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Inference Without Smoothing for Large Panels with Cross- Sectional and Temporal Dependence

Javier Hidalgo and Marcia M Schafgans

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: This paper addresses inference in large panel data models in the presence of both cross-sectional and temporal dependence of unknown form. We are interested in making inferences without relying on the choice of any smoothing parameter as is the case with the often employed HACestimator for the covariance matrix. To that end, we propose a cluster estimator for the asymptotic covariance of the estimators and a valid bootstrap which accommodates the nonparametric nature of both temporal and cross-sectional dependence. Our approach is based on the observation that the spectral representation of the fixed effect panel data model is such that the errors become approximately temporal uncorrelated. Our proposed bootstrap can be viewed as a wild bootstrap in the frequency domain. We present some Monte-Carlo simulations to shed some light on the small sample performance of our inferential procedure and illustrate our results using an empirical example.

Keywords: Large panel data models; cross-sectional strong-dependence; central Limit Theorems; clustering; discrete Fourier Transformation; nonparametric bootstrap algorithms (search for similar items in EconPapers)
JEL-codes: C12 C13 C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2017-12
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:597

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