Regularization for Spatial Panel Time Series Using the Adaptive LASSO

Clifford Lam and Pedro Souza ()

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

Abstract: This paper proposes a model for estimating the underlying cross-sectional dependence structure of a large panel of time series. Technical difficulties meant such a structure is usually assumed before further analysis. We propose to estimate this by penalizing the elements in the spatial weight matrices using the adaptive LASSO proposed by Zou (2006). Non-asymptotic oracle inequalities and the asymptotic sign consistency of the estimators are proved when the dimension of the time series can be larger than the sample size, and they tend to infinity jointly. Asymptotic normality of the LASSO/adaptive LASSO estimator for the model regression parameter is also presented. All the proofs involve non-standard analysis of LASSO/adaptive LASSO estimators, since our model, albeit like a standard regression, always has the response vector as one of the covariates. A block coordinate descent algorithm is introduced, with simulations and a real data analysis carried out to demonstrate the performance of our estimators.

Keywords: spatial econometrics; adaptive LASSO; sign consistency; asymptotic normality; non-asymptotic oracle inequalities; spatial weight matrices (search for similar items in EconPapers)
JEL-codes: C33 C4 C52 (search for similar items in EconPapers)
Date: 2014-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-geo and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:578

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