Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects

By Xun Lu, Liangjun Su. 2016

89 citations recorded by the source corpus.

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon and Weidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to zero. Further, we demonstrate that our shrinkage estimators of the nonzero slope parameters exhibit some oracle property. We conduct Monte Carlo simulations to demonstrate the superb finite-sample performance of the proposed method. We apply our method to study the determinants of economic growth and find that in addition to three common unobserved factors selected by our method, government consumption share has negative effects, whereas investment share and lagged economic growth have positive effects on economic growth.

Subfields: Demography, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/307835336352c724ab3ac19b00d8b6b9f7925ac6

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Citations in the corpus

On factor models with random missing: EM estimation, inference, and cross validation (2020, background): “See Giannone et al. (2008), Mariano and Murasawa (2010), Doz et al. (2011), Jungbacker et al. (2011), Pinheiro et al. (2013), Bańbura and Modugno (2014), and Marcellino and Sivec (2016) for variations on this latter approach.”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, unknown): “…(2002), Fernandez-Val and Lee (2013), Bonhomme and Manresa (2015), Moon and Weidner (2015), Moon and Weidner (2016), Kock (2016b), Manresa (2016), Lu and Su (2016), Su et al. (2016), Moon et al. (2018), Kock and Tang (2019), Bonhomme et al. (2019a), Bonhomme et al. (2019b), Gao and Li (2019),…”
On Performance of Shrinkage Methods – A Monte Carlo Study (2015, methodology): “All Rights Reserved Over the years, the LASSO - type methods have become popular methods for parameter estimation and variable selection due to their property of shrinking some of the model coefficients to exactly zero see [3], [4].”
Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects (2015, background): “Onatski (2010; 2012) and Chudik, Pesaran and Tosetti (2011)) might not be picked up at all or might only be estimated very inaccurately by the principal components estimator F̂R, in which case that factor is not properly accounted for in the LS estimation procedure.”
SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES (2015, methodology): “…panel data models with fixed or random effects, respectively; Liao and Phillips (2014) apply adaptive shrinkage techniques to cointegrated systems; Lu and Su (2013) apply adaptive group Lasso to select both relevant regressors and the number of unobserved factors in panel data models with…”