Dynamic Linear Panel Regression Models With Interactive Fixed Effects

Moon, Hyungsik Roger; Weidner, Martin

doi:10.1017/s0266466615000328

Cited by 130 publications

(28 citation statements)

References 39 publications

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“…58 More sophisticated models with interactive effects can be estimated when both N and T are large. See Moon and Weidner (2017) for recent advances in that direction. carried out.…”

Section: Resultsmentioning

confidence: 99%

Estimation of linear dynamic panel data models with time‐invariant regressors

Kripfganz

Schwarz

2019

J of Applied Econometrics

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We present a sequential approach to estimating a dynamic Hausman-Taylor model. We first estimate the coefficients of the time-varying regressors and subsequently regress the first-stage residuals on the time-invariant regressors. In comparison to estimating all coefficients simultaneously, this two-stage procedure is more robust against model misspecification, allows for a flexible choice of the first-stage estimator, and enables simple testing of the overidentifying restrictions. For correct inference, we derive analytical standard error adjustments. We evaluate the finite-sample properties with Monte Carlo simulations and apply the approach to a dynamic gravity equation for US outward foreign direct investment. 1 Schooling itself is a time-invariant regressor in his data set. Yet it is hard to argue that its coefficient is identified because Andini uses only the first differences of time-varying regressors as instruments. These instruments are generally assumed to be uncorrelated with any time-invariant variable. 526 2 Plümper and Troeger (2007) proposed a three-stage approach for the static model that they label "fixed effects vector decomposition." In a symposium on this method, Breusch, Ward, Nguyen, and Kompas (2011) and Greene (2011) show that the first two stages can be characterized by an instrumental variables estimation with a particular choice of instruments, and that the third stage is essentially meaningless. 3 Hoeffler (2002) argues similarly. 4 As Binder, Hsiao, and Pesaran (2005) and Bun and Windmeijer (2010) emphasize, GMM estimators might suffer from a weak instruments problem when the autoregressive parameter approaches unity or when the variance of the unobserved unit-specific effects is large. Moreover, the number of instruments can rapidly become large relative to the sample size. The consequences of instrument proliferation, summarized by Roodman (2009), range from biased coefficient and standard error estimates to weakened specification tests. 5 Our two-stage procedure fits into the framework of sequential estimators discussed by Newey (1984). While our paper is only concerned with linear panel data models, Honoré and Kesina (2017) recently suggested related two-stage approaches for some nonlinear models. They use a bootstrap procedure to obtain valid standard errors in contrast to our analytical standard error correction.

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“…58 More sophisticated models with interactive effects can be estimated when both N and T are large. See Moon and Weidner (2017) for recent advances in that direction. carried out.…”

Section: Resultsmentioning

confidence: 99%

Estimation of linear dynamic panel data models with time‐invariant regressors

Kripfganz

Schwarz

2019

J of Applied Econometrics

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“…Moon & Weidner (2015) show that Bai (2009) and Moon & Weidner (2017) results hold, with no loss of efficiency, when the exact number of latent factors M is unknown and only an upper bound is specified. Bai & Liao (2017) show that GLS estimation leads to an efficiency improvement over the Bai (2009) and Moon & Weidner (2017) OLS-type estimator. Greenaway-McGrevy et al (2012) establish √ NT -asymptotics for the OLS estimator by augmenting the regressors with the principal component estimator of the common factors extracted from the observable data.…”

Section: Introductionmentioning

confidence: 93%

“…Bai (2009) considers joint estimation of the constant regression coefficients and of the residuals' factor structure components through an iterative OLS procedure. The same estimator has been studied by Moon & Weidner (2017) under weaker conditions on the observed regressors. Moon & Weidner (2015) show that Bai (2009) and Moon & Weidner (2017) results hold, with no loss of efficiency, when the exact number of latent factors M is unknown and only an upper bound is specified.…”

Section: Introductionmentioning

confidence: 99%

“…The same estimator has been studied by Moon & Weidner (2017) under weaker conditions on the observed regressors. Moon & Weidner (2015) show that Bai (2009) and Moon & Weidner (2017) results hold, with no loss of efficiency, when the exact number of latent factors M is unknown and only an upper bound is specified. Bai & Liao (2017) show that GLS estimation leads to an efficiency improvement over the Bai (2009) and Moon & Weidner (2017) OLS-type estimator.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic panel are permitted. 4 Bai & Liao (2017) focus on the homogeneous parameter case, unlike us, and considers joint estimation of the latent factors and parameters, generalizing Bai (2009) and Moon & Weidner (2017). More importantly, the motivation of Bai & Liao (2017) differs drastically from ours because they focus on the GLS approach for an efficiency improvement of an estimator that already exhibits the conventional asymptotic properties under Bai & Liao (2017) assumptions, in particular iidness across time.…”

Section: Introductionmentioning

confidence: 99%

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Robust Nearly-Efficient Estimation of Large Panels With Factor Structures

Avarucci

Zaffaroni

2019

SSRN Journal

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This paper studies estimation of linear panel regression models with heterogeneous coefficients, when both the regressors and the residual contain a possibly common, latent, factor structure. Our theory is (nearly) efficient, because based on the GLS principle, and also robust to the specification of such factor structure, because it does not require any information on the number of factors nor estimation of the factor structure itself. We first show how the unfeasible GLS estimator not only affords an efficiency improvement but, more importantly, provides a bias-adjusted estimator with the conventional limiting distribution, for situations where the OLS is affected by a first-order bias. The technical challenge resolved in the paper is to show how these properties are preserved for a class of feasible GLS estimators in a double-asymptotics setting. Our theory is illustrated by means of Monte Carlo exercises and, then, with an empirical application using individual asset returns and firms' characteristics data.

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CCE in fixed‐T panels

Westerlund

Petrova

Norkutė

2019

J of Applied Econometrics

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Summary The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross‐section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large‐T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.

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Dynamic Linear Panel Regression Models With Interactive Fixed Effects

Cited by 130 publications

References 39 publications

Estimation of linear dynamic panel data models with time‐invariant regressors

Estimation of linear dynamic panel data models with time‐invariant regressors

Robust Nearly-Efficient Estimation of Large Panels With Factor Structures

CCE in fixed‐T panels

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