Estimation Of Panel Data Models Research Articles

Estimation of Panel Data Models with Two-Sided Censoring

It is straightforward to construct moment conditions for two-sided censored panel data regression models with strictly exogenous explanatory variables. The contribution of this note is to show that one set of these moment conditions uniquely identify the parameters of the model under a natural full-rank condition. The identification result suggests an estimator that is then applied to evaluate the portfolio allocation effect of a Danish tax reform that increased the after-tax capital income of bonds relative to stocks.

Weak and Strong Cross Section Dependence and Estimation of Large Panels

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double- indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain 'granularity' conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects. JEL Classification: C10, C31, C33

Pseudo Panels and Repeated Cross-Sections

textabstractIn many countries there is a lack of genuine panel data where specific individuals or firms are followed over time. However, repeated cross-sectional surveys may be available, where a random sample is taken from the population at consecutive points in time. In this paper we discuss the identification and estimation of panel data models from repeated cross sections. In particular, attention will be paid to linear models with fixed individual effects, to models containing lagged dependent variables and to discrete choice models.

The IMF's Reserves Template and Nominal Exchange Rate Volatility

The effects of the adoption of the IMF's International Reserves and Foreign Currency Liquidity Data Template on nominal exchange rate volatility are investigated for 48 countries. Estimation of panel data models indicates that nominal exchange rate volatility decreases following dissemination of reserves template data while the effects of indebtedness and reserve adequacy on volatility exhibit statistically significant changes.

Some Monte Carlo Evidence for Adaptive Estimation of Unit-Time Varying Heteroscedastic Panel Data Models

In this paper, we present an adaptive estimator for panel data model with unknown unit-time varying heteroscedastic error component of unknown form by using probability weighted moments rather than conventional kernel estimators already available in the literature and then evaluate the finite sample performance of the proposed estimator in terms of efficiency and testing of hypothesis. The Monte Carlo evidence suggests that the proposed estimator performs adequately under different data generated processes, especially for small samples that are the most practical situations.

Estimation of panel data models with binary indicators when treatment effects are not constant over time

We show that two commonly employed estimation procedures to deal with correlated unobserved heterogeneity in panel data models, within-groups and first-differenced OLS, can lead to very different estimates of treatment effects when these are not constant over time and treatment is a state that only changes occasionally. It is therefore important to allow for flexible time varying treatment effects when estimating panel data models with binary indicator variables as is illustrated by an example of the effects of marital status on mental wellbeing.

Estimation of Panel Data Models with Binary Indicators when Treatment Effects are not Constant over Time

We show that two commonly employed estimation procedures to deal with correlated unobserved heterogeneity in panel data models, within-groups and first-differenced OLS, can lead to very different estimates of treatment effects when these are not constant over time and treatment is a state that only changes occasionally. It is therefore important to allow for flexible time varying treatment effects when estimating panel data models with binary indicator variables as is illustrated by an example of the effects of marital status on mental wellbeing.

Fixed effects, random effects or Hausman–Taylor?: A pretest estimator

This paper suggests a pretest estimator based upon two Hausman tests as an alternative to the fixed effects or random effects estimators for panel data models. The bias and RMSE properties of these estimators are investigated using Monte Carlo experiments.

Identification and sequential estimation of panel data models with insufficient exclusion restrictions

Abstract This paper presents estimators for nonparametric panel data models with additive fixed effects. We analyze a model in which the entire regressor vector, consisting of time-varying as well as time-invariant regressors, is correlated with the individual fixed effect but there are insufficient exclusion restrictions to permit direct instrumental variables estimation of the model. It is shown that when the model satisfies the Additive Interactive Regression (AIR) representation of Andrews and Whang (Economic theory 6 (1990) 466–480), a sequential estimator consistently estimates the model. The limiting distribution of scalar nonlinear functionals of the model is shown to be standard normal. Finite sample properties of the estimator are considered in a Monte Carlo simulation study, and used to estimate the returns to education in a cohort of mature men from the National Longitudinal Survey.

On the Estimation of Panel Regression Models with Fixed Effects

This paper considers estimation of panel data models with fixed effects. First, we will show that a consistent unrestricted fixed estimator does not exist for autoregressive panel data models with initial conditions. We will derive necessary and sufficient conditions for the consistency of estimators for these models. In particular, we will show that various widely used GMM estimators for the conditional AR(1) panel model are inconsistent under trending fixed effects sequences. Next, we derive, justify, and compare restricted Fixed Effects GMM and (Q)ML estimators for this model. We find that the FEML estimator is asymptotically efficient, whereas the Modified ML estimator is not. We will also compare the fixed effects approach for estimating the conditional AR(1) panel model and covariance parameters in static panel data models with the correlated random effects approach.

GMM estimation in panel data models with measurement error

Griliches and Hausman (J. Econom. 32 (1986) 93) have introduced GMM estimation in panel data models with measurement error. We present a simple, systematic approach to derive moment conditions for such models under a variety of assumptions.

Efficient estimation of panel data models with strictly exogenous explanatory variables

With panel data, exogeneity assumptions imply many more moment conditions than standard estimators use. However, many of the moment conditions may be redundant, in the sense that they do not increase efficiency; if so, we may establish the standard estimators’ efficiency. We prove efficiency results for GLS in a model with unrestricted error covariance matrix, and for 3SLS in models where regressors and errors are correlated, such as the Hausman–Taylor model. For models with correlation between regressors and errors, and with unrestricted error covariance structure, we provide a simple estimator based on a GLS generalization of deviations from means.

Two-step estimation of panel data models with censored endogenous variables and selection bias

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.

Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data

In this paper we discuss the estimation of panel data models with sequential moment restrictions using symmetrically normalized GMM estimators. These estimators are asymptotically equivalent to standard GMM but are invariant to normalization and tend to have a smaller finite sample bias. They also have a very different behaviour compared to standard GMM when the instruments are poor. We study the properties of SN-GMM estimators in relation to GMM, minimum distance and pseudo maximum likelihood estimators for various versions of the AR(1) model with individual effects by mean of simulations. The emphasis is not in assessing the value of enforcing particular restrictions in the model; rather, we wish to evaluate the effects in small samples of using alternative estimating criteria that produce asymptotically equivalent estimators for fixed T and large N. Finally, as an empirical illustration, we estimate by SN-GMM employment and wage equations using panels of UK and Spanish firms.

Efficient estimation of panel data models with sequential moment restrictions

This paper considers asymptotically efficient estimation of the panel data models with sequential moment restrictions in an environment with i.i.d. observations. It is shown that the GMM estimator with an increasing set of instruments attains the semiparametric efficiency bound of the model. The estimator considered here allows for the heteroscedasticity of the unknown form.

The bias of some estimators for panel data models with measurement errors

The bias of some estimators for panel data models with measurement errors