Efficient estimation of heterogeneous coefficients in panel data models with common shocks

Li, Kunpeng; Cui, Guowei; Liu, Lu

doi:10.1016/j.jeconom.2019.08.011

Cited by 25 publications

(14 citation statements)

References 38 publications

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“…Notice that the limiting distribution of θ 2SIV is correctly centered, and thus no bias correction is required. As demonstrated by Cui et al (2020), the main intuition of this result lies in that F x Γ x,i is estimated from X i , whereas F y γ y,i is estimated from u i . Because V i , F y γ y,i , and ε i are independent from one another, any correlations that arise because of the estimation error of F y and F x are asymptotically negligible.…”

Section: Second-stage IV Estimatormentioning

confidence: 87%

“…However, θ 1SIV is asymptotically biased. Rather than bias correcting this estimator, Norkute et al (2021) and Cui et al (2020) put forward a second-stage estimator, which is free from asymptotic bias and is potentially more efficient. For this purpose, the first-stage estimator is useful because it provides a consistent estimate of the error term of the model, which is required to implement the second-stage IV estimator.…”

Section: First-stage IV Estimatormentioning

confidence: 99%

“…In this case, proposition 3.2 in Cui et al (2020) reveals that the second-stage estimator is asymptotically equivalent to a least-squares estimator obtained by regressing y i −F y γ y,i on X i − F x Γ x,i . Moreover, the authors show that θ 2SIV is asymptotically as efficient as the bias-corrected CCE and IPC estimators.…”

Section: Second-stage IV Estimatormentioning

confidence: 99%

“…The remainder of the article is organized as follows. Section 2, outlines the 2SIV approach developed by Norkute et al (2021) and Cui et al (2020) and discusses implementation with unbalanced panel data. Section 3 describes the syntax of the xtivdfreg command.…”

Section: Introductionmentioning

confidence: 99%

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Instrumental-variable estimation of large-T panel-data models with common factors

Kripfganz

Sarafidis

2021

The Stata Journal: Promoting communications on statistics and S

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In this article, we introduce the xtivdfreg command, which implements a general instrumental-variables (IV) approach for fitting panel-data models with many time-series observations, T, and unobserved common factors or interactive effects, as developed by Norkute et al. (2021, Journal of Econometrics 220: 416–446) and Cui et al. (2020a , ISER Discussion Paper 1101). The underlying idea of this approach is to project out the common factors from exogenous covariates using principal-components analysis and to run IV regression in both of two stages, using defactored covariates as instruments. The resulting two-stage IV estimator is valid for models with homogeneous or heterogeneous slope coefficients and has several advantages relative to existing popular approaches. In addition, the xtivdfreg command extends the two-stage IV approach in two major ways. First, the algorithm accommodates estimation of unbalanced panels. Second, the algorithm permits a flexible specification of instruments. We show that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular Stata ivregress command. Notably, unlike ivregress, xtivdfreg permits estimation of the two-way error-components paneldata model with heterogeneous slope coefficients.

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Section: Second-stage IV Estimatormentioning

confidence: 87%

Section: First-stage IV Estimatormentioning

confidence: 99%

Section: Second-stage IV Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Instrumental-variable estimation of large-T panel-data models with common factors

Kripfganz

Sarafidis

2021

The Stata Journal: Promoting communications on statistics and S

View full text Add to dashboard Cite

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“…The stratum phenomenon in a big data scenario can be usually elucidated so that the populations could be heterogeneous across strata due to the different dataset sources (Zhao et al, 2016). Several studies have investigated the crucial importance of stratified models and controlling latent heterogeneity with respect to the panel data models by regarding an individual as a stratum (Pesaran and Tosetti, 2011;Su and Jin, 2012;Song, 2013;Li and Lu, 2014;Chudik and Pesaran, 2015). However, the development of an alternative that only concentrates on modeling in each stratum was inadvisable because of little observations in each stratum causing "incidental parameter" issues such as panel data models (Hsiao and Pesaran, 2008;Lu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis for High-Dimensional Stratified Models

Huang¹,

Jiao²,

Wang³

et al. 2024

STAT SINICA

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In modern economic studies, the population heterogeneity of multiple strata and the high dimensionality of the predictors pose a major challenge. In this study, we introduce an integrative procedure that can be used to explore the information regarding group and sparsity structures for high-dimensional and heterogeneous stratified models. Further, we propose K-regression modeling as a hybrid of complex and simple models exhibiting arbitrary dependence on the stratum features, but linear dependence on other variables. K-regression models preeminently exhibit the following features:(i) they are essentially non-parametric with respect to the stratified feature, and parametric linearly effects in other variables with potentially integrative pattern because the effects and the corresponding sparsity structures can be the same for the strata in common groups but vary across different groups; (ii) the devised K-regression algorithm can automatically integrate the strata pertaining to common regression model and simultaneously estimate the corresponding effects simultaneously; (iii) the proposal quickly 1

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A regularization approach to common correlated effects estimation

Juodis

2022

J of Applied Econometrics

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Summary Cross‐section average‐augmented panel regressions introduced by Pesaran (2006) have been a popular empirical tool to estimate panel data models with common factors. However, the corresponding common correlated effects (CCEs) estimator can be sensitive to the number of cross‐section averages used and/or the static factor representation for observables. In this paper, we show that most of the corresponding problems documented in the literature can be solved once cross‐section averages are appropriately regularized, thus extending the applicability of the CCE setup. As the standard plug‐in variance estimators are not able to account for all sources of estimation uncertainty, we suggest the use of cross‐section bootstrap to construct confidence intervals. The proposed procedure is illustrated both using real and simulated data.

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Efficient estimation of heterogeneous coefficients in panel data models with common shocks

Cited by 25 publications

References 38 publications

Instrumental-variable estimation of large-T panel-data models with common factors

Instrumental-variable estimation of large-T panel-data models with common factors

Integrative Analysis for High-Dimensional Stratified Models

A regularization approach to common correlated effects estimation

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