Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2021

• 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 1-53–73, December.
• 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: ," 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.
• Luke Hartigan & Michelle Wright, 2021, "Financial Conditions and Downside Risk to Economic Activity in Australia," RBA Research Discussion Papers, Reserve Bank of Australia, number rdp2021-03, Mar, DOI: 10.47688/rdp2021-03.
• Pérez Forero, Fernando, 2021, "Predicción de variables macroeconómicas en el Perú a través un modelo BVAR con media cambiante en el tiempo," Working Papers, Banco Central de Reserva del Perú, number 2021-001, Mar.
• Gazi Salah Uddin & Ou Tang & Maziar Sahamkhadam & Farhad Taghizadeh-Hesary & Muhammad Yahya & Pontus Cerin & Jakob Rehme, 2021, "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers, Asian Development Bank Institute, number 1212, Jan.
• Karen Poghosyan & Ruben Poghosyan, 2021, "On the applicability of dynamic factor models for forecasting real GDP growth in Armenia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 61, pages 28-46.
• Robert Garafutdinov, 2021, "Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 62, pages 85-100.
• Nikita Fokin, 2021, "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), volume 63, pages 5-29.
• Necati Alp ERİLLİ, 2021, "Use of Trimean in Theil-Sen Regression Analysis," Bulletin of Economic Theory and Analysis, BETA Journals, volume 6, issue 1, pages 15-26.
• Necmettin Alpay Kocak, 2021, "Analysis of Relationship between the Consumer and Producer Prices in Turkey using Alternative Estimation Methods (Türkiye'de Tüketici ve Üretici Fiyatları Arasındaki İlişkinin Alternatif Tahmin Yöntem," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 12, issue 1, pages 33-47.
• Kaveh A. Adli & Ugur Sener, 2021, "Forecasting of the U.S. Steel Prices with LVAR and VEC Models," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 12, issue 3, pages 509-522.
• Juan de Lucio, 2021, "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
• Miguel Ángel Mendoza-González, 2021, "Apertura comercial, choques productivos y externalidades con ciclos espacio-tiempo en el crecimiento económico por entidad federativa en México, 1980-2018," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 50, pages 105-124.
• Sailesh Bhaghoe & Gavin Ooft, 2021, "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise, number 176, Mar.
• Anitha Rao & Mark Wiendling & Paul Ridgeway & Liz Kennedy & Harris A. Eyre & Paulo Pinho, 2021, "Bridging the Gap Between Medicine and Insurance: How to Leverage Data, Artificial Intelligence, and Neuroinformatics for Insurance and Financial Risk Management," Journal of Financial Transformation, Capco Institute, volume 54, pages 142-147.
• Sung Wook Hong & Seong-hwan Min, 2021, "Market Analysis of Key Manufacturing Segments Using News Data," Research Papers, Korea Institute for Industrial Economics and Trade, number 21/8, May.
• Solmaz Sadeghpour & Hassan Heidari & Seyyed Jamaleddin Mohseni Zenozi, 2021, "Study the effect of the monetary and financial shocks in the real sector of Iran's economy with considering of gharz-al -hasanah deposits in the context of a DSGE model," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, volume 8, issue 1, pages 89-114.
• Hwee Kwan Chow & Daniel Han, 2021, "Forecast Pooling or Information Pooling During Crises? MIDAS Forecasting of GDP in a Small Open Economy," Economics and Statistics Working Papers, Singapore Management University, School of Economics, number 6-2021, Jul.
• Weiwei ZHANG & Tiezhu SUN & Yechi MA & Zilong WANG, 2021, "New Evidence on the Information Content of Implied Volatility of S&P 500: Model-Free versus Model-Based," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 1, pages 109-121, December.
• Fatemeh SALIMIANRAD & Vali BORIMNEJAD & Sahar DEHYUORI, 2021, "Investigating the Relationship between Natural Capital and Sustainable Economic Growth using the General Equilibrium Model," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 3, pages 120-139, June.
• Kirill Dragun & Kris Boudt & Orimar Sauri & Steven Vanduffel, 2021, "Beta-Adjusted Covariance Estimation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium, Ghent University, Faculty of Economics and Business Administration, number 21/1010, Feb.
• George Varghese & Vinodh Madhavan, 2021, "Nonlinearity in Global Crude Oil Benchmarks: Disentangling the Effect of Time Aggregation," Journal of Emerging Market Finance, Institute for Financial Management and Research, volume 20, issue 3, pages 290-307, December, DOI: 10.1177/09726527211043013.
• Tomas Havranek & Ayaz Zeynalov, 2021, "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , volume 27, issue 1, pages 129-148, February, DOI: 10.1177/1354816619879584.
• Reyes Zárate, Francisco J & León López, Iván, 2021, "Estimaciones de riesgo ajustadas por distribución: una aplicación para portafolios de inversión integrados por activos nacionales / Distribution-Adjusted Risk Estimates: An Application to Domestic Ass," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, volume 11, issue 2, pages 117-146, julio-dic.
• Wojciech Charemza & Michal Lewandowski & Lukasz Wozny, 2021, "Efficiency in rewarding academic journal publications. The case of Poland," KAE Working Papers, Warsaw School of Economics, Collegium of Economic Analysis, number 2021-062, Feb.
• Alejandro Rodriguez Arana, 2021, "La expansion de Covid-19 en Mexico en 2020: un enfoque desde la econometria de series de tiempo," Sobre México. Revista de Economía, Sobre México. Temas en economía, volume 1, issue 3, pages 34-66.
• F. Benedetto & L. Mastroeni & P. Vellucci, 2021, "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, volume 299, issue 1, pages 1235-1252, April, DOI: 10.1007/s10479-019-03319-7.
• Chrysovalantis Gaganis & Panagiota Papadimitri & Menelaos Tasiou, 2021, "A multicriteria decision support tool for modelling bank credit ratings," Annals of Operations Research, Springer, volume 306, issue 1, pages 27-56, November, DOI: 10.1007/s10479-020-03516-9.
• Eric Séverin & David Veganzones, 2021, "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, volume 306, issue 1, pages 247-272, November, DOI: 10.1007/s10479-021-04183-0.
• Yuzhi Cai & Thanaset Chevapatrakul & Danilo V. Mascia, 2021, "How is price explosivity triggered in the cryptocurrency markets?," Annals of Operations Research, Springer, volume 307, issue 1, pages 37-51, December, DOI: 10.1007/s10479-021-04298-4.
• Christos Agiakloglou & Apostolos Tsimpanos, 2021, "Evaluating information criteria for selecting spatial processes," The Annals of Regional Science, Springer;Western Regional Science Association, volume 66, issue 3, pages 677-697, June, DOI: 10.1007/s00168-020-01033-y.
• David Volkmann, 2021, "Explaining S&P500 option returns: an implied risk-adjusted approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, volume 29, issue 2, pages 665-685, June, DOI: 10.1007/s10100-019-00666-5.
• Michal Mešťan & Ivan Králik & Matej Žofaj & Nikola Karkošiaková & Audrius Kabašinskas, 2021, "Projections of pension benefits in supplementary pension saving scheme in Slovakia," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, volume 29, issue 2, pages 687-712, June, DOI: 10.1007/s10100-019-00669-2.
• Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021, "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 44, issue 2, pages 863-882, December, DOI: 10.1007/s10203-021-00318-x.
• Paolo Angelis & Roberto Marchis & Mario Marino & Antonio Luciano Martire & Immacolata Oliva, 2021, "Betting on bitcoin: a profitable trading between directional and shielding strategies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 44, issue 2, pages 883-903, December, DOI: 10.1007/s10203-021-00324-z.
• Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021, "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 44, issue 2, pages 1063-1085, December, DOI: 10.1007/s10203-021-00354-7.
• Marcel Aloy & Floris Laly & Sébastien Laurent & Christelle Lecourt, 2021, "Modeling Time-Varying Conditional Betas. A Comparison of Methods with Application for REITs," Dynamic Modeling and Econometrics in Economics and Finance, Springer, in: Gilles Dufrénot & Takashi Matsuki, "Recent Econometric Techniques for Macroeconomic and Financial Data", DOI: 10.1007/978-3-030-54252-8_9.
• Alain Kabundi & Asithandile Mbelu, 2021, "Estimating a time-varying financial conditions index for South Africa," Empirical Economics, Springer, volume 60, issue 4, pages 1817-1844, April, DOI: 10.1007/s00181-020-01844-0.
• Yongchen Zhao, 2021, "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, volume 61, issue 1, pages 173-199, July, DOI: 10.1007/s00181-020-01864-w.
• Jack Fosten & Daniel Gutknecht, 2021, "Horizon confidence sets," Empirical Economics, Springer, volume 61, issue 2, pages 667-692, August, DOI: 10.1007/s00181-020-01891-7.
• Serdar Neslihanoglu & Stelios Bekiros & John McColl & Duncan Lee, 2021, "Multivariate time-varying parameter modelling for stock markets," Empirical Economics, Springer, volume 61, issue 2, pages 947-972, August, DOI: 10.1007/s00181-020-01896-2.
• Georges Tsafack & James Cataldo, 2021, "Backtesting and estimation error: value-at-risk overviolation rate," Empirical Economics, Springer, volume 61, issue 3, pages 1351-1396, September, DOI: 10.1007/s00181-020-01905-4.
• Edmond Berisha & David Gabauer & Rangan Gupta & Chi Keung Marco Lau, 2021, "Time-varying influence of household debt on inequality in United Kingdom," Empirical Economics, Springer, volume 61, issue 4, pages 1917-1933, October, DOI: 10.1007/s00181-020-01940-1.
• Angela Capolongo & Claudia Pacella, 2021, "Forecasting inflation in the euro area: countries matter!," Empirical Economics, Springer, volume 61, issue 5, pages 2477-2499, November, DOI: 10.1007/s00181-020-01959-4.
• Nima Nonejad, 2021, "Crude oil price point forecasts of the Norwegian GDP growth rate," Empirical Economics, Springer, volume 61, issue 5, pages 2913-2930, November, DOI: 10.1007/s00181-020-01964-7.
• Ahmet Akca & Ethem Çanakoğlu, 2021, "Adaptive stochastic risk estimation of firm operating profit," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, volume 48, issue 3, pages 463-504, September, DOI: 10.1007/s40812-021-00184-z.
• David Y. Aharon & Zaghum Umar & Xuan Vinh Vo, 2021, "Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 7, issue 1, pages 1-25, December, DOI: 10.1186/s40854-021-00274-w.
• Christian Stummer & Lars Lüpke & Markus Günther, 2021, "Beaming market simulation to the future by combining agent-based modeling with scenario analysis," Journal of Business Economics, Springer, volume 91, issue 9, pages 1469-1497, November, DOI: 10.1007/s11573-021-01046-9.
• Martin Pažický, 2021, "Predicting Recessions in Germany Using the German and the US Yield Curve," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 17, issue 3, pages 263-291, December, DOI: 10.1007/s41549-021-00061-7.
• Pelin Akyol & Kala Krishna & Jinwen Wang, 2021, "Taking PISA Seriously: How Accurate are Low-Stakes Exams?," Journal of Labor Research, Springer, volume 42, issue 2, pages 184-243, June, DOI: 10.1007/s12122-021-09317-8.
• Filippo Massari, 2021, "Price probabilities: a class of Bayesian and non-Bayesian prediction rules," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), volume 72, issue 1, pages 133-166, July, DOI: 10.1007/s00199-020-01270-4.
• Annalisa Cristini & Piero Ferri, 2021, "Nonlinear models of the Phillips curve," Journal of Evolutionary Economics, Springer, volume 31, issue 4, pages 1129-1155, September, DOI: 10.1007/s00191-021-00736-5.
• Ali Habibnia & Esfandiar Maasoumi, 2021, "Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 19, issue 1, pages 363-381, December, DOI: 10.1007/s40953-021-00275-7.
• Roberto S. Mariano & Suleyman Ozmucur, 2021, "Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 19, issue 1, pages 383-400, December, DOI: 10.1007/s40953-021-00276-6.
• Subhash C. Sharma & Anil K. Bera, 2021, "Estimation of Random Components and Prediction in One and Two-Way Error Component Regression Models," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 19, issue 1, pages 419-441, December, DOI: 10.1007/s40953-021-00278-4.
• Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021, "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, volume 14, issue 2, pages 111-128, August, DOI: 10.1007/s12076-021-00268-3.
• Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021, "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, volume 60, issue C, pages 56-73, DOI: 10.1016/j.jempfin.2020.11.003.
• Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021, "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, volume 62, issue C, pages 179-201, DOI: 10.1016/j.jempfin.2021.03.003.
• Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021, "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, volume 62, issue C, pages 46-61, DOI: 10.1016/j.jempfin.2021.01.007.
• Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021, "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, volume 63, issue C, pages 252-269, DOI: 10.1016/j.jempfin.2021.07.009.
• Han, Yang & Jiao, Anqi & Ma, Jun, 2021, "The predictive power of Nelson–Siegel factor loadings for the real economy," Journal of Empirical Finance, Elsevier, volume 64, issue C, pages 95-127, DOI: 10.1016/j.jempfin.2021.04.008.
• Algieri, Bernardina & Leccadito, Arturo & Tunaru, Diana, 2021, "Risk premia in electricity derivatives markets," Energy Economics, Elsevier, volume 100, issue C, DOI: 10.1016/j.eneco.2021.105300.
• Lyócsa, Štefan & Todorova, Neda, 2021, "What drives volatility of the U.S. oil and gas firms?," Energy Economics, Elsevier, volume 100, issue C, DOI: 10.1016/j.eneco.2021.105367.
• Patra, Saswat, 2021, "Revisiting value-at-risk and expected shortfall in oil markets under structural breaks: The role of fat-tailed distributions," Energy Economics, Elsevier, volume 101, issue C, DOI: 10.1016/j.eneco.2021.105452.
• Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021, "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, volume 102, issue C, DOI: 10.1016/j.eneco.2021.105494.
• Özen, Kadir & Yıldırım, Dilem, 2021, "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, volume 103, issue C, DOI: 10.1016/j.eneco.2021.105573.
• Nonejad, Nima, 2021, "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, volume 104, issue C, DOI: 10.1016/j.eneco.2021.105635.
• Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021, "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, volume 104, issue C, DOI: 10.1016/j.eneco.2021.105689.
• Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021, "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, volume 93, issue C, DOI: 10.1016/j.eneco.2019.104481.
• Dai, Zhifeng & Zhou, Huiting & Kang, Jie & Wen, Fenghua, 2021, "The skewness of oil price returns and equity premium predictability," Energy Economics, Elsevier, volume 94, issue C, DOI: 10.1016/j.eneco.2020.105069.
• Uniejewski, Bartosz & Weron, Rafał, 2021, "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, volume 95, issue C, DOI: 10.1016/j.eneco.2021.105121.
• Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021, "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, volume 96, issue C, DOI: 10.1016/j.eneco.2021.105118.
• He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021, "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, volume 97, issue C, DOI: 10.1016/j.eneco.2021.105189.
• Dai, Zhifeng & Kang, Jie, 2021, "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, volume 97, issue C, DOI: 10.1016/j.eneco.2021.105205.
• Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan, 2021, "Geopolitical risk and forecastability of tail risk in the oil market: Evidence from over a century of monthly data," Energy, Elsevier, volume 235, issue C, DOI: 10.1016/j.energy.2021.121333.
• Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021, "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, volume 78, issue C, DOI: 10.1016/j.irfa.2021.101899.
• Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2021, "VCRIX — A volatility index for crypto-currencies," International Review of Financial Analysis, Elsevier, volume 78, issue C, DOI: 10.1016/j.irfa.2021.101915.
• Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021, "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, volume 38, issue C, DOI: 10.1016/j.frl.2020.101490.
• Chen, Wen & Minney, Aaron & Toscas, Peter & Koo, Bonsoo & Zhu, Zili & Pantelous, Athanasios A., 2021, "Personalised drawdown strategies and partial annuitisation to mitigate longevity risk," Finance Research Letters, Elsevier, volume 39, issue C, DOI: 10.1016/j.frl.2020.101644.
• Nonejad, Nima, 2021, "Predicting equity premium by conditioning on macroeconomic variables: A prediction selection strategy using the price of crude oil," Finance Research Letters, Elsevier, volume 41, issue C, DOI: 10.1016/j.frl.2020.101792.
• Ly, Kim Tien, 2021, "A COVID-19 forecasting system using adaptive neuro-fuzzy inference," Finance Research Letters, Elsevier, volume 41, issue C, DOI: 10.1016/j.frl.2020.101844.
• Chatziantoniou, Ioannis & Degiannakis, Stavros & Delis, Panagiotis & Filis, George, 2021, "Forecasting oil price volatility using spillover effects from uncertainty indices," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2020.101885.
• Bouri, Elie & Gupta, Rangan & Majumdar, Anandamayee & Subramaniam, Sowmya, 2021, "Time-varying risk aversion and forecastability of the US term structure of interest rates," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2021.101924.
• Bouri, Elie & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021, "Forecasting power of infectious diseases-related uncertainty for gold realized variance," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2021.101936.
• Guidolin, Massimo & Pedio, Manuela, 2021, "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," Finance Research Letters, Elsevier, volume 42, issue C, DOI: 10.1016/j.frl.2021.101943.
• Umar, Zaghum & Riaz, Yasir & Zaremba, Adam, 2021, "Patterns of Spillover in Energy, Agricultural, and Metal Markets: A Connectedness Analysis for Years 1780-2020," Finance Research Letters, Elsevier, volume 43, issue C, DOI: 10.1016/j.frl.2021.101999.
• Hoang, Khoa & Cannavan, Damien & Huang, Ronghong & Peng, Xiaowen, 2021, "Predicting stock returns with implied cost of capital: A partial least squares approach," Journal of Financial Markets, Elsevier, volume 53, issue C, DOI: 10.1016/j.finmar.2020.100576.
• Knill, April M. & Lee, Bong Soo & Ang, James, 2021, "Leveling of the playing field and corporate financing patterns around the world," Global Finance Journal, Elsevier, volume 47, issue C, DOI: 10.1016/j.gfj.2020.100515.
• Battiston, Pietro & Gamba, Simona, 2021, "COVID-19: R0 is lower where outbreak is larger," Health Policy, Elsevier, volume 125, issue 2, pages 141-147, DOI: 10.1016/j.healthpol.2020.10.017.
• Buonanno, Paolo & Puca, Marcello, 2021, "Using newspaper obituaries to “nowcast” daily mortality: Evidence from the Italian COVID-19 hot-spots," Health Policy, Elsevier, volume 125, issue 4, pages 535-540, DOI: 10.1016/j.healthpol.2021.01.006.
• Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021, "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, volume 100, issue C, pages 296-308, DOI: 10.1016/j.insmatheco.2021.06.004.
• Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021, "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, volume 100, issue C, pages 30-58, DOI: 10.1016/j.insmatheco.2021.04.007.
• Taylor, Greg, 2021, "A special Tweedie sub-family with application to loss reserving prediction error," Insurance: Mathematics and Economics, Elsevier, volume 101, issue PB, pages 262-288, DOI: 10.1016/j.insmatheco.2021.08.002.
• Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021, "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, volume 96, issue C, pages 31-52, DOI: 10.1016/j.insmatheco.2020.10.003.
• Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021, "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, volume 99, issue C, pages 200-221, DOI: 10.1016/j.insmatheco.2021.03.025.
• Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021, "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, volume 99, issue C, pages 9-24, DOI: 10.1016/j.insmatheco.2021.01.002.
• Urom, Christian & Ndubuisi, Gideon & Ozor, Jude, 2021, "Economic activity, and financial and commodity markets’ shocks: An analysis of implied volatility indexes," International Economics, Elsevier, volume 165, issue C, pages 51-66, DOI: 10.1016/j.inteco.2020.11.005.
• Bessler, Wolfgang & Taushanov, Georgi & Wolff, Dominik, 2021, "Optimal asset allocation strategies for international equity portfolios: A comparison of country versus industry optimization," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 72, issue C, DOI: 10.1016/j.intfin.2021.101343.
• Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021, "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 73, issue C, DOI: 10.1016/j.intfin.2021.101353.
• Breen, John David & Hu, Liang, 2021, "The predictive content of oil price and volatility: New evidence on exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 75, issue C, DOI: 10.1016/j.intfin.2021.101454.
• Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021, "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, volume 37, issue 1, pages 360-377, DOI: 10.1016/j.ijforecast.2020.06.006.
• Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo, 2021, "Realized volatility forecasting: Robustness to measurement errors," International Journal of Forecasting, Elsevier, volume 37, issue 1, pages 44-57, DOI: 10.1016/j.ijforecast.2020.02.009.
• Costantini, Mauro & Kunst, Robert M., 2021, "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 445-460, DOI: 10.1016/j.ijforecast.2020.06.010.
• Clements, Michael P., 2021, "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 634-646, DOI: 10.1016/j.ijforecast.2020.08.003.
• Greenwood-Nimmo, Matthew & Nguyen, Viet Hoang & Shin, Yongcheol, 2021, "Measuring the Connectedness of the Global Economy," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 899-919, DOI: 10.1016/j.ijforecast.2020.10.003.
• Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021, "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 941-948, DOI: 10.1016/j.ijforecast.2020.10.005.
• Ganics, Gergely & Odendahl, Florens, 2021, "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, volume 37, issue 2, pages 971-999, DOI: 10.1016/j.ijforecast.2020.11.001.
• Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021, "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, volume 37, issue 3, pages 1247-1260, DOI: 10.1016/j.ijforecast.2021.02.007.
• Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas, 2021, "Predicting benchmarked US state employment data in real time," International Journal of Forecasting, Elsevier, volume 37, issue 3, pages 1261-1275, DOI: 10.1016/j.ijforecast.2021.02.006.