Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data

Min Seong Kim

No 2021-04, Working papers from University of Connecticut, Department of Economics

Abstract: In this paper, we propose the time-series average of spatial HAC estimators for the variance of the estimated common factors under the approximate factor structure. Based on this, we provide the cofidence interval for the conditional mean of the diffusion-index forecasting model with cross-sectional heteroskedasticity and dependence of the idiosyncratic errors. We establish the asymptotics under very mild conditions, and no prior information about the dependence structure is needed to implement our procedure. We employ a bootstrap to select the bandwidth parameter. Simulation studies show that our procedure performs well in finite samples. We apply the proposed confidence interval to the problem of forecasting the unemployment rate using data by Ludvigson and Ng (2010).

Keywords: Approximate factor structure; Bandwidth selection; Diffusion index forecast; Robust inference; Spatial HAC estimator (search for similar items in EconPapers)
JEL-codes: C12 C31 C38 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2021-03
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:uct:uconnp:2021-04

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