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

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  • Min Seong Kim

    (University of Connecticut)

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).

Suggested Citation

  • Min Seong Kim, 2021. "Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data," Working papers 2021-04, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2021-04
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    References listed on IDEAS

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    Cited by:

    1. Zhenhao Gong & Min Seong Kim, 2024. "Improved inference for interactive fixed effects model under cross-sectional dependence," Empirical Economics, Springer, vol. 67(2), pages 727-760, August.

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    More about this item

    Keywords

    Approximate factor structure; Bandwidth selection; Diffusion index forecast; Robust inference; Spatial HAC estimator;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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