Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2019

• 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.
• Chen, Jian & Jiang, Fuwei & Xue, Shuyu & Yao, Jiaquan, 2019, "The world predictive power of U.S. equity market skewness risk," Journal of International Money and Finance, Elsevier, volume 96, issue C, pages 210-227, DOI: 10.1016/j.jimonfin.2019.05.003.
• Xu, Zhongxiang & Chevapatrakul, Thanaset & Li, Xiafei, 2019, "Return asymmetry and the cross section of stock returns," Journal of International Money and Finance, Elsevier, volume 97, issue C, pages 93-110, DOI: 10.1016/j.jimonfin.2019.06.005.
• Brei, Michael & Moreno, Ramon, 2019, "Reserve requirements and capital flows in Latin America," Journal of International Money and Finance, Elsevier, volume 99, issue C, DOI: 10.1016/j.jimonfin.2019.102079.
• Kim, Insu & Kim, Young Se, 2019, "Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach," Journal of Macroeconomics, Elsevier, volume 62, issue C, DOI: 10.1016/j.jmacro.2019.103139.
• 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.
• Muhammad Jahanzeb Malik & Muhammad Nadim Hanif, 2019, "Learning from Errors While Forecasting Inflation: A Case for Intercept Correction," International Econometric Review (IER), Economic Research Association, volume 11, issue 1, pages 24-38, April.
• Zümre Özdemir Güler & Mehmet Akif Bakır, 2019, "Performance of Methods Determining Structural Break in Linear Regression Models," International Econometric Review (IER), Economic Research Association, volume 11, issue 2, pages 70-83, September.
• 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.
• François Legendre, 2019, "The Emergence and Consolidation of Microsimulation Methods in France
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• 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
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• 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.