthew & Peck, Richard, 2019, "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," SocArXiv, Center for Open Science, number bkrn8, Jun, DOI: 10.31219/osf.io/bkrn8.
• Mikhail Anufriev & Cars Hommes & Tomasz Makarewicz, 2019, "Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments," Journal of the European Economic Association, European Economic Association, volume 17, issue 5, pages 1538-1584.
• Kevin Sheppard & Wen Xu, 2019, "Factor High-Frequency-Based Volatility (HEAVY) Models," Journal of Financial Econometrics, Oxford University Press, volume 17, issue 1, pages 33-65.
• Gong Cheng & Javier Diaz-Cassou & Aitor Erce, 2019, "The macroeconomic effects of official debt restructuring: evidence from the Paris Club," Oxford Economic Papers, Oxford University Press, volume 71, issue 2, pages 344-363.
• Alonso Cifuentes, Julio César & Díaz, Javier Gustavo & Estrada, Daniela & Figueroa, César Alfonso & Tamura, Gabriel, 2019, "Empleando modelos jerárquicos para encontrar el mejor modelo para pronosticar los galones de gasolina corriente demandados en Bogotá (Colombia) || Use of hierarchical models to find the best model to forecast the gallons of regular gasoline demanded ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 28, issue 1, pages 113-123, December.
• Dominik Wolff & Ulrich Neugebauer, 2019, "Tree-based machine learning approaches for equity market predictions," Journal of Asset Management, Palgrave Macmillan, volume 20, issue 4, pages 273-288, July, DOI: 10.1057/s41260-019-00125-5.
• Mauricio Zevallos, 2019, "A Note on Forecasting Daily Peruvian Stock Market VolatilityRisk Using Intraday Returns," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, volume 42, issue 84, pages 94-101.
• Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019, "Online Estimation of DSGE Models," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 19-014, Jul.
• Francis X. Diebold & Glenn D. Rudebusch, 2019, "Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 20-001, Dec.
• Maria Kovacova & Tomas Kliestik & Katarina Valaskova & Pavol Durana & Zuzana Juhaszova, 2019, "Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries," Oeconomia Copernicana, Institute of Economic Research, volume 10, issue 4, pages 743-772, December, DOI: 10.24136/oc.2019.034.
• Raul V. Fabella & Geoffrey Ducanes, 2019, "Power Industry Disruptors and Prospects of the Electricity Demand in the Greater Metro-Manila Area," UP School of Economics Discussion Papers, University of the Philippines School of Economics, number 201901, Mar.
• Asongu, Simplice & Nnanna, Joseph, 2019, "Foreign aid, instability and governance in Africa," MPRA Paper, University Library of Munich, Germany, number 101087, Jan.
• Rossi, Barbara & Wang, Yiru, 2019, "Vector autoregressive-based Granger causality test in the presence of instabilities," MPRA Paper, University Library of Munich, Germany, number 101492, Dec.
• Breitenstein, Miriam & Anke, Carl-Philipp & Nguyen, Duc Khuong & Walther, Thomas, 2019, "Stranded Asset Risk and Political Uncertainty: The Impact of the Coal Phase-out on the German Coal Industry," MPRA Paper, University Library of Munich, Germany, number 101763, Oct.
• Doojav, Gan-Ochir & Damdinjav, Davaasukh, 2019, "The policy-driven boom and bust in the housing market: Evidence from Mongolia," MPRA Paper, University Library of Munich, Germany, number 102933, revised 2019.
• Mukherjee, Paramita & Coondoo, Dipankor & Lahiri, Poulomi, 2019, "Forecasting Hourly Prices in Indian Spot Electricity Market," MPRA Paper, University Library of Munich, Germany, number 103161.
• Ngomba Bodi, Francis Ghislain & Bikai, Landry, 2019, "Les prévisions conditionnelles sont-elles plus précises que les prévisions inconditionnelles dans les projections de croissance et d’inflation en zone CEMAC ?
  [Should conditional forecasts of inflation and real growth more accurate than unconditio," MPRA Paper, University Library of Munich, Germany, number 116432.
• Nyoni, Thabani, 2019, "Is the United States of America (USA) really being made great again? witty insights from the Box-Jenkins ARIMA approach," MPRA Paper, University Library of Munich, Germany, number 91353, Jan.
• Nyoni, Thabani, 2019, "Modeling and forecasting population in Bangladesh: a Box-Jenkins ARIMA approach," MPRA Paper, University Library of Munich, Germany, number 91394, Jan.
• Nyoni, Thabani, 2019, "Where is Kenya being headed to? Empirical evidence from the Box-Jenkins ARIMA approach," MPRA Paper, University Library of Munich, Germany, number 91395, Jan.
• Nyoni, Thabani, 2019, "Is Nigeria's economy progressing or backsliding? Implications from ARIMA models," MPRA Paper, University Library of Munich, Germany, number 91396, Jan.