Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2023

• Bårdsen, Gunnar & Nymoen, Ragnar, 2023, "Dynamic time series modelling and forecasting of COVID-19 in Norway," Memorandum, Oslo University, Department of Economics, number 3/2023, May.
• Vladimir Sviyazov, 2023, "Is There a Weekend Effect? Russian Stock Market Research Based on Fuzzy Systems," HSE Economic Journal, National Research University Higher School of Economics, volume 27, issue 3, pages 412-434.
• Watanabe, Toshiaki & Nakajima, Jouchi, 2023, "High-frequency realized stochastic volatility model," Discussion paper series, Hitotsubashi Institute for Advanced Study, Hitotsubashi University, number HIAS-E-127, Jan.
• Kouach Yassine & EL Attar Abderrahim & EL Hachloufi Mostafa, 2023, "Retakaful Contributions Model Using Machine Learning Techniques," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, volume 9, issue 3, pages 511-532, September, DOI: https://doi.org/10.21098/jimf.v9i3..
• Saurabh Ghosh & Abhishek Ranjan, 2023, "A Machine Learning Approach to GDP Nowcasting: An Emerging Market Experience," Bulletin of Monetary Economics and Banking, Bank Indonesia, volume 26, issue Special I, pages 33-54, February, DOI: https://doi.org/10.59091/1410-8046..
• Fortin, Ines & Hlouskova, Jaroslava, 2023, "Regime-dependent nowcasting of the Austrian economy," IHS Working Paper Series, Institute for Advanced Studies, number 51, Dec.
• Marcus Buckmann & Andreas Joseph, 2023, "An Interpretable Machine Learning Workflow with an Application to Economic Forecasting," International Journal of Central Banking, International Journal of Central Banking, volume 19, issue 4, pages 449-522, October.
• Caterina Lepore & Roshen Fernando, 2023, "Global Economic Impacts of Physical Climate Risks," IMF Working Papers, International Monetary Fund, number 2023/183, Sep.
• José Eduardo Medina Reyes & Agustín Ignacio Cabrera Llanos & Salvador Cruz Aké, 2023, "Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 18, issue 3, pages 1-22, Julio - S.
• Enrique R. Casares & María Guadalupe García-Salazar & Leobardo Pedro Plata Pérez & José Manuel Ramos Varela, 2023, "Deuda externa y crecimiento económico. Una calibración para México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 18, issue 3, pages 1-24, Julio - S.
• Patrycja Klusak & Matthew Agarwala & Matt Burke & Moritz Kraemer & Kamiar Mohaddes, 2023, "Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness," Management Science, INFORMS, volume 69, issue 12, pages 7468-7491, December, DOI: 10.1287/mnsc.2023.4869.
• Marica Valente & Timm Gries & Lorenzo Trapani, 2023, "Informal employment from migration shocks," Working Papers, Faculty of Economics and Statistics, Universität Innsbruck, number 2023-09, Sep.
• Marc Burri, 2023, "Do daily lead texts help nowcasting GDP growth?," IRENE Working Papers, IRENE Institute of Economic Research, number 23-02, Jul.
• Sinem Kutlu Horvath & Ipek M. Yurttaguler, 2023, "Modeling Exchange Rate Volatility in Türkiye: An Empirical Research," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, volume 10, issue 2, pages 435-455, July, DOI: 10.26650/JEPR1217028.
• van den Berg, Gerard J. & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2023, "Predicting Re-Employment: Machine Learning versus Assessments by Unemployed Workers and by Their Caseworkers," IZA Discussion Papers, IZA Network @ LISER, number 16426, Sep.
• Dimitrios D. Thomakos & Marilou Ioakimidis & Konstantinos Eleftheriou, 2023, "Forecasting Tourism Demand for Medical Services," Journal of Developing Areas, Tennessee State University, College of Business, volume 57, issue 3, pages 315-320, July-Sept.
• Maiti,Dibyendu & Khari,Bhavna, 2023, "Digitalisation, Governance and the Informal Sector," IDE Discussion Papers, Institute of Developing Economies, Japan External Trade Organization(JETRO), number 898, May.
• Kachour Maher & Bakouch Hassan S. & Mohammadi Zohreh, 2023, "A New INAR(1) Model for ℤ-Valued Time Series Using the Relative Binomial Thinning Operator," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, volume 243, issue 2, pages 125-152, April, DOI: 10.1515/jbnst-2022-0059.
• Collischon Matthias, 2023, "Identifying Supervisory or Managerial Status in German Administrative Records," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, volume 243, issue 2, pages 183-195, April, DOI: 10.1515/jbnst-2022-0035.

2022

• Rubesam, Alexandre, 2022, "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, volume 51, issue PB, DOI: 10.1016/j.ememar.2022.100891.
• Zhang, Han & Guo, Bin & Liu, Lanbiao, 2022, "The time-varying bond risk premia in China," Journal of Empirical Finance, Elsevier, volume 65, issue C, pages 51-76, DOI: 10.1016/j.jempfin.2021.11.004.
• Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022, "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, volume 106, issue C, DOI: 10.1016/j.eneco.2021.105760.
• Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022, "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, volume 106, issue C, DOI: 10.1016/j.eneco.2021.105802.
• Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022, "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, volume 107, issue C, DOI: 10.1016/j.eneco.2022.105811.
• Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022, "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105862.
• Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022, "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105934.
• Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022, "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, volume 108, issue C, DOI: 10.1016/j.eneco.2022.105936.
• Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022, "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105947.
• Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022, "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105960.
• Umar, Zaghum & Aharon, David Y. & Esparcia, Carlos & AlWahedi, Wafa, 2022, "Spillovers between sovereign yield curve components and oil price shocks," Energy Economics, Elsevier, volume 109, issue C, DOI: 10.1016/j.eneco.2022.105963.
• Xing, Li-Min & Zhang, Yue-Jun, 2022, "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, volume 110, issue C, DOI: 10.1016/j.eneco.2022.106014.
• Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022, "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, volume 110, issue C, DOI: 10.1016/j.eneco.2022.106021.
• Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022, "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, volume 112, issue C, DOI: 10.1016/j.eneco.2022.106125.
• Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022, "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106229.
• Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022, "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106285.
• Alturki, Sultan & Olson, Eric, 2022, "Oil sentiment and the U.S. inflation premium," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106317.
• Nonejad, Nima, 2022, "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, volume 115, issue C, DOI: 10.1016/j.eneco.2022.106395.
• Huo, Da & Zhang, Xiaotao & Meng, Shuang & Wu, Gang & Li, Junhang & Di, Ruoqi, 2022, "Green finance and energy efficiency: Dynamic study of the spatial externality of institutional support in a digital economy by using hidden Markov chain," Energy Economics, Elsevier, volume 116, issue C, DOI: 10.1016/j.eneco.2022.106431.
• Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022, "Forecasting oil prices: New approaches," Energy, Elsevier, volume 238, issue PC, DOI: 10.1016/j.energy.2021.121968.
• Kuang, Wei, 2022, "The economic value of high-frequency data in equity-oil hedge," Energy, Elsevier, volume 239, issue PA, DOI: 10.1016/j.energy.2021.121904.
• Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2022, "Geopolitical risk trends and crude oil price predictability," Energy, Elsevier, volume 258, issue C, DOI: 10.1016/j.energy.2022.124824.
• Ellington, Michael & Stamatogiannis, Michalis P. & Zheng, Yawen, 2022, "A study of cross-industry return predictability in the Chinese stock market," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102249.
• Nonejad, Nima, 2022, "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102251.
• Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022, "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102277.
• Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan & Gabauer, David, 2022, "Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios," International Review of Financial Analysis, Elsevier, volume 83, issue C, DOI: 10.1016/j.irfa.2022.102300.
• Sattarhoff, Cristina & Gronwald, Marc, 2022, "Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence," International Review of Financial Analysis, Elsevier, volume 84, issue C, DOI: 10.1016/j.irfa.2022.102403.
• Alanya-Beltran, Willy, 2022, "Modelling stock returns volatility with dynamic conditional score models and random shifts," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102121.
• Sheng, Xin & Gupta, Rangan & Salisu, Afees A. & Bouri, Elie, 2022, "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102125.
• Salisu, Afees A. & Tchankam, Jean Paul, 2022, "US Stock return predictability with high dimensional models," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102194.
• Kutuk, Yasin & Barokas, Lina, 2022, "Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102198.
• Duan, Yuejiao & Goodell, John W. & Li, Haoran & Li, Xinming, 2022, "Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set," Finance Research Letters, Elsevier, volume 46, issue PA, DOI: 10.1016/j.frl.2021.102273.
• Nonejad, Nima, 2022, "Forecasting crude oil price volatility out-of-sample using news-based geopolitical risk index: What forms of nonlinearity help improve forecast accuracy the most?," Finance Research Letters, Elsevier, volume 46, issue PA, DOI: 10.1016/j.frl.2021.102310.
• Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš, 2022, "YOLO trading: Riding with the herd during the GameStop episode," Finance Research Letters, Elsevier, volume 46, issue PA, DOI: 10.1016/j.frl.2021.102359.
• Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan, 2022, "Oil tail risks and the forecastability of the realized variance of oil-price: Evidence from over 150 years of data," Finance Research Letters, Elsevier, volume 46, issue PB, DOI: 10.1016/j.frl.2021.102378.
• Doan, Bao & Lee, John B. & Liu, Qianqiu & Reeves, Jonathan J., 2022, "Beta measurement with high frequency returns," Finance Research Letters, Elsevier, volume 47, issue PA, DOI: 10.1016/j.frl.2021.102632.
• Su, Hao & Ying, Chengwei & Zhu, Xiaoneng, 2022, "Disaster risk matters in the bond market," Finance Research Letters, Elsevier, volume 47, issue PA, DOI: 10.1016/j.frl.2022.102764.
• Nonejad, Nima, 2022, "An interesting finding about the ability of geopolitical risk to forecast aggregate equity return volatility out-of-sample," Finance Research Letters, Elsevier, volume 47, issue PB, DOI: 10.1016/j.frl.2022.102710.
• Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled, 2022, "How do financial and commodity markets volatility react to real economic activity?," Finance Research Letters, Elsevier, volume 47, issue PB, DOI: 10.1016/j.frl.2022.102733.
• Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022, "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102856.
• Achakzai, Muhammad Atif Khan & Juan, Peng, 2022, "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102915.
• González-Pla, Francisco & Lovreta, Lidija, 2022, "Modeling and forecasting firm-specific volatility: The role of asymmetry and long-memory," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102931.
• Mei, Dexiang & Xie, Yutang, 2022, "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.103028.
• Jiménez, Inés & Mora-Valencia, Andrés & Perote, Javier, 2022, "Has the interaction between skewness and kurtosis of asset returns information content for risk forecasting?," Finance Research Letters, Elsevier, volume 49, issue C, DOI: 10.1016/j.frl.2022.103105.
• Bouri, Elie & Christou, Christina & Gupta, Rangan, 2022, "Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models," Finance Research Letters, Elsevier, volume 49, issue C, DOI: 10.1016/j.frl.2022.103193.
• Papík, Mário & Papíková, Lenka, 2022, "Detecting accounting fraud in companies reporting under US GAAP through data mining," International Journal of Accounting Information Systems, Elsevier, volume 45, issue C, DOI: 10.1016/j.accinf.2022.100559.
• Al-Mudafer, Muhammed Taher & Avanzi, Benjamin & Taylor, Greg & Wong, Bernard, 2022, "Stochastic loss reserving with mixture density neural networks," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 144-174, DOI: 10.1016/j.insmatheco.2022.03.010.
• Ang, Zi Qing & Lee, See Keong, 2022, "Hierarchical Bayesian Gaussian process regression model for loss reserving using combinations of squared exponential kernels," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 54-63, DOI: 10.1016/j.insmatheco.2022.03.008.
• Henckaerts, Roel & Antonio, Katrien, 2022, "The added value of dynamically updating motor insurance prices with telematics collected driving behavior data," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 79-95, DOI: 10.1016/j.insmatheco.2022.03.011.
• Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022, "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, volume 106, issue C, pages 115-127, DOI: 10.1016/j.insmatheco.2022.06.001.
• Hu, Changyue & Quan, Zhiyu & Chong, Wing Fung, 2022, "Imbalanced learning for insurance using modified loss functions in tree-based models," Insurance: Mathematics and Economics, Elsevier, volume 106, issue C, pages 13-32, DOI: 10.1016/j.insmatheco.2022.04.010.
• Steinmetz, Julia & Jentsch, Carsten, 2022, "Asymptotic theory for Mack's model," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 223-268, DOI: 10.1016/j.insmatheco.2022.08.007.
• Verschuren, Robert Matthijs, 2022, "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 379-392, DOI: 10.1016/j.insmatheco.2022.09.007.
• Xu, Shuzhe & Zhang, Chuanlong & Hong, Don, 2022, "BERT-based NLP techniques for classification and severity modeling in basic warranty data study," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 57-67, DOI: 10.1016/j.insmatheco.2022.07.013.
• Zhang, Dan & Farnoosh, Arash & Lantz, Frédéric, 2022, "Does something change in the oil market with the COVID-19 crisis?," International Economics, Elsevier, volume 169, issue C, pages 252-268, DOI: 10.1016/j.inteco.2022.01.008.
• Ferrara, Laurent & Yapi, Joseph, 2022, "Measuring exchange rate risks during periods of uncertainty," International Economics, Elsevier, volume 170, issue C, pages 202-212, DOI: 10.1016/j.inteco.2022.04.001.
• He, Mengxi & Zhang, Yaojie, 2022, "Climate policy uncertainty and the stock return predictability of the oil industry," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 81, issue C, DOI: 10.1016/j.intfin.2022.101675.
• Martin, Gael M. & Loaiza-Maya, Rubén & Maneesoonthorn, Worapree & Frazier, David T. & Ramírez-Hassan, Andrés, 2022, "Optimal probabilistic forecasts: When do they work?," International Journal of Forecasting, Elsevier, volume 38, issue 1, pages 384-406, DOI: 10.1016/j.ijforecast.2021.05.008.
• Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2022, "Forecasting realized volatility of agricultural commodities," International Journal of Forecasting, Elsevier, volume 38, issue 1, pages 74-96, DOI: 10.1016/j.ijforecast.2019.08.011.
• Lahiri, Kajal & Yang, Cheng, 2022, "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, volume 38, issue 2, pages 545-566, DOI: 10.1016/j.ijforecast.2021.10.005.
• Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022, "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, volume 38, issue 2, pages 596-612, DOI: 10.1016/j.ijforecast.2020.12.005.
• Larson, William D. & Sinclair, Tara M., 2022, "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, volume 38, issue 2, pages 635-647, DOI: 10.1016/j.ijforecast.2021.01.001.
• Conlon, Thomas & Cotter, John & Eyiah-Donkor, Emmanuel, 2022, "The illusion of oil return predictability: The choice of data matters!," Journal of Banking & Finance, Elsevier, volume 134, issue C, DOI: 10.1016/j.jbankfin.2021.106331.
• Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022, "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, volume 134, issue C, DOI: 10.1016/j.jbankfin.2021.106339.
• Taylor, James W., 2022, "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, volume 140, issue C, DOI: 10.1016/j.jbankfin.2022.106519.
• Huang, Rong & Pilbeam, Keith & Pouliot, William, 2022, "Are macroeconomic forecasters optimists or pessimists? A reassessment of survey based forecasts," Journal of Economic Behavior & Organization, Elsevier, volume 197, issue C, pages 706-724, DOI: 10.1016/j.jebo.2022.03.012.
• Benchimol, Jonathan & El-Shagi, Makram & Saadon, Yossi, 2022, "Do expert experience and characteristics affect inflation forecasts?," Journal of Economic Behavior & Organization, Elsevier, volume 201, issue C, pages 205-226, DOI: 10.1016/j.jebo.2022.06.025.
• Obaid, Khaled & Pukthuanthong, Kuntara, 2022, "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, volume 144, issue 1, pages 273-297, DOI: 10.1016/j.jfineco.2021.06.002.
• Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022, "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, volume 112, issue C, DOI: 10.1016/j.foodpol.2022.102345.
• Kishor, N. Kundan & Marfatia, Hardik A. & Nam, Gooan & Rizi, Majid Haghani, 2022, "The local employment effect of house prices: Evidence from U.S. States," Journal of Housing Economics, Elsevier, volume 55, issue C, DOI: 10.1016/j.jhe.2021.101805.
• Degiannakis, Stavros & Filis, George, 2022, "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, volume 123, issue C, DOI: 10.1016/j.jimonfin.2021.102594.
• Casabianca, Elizabeth Jane & Catalano, Michele & Forni, Lorenzo & Giarda, Elena & Passeri, Simone, 2022, "A machine learning approach to rank the determinants of banking crises over time and across countries," Journal of International Money and Finance, Elsevier, volume 129, issue C, DOI: 10.1016/j.jimonfin.2022.102739.
• Dai, Peng-Fei & Xiong, Xiong & Duc Huynh, Toan Luu & Wang, Jiqiang, 2022, "The impact of economic policy uncertainties on the volatility of European carbon market," Journal of Commodity Markets, Elsevier, volume 26, issue C, DOI: 10.1016/j.jcomm.2021.100208.
• Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2022, "Common factors and the dynamics of cereal prices. A forecasting perspective," Journal of Commodity Markets, Elsevier, volume 28, issue C, DOI: 10.1016/j.jcomm.2021.100240.
• Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022, "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, volume 28, issue C, DOI: 10.1016/j.jcomm.2022.100248.
• Stolbov, Mikhail & Shchepeleva, Maria, 2022, "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, volume 25, issue C, DOI: 10.1016/j.jeca.2021.e00238.
• Liu, Guangqiang & Guo, Xiaozhu, 2022, "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102481.
• Salisu, Afees A. & Gupta, Rangan & Ji, Qiang, 2022, "Forecasting oil prices over 150 years: The role of tail risks," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102508.
• Yan, Xiang & Bai, Jiancheng & Li, Xiafei & Chen, Zhonglu, 2022, "Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102521.
• Salisu, Afees A. & Gupta, Rangan & Karmakar, Sayar & Das, Sonali, 2022, "Forecasting output growth of advanced economies over eight centuries: The role of gold market volatility as a proxy of global uncertainty," Resources Policy, Elsevier, volume 75, issue C, DOI: 10.1016/j.resourpol.2021.102527.
• Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022, "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, volume 76, issue C, DOI: 10.1016/j.resourpol.2022.102570.
• Gupta, Rangan & Pierdzioch, Christian & Salisu, Afees A., 2022, "Oil-price uncertainty and the U.K. unemployment rate: A forecasting experiment with random forests using 150 years of data," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102662.
• Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022, "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102667.
• Gupta, Rangan & Pierdzioch, Christian, 2022, "Climate risks and forecastability of the realized volatility of gold and other metal prices," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102681.
• Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022, "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102732.
• Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022, "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, volume 78, issue C, DOI: 10.1016/j.resourpol.2022.102884.
• Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022, "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, volume 78, issue C, DOI: 10.1016/j.resourpol.2022.102903.
• Liu, Guangqiang & Zeng, Qing & Lei, Juan, 2022, "Dynamic risks from climate policy uncertainty: A case study for the natural gas market," Resources Policy, Elsevier, volume 79, issue C, DOI: 10.1016/j.resourpol.2022.103014.
• Zhao, Jing, 2022, "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, volume 79, issue C, DOI: 10.1016/j.resourpol.2022.103031.
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• Islam, Md. Monirul & Irfan, Muhammad & Shahbaz, Muhammad & Vo, Xuan Vinh, 2022, "Renewable and non-renewable energy consumption in Bangladesh: The relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality," Renewable Energy, Elsevier, volume 184, issue C, pages 1130-1149, DOI: 10.1016/j.renene.2021.12.020.
• Salisu, Afees A. & Cuñado, Juncal & Gupta, Rangan, 2022, "Geopolitical risks and historical exchange rate volatility of the BRICS," International Review of Economics & Finance, Elsevier, volume 77, issue C, pages 179-190, DOI: 10.1016/j.iref.2021.09.017.
• Umar, Zaghum & Riaz, Yasir & Aharon, David Y., 2022, "Network connectedness dynamics of the yield curve of G7 countries," International Review of Economics & Finance, Elsevier, volume 79, issue C, pages 275-288, DOI: 10.1016/j.iref.2022.02.052.
• Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je & Gau, Yin-Feng, 2022, "Risk-return trade-off in the Australian Securities Exchange: Accounting for overnight effects, realized higher moments, long-run relations, and fractional cointegration," International Review of Economics & Finance, Elsevier, volume 80, issue C, pages 384-401, DOI: 10.1016/j.iref.2022.02.057.
• Jiménez, Inés & Mora-Valencia, Andrés & Perote, Javier, 2022, "Semi-nonparametric risk assessment with cryptocurrencies," Research in International Business and Finance, Elsevier, volume 59, issue C, DOI: 10.1016/j.ribaf.2021.101567.
• Kristóf, Tamás & Virág, Miklós, 2022, "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, volume 61, issue C, DOI: 10.1016/j.ribaf.2022.101644.
• Wang, Yaqi & Wang, Chunfeng & Sensoy, Ahmet & Yao, Shouyu & Cheng, Feiyang, 2022, "Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning," Research in International Business and Finance, Elsevier, volume 62, issue C, DOI: 10.1016/j.ribaf.2022.101683.
• Su, Yuandong & Lu, Xinjie & Zeng, Qing & Huang, Dengshi, 2022, "Good air quality and stock market returns," Research in International Business and Finance, Elsevier, volume 62, issue C, DOI: 10.1016/j.ribaf.2022.101723.
• Tripathy, Naliniprava, 2022, "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, volume 63, issue C, DOI: 10.1016/j.ribaf.2022.101782.
• Naimoli, Antonio, 2022, "Modelling the persistence of Covid-19 positivity rate in Italy," Socio-Economic Planning Sciences, Elsevier, volume 82, issue PA, DOI: 10.1016/j.seps.2022.101225.
• Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022, "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, volume 182, issue C, DOI: 10.1016/j.techfore.2022.121810.
• Liu Yang & Kajal Lahiri & Adrian Pagan, 2022, "Getting the ROC into Sync," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2022-01, Jan.
• James Morley & Trung Duc Tran & Benjamin Wong, 2022, "A Simple Correction for Misspecification in Trend-Cycle Decompositions with an Application to Estimating r," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2022-02, Jan, revised Mar 2023.
• Fabio Calonaci & George Kapetanios & Simon Price, 2022, "Stock Returns Predictability with Unstable Predictors," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2022-04, Jan.
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• Jarvis, Stephen & Deschenes, Olivier & Jha, Akshaya, 2022, "The private and external costs of Germany’s nuclear phase-out," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 113634, Jun.
• Oparina, Ekaterina & Kaiser, Caspar & Gentile, Niccoló & Tkatchenko, Alexandre & Clark, Andrew E. & De Neve, Jan-Emmanuel & D'Ambrosio, Conchita, 2022, "Human wellbeing and machine learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 117955, Jul.
• Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022, "Efficient Combined Estimation under Structural Breaks," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling", DOI: 10.1108/S0731-90532021000043A007.
• Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2022, "Smooth Robust Multi-Horizon Forecasts," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling", DOI: 10.1108/S0731-90532021000043A008.
• Luca Nocciola, 2022, "Finite Sample Forecast Properties and Window Length Under Breaks in Cointegrated Systems," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling", DOI: 10.1108/S0731-90532021000043A009.
• Luis Uzeda, 2022, "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, Emerald Group Publishing Limited, "Essays in Honour of Fabio Canova", DOI: 10.1108/S0731-90532022000044A003.
• Xuan Liu & G. Cornelis van Kooten & Eric Martin Gerbrandt & Jun Duan, 2022, "Prospects for weather-indexed insurance for blueberry growers," Agricultural Finance Review, Emerald Group Publishing Limited, volume 83, issue 2, pages 333-351, December, DOI: 10.1108/AFR-05-2022-0059.
• Peterson K. Ozili & Thankom G. Arun, 2022, "Does economic policy uncertainty affect bank profitability?," International Journal of Managerial Finance, Emerald Group Publishing Limited, volume 19, issue 4, pages 803-830, August, DOI: 10.1108/IJMF-04-2022-0177.
• Davide Fiaschi & Cristina Tealdi, 2022, "Young people between education and the labour market during the COVID-19 pandemic in Italy," International Journal of Manpower, Emerald Group Publishing Limited, volume 43, issue 7, pages 1719-1757, July, DOI: 10.1108/IJM-06-2021-0352.
• Fariq Rahadiyan Chalik & Taufik Faturohman, 2022, "Customer Satisfaction of E-wallet User: An Adoption of Information System Success Model," International Symposia in Economic Theory and Econometrics, Emerald Group Publishing Limited, "Quantitative Analysis of Social and Financial Market Development", DOI: 10.1108/S1571-038620220000030005.
• Thi Thu Ha Nguyen & Salma Ibrahim & George Giannopoulos, 2022, "Detecting earnings management: a comparison of accrual and real earnings manipulation models," Journal of Applied Accounting Research, Emerald Group Publishing Limited, volume 24, issue 2, pages 344-379, August, DOI: 10.1108/JAAR-08-2021-0217.
• Edmond Berisha & David Gabauer & Rangan Gupta & Jacobus Nel, 2022, "Time-varying predictability of financial stress on inequality in United Kingdom," Journal of Economic Studies, Emerald Group Publishing Limited, volume 50, issue 5, pages 987-1007, August, DOI: 10.1108/JES-02-2022-0103.
• Smita Roy Trivedi, 2022, "A volatile mind? Experimental evidence on dealers' biases and market volatility," Review of Behavioral Finance, Emerald Group Publishing Limited, volume 15, issue 4, pages 550-569, March, DOI: 10.1108/RBF-10-2021-0223.
• Ioannis Georgakopoulos & Dimitrios Piromalis & Panagiotis S. Makrygiannis & Vassilis Zakopoulos & Christos Drosos, 2022, "A Robust Risk Model to Identify Factors that Affect Students’ Critical Achievement in Remote Lab Courses," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), volume 0, issue 3, pages 3-22.
• Vassilis Zakopoulos & Ioannis Georgakopoulos & Pelagia Kontaxaki, 2022, "Developing a Risk Model to Control Attrition by Analyzing Students’ Academic and Nonacademic Data," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2, pages 350-366.
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• Lorena Skufi & Adam Gersl, 2022, "Using Macro-Financial Models to Simulate Macroeconomic Developments During the Covid-19 Pandemic: The Case of Albania," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2022/24, Sep, revised Sep 2022.
• Kirstin Hubrich & Daniel F. Waggoner, 2022, "The Transmission of Financial Shocks and Leverage of Financial Institutions: An Endogenous Regime-Switching Framework," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2022-5, Jun, DOI: 10.29338/wp2022-05.
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• Ina Hajdini, 2022, "Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model," Working Papers, Federal Reserve Bank of Cleveland, number 22-03R, Feb, revised 06 Mar 2023, DOI: 10.26509/frbc-wp-202203r.
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• Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022, "Constructing Fan Charts from the Ragged Edge of SPF Forecasts," Working Papers, Federal Reserve Bank of Cleveland, number 22-36, Nov, DOI: 10.26509/frbc-wp-202236.
• Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022, "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers, Federal Reserve Bank of Cleveland, number 22-37, Nov, DOI: 10.26509/frbc-wp-202237.
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• Andrey Yu. Nevela & Victor A. Lapshin, 2022, "Model Risk and Basic Approaches to its Estimation on Example of Market Risk Models," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 91-112, April, DOI: 10.31107/2075-1990-2022-2-91-112.
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• Tsukhlo Sergey, 2022, "Russian industrial sector in 2021: (based on surveys’ findings)," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2022-1203, revised 2022.
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• Andrey V. Polbin & Andrey V. Shumilov, 2022, "Forecasting Output Growth of Russian Manufacturing Industries Using Panel Data Models
  [Об Использовании Моделей Панельных Данных Для Прогнозирования Темпов Роста Отраслей Российской Обрабатывающей Промышленности]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 2, pages 15-19, February.
• Vadim Ye. Zyamalov, 2022, "Applying the Multi Regime Models to the Modelling the Dynamics of Financial Time Series
  [Использование Многорежимных Моделей Для Моделирования Динамики Финансовых Временных Рядов]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 5, pages 13-19, May.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "IMF and World Bank Downgraded World Economy Growth Forecast for 2022–2023 and Raised Inflation Projection for 2022
  [Мвф И Всемирный Банк Понизили Прогноз Роста Мировой Экономики В 2022–2023 Гг. И Повысили Прогноз Инфляции На 2022 Г]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 6, pages 4-14, June.
• Natalia S. Nikitina, 2022, "Forecasting the Real Estate Price Index in Russia
  [Прогнозирование Индекса Цен На Недвижимость В России]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 6, pages 23-28, June.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "G20 Countries Tightened Their Monetary Policies in May 2022, Global Economic Outlook Revised Downward
  [В Мае 2022 Г. Продолжилось Ужесточение Монетарной Политики В Странах G20, Прогнозы Роста Мировой Экономики Ухудшались]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 7, pages 4-14, July.
• Andrey V. Zubarev & Mariya A. Kirillova, 2022, "Estimating the Decline in Russia's GDP Due to the Trade Restrictions with the EU, the US, Great Britain and Japan
  [Оценка Потерь Ввп Из-За Ограничения Странами Ес, Сша, Великобританией И Японией Торговли С Россией]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 8, pages 21-23, August.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "Global Economic Development: Worsening Forecasts
  [Развитие Мировой Экономики: Ухудшение Прогнозов]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 9, pages 4-14, September.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "Social and Economic Situation in G20 Countries: Outlooks are Getting Worse
  [Социально-Экономическое Положение В Странах G20: Прогнозы Ухудшаются]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 10, pages 4-12, October.
• Andrey V. Polbin & Andrey V. Shumilov, 2022, "Об Использовании Моделей Панельных Данных Для Прогнозирования Темпов Роста Отраслей Российской Обрабатывающей Промышленности," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 2, pages 15-19, February.
• Vadim Ye. Zyamalov, 2022, "Использование Многорежимных Моделей Для Моделирования Динамики Финансовых Временных Рядов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 5, pages 13-19, May.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "Мвф И Всемирный Банк Понизили Прогноз Роста Мировой Экономики В 2022–2023 Гг. И Повысили Прогноз Инфляции На 2022 Г," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 6, pages 4-14, June.
• Natalia S. Nikitina, 2022, "Прогнозирование Индекса Цен На Недвижимость В России," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 6, pages 23-28, June.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "В Мае 2022 Г. Продолжилось Ужесточение Монетарной Политики В Странах G20, Прогнозы Роста Мировой Экономики Ухудшались," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 7, pages 4-14, July.
• Andrey V. Zubarev & Mariya A. Kirillova, 2022, "Оценка Потерь Ввп Из-За Ограничения Странами Ес, Сша, Великобританией И Японией Торговли С Россией," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 8, pages 21-23, August.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "Развитие Мировой Экономики: Ухудшение Прогнозов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 9, pages 4-14, September.
• Urmat K. Dzhunkeev & Yury N. Perevyshin & Pavel V. Trunin & Maria I. Chembulatova, 2022, "Социально-Экономическое Положение В Странах G20: Прогнозы Ухудшаются," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 10, pages 4-12, October.
• Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022, "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, volume 15, issue 22, pages 1-26, November.
• William A. Barnett & Kun He & Jingtong He, 2022, "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," JRFM, MDPI, volume 15, issue 10, pages 1-17, October.