Panel Data in Econometrics

xtdpdgmm implements generalized method of moments estimators for linear dynamic panel data models. Linear moment conditions can be combined with the nonlinear moment conditions suggested by Ahn and Schmidt (1995). The Windmeijer (2005) finite-sample standard error correction is implemented for estimators with and without nonlinear moment conditions. Available postestimation statistics include the Arellano-Bond test for autocorrelation of the residuals and Hansen's J-test for the validity of the overidentifying restrictions.

XTDPDGMM: Stata module to perform generalized method of moments estimation of linear dynamic panel data models

Initial conditions and Blundell–Bond estimators

Deregulation, efficiency and policy determination: An analysis of Australia's electricity distribution sector

Current research has established a clear negative impact of technological sources of stress on work-related outcomes. This study proposes that the invasion of technology into the private sphere is not always perceived negatively by individuals per se. The authors used structural equation modeling with a sample of 479 employees in Germany to test whether techno-invasion (technology invading personal life) can lead to emotional exhaustion as well as eustress (positive stress). In doing so, we advance our understanding of the dual effects technology has on an individual's life. The study further examines to what extent individuals themselves and their resources are responsible for the effects of technological stressors on stress responses. Our empirical results confirm that techno-invasion increases both positive and negative stress responses. Individual resources as mediators are shown to play a key role in the stressor-response relationship. Unexpectedly, the six tested resources do not have similar effects on stress responses. The authors discuss practical implications and future research directions.

https://www.econstor.eu/bitstream/10419/179471/1/1023330156.pdf

Searching for the silver linings of techno-invasion

We study the estimation of the lag parameter of linear dynamic panel data models with first order dynamics based on the quadratic Ahn and Schmidt (1995) moment conditions. Our contribution is twofold: First, we show that extending the standard assumptions by mean stationarity and time series homoscedasticity and employing these assumptions in estimation restores standard asymptotics and mitigates the non-standard distributions found in the literature. Second, we consider an IV estimator based on the quadratic moment conditions that consistently identifies the true population parameter under standard assumptions. Standard asymptotics hold for the estimator when the cross section dimension is large and the time series dimension is finite. We also suggest a data-driven approach to obtain standard errors and confidence intervals that preserves the time series dependence structure in the data.

https://www.econstor.eu/bitstream/10419/204583/1/167818893X.pdf

Practical aspects of using quadratic moment conditions in linear dynamic panel data models

We propose an instrumental variables (IV) estimator based on nonlinear (in param- eters) moment conditions for estimating linear dynamic panel data models and derive the large sample properties of the estimator. We assume that the only explanatory variable in the model is one lag of the dependent variable and consider the setting where the absolute value of the true lag parameter is smaller or equal to one, the cross section dimension is large, and the time series dimension is either fixed or large. Estimation of the lag parameter involves solving a quadratic equation and we find that the lag parameter is point identified in the unit root case; otherwise, two distinct roots (solutions) result. We propose a selection rule that identifies the consistent root asymptotically in the latter case and derive the asymptotic distribution of the estimator for the unit root case and for the case when the absolute value of the lag parameter is smaller than one.

https://www.econstor.eu/bitstream/10419/204582/1/1678188328.pdf

Large sample properties of an IV estimator based on the Ahn and Schmidt moment conditions

/pdf/pdynmc-a-package-for-estimating-linear-dynamic-panel-data-3zyub0nfvz.pdf

pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions

pdynmc is an R-package for IV- and GMM-estimation of linear dynamic panel data models that are based on linear and nonlinear moment conditions as proposed by Anderson and Hsiao (1982), Holtz-Eakin, Newey, and Rosen (1988), Arellano and Bover (1995), and Ahn and Schmidt (1995). This paper describes the functionality of the package and the options regarding instrument type, covariate type, estimation methodology, general configuration, specification testing and inference from the perspective of an applied statistician. The illustrations are based on a publicly available panel data set. Additionally, we link our implementation to other software and packages for GMM-estimation of linear dynamic panel data models.

https://www.econstor.eu/bitstream/10419/204584/1/1678189383.pdf

Pdynmc - An R-package for estimating linear dynamic panel data models based on linear and nonlinear moment conditions

We conduct a narrow replication of Browning and Collado (Journal of Applied Econometrics 2007; 22(3): 625-640). They estimate a linear panel AR(1) version of an Engel curve for six consumption composites using iterated GMM. We find that the coefficient estimates and standard errors differ from the reported results when we use their instrument set; in particular, we find habit formation in non-durable services and no state dependence in small durables. Despite finding evidence for weak instruments, our results support most of the claims made in the original paper and are also unable to detect intertemporal dependence strong enough to resolve existing macro puzzles.

https://www.econstor.eu/bitstream/10419/204574/1/1678184659.pdf

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