Long difference instrumental variables estimation for dynamic panel models with fixed effects

Hahn, Jinyong; Hausman, Jerry A.; Kuersteiner, Guido M.

doi:10.1016/j.jeconom.2006.07.005

Cited by 149 publications

(162 citation statements)

References 36 publications

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“…Instead, we shall try to decrease the estimated standard errors and mitigate finite sample bias by examining whether the three regressors which we treated as endogenous could actually be classified such that additional and stronger instruments might be used. However, before we do that, just for illustrative purposes, we present again AB1 and AB2 results for the model specification and instrument set as used in column (2), but now not robustified AB1 in column (5) and not Windmeijer corrected AB2 in column (6). For most coefficients column (5) suggests smaller standard errors than column (2), but given the detected heteroskedasticity we know these are deceitful inconsistent standard deviation estimates.…”

Section: Resultsmentioning

confidence: 84%

“…Note that in this DGP x it is either strictly exogenous (ρ vε = 0) or otherwise endogenous 6 ; the only weakly exogenous regressor is y i,t−1 . Regressor x it may be affected contemporaneously by two independent individual specific effects when π η = 0 and π λ = 0, but also with delays if ξ = 0.…”

Section: Simulation Designmentioning

confidence: 99%

“…The cross-sectional heteroskedasticity is determined by both η • i and λ • i , the two standardized individual effects, and is thus associated with the regressors x it and y i,t−1 . It follows a lognormal pattern when both η • i and λ • i are standard normal, because we take 6 If we would strictly follow the notation of the earlier sections the coefficient β should actually be called δ when ρ vε = 0.…”

Section: Simulation Designmentioning

confidence: 99%

“…As a rule, they do not consider various empirically relevant issues in conjunction, such as: (i) occurrence and the possible endogeneity of regressors additional to the lagged dependent variable, (ii) occurrence of individual effect (non-)stationarity of both the lagged dependent variable and other regressors, (iii) cross-section and/or time-series heteroskedasticity of the idiosyncratic disturbances, and (iv) variation in signal-to-noise ratios and in the relative prominence of individual effects. For example: the simulation results in Arellano and Bover [3], Hahn and Kuersteiner [4], Alvarez and Arellano [5], Hahn et al [6], Kiviet [7], Kruiniger [8], Okui [9], Roodman [10], Hayakawa [11] and Han and Phillips [12] just concern the panel AR(1) model under homoskedasticity. Although an extra regressor is included in the simulation studies in Arellano and Bond [1], Kiviet [13], Bowsher [14], Hsiao et al [15], Bond and Windmeijer [16], Bun and Carree [17,18], Bun and Kiviet [19], Gouriéroux et al [20], Hayakawa [21], Dhaene and Jochmans [22], Flannery and Hankins [23], Everaert [24] and Kripfganz and Schwarz [25], this regressor is (weakly-)exogenous and most experiments just concern homoskedastic disturbances and stationarity regarding the impact of individual effects.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

Kiviet

Pleus²,

Poldermans

2017

Econometrics

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Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation, the effects are examined in terms of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification and coefficient restrictions show serious deficiencies. The variance of the individual effects is shown to be a major determinant of the poor quality of most asymptotic approximations; therefore, the accurate estimation of this nuisance parameter is investigated. A modification of GMM is found to have some potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample is not too small. Finally, all techniques are employed to actual data and lead to insights which differ considerably from those published earlier.

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Section: Resultsmentioning

confidence: 84%

Section: Simulation Designmentioning

confidence: 99%

Section: Simulation Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

Kiviet

Pleus²,

Poldermans

2017

Econometrics

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“…We consider the cases of (T, N ) = (10, 100), (10, 500), (15, 100), (15,300), (20,100), (20,200), (50, 100), and (100, 100). For the AR(1) model, we set α 1 = 0.5, 0.9, and for the AR(2) model, we set (α 1 , α 2 ) = (0.6, −0.1), (0.6, 0.3).…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

A SIMPLE EFFICIENT INSTRUMENTAL VARIABLE ESTIMATOR FOR PANEL AR(p) MODELS WHEN BOTHNANDTARE LARGE

Hayakawa¹

2009

Econom. Theory

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In this paper, we show that for panel AR(p) models, an instrumental variable (IV) estimator with instruments deviated from past means has the same asymptotic distribution as the infeasible optimal IV estimator when both N and T , the dimensions of the cross section and the time series, are large. If we assume that the errors are normally distributed, the asymptotic variance of the proposed IV estimator is shown to attain the lower bound when both N and T are large. A simulation study is conducted to assess the estimator.Keywords: panel AR(p) models, the optimal instruments, instruments deviated from past means.

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Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

Millimet

McDonough

2016

J. Appl. Econ.

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Summary With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators break down when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non‐uniform intervals and no solution is currently available when time‐varying covariates are included in the model. In this paper, we propose two new estimators for dynamic panel data models when data are irregularly spaced and compare their finite‐sample performance to the näive application of existing estimators. We illustrate the practical importance of this issue in an application concerning early childhood development. Copyright © 2016 John Wiley & Sons, Ltd.

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Long difference instrumental variables estimation for dynamic panel models with fixed effects

Cited by 149 publications

References 36 publications

Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

A SIMPLE EFFICIENT INSTRUMENTAL VARIABLE ESTIMATOR FOR PANEL AR(p) MODELS WHEN BOTHNANDTARE LARGE

Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

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