Time-varying Individual Effects Research Articles

Quasi-maximum likelihood estimation of short panel data models with time-varying individual effects

Quasi-maximum likelihood estimation of short panel data models with time-varying individual effects

Time-varying individual effects in a panel data probit model with an application to female labor force participation

In this article, we study a panel data probit model with time-varying individual effects. Under a Gaussian assumption on the error term, we show the common slope coefficients and time factors can be identified up to scale. We propose for the identified parameters a multi-stage estimation procedure. The proposed estimators have a closed form expression, thus are very convenient to compute even in large samples. We establish the root-n consistency as well as asymptotically normality for these estimators. Using a German socioeconomic panel dataset, we estimate the labor supply function for married women. We find that the unobserved skill premium is positive and shows an upward trend during 2008–2012, in support of the biased technological progress hypothesis.

Identification without assuming mean stationarity: quasi–maximum likelihood estimation of dynamic panel models with endogenous regressors

SummaryLinear generalised method of moments (GMM) estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper, we propose new random- and fixed-effects limited-information quasi–maximum likelihood estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike system GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first-stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than nonlinear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (quasi–) maximum likelihood estimators in terms of precision, robustness, and/or ease of computation.

Quasi ML Estimation of the Panel AR(1) Model with Additional Regressors

In this paper we discuss limited information and full information random effects and fixed effects Quasi ML estimators for panel AR(1) models with additional regressors. We also consider related GMM estimators. The models allow for arbitrary initial conditions and heteroskedasticity and are extensions of the models considered in Kruiniger (2013. Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions. Journal of Econometrics 173, 175-188). Among other things, we distinguish between the case where the regressors are strictly exogenous and the case where they are predetermined with respect to the idiosyncratic errors, including the case where they are weakly exogenous; we consider the possibility that the regressors are correlated with the individual effects; and we discuss estimation of models with time-varying individual effects. Finally, we stress the advantages of an LI approach over an FI approach such as robustness, computational ease and in some cases the higher precision of the resulting estimators.

Panel data models with multiple time-varying individual effects

This paper considers a panel data model with time-varying individual effects. The data are assumed to contain a large number of cross-sectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixed-effects model in that the effects are assumed to be correlated with the regressors. The unobservable individual effects are assumed to have a factor structure. For consistent estimation of the model, it is important to estimate the true number of individual effects. We propose a generalized methods of moments procedure by which both the number of individual effects and the regression coefficients can be consistently estimated. Some important identification issues are also discussed. Our simulation results indicate that the proposed methods produce reliable estimates.

Fixed-Effect Estimation of Highly-Mobile Production Technologies

We consider fixed-effect estimation of a production function where inputs and outputs vary over time, space, and cross-sectional unit. Variability in the spatial dimension allows for time-varying individual effects, without parametric assumptions on the effects. Asymptotics along the spatial dimension provide consistency and normality of the marginal products. A finite-sample example is provided: a production function for bottom-trawler fishing vessels in the flatfish fisheries of the Bering Sea. We find significant spatial variability of output (catch) which we exploit in estimation of a harvesting function.

A time-varying common risk factor affecting corporate yield spreads

A time-varying common risk factor affecting corporate yield spreads is modelled by extending a panel data model. The panel data model accommodates a common factor, which is associated with time-varying individual effects. The factor multiplied by a bond-specific unobservable is identified as a systematic risk premium. In disentangling the systematic risk premium, both credit and liquidity risks are evaluated; the credit risk is assessed by bond rating, and the liquidity risk is indirectly measured by discrepancy in quoted yields by brokerage firms. Parameters are estimated by the generalized method of moments procedure. The model is tested on the corporate bond market in Japan. Empirical results show that the time-varying common risk factor is successfully estimated together with credit and liquidity risks.

Stochastic frontier models with multiple time-varying individual effects

This paper proposes a flexible time-varying stochastic frontier model. Similarly to Lee and Schmidt [1993, In: Fried H, Lovell CAK, Schmidt S (eds) The measurement of productive efficiency: techniques and applications. Oxford University Press, Oxford], we assume that individual firms’ technical inefficiencies vary over time. However, the model, which we call the “multiple time-varying individual effects” model, is more general in that it allows multiple factors determining firm-specific time-varying technical inefficiencies. This allows the temporal pattern of inefficiency to vary over firms. The number of such factors can be consistently estimated. The model is applied to data on Indonesian rice farms, and the changes in the efficiency rankings of farms over time demonstrate the model’s flexibility.

Consistent Estimation of Dynamic Panel Data Models with Time-Varying Individual Effects

Consistent Estimation of Dynamic Panel Data Models with Time-Varying Individual Effects

GMM estimation of linear panel data models with time-varying individual effects

This paper considers models for panel data in which the individual effects vary over time. The temporal pattern of variation is arbitrary, but it is the same for all individuals. The model thus allows one to control for time-varying unobservables that are faced by all individuals (e.g., macro-economic events) and to which individuals may respond differently. A generalized within estimator is consistent under strong assumptions on the errors, but it is dominated by a generalized method of moments estimator. This is perhaps surprising, because the generalized within estimator is the MLE under normality. The efficiency gains from imposing second-moment error assumptions are evaluated; they are substantial when the regressors and effects are weakly correlated.