Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2024

• Luke Hartigan & Tom Rosewall, 2024, "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," Working Papers, University of Sydney, School of Economics, number 2024-15, Jul.
• Monica Billio & Roberto Casarin & Matteo Iacopini, 2024, "Bayesian Markov-Switching Tensor Regression for Time-Varying Networks," Journal of the American Statistical Association, Taylor & Francis Journals, volume 119, issue 545, pages 109-121, January, DOI: 10.1080/01621459.2022.2102502.
• Liu Yang & Kajal Lahiri & Adrian Pagan, 2024, "Getting the ROC into Sync," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 42, issue 1, pages 109-121, January, DOI: 10.1080/07350015.2022.2154778.
• James Morley & Trung Duc Tran & Benjamin Wong, 2024, "A Simple Correction for Misspecification in Trend-Cycle Decompositions with an Application to Estimating r," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 42, issue 2, pages 665-680, April, DOI: 10.1080/07350015.2023.2221974.
• Davide Delle Monache & Andrea De Polis & Ivan Petrella, 2024, "Modeling and Forecasting Macroeconomic Downside Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 42, issue 3, pages 1010-1025, July, DOI: 10.1080/07350015.2023.2277171.
• David Ardia & Arnaud Dufays & Carlos Ordás Criado, 2024, "Linking Frequentist and Bayesian Change-Point Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 42, issue 4, pages 1155-1168, October, DOI: 10.1080/07350015.2023.2293166.
• Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2024, "Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 42, issue 4, pages 1302-1317, October, DOI: 10.1080/07350015.2024.2310020.
• Raffaele Mattera & George Athanasopoulos & Rob Hyndman, 2024, "Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering," Quantitative Finance, Taylor & Francis Journals, volume 24, issue 11, pages 1641-1667, November, DOI: 10.1080/14697688.2024.2412687.
• Mihaela Simionescu & Nicolas Schneider & Beata Gavurova, 2024, "A Bayesian vector-autoregressive application with time-varying parameters on the monetary shocks–production network nexus," Journal of Applied Economics, Taylor & Francis Journals, volume 27, issue 1, pages 2395114-239, December, DOI: 10.1080/15140326.2024.2395114.
• Salih Zeki Atilgan & Tarik Aydogdu & Mehmet Selman Colak & Muhammed Hasan Yilmaz, 2024, "Anticipating Credit Developments with Regularization and Shrinkage Methods: Evidence for Turkish Banking Industry," Working Papers, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, number 2402.
• Anne Opschoor & Dewi Peerlings & Luca Rossini & Andre Lucas, 2024, "Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 24-049/III, Jul.
• Gabriele Mingoli, 2024, "Modeling Common Bubbles: A Mixed Causal Non-Causal Dynamic Factor Model," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 24-072/III, Nov.
• Pierluigi Vallarino, 2024, "Dynamic kernel models," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 24-082/III, Dec.
• Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2024, "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," The Review of Economics and Statistics, MIT Press, volume 106, issue 5, pages 1403-1417, September, DOI: 10.1162/rest_a_01213.
• Felix Haase, 2024, "Sum-of-the-Parts Revised: Economic Regimes and Flexible Probabilities," Research Papers in Economics, University of Trier, Department of Economics, number 2024-10.
• Fayssal Jamhamed & Franck Martin & Fabien Rondeau & Josué Thélissaint & Stéphane Tufféry, 2024, "Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models," Economics Working Paper Archive (University of Rennes & University of Caen), Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS, number 2024-13, Dec.
• Tae-Hwy Lee & Tao Wang, 2024, "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers, University of California at Riverside, Department of Economics, number 202412, Dec.
• Zacharias Psaradakis & Martin Sola & Francisco Rapetti & Patricio Yunis, 2024, "The Role of Consumer Sentiment in the Stock Market: A Multivariate Dynamic Mixture Model with Threshold Effects," Department of Economics Working Papers, Universidad Torcuato Di Tella, number 2024_01, Apr.
• Zacharias Psaradakis & Martin Sola & Nicola Spagnolo & Patricio Yunis, 2024, "Predictive Accuracy of Impulse Responses Estimated Using Local Projections and Vector Autoregressions," Department of Economics Working Papers, Universidad Torcuato Di Tella, number 2024_02, May.
• Amelie BARBIER-GAUCHARD & Emmanouil SOFIANOS, 2024, "Forecasting Public Debt in the Euro Area Using Machine Learning: Decision Tools for Financial Markets," Working Papers of BETA, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg, number 2024-47.
• Paula Barro, 2024, "Imputación de ingresos del hogar en la Encuesta de Uso del Tiempo de Uruguay 2021-2022. Documento metodológico," Documentos de Trabajo (working papers), Instituto de Economía - IECON, number 24-19, Dec.
• Elena G. Shershneva, 2024, "CAMELS parameters’ impact on the risk of losing financial stability: The case of Russian banks," Journal of New Economy, Ural State University of Economics, volume 25, issue 2, pages 130-152, July, DOI: 10.29141/2658-5081-2024-25-2-7.
• Valdemar J. Undji & Johannes P. S. Sheefeni, 2024, "A factor-based framework for stress-testing the Namibian banking sector," Journal of New Economy, Ural State University of Economics, volume 25, issue 3, pages 112-137, December, DOI: 10.29141/2658-5081-2024-25-3-6.
• HABIBI, Reza, 2024, "A Note On The Early Warning System Of Change Points: Combination Of Regime Switching And Threshold Models," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", volume 28, issue 2, pages 6-18, June.
• ANGHEL, Bogdan Ionuț, 2024, "Predicting Stock Price Direction Of Eurozone Banks: Can Deep Learning Techniques Outperform Traditional Models?," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", volume 28, issue 4, pages 29-42, December.
• Yordan Petkov, 2024, "Forecasting The Number Of Human Resources In The Organization Using Markov Chains," INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE "HUMAN RESOURCE MANAGEMENT", University of Economics - Varna, issue 1, pages 80-88.
• Svetlana Todorova, 2024, "Hedonic Modelling of Real Estate Prices in Varna," Stroitelno predpriemachestvo i nedvizhima sobstvenost = Construction Entrepreneurship and Real Property, University of Economics Varna, issue 1, pages 65-79.
• Radojković Ivan D. & Radović Ognjen V. & Stevanović Kristina R., 2024, "Modeling the Volatility of Returns on Investment Units of Voluntary Pension Funds in Serbia," Economic Themes, Sciendo, volume 62, issue 4, pages 541-560, DOI: 10.2478/ethemes-2024-0029.
• Mirescu Lucian & Popescu Liviu, 2024, "Forecasts of Performance Indicators in the Health System Using the Arima Method," Journal of Social and Economic Statistics, Sciendo, volume 13, issue 1, pages 1-22, DOI: 10.2478/jses-2024-0005.
• Mirescu Lucian & Popescu Liviu, 2024, "Forecasts on the Evolution of Human Resources in the Health System in Romania Using the Arima Method," Timisoara Journal of Economics and Business, Sciendo, volume 17, issue 1, pages 65-112, DOI: 10.2478/tjeb-2024-0004.
• Naz Farah & Lutfullah Tooba & Zahra Kanwal, 2024, "COVID-19 and Seasonality in Monthly Returns: a Firm Level Analysis of PSX," Zagreb International Review of Economics and Business, Sciendo, volume 27, issue 1, pages 201-230, DOI: 10.2478/zireb-2024-0010.
• Bartosz Bieganowski & Robert Ślepaczuk, 2024, "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-03.
• Kamil Kashif & Robert Ślepaczuk, 2024, "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-07.
• Sugarbayar Enkhbayar & Robert Ślepaczuk, 2024, "Predictive modeling of foreign exchange trading signals using machine learning techniques," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-10.
• Maciej Wysocki & Robert Ślepaczuk, 2024, "Construction and Hedging of Equity Index Options Portfolios," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-14.
• Stanisław Łaniewski & Robert Ślepaczuk, 2024, "Enhancing literature review with NLP methods Algorithmic investment strategies case," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-16.
• Filip Stefaniuk & Robert Ślepaczuk, 2024, "The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Me," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-27.
• Vasily Astrov & Artem Kochnev & Vincent Stamer & Feodora Teti, 2024, "The Russian Economy Amidst the War and Sanctions," Russia Monitor, The Vienna Institute for International Economic Studies, wiiw, number 1, Jan.
• Sulkhan Chavleishvili & Simone Manganelli, 2024, "Forecasting and stress testing with quantile vector autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 39, issue 1, pages 66-85, January, DOI: 10.1002/jae.3009.
• Jia Liu & John M. Maheu & Yong Song, 2024, "Identification and forecasting of bull and bear markets using multivariate returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 39, issue 5, pages 723-745, August, DOI: 10.1002/jae.3048.
• James Mitchell & Aubrey Poon & Dan Zhu, 2024, "Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 39, issue 5, pages 790-812, August, DOI: 10.1002/jae.3049.
• Florian Huber & Gary Koop, 2024, "Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 39, issue 7, pages 1301-1320, November, DOI: 10.1002/jae.3087.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024, "Business applications and state‐level stock market realized volatility: A forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 2, pages 456-472, March, DOI: 10.1002/for.3042.
• Peter McAdam & Anders Warne, 2024, "Density forecast combinations: The real‐time dimension," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 5, pages 1153-1172, August, DOI: 10.1002/for.3068.
• Pablo Pincheira Brown & Nicolás Hardy, 2024, "Correlation‐based tests of predictability," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 6, pages 1835-1858, September, DOI: 10.1002/for.3081.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024, "Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 6, pages 2088-2125, September, DOI: 10.1002/for.3106.
• Chenxing Li & John M. Maheu & Qiao Yang, 2024, "An infinite hidden Markov model with stochastic volatility," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 6, pages 2187-2211, September, DOI: 10.1002/for.3123.
• Pablo Pincheira Brown & Nicolás Hardy, 2024, "The mean squared prediction error paradox," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 6, pages 2298-2321, September, DOI: 10.1002/for.3129.
• Utkarsh Kumar & Wasim Ahmad & Gazi Salah Uddin, 2024, "Bayesian Markov switching model for BRICS currencies' exchange rates," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 6, pages 2322-2340, September, DOI: 10.1002/for.3128.
• Jesus Crespo Cuaresma & Ines Fortin & Jaroslava Hlouskova & Michael Obersteiner, 2024, "Regime‐dependent commodity price dynamics: A predictive analysis," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 7, pages 2822-2847, November, DOI: 10.1002/for.3152.
• Arabinda Basistha & Richard Startz, 2024, "Measuring persistent global economic factors with output, commodity price, and commodity currency data," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 7, pages 2860-2885, November, DOI: 10.1002/for.3139.
• Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024, "Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, volume 56, issue 5, pages 1099-1127, August, DOI: 10.1111/jmcb.13121.
• William A. Barnett & Marcelle Chauvet & Danilo Leiva‐Leon & Liting Su, 2024, "The Credit‐Card‐Services Augmented Divisia Monetary Aggregates," Journal of Money, Credit and Banking, Blackwell Publishing, volume 56, issue 5, pages 1163-1202, August, DOI: 10.1111/jmcb.13088.
• Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2024, "From Fixed‐Event to Fixed‐Horizon Density Forecasts: Obtaining Measures of Multihorizon Uncertainty from Survey Density Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, volume 56, issue 7, pages 1675-1704, October, DOI: 10.1111/jmcb.13105.
• De Polis, Andrea & Melosi, Leonardo & Petrella, Ivan, 2024, "The Taming of the Skew : Asymmetric Inflation Risk and Monetary Policy," The Warwick Economics Research Paper Series (TWERPS), University of Warwick, Department of Economics, number 1530.
• Long Thanh Giang & Aiko Kikkawa & Donghyun Park, 2024, "Health Capacity to Work among Older Adults in Viet Nam," Asian Development Review (ADR), World Scientific Publishing Co. Pte. Ltd., volume 41, issue 01, pages 195-225, March, DOI: 10.1142/S0116110524400080.
• Rangan Gupta & Savanah Hall & Christian Pierdzioch, 2024, "Realized Stock Market Volatility of the United States: The Role of Employee Sentiment," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., volume 19, issue 02, pages 1-21, June, DOI: 10.1142/S2010495224500064.
• Jacobus Nel & Rangan Gupta & Mark E. Wohar & Christian Pierdzioch, 2024, "Climate Risks And Predictability Of Commodity Returns And Volatility: Evidence From Over 750 Years Of Data," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., volume 15, issue 04, pages 1-40, November, DOI: 10.1142/S2010007824500039.
• Nguyen Anh Phong & Phan Huy Tam & Nguyen Thanh Tung, 2024, "Identifying Fraud Financial Reports Based on Signs of Income Management Using Machine Learning Technology: The Case of Listed Companies in Vietnam," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., volume 15, issue 02, pages 1-16, June, DOI: 10.1142/S1793993324500133.
• Russell R Barton, 2024, "Predictive Analytics for Business using R," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 13856, ISBN: ARRAY(0x84fb3e98).
• Granziera, Eleonora & Jalasjoki, Pirkka & Paloviita, Maritta, 2024, "The bias of the ECB inflation projections: A State-dependent analysis," Bank of Finland Research Discussion Papers, Bank of Finland, number 4/2024.
• Clark, Todd E. & Ganics, Gergely & Mertens, Elmar, 2024, "Constructing fan charts from the ragged edge of SPF forecasts," Discussion Papers, Deutsche Bundesbank, number 38/2024.
• Schnorrenberger, Richard & Schwind, Patrick & Wieland, Elisabeth, 2024, "Forecasting HICP package holidays with forward-looking booking data," Technical Papers, Deutsche Bundesbank, number 04/2024.
• Herbst, Tobias & Roling, Christoph, 2024, "A top-down loan-level stress test for banks' corporate credit risk: Application to risks from commercial real estate markets," Technical Papers, Deutsche Bundesbank, number 09/2024.
• Lux, Thomas, 2024, "Lack of identification of parameters in a simple behavioral macroeconomic model," Economics Working Papers, Christian-Albrechts-University of Kiel, Department of Economics, number 2024-02.
• Kronenberg, Philipp, 2024, "A High-Frequency GDP Indicator for Switzerland," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 330303, DOI: 10.2139/ssrn.4875922.
• Huo, Shutong & Feng, Derek & Gill, Thomas M. & Chen, Xi, 2024, "Childhood Circumstances and Health of American and Chinese Older Adults: A Machine Learning Evaluation of Inequality of Opportunity in Health," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1384.
• Greyling, Talita & Rossouw, Stephanié, 2024, "Development and validation of a real-time happiness index using Google TrendsTM," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1493.
• Bantle, Melissa, 2024, "Screen for collusive behavior: A machine learning approach," Hohenheim Discussion Papers in Business, Economics and Social Sciences, University of Hohenheim, Faculty of Business, Economics and Social Sciences, number 01-2024.
• Paul, Joseph R. & Schaffer, Mark E., 2024, "An introduction to conformal inference for economists," Accountancy, Economics, and Finance Working Papers, Heriot-Watt University, Department of Accountancy, Economics, and Finance, number 2024-13.
• Holtemöller, Oliver & Kozyrev, Boris, 2024, "Forecasting economic activity using a neural network in uncertain times: Monte Carlo evidence and application to the German GDP," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 6/2024.
• Heinisch, Katja, 2024, "Step by step - A quarterly evaluation of EU Commission's GDP forecasts," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 22/2024.
• Deschermeier, Philipp, 2024, "IW-Bevölkerungsprognose: Eine Datengrundlage zur Gestaltung der Herausforderungen des demografischen Wandels auf Basis des Zensus 2022
  [IW population forecast 2024 - Essential data for meeting the challenges of demographic change on the basis of t," IW-Trends – Vierteljahresschrift zur empirischen Wirtschaftsforschung, Institut der deutschen Wirtschaft (IW) / German Economic Institute, volume 51, issue 3, pages 67-87, DOI: 10.2373/1864-810X.24-03-04.
• Foltas, Alexander, 2024, "Inefficient forecast narratives: A BERT-based approach," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 45, DOI: 10.18452/29133.
• Wolf, Elias & Montes-Galdón, Carlos & Paredes, Joan, 2024, "Conditional density forecasting: a tempered importance sampling approach," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges, Verein für Socialpolitik / German Economic Association, number 302442.

2023

• Paul M. Torrens, 2023, "Agent models of customer journeys on retail high streets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, volume 18, issue 1, pages 87-128, January, DOI: 10.1007/s11403-022-00350-z.
• Mustafa Yurtsever, 2023, "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), volume 57, issue 1, pages 1-9, December, DOI: 10.1186/s12651-023-00345-8.
• Zouheir Mighri & Raouf Jaziri, 2023, "Long-Memory, Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 21, issue 1, pages 41-97, March, DOI: 10.1007/s40953-022-00331-w.
• Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023, "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 21, issue 1, pages 213-234, March, DOI: 10.1007/s40953-022-00335-6.
• Samuel F. Onipede & Nafiu A. Bashir & Jamaladeen Abubakar, 2023, "Small open economies and external shocks: an application of Bayesian global vector autoregression model," Quality & Quantity: International Journal of Methodology, Springer, volume 57, issue 2, pages 1673-1699, April, DOI: 10.1007/s11135-022-01423-8.
• Afees A. Salisu & Abdulsalam Abidemi Sikiru & Philip C. Omoke, 2023, "COVID-19 pandemic and financial innovations," Quality & Quantity: International Journal of Methodology, Springer, volume 57, issue 4, pages 3885-3904, August, DOI: 10.1007/s11135-022-01540-4.
• Matthias Breuer & Harm H. Schütt, 2023, "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, volume 28, issue 2, pages 726-768, June, DOI: 10.1007/s11142-021-09654-0.
• Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023, "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, volume 128, issue 6, pages 3313-3335, June, DOI: 10.1007/s11192-023-04699-1.
• Sylvia Kaufmann, 2023, "Covid-19 outbreak and beyond: a retrospect on the information content of short-time workers for GDP now- and forecasting," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, volume 159, issue 1, pages 1-10, December, DOI: 10.1186/s41937-023-00106-x.
• Stavros Degiannakis, 2023, "The D-model for GDP nowcasting," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, volume 159, issue 1, pages 1-33, December, DOI: 10.1186/s41937-023-00109-8.
• Peter Kugler & George Sheldon, 2023, "A monthly leading indicator of Swiss GDP growth based on Okun’s law," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, volume 159, issue 1, pages 1-14, December, DOI: 10.1186/s41937-023-00115-w.
• Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023, "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, volume 3, issue 1, pages 1-32, January, DOI: 10.1007/s43546-022-00384-2.
• Pami Dua & Rajiv Ranjan & Deepika Goel, 2023, "Forecasting the INR/USD Exchange Rate: A BVAR Framework," Springer Books, Springer, chapter 0, in: Pami Dua, "Macroeconometric Methods", DOI: 10.1007/978-981-19-7592-9_8.
• Timothy Neal, 2023, "The Importance of External Weather Effects in Projecting the Economic Impacts of Climate Change," Discussion Papers, School of Economics, The University of New South Wales, number 2023-09, Jun.
• Clements, Adam & Vasnev, Andrey L., 2023, "Combining simple multivariate HAR-like models for portfolio construction," Working Papers, University of Sydney Business School, Discipline of Business Analytics, number BAWP-2023-03, Nov.
• Kajal Lahiri & Junyan Zhang & Yongchen Zhao, 2023, "Inefficiency in social security trust funds forecasts," Applied Economics Letters, Taylor & Francis Journals, volume 30, issue 10, pages 1353-1357, June, DOI: 10.1080/13504851.2022.2053649.
• Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023, "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, volume 29, issue 14, pages 1579-1597, September, DOI: 10.1080/1351847X.2022.2137422.
• Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023, "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, volume 24, issue 1, pages 111-122, January, DOI: 10.1080/15427560.2021.1949719.
• Monica Billio & Roberto Casarin & Matteo Iacopini & Sylvia Kaufmann, 2023, "Bayesian Dynamic Tensor Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 41, issue 2, pages 429-439, April, DOI: 10.1080/07350015.2022.2032721.
• Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2023, "Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 41, issue 2, pages 523-537, April, DOI: 10.1080/07350015.2022.2039159.
• Laurent Ferrara & Anna Simoni, 2023, "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 41, issue 4, pages 1188-1202, October, DOI: 10.1080/07350015.2022.2116025.
• Zhongchen Song & Tom Coupé, 2023, "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, volume 21, issue 3, pages 429-463, July, DOI: 10.1080/14765284.2022.2161175.
• Constantin Rudolf Salomo Bürgi, 2023, "How to deal with missing observations in surveys of professional forecasters," Journal of Applied Economics, Taylor & Francis Journals, volume 26, issue 1, pages 2185975-218, December, DOI: 10.1080/15140326.2023.2185975.
• Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023, "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, volume 18, issue 4, pages 530-551, October, DOI: 10.1080/17421772.2023.2199034.
• Benjamin Monnery & Fran ois-Charles Wolff, 2023, "Is participatory democracy in line with social protest ? Evidence from the French Yellow Vests movement," TEPP Working Paper, TEPP, number 2023-07.
• Daan Opschoor & Dick van Dijk, 2023, "Slow Expectation-Maximization Convergence in Low-Noise Dynamic Factor Models," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 23-018/III, Apr.
• Francisco Blasques & Siem Jan Koopman & Gabriele Mingoli, 2023, "Observation-Driven filters for Time- Series with Stochastic Trends and Mixed Causal Non-Causal Dynamics," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 23-065/III, Oct, revised 01 Mar 2024.
• Gabriel Mathy & Yongchen Zhao, 2023, "Could Diffusion Indexes Have Forecasted the Great Depression?," Working Papers, Towson University, Department of Economics, number 2023-05, Sep, revised Sep 2023.
• Yongchen Zhao, 2023, "Uncertainty of Household Inflation Expectations: Reconciling Point and Density Forecasts," Working Papers, Towson University, Department of Economics, number 2023-09, Dec, revised Dec 2023.
• Travis J. Berge, 2023, "Time-Varying Uncertainty of the Federal Reserve's Output Gap Estimate," The Review of Economics and Statistics, MIT Press, volume 105, issue 5, pages 1191-1206, September, DOI: 10.1162/rest_a_01102.
• Abir HASSAN & Mahbubul Md. ALAM & Azmaine FAEIQUE, 2023, "Forecasting Monthly Inflation in Bangladesh: A Seasonal Autoregressive Moving Average (SARIMA) Approach," Journal of Economics and Financial Analysis, Tripal Publishing House, volume 7, issue 2, pages 25-43, DOI: 10.1991/jefa.v7i2.a61.
• Marco Del Negro & Marc P. Giannoni & Christina Patterson, 2023, "The Forward Guidance Puzzle," Journal of Political Economy Macroeconomics, University of Chicago Press, volume 1, issue 1, pages 43-79, DOI: 10.1086/724214.
• Jianghao Chu & Tae-Hwy Lee & Aman Ullah, 2023, "Asymmetric AdaBoost for High-dimensional Maximum Score Regression," Working Papers, University of California at Riverside, Department of Economics, number 202306, Aug.
• Tae-Hwy Lee & Tao Wang, 2023, "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers, University of California at Riverside, Department of Economics, number 202307, Sep.
• Tae-Hwy Lee & Ekaterina Seregina & Yaojue Xu, 2023, "Elicitability and Encompassing for Volatility Forecasts by Bregman Functions," Working Papers, University of California at Riverside, Department of Economics, number 202311, Sep.
• Vladimir M. Markovic & Nikola Radivojevic & Tatjana Ivanovic & Slobodan Radisic & Nenad Novakovic, 2023, "The quantum harmonic oscillator expected shortfall model," Estudios de Economia, University of Chile, Department of Economics, volume 50, issue 2 Year 20, pages 233-261, December.
• Mihnea Constantinescu, 2023, "Sparse Warcasting," Working Papers, National Bank of Ukraine, number 01/2023, Jun.
• Alejo Estavillo & Gabriela Mordecki, 2023, "Nowcasting del PIB para Uruguay en base a un modelo de ecuaciones puente," Documentos de Trabajo (working papers), Instituto de Economía - IECON, number 23-26, Dec.
• Dietrich, Stephan & Malerba, Daniele & Gassmann, Franziska, 2023, "Predicting social assistance beneficiaries," MERIT Working Papers, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT), number 2023-007, Mar.
• Jaromir Hurnik & Vatcharin Sirimaneetham, 2023, "A long-term approach for analysing public debt sustainability: a case study of Mongolia," MPDD Working Paper Series, United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), number WP/23/02, Dec.
• Lis Szymon & Chlebus Marcin, 2023, "Combining forecasts? Keep it simple," Central European Economic Journal, Sciendo, volume 10, issue 57, pages 343-370, January, DOI: 10.2478/ceej-2023-0020.
• Sabek Amine, 2023, "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, volume 9, issue 1, pages 16-32, July, DOI: 10.2478/crebss-2023-0002.
• Berezka Kateryna & Kovalchuk Olha, 2023, "The Application of Association Rules to Detect the Effects of Vaccinations against Covid-19 in the EU-27. Preliminary Estimates," Econometrics. Advances in Applied Data Analysis, Sciendo, volume 27, issue 1, pages 1-16, March, DOI: 10.15611/eada.2023.1.01.
• Souto Hugo Gobato & Moradi Amir, 2023, "Forecasting realized volatility through financial turbulence and neural networks," Economics and Business Review, Sciendo, volume 9, issue 2, pages 133-159, April, DOI: 10.18559/ebr.2023.2.737.
• Mishra Akshay Kumar & Kumar Rahul & Bal Debi Prasad, 2023, "ESG Volatility Prediction Using GARCH and LSTM Models," Financial Internet Quarterly (formerly e-Finanse), Sciendo, volume 19, issue 4, pages 97-114, December, DOI: 10.2478/fiqf-2023-0029.
• Vasilev Julian & Sulova Snezhana, 2023, "An Approach for the In-Depth Data Analysis of the Marine Traffic of Independent Nearby Ports," Folia Oeconomica Stetinensia, Sciendo, volume 23, issue 2, pages 402-426, December, DOI: 10.2478/foli-2023-0038.
• Suleiman Ahmad Abubakar & Othman Mahmod & Daud Hanita & Abdullah Mohd Lazim & Kadir Evizal Abdul & Kane Ibrahim Lawal & Husin Abdullah, 2023, "Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models," Real Estate Management and Valuation, Sciendo, volume 31, issue 3, pages 20-31, September, DOI: 10.2478/remav-2023-0018.
• Ciocîrlan Cecilia & Zwak-Cantoriu Maria-Cristina & Stancea Andreea & Plăcintă Dimitrie-Daniel, 2023, "European Macroeconomic Dynamics on Financial Markets and Economic Policy: A Cross Country Study for Spillover Effects," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, volume 68, issue 3, pages 40-63, December, DOI: 10.2478/subboec-2023-0014.
• Leo Krippner, 2023, "Estimating and Applying Autoregression Models Via Their Eigensystem Representation," Working Papers in Economics, University of Waikato, number 23/09, Dec.
• Maudud Hassan Uzzal & Robert Ślepaczuk, 2023, "The performance of time series forecasting based on classical and machine learning methods for S&P 500 index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-05.
• Konrad Lewszyk & Piotr Wójcik, 2023, "Modelling Subjective Attractiveness," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-06.
• Karol Chojnacki & Robert Ślepaczuk, 2023, "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were compa," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-15.
• Damian Ślusarczyk & Robert Ślepaczuk, 2023, "Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-17.
• Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023, "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-20.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-23.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-25.
• Sahil Teymurzade & Robert Ślepaczuk, 2023, "Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-27.
• Amor Aniss Benmoussa, Reinhard Ellwanger, Stephen Snudden, 2023, "Carpe Diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?," LCERPA Working Papers, Laurier Centre for Economic Research and Policy Analysis, number bm0141, Dec.
• Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023, "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers, Laurier Centre for Economic Research and Policy Analysis, number bm0142, Dec.
• Gary Koop & Dimitris Korobilis, 2023, "Bayesian Dynamic Variable Selection In High Dimensions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, volume 64, issue 3, pages 1047-1074, August, DOI: 10.1111/iere.12623.
• Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023, "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, volume 64, issue 3, pages 979-1022, August, DOI: 10.1111/iere.12619.
• Pablo Guerróon‐Quintana & Molin Zhong, 2023, "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 38, issue 3, pages 295-320, April, DOI: 10.1002/jae.2951.
• James Mitchell & Martin Weale, 2023, "Censored density forecasts: Production and evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 38, issue 5, pages 714-734, August, DOI: 10.1002/jae.2972.
• Eiji Goto & Jan P.A.M. Jacobs & Tara M. Sinclair & Simon van Norden, 2023, "Employment reconciliation and nowcasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 38, issue 7, pages 1007-1017, November, DOI: 10.1002/jae.2995.
• William A. Barnett & Sohee Park, 2023, "Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., volume 42, issue 2, pages 331-346, March, DOI: 10.1002/for.2910.
• Stephen G. Hall & George S. Tavlas & Yongli Wang, 2023, "Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations," Journal of Forecasting, John Wiley & Sons, Ltd., volume 42, issue 3, pages 514-529, April, DOI: 10.1002/for.2948.
• Carlos Cañizares Martínez & Gabe J. de Bondt & Arne Gieseck, 2023, "Forecasting housing investment," Journal of Forecasting, John Wiley & Sons, Ltd., volume 42, issue 3, pages 543-565, April, DOI: 10.1002/for.2946.
• Matteo Bonato & Oğuzhan Çepni & Rangan Gupta & Christian Pierdzioch, 2023, "El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., volume 42, issue 4, pages 785-801, July, DOI: 10.1002/for.2914.
• Donato Ceci & Andrea Silvestrini, 2023, "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., volume 42, issue 7, pages 1569-1593, November, DOI: 10.1002/for.2958.
• Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023, "Policy uncertainty and stock market volatility revisited: The predictive role of signal quality," Journal of Forecasting, John Wiley & Sons, Ltd., volume 42, issue 8, pages 2307-2321, December, DOI: 10.1002/for.3016.
• Stavros Degiannakis & George Filis & Grigorios Siourounis & Lorenzo Trapani, 2023, "Superkurtosis," Journal of Money, Credit and Banking, Blackwell Publishing, volume 55, issue 8, pages 2061-2091, December, DOI: 10.1111/jmcb.12988.
• Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2023, "Forecasting with a panel Tobit model," Quantitative Economics, Econometric Society, volume 14, issue 1, pages 117-159, January, DOI: 10.3982/QE1505.
• Szydlo, Jan, 2023, "Forecasting Credit Dynamics : VAR, VECM or modern Factor-Augmented VAR approach?," Warwick-Monash Economics Student Papers, Warwick Monash Economics Student Papers, number 63.
• Christian Pierdzioch & Sebastian Rohloff & Roland Von Campe, 2023, "On the Predictive Value of the (Shadow) Real Interest Rate for the Realized Volatility of Gold-Price Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., volume 18, issue 01, pages 1-16, March, DOI: 10.1142/S2010495222410019.
• Marc S. Paolella & PaweÅ‚ Polak, 2023, "Density and Risk Prediction with Non-Gaussian COMFORT Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., volume 18, issue 01, pages 1-37, March, DOI: 10.1142/S2010495222500336.
• Francisca Mendonça Souza & Claudia Aline de Souza Ramser & Adriano Mendonça Souza & Claudimar Pereira da Veiga, 2023, "Spillover Effects in the Presence of Structural Breaks, Persistence and Conditioned Heteroscedasticity," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., volume 18, issue 02, pages 1-51, June, DOI: 10.1142/S2010495222500348.
• Fazlul Miah, 2023, "News And Information Rigidity: Further Evidence From Gdp Growth Forecasts," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., volume 23, issue 01n04, pages 1-21, December, DOI: 10.1142/S2194565924500039.
• Mohamad Hassan Shahrour & Mostafa Dekmak, 2023, "Intelligent stock prediction: A neural network approach," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 10, issue 01, pages 1-14, March, DOI: 10.1142/S2424786322500165.
• Bolin Lei & Yuping Song, 2023, "The impact of contagion effects of media reports, investors’ sentiment and attention on the stock market based on HAR-RV model," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 10, issue 02, pages 1-54, June, DOI: 10.1142/S242478632350010X.
• Sanjeev Kadam & Anshul Agrawal & Aryan Bajaj & Rachit Agarwal & Rameesha Kalra & Jaymin Shah, 2023, "Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., volume 14, issue 03, pages 1-15, October, DOI: 10.1142/S179399332350014X.
• Alexander Correa, 2023, "Predicting Business Bankruptcy in Colombian SMEs: A Machine Learning Approach," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., volume 14, issue 03, pages 1-21, October, DOI: 10.1142/S1793993323500278.
• Benedict Foo & Deng Yao Koh & Juan Pang Tan & Wenjie Wang, 2023, "FORECASTING SINGAPORE’s ECONOMY USING STATISTICAL LEARNING AND FACTOR MODELS," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., volume 68, issue 02, pages 319-353, DOI: 10.1142/S0217590822500655.
• Qu Feng & Shihao Zhou, 2023, "Electricity Market Liberalization And Efficiency: Evidence From Singapore," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., volume 68, issue 03, pages 651-669, June, DOI: 10.1142/S0217590822500667.
• Young Bin Ahn & Yoichi Tsuchiya, 2023, "Directional Accuracy Of Singapore’S Macroeconomic Forecasts," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., volume 68, issue 05, pages 1569-1583, September, DOI: 10.1142/S0217590819500541.
• Arabinda Basistha & Richard Startz, 2023, "Measuring Persistent Global Economic Factors with Output, Commodity Price, and Commodity Currency Data," Working Papers, Department of Economics, West Virginia University, number 23-05, Oct.
• Xin Jing & Jin Seo Cho, 2023, "Forecasting the Confirmed COVID-19 Cases Using Modal Regression," Working papers, Yonsei University, Yonsei Economics Research Institute, number 2023rwp-217, Jun.
• Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2023, "Shadow-rate VARs," Discussion Papers, Deutsche Bundesbank, number 14/2023.
• Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023, "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers, Deutsche Bundesbank, number 34/2023.
• Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 39, issue 3, pages 1145-1162, DOI: 10.1016/j.ijforecast.2022.04.009.
• Stempel, Daniel & Zahner, Johannes, 2023, "Whose inflation rates matter most? A DSGE model and machine learning approach to monetary policy in the Euro area," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 188.
• Baumgärtner, Martin & Zahner, Johannes, 2023, "Whatever it takes to understand a central banker: Embedding their words using neural networks," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 194.
• Drygalla, Andrej & Heinisch, Katja & Holtemöller, Oliver & Lindner, Axel & Sardone, Alessandro & Schult, Christoph & Schultz, Birgit & Zeddies, Götz, 2023, "Grüne Transformation und Schuldenbremse: Implikationen zusätzlicher Investitionen für öffentliche Finanzen und privaten Konsum," Konjunktur aktuell, Halle Institute for Economic Research (IWH), volume 11, issue 4, pages 141-159.
• Frank, Johannes, 2023, "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, number 03/2023.
• Born, Benjamin & Enders, Zeno & Menkhoff, Manuel & Müller, Gernot J. & Niemann, Knut, 2023, "Firm expectations and news: Micro v macro," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 43, DOI: 10.18452/26901.
• Billio, Monica & Casarin, Roberto & Costola, Michele & Veggente, Veronica, 2023, "Learning from experts: Energy efficiency in residential buildings," SAFE Working Paper Series, Leibniz Institute for Financial Research SAFE, number 403, DOI: 10.2139/ssrn.4596682.
• Ganics, Gergely & Mertens, Elmar & Clark, Todd E., 2023, "What Is the Predictive Value of SPF Point and Density Forecasts?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277622.
• Stempel, Daniel & Zahner, Johannes, 2023, "Whose Inflation Rates Matter Most? A DSGE Model and Machine Learning Approach to Monetary Policy in the Euro Area," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277627.
• Holtemöller, Oliver & Kozyrev, Boris, 2023, "Forecasting Economic Activity with a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to German GDP," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277688.
• Strunz, Franziska & Gödl, Maximilian, 2023, "An Evaluation of Professional Forecasts for the German Economy," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277707.
• Conrad, Christian & Lahiri, Kajal, 2023, "Heterogeneous expectations among professional forecasters," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 23-062.
• Janßen, Rebecca & Ribar, Matthew K., 2023, "In vi(vi)no veritas? Expertise, review accuracy and reputation inflation," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 23-075.
• Iva Tolic Mandic & Sanja Tisma & Daniela Angelina Jelincic & Damir Demonja, 2023, "Smart Solutions for Sustainable Tourism Pearls: How to Live Between Culture and Tourism in Dubrovnik," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, volume 21, issue 3, pages 272-296.
• Nithin Mani & Alok Kumar Mishra & Jijin Pandikasala, 2023, "How Serious is India’s Nonperforming Assets Crisis? A Structural Satellite Version of the Financial-Macroeconometric Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, volume 30, issue 4, pages 761-794, December, DOI: 10.1007/s10690-023-09397-9.
• Ruzhen Yan & Ding Yue & Xu Wu & Wei Gao, 2023, "Multiscale Multifractal Detrended Fluctuation Analysis and Trend Identification of Liquidity in the China's Stock Markets," Computational Economics, Springer;Society for Computational Economics, volume 61, issue 2, pages 487-511, February, DOI: 10.1007/s10614-021-10215-5.
• Yushu Li & Hyunjoo Kim Karlsson, 2023, "Investigating the Asymmetric Behavior of Oil Price Volatility Using Support Vector Regression," Computational Economics, Springer;Society for Computational Economics, volume 61, issue 4, pages 1765-1790, April, DOI: 10.1007/s10614-022-10266-2.
• Jan G. De Gooijer, 2023, "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 1, pages 407-424, June, DOI: 10.1007/s10614-022-10289-9.
• Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023, "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 2, pages 663-687, August, DOI: 10.1007/s10614-022-10305-y.
• Ba Chu & Shafiullah Qureshi, 2023, "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1567-1609, December, DOI: 10.1007/s10614-022-10312-z.
• Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023, "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, volume 171, issue 1, pages 85-117, March, DOI: 10.1007/s10645-023-09418-y.
• Ines Fortin & Jaroslava Hlouskova & Leopold Sögner, 2023, "Financial and economic uncertainties and their effects on the economy," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, volume 50, issue 2, pages 481-521, May, DOI: 10.1007/s10663-023-09570-3.
• Ying Fan & Abdullah Yavas, 2023, "Price Dynamics in Public and Private Commercial Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, volume 67, issue 1, pages 150-190, July, DOI: 10.1007/s11146-020-09773-6.
• Benjamin Monnery & François-Charles Wolff, 2023, "Is participatory democracy in line with social protest? Evidence from the French Yellow Vests movement," Public Choice, Springer, volume 197, issue 1, pages 283-309, October, DOI: 10.1007/s11127-023-01105-5.
• Maxim Ulrich & Lukas Zimmer & Constantin Merbecks, 2023, "Implied volatility surfaces: a comprehensive analysis using half a billion option prices," Review of Derivatives Research, Springer, volume 26, issue 2, pages 135-169, October, DOI: 10.1007/s11147-023-09195-5.
• Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023, "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, volume 61, issue 2, pages 601-631, August, DOI: 10.1007/s11156-023-01158-z.
• Misik, Sándor, 2023, "Korrelációbecslés a forintpiacon
  [Correlation forecasting on the Hungarian forint market]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), volume 0, issue 7, pages 772-794, DOI: 10.18414/KSZ.2023.7-8.772.
• Kristóf, Tamás & Márton, András & Fiáth, Attila, 2023, "Állami energiavállalatok pénzügyi teljesítménye Magyarországon a koronavírus-járvány előtt és alatt
  [Financial performance of publicly owned energy companies in Hungary before and during the COVID crisis]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), volume 0, issue 10, pages 1057-1076, DOI: 10.18414/KSZ.2023.10.1057.
• Chaoyi Chen & Yiguo Sun & Yao Rao, 2023, "Threshold MIDAS Forecasting of Inflation Rate," Working Papers, University of Liverpool, Department of Economics, number 202314, Jul.
• Lorena Skufi & Adam Geršl, 2023, "Using Macrofinancial Models to Simulate Macroeconomic Developments During the COVID-19 Pandemic: The Case of Albania," Eastern European Economics, Taylor & Francis Journals, volume 61, issue 5, pages 517-553, September, DOI: 10.1080/00128775.2023.2215229.
• Gabor Szigel & Boldizsar Istvan Gyurus, 2023, "Are Default Rate Time Series Stationary? A Practical Approach for Banking Experts," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), volume 22, issue 4, pages 107-135.
• Viola Monostoriné Grolmusz, 2023, "Optimal Forecast Combination Under Asymmetric Loss and Regime-Switching," MNB Working Papers, Magyar Nemzeti Bank (Central Bank of Hungary), number 2023/3.
• Viola Monostoriné Grolmusz, 2023, "Recovering Stock Analysts’ Loss Functions from Buy/Sell Recommendations," MNB Working Papers, Magyar Nemzeti Bank (Central Bank of Hungary), number 2023/4.
• Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023, "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 1/23.
• Chaya Weerasinghe & Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2023, "ABC-based Forecasting in State Space Models," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 12/23.
• George Athanasopoulos & Rob J Hyndman & Raffaele Mattera, 2023, "Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 17/23.
• David T. Frazier & Ryan Covey & Gael M. Martin & Donald S. Poskitt, 2023, "Solving the Forecast Combination Puzzle," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 18/23.