City Research Online

Abstract

This paper builds a mixed‐frequency panel data model for nowcasting economic variables across many countries. The model extends the mixed‐frequency panel vector autoregression (MF‐PVAR) to allow for heterogeneous coefficients and a multifactor error structure to model cross‐sectional dependence. We propose a modified common correlated effects (CCE) estimation technique which performs well in simulations. The model is applied in two distinct settings: nowcasting gross domestic product (GDP) growth for a pool of advanced and emerging economies and nowcasting inflation across many European countries. Our method is capable of beating standard benchmark models and can produce updated nowcasts whenever data releases occur in any country in the panel.

Publication Type:	Article
Additional Information:	© 2023 The Authors. Journal of Applied Econometrics published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	calendar effects, common correlated effects, cross-sectional dependence, multifactor errors, mixed data sampling (MIDAS), nowcasting
Subjects:	H Social Sciences > HB Economic Theory
Departments:	Bayes Business School Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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