Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2021

• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312R2, Nov, revised Oct 2022.
• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312R3, Nov, revised Jan 2023.
• Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2312R4, Nov, revised Jun 2023.
• Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2021, "Industrial Output Growth Forecast: A Machine Learning Approach Based on Cross-Validation," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot GmbH, Berlin, volume 67, issue 4, pages 337-351, DOI: 10.3790/aeq.67.4.337.
• Lukas Boer & Andrea Pescatori & Martin Stuermer, 2021, "Energy Transition Metals," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1976.
• Bastien Lextrait, 2021, "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2021-25.
• Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2021, "Using machine learning and big data to analyse the business cycle," Economic Bulletin Articles, European Central Bank, volume 5.
• Alogoskoufis, Spyros & Dunz, Nepomuk & Emambakhsh, Tina & Hennig, Tristan & Kaijser, Michiel & Kouratzoglou, Charalampos & Muñoz, Manuel A. & Parisi, Laura & Salleo, Carmelo, 2021, "ECB’s economy-wide climate stress test," Occasional Paper Series, European Central Bank, number 281, Sep.
• Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2021, "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Working Paper Series, European Central Bank, number 2510, Jan.
• Le Mezo, Helena & Ferrari Minesso, Massimo, 2021, "Text-based recession probabilities," Working Paper Series, European Central Bank, number 2516, Jan.
• Dalhaus, Tatjana & Schaumburg, Julia & Sekhposyan, Tatevik, 2021, "Networking the yield curve: implications for monetary policy," Working Paper Series, European Central Bank, number 2532, Mar.
• Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2021, "Economic predictions with big data: the illusion of sparsity," Working Paper Series, European Central Bank, number 2542, Apr.
• Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021, "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series, European Central Bank, number 2543, May.
• Bobeica, Elena & Hartwig, Benny, 2021, "The COVID-19 shock and challenges for time series models," Working Paper Series, European Central Bank, number 2558, May.
• Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021, "The time-varying evolution of inflation risks," Working Paper Series, European Central Bank, number 2600, Oct.
• Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021, "A mixed frequency BVAR for the euro area labour market," Working Paper Series, European Central Bank, number 2601, Oct.
• Garcia, Pablo & Jacquinot, Pascal & Lenarčič, Črt & Lozej, Matija & Mavromatis, Kostas, 2021, "Global models for a global pandemic: the impact of COVID-19 on small euro area economies," Working Paper Series, European Central Bank, number 2603, Oct.
• Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021, "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series, European Central Bank, number 2604, Oct.
• Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021, "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series, European Central Bank, number 2614, Nov.
• Sokol, Andrej, 2021, "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series, European Central Bank, number 2624, Dec.
• Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021, "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, International Journal of Economics and Financial Issues, volume 11, issue 1, pages 49-60.
• Ernie Hendrawaty & Rialdi Azhar & Fajrin Satria Dwi Kesumah & Sari Indah Oktanti Sembiring & Mega Metalia, 2021, "Modelling and Forecasting Crude Oil Prices during COVID-19 Pandemic," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 2, pages 149-154.
• Santiago Gall n & Jorge Barrientos, 2021, "Forecasting the Colombian Electricity Spot Price under a Functional Approach," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 2, pages 67-74.
• Chaido Dritsaki & Dimitrios Niklis & Pavlos Stamatiou, 2021, "Oil Consumption Forecasting using ARIMA Models: An Empirical Study for Greece," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 4, pages 214-224.
• Mohamed Defaf & Mohamed Tkiouat, 2021, "A Review on Prospective Energy Models: The Moroccan Case," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 4, pages 14-23.
• Saring Suhendro & Mega Matalia & Sari Indah Oktanti Sembiring, 2021, "Public Sector Policy of Estimating Model for Renewable Energy," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 5, pages 609-613.
• Bharat Kumar Meher & Iqbal Thonse Hawaldar & Mathew Thomas Gil & Deebom Zorle Dum, 2021, "Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 6, pages 489-502.
• Thu, Le Ha & Leon-Gonzalez, Roberto, 2021, "Forecasting macroeconomic variables in emerging economies," Journal of Asian Economics, Elsevier, volume 77, issue C, DOI: 10.1016/j.asieco.2021.101403.
• Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021, "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, volume 127, issue C, DOI: 10.1016/j.jedc.2021.104119.
• Gelfer, Sacha, 2021, "Evaluating the forecasting power of an open-economy DSGE model when estimated in a data-Rich environment," Journal of Economic Dynamics and Control, Elsevier, volume 129, issue C, DOI: 10.1016/j.jedc.2021.104177.
• Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021, "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, volume 69, issue C, pages 208-224, DOI: 10.1016/j.eap.2020.12.008.
• Gangopadhyay, Kausik & Mondal, Debasis, 2021, "Productivity, relative sectoral prices, and total factor productivity: Theory and evidence," Economic Modelling, Elsevier, volume 100, issue C, DOI: 10.1016/j.econmod.2021.105509.
• Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021, "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, volume 101, issue C, DOI: 10.1016/j.econmod.2021.105522.
• Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021, "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, volume 101, issue C, DOI: 10.1016/j.econmod.2021.105531.
• Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021, "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, volume 102, issue C, DOI: 10.1016/j.econmod.2021.105556.
• Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021, "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, volume 103, issue C, DOI: 10.1016/j.econmod.2021.105614.
• Lucey, Brian & Ren, Boru, 2021, "Does news tone help forecast oil?," Economic Modelling, Elsevier, volume 104, issue C, DOI: 10.1016/j.econmod.2021.105635.
• Cross, Jamie L. & Hou, Chenghan & Trinh, Kelly, 2021, "Returns, volatility and the cryptocurrency bubble of 2017–18," Economic Modelling, Elsevier, volume 104, issue C, DOI: 10.1016/j.econmod.2021.105643.
• Casarin, Roberto & Costantini, Mauro & Paradiso, Antonio, 2021, "On the role of dependence in sticky price and sticky information Phillips curve: Modelling and forecasting," Economic Modelling, Elsevier, volume 105, issue C, DOI: 10.1016/j.econmod.2021.105644.
• Lourenço, Nuno & Gouveia, Carlos Melo & Rua, António, 2021, "Forecasting tourism with targeted predictors in a data-rich environment," Economic Modelling, Elsevier, volume 96, issue C, pages 445-454, DOI: 10.1016/j.econmod.2020.03.030.
• Frömmel, Michael & Midiliç, Murat, 2021, "Daily currency interventions in an emerging market: Incorporating reserve accumulation to the reaction function," Economic Modelling, Elsevier, volume 97, issue C, pages 461-476, DOI: 10.1016/j.econmod.2020.09.020.
• Mora-Valencia, Andrés & Rodríguez-Raga, Santiago & Vanegas, Esteban, 2021, "Skew index: Descriptive analysis, predictive power, and short-term forecast," The North American Journal of Economics and Finance, Elsevier, volume 56, issue C, DOI: 10.1016/j.najef.2020.101356.
• Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021, "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, volume 56, issue C, DOI: 10.1016/j.najef.2021.101383.
• Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021, "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101421.
• Figà-Talamanca, Gianna & Focardi, Sergio & Patacca, Marco, 2021, "Regime switches and commonalities of the cryptocurrencies asset class," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101425.
• Pérez-Rodríguez, Jorge V. & Andrada-Félix, Julián & Rachinger, Heiko, 2021, "Testing the forward volatility unbiasedness hypothesis in exchange rates under long-range dependence," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101438.
• Christou, Christina & Gupta, Rangan & Jawadi, Fredj, 2021, "Does inequality help in forecasting equity premium in a panel of G7 countries?," The North American Journal of Economics and Finance, Elsevier, volume 57, issue C, DOI: 10.1016/j.najef.2021.101456.
• Shu, Min & Song, Ruiqiang & Zhu, Wei, 2021, "The ‘COVID’ crash of the 2020 U.S. Stock market," The North American Journal of Economics and Finance, Elsevier, volume 58, issue C, DOI: 10.1016/j.najef.2021.101497.
• Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021, "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, volume 58, issue C, DOI: 10.1016/j.najef.2021.101540.
• Lyu, Yifei & Nie, Jun & Yang, Shu-Kuei X., 2021, "Forecasting US economic growth in downturns using cross-country data," Economics Letters, Elsevier, volume 198, issue C, DOI: 10.1016/j.econlet.2020.109668.
• Aguilar, Pablo & Ghirelli, Corinna & Pacce, Matías & Urtasun, Alberto, 2021, "Can news help measure economic sentiment? An application in COVID-19 times," Economics Letters, Elsevier, volume 199, issue C, DOI: 10.1016/j.econlet.2021.109730.
• Qiu, Yue, 2021, "Complete subset least squares support vector regression," Economics Letters, Elsevier, volume 200, issue C, DOI: 10.1016/j.econlet.2021.109737.
• Li, Yiyun & Law, Keith K.F., 2021, "Systematic risk in pairs trading and dynamic parameterization," Economics Letters, Elsevier, volume 202, issue C, DOI: 10.1016/j.econlet.2021.109842.
• Fang, Ming & Taylor, Stephen, 2021, "A machine learning based asset pricing factor model comparison on anomaly portfolios," Economics Letters, Elsevier, volume 204, issue C, DOI: 10.1016/j.econlet.2021.109919.
• Ardia, David & Bluteau, Keven & Kassem, Alaa, 2021, "A century of Economic Policy Uncertainty through the French–Canadian lens," Economics Letters, Elsevier, volume 205, issue C, DOI: 10.1016/j.econlet.2021.109938.
• Du, Zaichao & Pei, Pei, 2021, "A simple and robust counterfactual impact evaluation," Economics Letters, Elsevier, volume 207, issue C, DOI: 10.1016/j.econlet.2021.110015.
• Karmakar, Sayar & Demirer, Riza & Gupta, Rangan, 2021, "Bitcoin mining activity and volatility dynamics in the power market," Economics Letters, Elsevier, volume 209, issue C, DOI: 10.1016/j.econlet.2021.110111.
• Hayashi, Fumio & Tachi, Yuta, 2021, "The nowcast revision analysis extended," Economics Letters, Elsevier, volume 209, issue C, DOI: 10.1016/j.econlet.2021.110112.
• Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021, "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, volume 220, issue 1, pages 2-22, DOI: 10.1016/j.jeconom.2020.08.010.
• Korolev, Ivan, 2021, "Identification and estimation of the SEIRD epidemic model for COVID-19," Journal of Econometrics, Elsevier, volume 220, issue 1, pages 63-85, DOI: 10.1016/j.jeconom.2020.07.038.
• Perera, Indeewara & Silvapulle, Mervyn J., 2021, "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, volume 221, issue 1, pages 1-24, DOI: 10.1016/j.jeconom.2020.01.022.
• Pathak, Parag A. & Shi, Peng, 2021, "How well do structural demand models work? Counterfactual predictions in school choice," Journal of Econometrics, Elsevier, volume 222, issue 1, pages 161-195, DOI: 10.1016/j.jeconom.2020.07.031.
• Mogliani, Matteo & Simoni, Anna, 2021, "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, volume 222, issue 1, pages 833-860, DOI: 10.1016/j.jeconom.2020.07.022.
• Sun, Yuying & Hong, Yongmiao & Lee, Tae-Hwy & Wang, Shouyang & Zhang, Xinyu, 2021, "Time-varying model averaging," Journal of Econometrics, Elsevier, volume 222, issue 2, pages 974-992, DOI: 10.1016/j.jeconom.2020.02.006.
• Liao, Jun & Zou, Guohua & Gao, Yan & Zhang, Xinyu, 2021, "Model averaging prediction for time series models with a diverging number of parameters," Journal of Econometrics, Elsevier, volume 223, issue 1, pages 190-221, DOI: 10.1016/j.jeconom.2020.10.004.
• Su, Jiun-Hua, 2021, "Model selection in utility-maximizing binary prediction," Journal of Econometrics, Elsevier, volume 223, issue 1, pages 96-124, DOI: 10.1016/j.jeconom.2020.07.052.
• Ahsan, Md. Nazmul & Dufour, Jean-Marie, 2021, "Simple estimators and inference for higher-order stochastic volatility models," Journal of Econometrics, Elsevier, volume 224, issue 1, pages 181-197, DOI: 10.1016/j.jeconom.2021.03.008.
• Andersen, Torben G. & Varneskov, Rasmus T., 2021, "Consistent inference for predictive regressions in persistent economic systems," Journal of Econometrics, Elsevier, volume 224, issue 1, pages 215-244, DOI: 10.1016/j.jeconom.2020.04.051.
• Emerson Abraham Jackson, 2021, "Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, volume 8, issue 1, pages 29-43, January, DOI: 10.26650/JEPR795665.
• Sergio Navarro Palacios, 2021, "Limitaciones de las metodologías basadas en opiniones de expertos. El caso de la automatización del empleo," Working Papers. Serie EC, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie), number 2021-01, Apr.
• Fiaschi, Davide & Tealdi, Cristina, 2021, "A General Methodology to Measure Labour Market Dynamics," IZA Discussion Papers, IZA Network @ LISER, number 14254, Apr.
• Sansone, Dario & Zhu, Anna, 2021, "Using Machine Learning to Create an Early Warning System for Welfare Recipients," IZA Discussion Papers, IZA Network @ LISER, number 14377, May.
• Fiaschi, Davide & Tealdi, Cristina, 2021, "Young People between Education and the Labour Market during the COVID-19 Pandemic in Italy," IZA Discussion Papers, IZA Network @ LISER, number 14479, Jun.
• Rabia Ã-zdemir & Munevver Turanli, 2021, "Comparison of machine learning classification algorithms for purchasing forecast," JOURNAL OF LIFE ECONOMICS, Holistence Publications, volume 8, issue 1, pages .59-68, January, DOI: 10.15637/jlecon.8.1.06.
• Florian, Huber & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2021, "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs," JRC Working Papers in Economics and Finance, Joint Research Centre, European Commission, number 2021-01, Mar.
• Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2021, "Efficient Combined Estimation under Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202107, Jan.
• William A. Barnett & Sohee Park, 2021, "Forecasting Inflation and Output Growth with Credit-Card-Augmented Divisia Monetary Aggregates," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202120, Oct, revised Oct 2021.
• Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022, "Optimal Forecast under Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202207, Jan.
• Kartikay Gupta & Niladri Chatterjee, 2021, "Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, volume 28, issue 4, pages 667-689, December, DOI: 10.1007/s10690-021-09341-9.
• Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021, "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, volume 57, issue 1, pages 149-181, January, DOI: 10.1007/s10614-020-10039-9.
• Indranil Ghosh & Manas K. Sanyal & R. K. Jana, 2021, "Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH," Computational Economics, Springer;Society for Computational Economics, volume 57, issue 2, pages 503-527, February, DOI: 10.1007/s10614-019-09965-0.
• Athanasios Tsadiras & Maria Pempetzoglou & Iosif Viktoratos, 2021, "Making Predictions of Global Warming Impacts Using a Semantic Web Tool that Simulates Fuzzy Cognitive Maps," Computational Economics, Springer;Society for Computational Economics, volume 58, issue 3, pages 715-745, October, DOI: 10.1007/s10614-020-10025-1.
• Oscar Claveria, 2021, "Uncertainty indicators based on expectations of business and consumer surveys," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, volume 48, issue 2, pages 483-505, May, DOI: 10.1007/s10663-020-09479-1.
• Eduard Baitinger & Samuel Flegel, 2021, "The better turbulence index? Forecasting adverse financial markets regimes with persistent homology," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, volume 35, issue 3, pages 277-308, September, DOI: 10.1007/s11408-020-00377-x.
• Alfred Michael Dockery & Mark N. Harris & Nicholas Holyoak & Ranjodh B. Singh, 2021, "A methodology for projecting sparse populations and its application to remote Indigenous communities," Journal of Geographical Systems, Springer, volume 23, issue 1, pages 37-61, January, DOI: 10.1007/s10109-020-00329-z.
• Niall Newsham & Francisco Rowe, 2021, "Projecting the demographic impact of Syrian migration in a rapidly ageing society, Germany," Journal of Geographical Systems, Springer, volume 23, issue 2, pages 231-261, April, DOI: 10.1007/s10109-018-00290-y.
• Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021, "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, volume 62, issue 2, pages 283-317, February, DOI: 10.1007/s11146-020-09745-w.
• Qingjing Zhang & Taufiq Choudhry & Jing-Ming Kuo & Xiaoquan Liu, 2021, "Does liquidity drive stock market returns? The role of investor risk aversion," Review of Quantitative Finance and Accounting, Springer, volume 57, issue 3, pages 929-958, October, DOI: 10.1007/s11156-021-00966-5.
• Cem Cakmakli & Verda Ozturk, 2021, "Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing," Koç University-TUSIAD Economic Research Forum Working Papers, Koc University-TUSIAD Economic Research Forum, number 2110, Jul.
• Magdalena Cornejo, 2021, "Forecasting crop yields through climate variables using mixed frequency data. The case of Argentine soybeans," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, volume 67, pages 93-106, January-D.
• Alexander Correa, 2021, "Forecasting Tourist Arrivals to Colombia from Google Trends Search Criteria," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 95, pages 105-134, July-Dece, DOI: 10.17533/udea.le.n95a343462.
• Jamal Bouoiyour, Refk Selmi, 2021, "The financial costs of terrorism: evidence from Germany," European Journal of Comparative Economics, Cattaneo University (LIUC), volume 18, issue 1, pages 87-104, June.
• Muhammad Ejaz & Javed Iqbal, 2021, "Estimation and Forecasting of Industrial Production Index," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, volume 26, issue 1, pages 1-30, Jan-June.
• Muhammad Zubair Mumtaz, 2021, "Predicting Stock Indices Trends using Neuro-fuzzy Systems in COVID-19," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, volume 26, issue 2, pages 1-18, July-Dec.
• Boriss Siliverstovs, 2021, "Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth," Working Papers, Latvijas Banka, number 2021/01, Feb.
• Andreï Kostyrka & Dmitry Igorevich Malakhov,, 2021, "The good, the bad, and the asymmetric: Evidence from a new conditional density model," DEM Discussion Paper Series, Department of Economics at the University of Luxembourg, number 21-09.
• Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021, "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers, National Bank of the Republic of North Macedonia, number 2021-03.
• Martin Baumgaertner & Johannes Zahner, 2021, "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202130.
• Erik Heilmann & Janosch Henze & Heike Wetzel, 2021, "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202135.
• Weshah Razzak, 2021, "The Ownership of Oil, Democracy, and Iraq's Past, Present and Future," Discussion Papers, School of Accountancy, Economics and Finance, Massey University, New Zealand, number 2102.
• Yoonseok Lee & Donggyu Sul, 2021, "Depth-Weighted Forecast Combination: Application to COVID-19 Cases," Center for Policy Research Working Papers, Center for Policy Research, Maxwell School, Syracuse University, number 238, Feb.
• João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021, "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, volume 57, issue 15, pages 4312-4329, December, DOI: 10.1080/1540496X.2020.1808458.
• Kadir Özen & Dilem Yıldırım, 2021, "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers, ERC - Economic Research Center, Middle East Technical University, number 2101, Apr, revised Apr 2021.
• Jean-David Fermanian & Dominique Guégan, 2021, "Fair learning with bagging," Documents de travail du Centre d'Economie de la Sorbonne, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, number 21034, Nov.
• George Athanasopoulos & Nikolaos Kourentzes, 2021, "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 10/21.
• Sium Bodha Hannadige & Jiti Gao & Mervyn J Silvapulle & Param Silvapulle, 2021, "Time Series Forecasting Using a Mixture of Stationary and Nonstationary Predictors," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 6/21.
• David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021, "Loss-Based Variational Bayes Prediction," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 8/21.
• Andres Algaba & Samuel Borms & Kris Boudt & Brecht Verbeken, 2021, "Daily news sentiment and monthly surveys: A mixed–frequency dynamic factor model for nowcasting consumer confidence," Working Paper Research, National Bank of Belgium, number 396, Feb.
• Sylwia Radomska, 2021, "Prognozowanie indeksu WIG20 za pomocą sieci neuronowych NARX i metody SVM," Bank i Kredyt, Narodowy Bank Polski, volume 52, issue 5, pages 457-472.
• Anirban Basu & Noah Hammarlund & Sara Khor & Aasthaa Bansal, 2021, "Understanding Algorithmic Discrimination in Health Economics Through the Lens of Measurement Errors," NBER Working Papers, National Bureau of Economic Research, Inc, number 29413, Oct.
• Ali Hortaçsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," NBER Working Papers, National Bureau of Economic Research, Inc, number 29508, Nov.
• Frank Schorfheide & Dongho Song, 2021, "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," NBER Working Papers, National Bureau of Economic Research, Inc, number 29535, Dec.
• Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2021, "Nowcasting India's Quarterly GDP Growth: A Factor Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," NCAER Working Papers, National Council of Applied Economic Research, number 130, Oct.
• Novikova, T. & Tsyplakov, A., 2021, "Social policy development based on a combination of agent-oriented and inter-industrial approaches," Journal of the New Economic Association, New Economic Association, volume 52, issue 4, pages 12-36, DOI: 10.31737/2221-2264-2021-52-4-1.
• Ali Hortaçsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021, "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Working Papers, NET Institute, number 21-09, Oct.
• Bhattacharya, Rudrani & Bhandari, Bornali & Mundle, Sudipto, 2021, "Nowcasting India's Quarterly GDP Growth: A Factor Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Working Papers, National Institute of Public Finance and Policy, number 21/357, Oct.
• Mirjana Miletic, Aleksandar Tomin, Andjelka Djordjevic & Mirjana Miletic & Aleksandar Tomin & Andjelka Djordjevic, 2021, "Interest rate pass-through in Serbia: evidence from individual bank data," Working Papers Bulletin, National Bank of Serbia, number 4, Sep.
• George Kapetanios & Fotis Papailias, 2021, "UK Economic Conditions during the Pandemic: Assessing the Economy using ONS Faster Indicators," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2021-10, Aug.
• Alex Botsis & Christoph Gortz & Plutarchos Sakellaris, 2021, "Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms' Forecasts," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2021-14, Oct.
• Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2021, "Smooth Robust Multi-Horizon Forecasts," Economics Papers, Economics Group, Nuffield College, University of Oxford, number 2021-W01, Jan.
• Victor Yotzov, 2021, "Commodity Market Structure and Risk Factor Analysis in Bulgaria," Godishnik na UNSS, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 53-73, December, DOI: 10.37075/YB.2021.2.04.
• Iva Raycheva, 2021, "Child Poverty among European Countries and Bulgaria’s Place among Them. Statistical Analysis of Convergence," Ikonomiceski i Sotsialni Alternativi, University of National and World Economy, Sofia, Bulgaria, issue 3, pages 37-51, September.
• Kensuke Tanaka, 2021, "Forecasting developing Asian economies during normal times and large external shocks: Approaches and challenges," OECD Development Centre Working Papers, OECD Publishing, number 345, May, DOI: 10.1787/5a1c4c48-en.
• Gerhard Fenz & Helmut Stix, 2021, "Monitoring the economy in real time with the weekly OeNB GDP indicator: background, experience and outlook," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20-Q1/, pages 17-40.
• Lidia Vesa & Marcel Ioan Boloş & Claudia Diana Sabău-Popa, 2021, "Inventory Decision In Vuca World Using Economic Logic Quantity," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, volume 1, issue 1, pages 251-267, July.
• Ionuţ Gavriş & Valentin Toader, 2021, "The Probability Of Uncertainty: Romania’S Growth Perspectives," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, volume 1, issue 1, pages 71-81, July.
• Rachel R. Cheti & Bahati Ilembo, 2021, "Vector Autoregressive Approach After First Differencing: A Time Series Analysis Of Inflation And Its Determinants In Tanzania," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, volume 6, issue 2, pages 43-56, September, DOI: http://doi.org/10.47535/1991ojbe128.
• Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2021, "Online estimation of DSGE models," The Econometrics Journal, Royal Economic Society, volume 24, issue 1, pages 33-58.
• Fred Liu & Lars Stentoft, 2021, "Regulatory Capital and Incentives for Risk Model Choice under Basel 3
  [Procyclical Leverage and Value-at-Risk]," Journal of Financial Econometrics, Oxford University Press, volume 19, issue 1, pages 53-96.
• Apaar Sadhwani & Kay Giesecke & Justin Sirignano, 2021, "Deep Learning for Mortgage Risk
  [The Subprime Virus]," Journal of Financial Econometrics, Oxford University Press, volume 19, issue 2, pages 313-368.
• Giuseppe Buccheri & Fulvio Corsi, 2021, "HARK the SHARK: Realized Volatility Modeling with Measurement Errors and Nonlinear Dependencies," Journal of Financial Econometrics, Oxford University Press, volume 19, issue 4, pages 614-649.
• Giorgio Calzolari & Roxana Halbleib & Aygul Zagidullina, 2021, "A Latent Factor Model for Forecasting Realized Variances
  [Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk]," Journal of Financial Econometrics, Oxford University Press, volume 19, issue 5, pages 860-909.
• Steven Lehrer & Tian Xie & Tao Zeng, 2021, "Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures?
  [Regression Models with Mixed Sampling Frequencies]," Journal of Financial Econometrics, Oxford University Press, volume 19, issue 5, pages 910-933.
• Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021, "Bond Risk Premiums with Machine Learning
  [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, volume 34, issue 2, pages 1046-1089.
• Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021, "Corrigendum: Bond Risk Premiums with Machine Learning
  [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, volume 34, issue 2, pages 1090-1103.
• Romero Martínez, Mariano & Carmona Ibáñez, Pedro & Pozuelo Campillo, José, 2021, "Utilidad del Deep Learning en la predicción del fracaso empresarial en el ámbito europeo || The usefulness of Deep Learning in the prediction of business failure at the European level," 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 32, issue 1, pages 392-414, December, DOI: https://doi.org/10.46661/revmetodos.
• Wolfgang Bessler & Georgi Taushanov & Dominik Wolff, 2021, "Factor investing and asset allocation strategies: a comparison of factor versus sector optimization," Journal of Asset Management, Palgrave Macmillan, volume 22, issue 6, pages 488-506, October, DOI: 10.1057/s41260-021-00225-1.
• János Varga & Jan in ’t Veld, 2021, "The Impact of the EU Cohesion Policy Spending: A Model-Based Assessment," Studies in Economic Transition, Palgrave Macmillan, chapter 0, in: Michael Landesmann & István P. Székely, "Does EU Membership Facilitate Convergence? The Experience of the EU's Eastern Enlargement - Volume II", DOI: 10.1007/978-3-030-57702-5_5.
• Alessandro Bitetto & Paola Cerchiello & Stefano Filomeni & Alessandra Tanda & Barbara Tarantino, 2021, "Machine Learning and Credit Risk: Empirical Evidence from SMEs," DEM Working Papers Series, University of Pavia, Department of Economics and Management, number 201, Feb.
• Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021, "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series, University of Pavia, Department of Economics and Management, number 205, Oct.
• Mario Papik & Lenka Papikova, 2021, "Application of selected data mining techniques in unintentional accounting error detection," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 16, issue 1, pages 185-201, March, DOI: 10.24136/eq.2021.007.
• Krzysztof Waliszewski & Anna Warchlewska, 2021, "Comparative analysis of Poland and selected countries in terms of household financial behaviour during the COVID-19 pandemic," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 16, issue 3, pages 577-615, September, DOI: 10.24136/eq.2021.021.
• Andrea Kolková & Aleksandr Kljuènikov, 2021, "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, volume 12, issue 4, pages 1063-1094, December, DOI: 10.24136/oc.2021.035.
• Manuel M. F. Martins & Fabio Verona, 2021, "Inflation Dynamics and Forecast: Frequency Matters," CEF.UP Working Papers, Universidade do Porto, Faculdade de Economia do Porto, number 2101, Jun.
• Valerio Mendoza, Octasiano & Borsi, Mihály Tamás & Comim, Flavio, 2021, "Human capital dynamics in China: Evidence from a club convergence approach," MPRA Paper, University Library of Munich, Germany, number 105200, Jan.
• Pincheira, Pablo & Hardy, Nicolas, 2021, "The Mean Squared Prediction Error Paradox," MPRA Paper, University Library of Munich, Germany, number 107403, Apr.
• VINTU, Denis, 2021, "GDP Modelling and Forecasting Using ARIMA. An Empirical Assessment for Innovative Economy Formation," MPRA Paper, University Library of Munich, Germany, number 107603, Apr, revised 11 Feb 2021.
• Pincheira, Pablo & Hardy, Nicolas & Bentancor, Andrea & Henriquez, Cristóbal & Tapia, Ignacio, 2021, "Forecasting Base Metal Prices with an International Stock Index," MPRA Paper, University Library of Munich, Germany, number 107828, May.
• Ofori, Isaac K, 2021, "Catching The Drivers of Inclusive Growth In Sub-Saharan Africa: An Application of Machine Learning," MPRA Paper, University Library of Munich, Germany, number 108622.
• Youssef, Jamile & Ishker, Nermeen & Fakhreddine, Nour, 2021, "GDP Forecast of the Biggest GCC Economies Using ARIMA," MPRA Paper, University Library of Munich, Germany, number 108912, Jun.
• Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021, "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper, University Library of Munich, Germany, number 109137, Apr.
• Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021, "Evaluation of technology clubs by clustering: A cautionary note," MPRA Paper, University Library of Munich, Germany, number 109138, May.
• Kindop, Igor, 2021, "Ubiquitous multimodality in mixed causal-noncausal processes," MPRA Paper, University Library of Munich, Germany, number 109594, Jul, revised 04 Sep 2021.
• Kadanga, Mayo Takémsi Norris & Togbenu, Fo-Kossi Edem, 2021, "Modélisation et prévision du nombre d’infections au coronavirus au Togo: une approche par un modèle ARIMA avec le logiciel R
  [Modeling and forecasting the number of coronavirus infections in Togo: an ARIMA model approach with R software]," MPRA Paper, University Library of Munich, Germany, number 109893, Sep.
• Ogbonna, Ahamuefula & Olubusoye, Olusanya E, 2021, "Tail Risks and Stock Return Predictability: Evidence From Asia-Pacific," MPRA Paper, University Library of Munich, Germany, number 109922, Apr.
• Adekunle, Wasiu & Bekoe, William & Badmus, Sheriff & Anagun, Michael & Alimi, Wasiu, 2021, "Nexus Between Fiscal Discipline And The Budget Process In Africa: Evidence From Nigeria," MPRA Paper, University Library of Munich, Germany, number 110061, Oct.
• Barnett, William & Park, Sohee, 2021, "Forecasting Inflation and Output Growth with Credit-Card-Augmented Divisia Monetary Aggregates," MPRA Paper, University Library of Munich, Germany, number 110298, Oct.
• Boer, Lukas & Pescatori, Andrea & Stuermer, Martin, 2021, "Energy Transition Metals," MPRA Paper, University Library of Munich, Germany, number 110364, Oct.
• Fantazzini, Dean & Calabrese, Raffaella, 2021, "Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure," MPRA Paper, University Library of Munich, Germany, number 110391.
• Fantazzini, Dean & Pushchelenko, Julia & Mironenkov, Alexey & Kurbatskii, Alexey, 2021, "Forecasting internal migration in Russia using Google Trends: Evidence from Moscow and Saint Petersburg," MPRA Paper, University Library of Munich, Germany, number 110452.
• Bhadury, Soumya & Ghosh, Saurabh & Gopalakrishnan, Pawan, 2021, "In quest for policy 'silver bullets' towards triggering a v-shaped recovery," MPRA Paper, University Library of Munich, Germany, number 110905, Dec.
• Degiannakis, Stavros, 2021, "Stock market as a nowcasting indicator for real investment," MPRA Paper, University Library of Munich, Germany, number 110914, Dec.
• Mestiri, Sami, 2021, "Modelling the volatility of Bitcoin returns using Nonparametric GARCH models," MPRA Paper, University Library of Munich, Germany, number 111116, Dec.
• Korobilis, Dimitris & Shimizu, Kenichi, 2021, "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper, University Library of Munich, Germany, number 111631, Dec.
• G.K., Chetan Kumar & K.B., Rangappa & S., Suchitra, 2021, "Analyzing Interlinkages between Financial and Real Estate Sector in the aftermath of COVID-19's Second wave: An Econometric Approach using VECM model," MPRA Paper, University Library of Munich, Germany, number 112440, Dec.
• Abdullah, Muhammad & Gul, Zarro & Waseem, Faiza & Islam, Tanweer, 2021, "The State of Pakistan’s Economy and the Ineffectiveness of Monetary Policy," MPRA Paper, University Library of Munich, Germany, number 112678.
• Bradrania, Reza & Pirayesh Neghab, Davood, 2021, "State-dependent asset allocation using neural networks," MPRA Paper, University Library of Munich, Germany, number 115254, Feb.
• Fasano, Augusto & Rebaudo, Giovanni & Durante, Daniele & Petrone, Sonia, 2021, "A closed-form filter for binary time series," MPRA Paper, University Library of Munich, Germany, number 122349.
• Xin Sheng & Rangan Gupta & Afees A. Salisu & Elie Bouri, 2021, "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Working Papers, University of Pretoria, Department of Economics, number 202101, Jan.
• Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar, 2021, "Uncertainty and Predictability of Real Housing Returns in the United Kingdom: A Regional Analysis," Working Papers, University of Pretoria, Department of Economics, number 202102, Jan.
• Afees A. Salisu & Umar Bida Ndako & Rangan Gupta, 2021, "Forecasting US Output Growth with Large Information Sets," Working Papers, University of Pretoria, Department of Economics, number 202103, Jan.
• Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021, "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers, University of Pretoria, Department of Economics, number 202111, Feb.
• Riza Demirer & Rangan Gupta & He Li & Yu You, 2021, "Financial Vulnerability and Volatility in Emerging Stock Markets: Evidence from GARCH-MIDAS Models," Working Papers, University of Pretoria, Department of Economics, number 202112, Feb.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021, "Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis," Working Papers, University of Pretoria, Department of Economics, number 202114, Feb.
• Afees A. Salisu & Rangan Gupta & Qiang Ji, 2021, "Forecasting Oil Price over 150 Years: The Role of Tail Risks," Working Papers, University of Pretoria, Department of Economics, number 202120, Mar.
• Afees A. Salisu & Rangan Gupta & Riza Demirer, 2021, "Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model," Working Papers, University of Pretoria, Department of Economics, number 202121, Mar.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta, 2021, "Geopolitical Risk and Forecastability of Tail Risk in the Oil Market: Evidence from Over a Century of Monthly Data," Working Papers, University of Pretoria, Department of Economics, number 202122, Mar.
• Afees A. Salisu & Rangan Gupta & Christian Pierdzioch, 2021, "Predictability of Tail Risks of Canada and the U.S. Over a Century: The Role of Spillovers and Oil Tail Risks," Working Papers, University of Pretoria, Department of Economics, number 202127, Apr.
• Afees A. Salisu & Rangan Gupta & Sayar Karmakar & Sonali Das, 2021, "Forecasting Output Growth of Advanced Economies Over Eight Centuries: The Role of Gold Market Volatility as a Proxy of Global Uncertainty," Working Papers, University of Pretoria, Department of Economics, number 202133, May.
• Rangan Gupta & Christian Pierdzioch, 2021, "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers," Working Papers, University of Pretoria, Department of Economics, number 202135, May.
• Rangan Gupta & Christian Pierdzioch, 2021, "Uncertainty, Spillovers, and Forecasts of the Realized Variance of Gold Returns," Working Papers, University of Pretoria, Department of Economics, number 202137, May.
• Afees A. Salisu & Rangan Gupta, 2021, "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers, University of Pretoria, Department of Economics, number 202144, Jun.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta, 2021, "Oil Tail Risks and the Forecastability of the Realized Variance of Oil-Price: Evidence from Over 150 Years of Data," Working Papers, University of Pretoria, Department of Economics, number 202146, Jun.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & David Gabauer, 2021, "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," Working Papers, University of Pretoria, Department of Economics, number 202161, Sep.
• Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & Renee van Eyden, 2021, "Climate Risks and U.S. Stock-Market Tail Risks: A Forecasting Experiment Using over a Century of Data," Working Papers, University of Pretoria, Department of Economics, number 202165, Sep.
• Sayar Karmakar & Riza Demirer & Rangan Gupta, 2021, "Bitcoin Mining Activity and Volatility Dynamics in the Power Market," Working Papers, University of Pretoria, Department of Economics, number 202166, Sep.
• Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risks and Forecastability of the Realized Volatility of Gold and Other Metal Prices," Working Papers, University of Pretoria, Department of Economics, number 202172, Oct.
• Jiqian Wang & Rangan Gupta & Oguzhan Cepni & Feng Ma, 2021, "Forecasting International REITs Volatility: The Role of Oil-Price Uncertainty," Working Papers, University of Pretoria, Department of Economics, number 202173, Oct.
• Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Working Papers, University of Pretoria, Department of Economics, number 202175, Oct.
• Rangan Gupta & Christian Pierdzioch, 2021, "Forecasting the Realized Variance of Oil-Price Returns: A Disaggregated Analysis of the Role of Uncertainty and Geopolitical Risk," Working Papers, University of Pretoria, Department of Economics, number 202176, Oct.
• Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risk and the Volatility of Agricultural Commodity Price Fluctuations: A Forecasting Experiment," Working Papers, University of Pretoria, Department of Economics, number 202177, Nov.
• Ruipeng Liu & Mawuli Segnon & Rangan Gupta & Elie Bouri, 2021, "Conventional and Unconventional Monetary Policy Rate Uncertainty and Stock Market Volatility: A Forecasting Perspective," Working Papers, University of Pretoria, Department of Economics, number 202178, Nov.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021, "El Nino, La Nina, and Forecastability of the Realized Variance of Agricultural Commodity Prices: Evidence from a Machine Learning Approach," Working Papers, University of Pretoria, Department of Economics, number 202179, Nov.
• Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021, "Climate Risks and Forecasting Stock-Market Returns in Advanced Economies Over a Century," Working Papers, University of Pretoria, Department of Economics, number 202183, Nov.
• Paulina Ziembińska, 2021, "Quality of Tests of Expectation Formation for Revised Data," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, volume 13, issue 4, pages 405-453, December.
• Bhumjai Tangsawasdirat & Suranan Tanpoonkiat & Burasakorn Tangsatchanan, 2021, "Credit Risk Database: Credit Scoring Models for Thai SMEs," PIER Discussion Papers, Puey Ungphakorn Institute for Economic Research, number 168, Nov.
• Eliud Silva & Corey Sparks, 2021, "Hierarchical forecasts of Diabetes mortality in Mexico by marginalization and sex to establish resource allocation," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., volume 18, issue 2, pages 82-98, Julio-Dic.