Macroeconomic real-time forecasts of univariate models with flexible error structures
Trinh, Kelly, Zhang, Bo, and Hou, Chenghan (2025) Macroeconomic real-time forecasts of univariate models with flexible error structures. Journal of Forecasting, 44 (1). pp. 59-78.
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Abstract
This paper investigates the importance of flexible error structure specifications in two widely used univariate models, namely, autoregressive and unobserved component models, in fitting and forecasting 20 significant US macroeconomic variables. The in-sample estimation reveals that the models with flexible error structures provide better in-sample fit than the univariate models with homoscedastic errors. Furthermore, the density forecast analysis suggests that accommodating heavy tail, stochastic volatility, and serial correlation in error structures leads to significant improvements in short-term forecasts. For most macroeconomic variables, the univariate models tend to yield more accurate one-step-ahead forecasts than the multivariate (vector autoregressive) models in terms of both point and density forecasts.
| Item ID: | 88662 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1099-131X |
| Keywords: | autoregressive moving average, real-time forecasting, stochastic volatility |
| Copyright Information: | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2024 The Author(s). Journal of Forecasting published by John Wiley & Sons Ltd. |
| Date Deposited: | 13 May 2026 02:07 |
| FoR Codes: | 38 ECONOMICS > 3802 Econometrics > 380203 Economic models and forecasting @ 100% |
| SEO Codes: | 15 ECONOMIC FRAMEWORK > 1502 Macroeconomics > 150299 Macroeconomics not elsewhere classified @ 100% |
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