The fixed effects estimator of technical efficiency

Firms and organizations, public or private, often operate on markets characterized by non-competitiveness. For example agricultural activities in the western world are heavily subsidized and electricity is supplied by firms with market power. In general it is probably more difficult to find firms that act on highly competitive markets, than firms that are not. To measure different types of inefficiencies, due to this lack of competitiveness, has been an ongoing issue, since at least the 1950s when several definitions of inefficiency was proposed and since the late 1970s as stochastic frontier analysis. In all three articles presented in this thesis the stochastic frontier analysis approach is considered. Furthermore, in all three articles focus is on technical inefficiency. The ways to estimate technical inefficiency, based on stochastic frontier models, are numerous. However, focus in this thesis is on fixed effects panel data estimators. This is mainly for two reasons. First, the fixed effects analysis does not demand explicit distributional assumptions of the inefficiency and the random error of the model. Secondly, the analysis does not require the random effects assumption of independence between the firm specific inefficiency and the inputs selected by the very same firm. These two properties are exclusive for fixed effects estimation, compared to other stochastic frontier estimators. There are of course flaws attached to fixed effects analysis as well, and the contribution of this thesis is to probe some of these flaws, and to propose improvements and tools to identify the worst case scenarios. For example the fixed effects estimator is seriously upward biased in some cases, i.e. inefficiency is overestimated. This could lead to false conclusions, like e.g. that subsidies in agriculture lead to severely inefficient farmers even if these farmers in reality are quite homogenous. In this thesis estimators to reduce bias as well as mean square error are proposed and statistical diagnostics are designed to identify worst case scenarios for the fixed effects estimator as well as for other estimators. The findings can serve as important tools for the applied researcher, to obtain better approximations of technical inefficiency

Accounting for Unobserved Country Heterogeneity in Happiness Research: Country Fixed Effects versus Region Fixed Effects

Many empirical studies are ambiguous about whether good formal institutions are conducive to subjective well-being or not. Possibly, this ambiguity is caused by cross-section models that do not account for unobserved cultural and institutional effects. Using the World Value Survey 1980-2005, this paper supports a positive relation in a country panel framework that accounts for unobserved, time-invariant country heterogeneity. This study also shows that using supra-national region dummies (by geography or language) in a country-random effects model appears to be a sufficient substitution for omitted country fixed effects.Happiness; life satisfaction; subjective well-being; quality of life; institutions; democracy; rule of law; political constraints; policy implications; panel econometrics

Using backward means to eliminate individual effects from dynamic panels

The within-groups estimator is inconsistent in dynamic panels with fixed T since the sample mean used to eliminate the individual effects from the lagged dependent variable is correlated with the error term. This paper suggests to eliminate individual effects from an AR(1) panel using backward means as an alternative to sample means. Using orthogonal deviations of the lagged dependent variable from its backward mean yields an estimator that is still inconsistent for fixed T but the inconsistency is shown to be negligibly small. A Monte Carlo simulation shows that this alternative estimator has superior small sample properties compared to conventional fixed effects, bias-corrected fixed effects and GMM estimators. Interestingly, it is also consistent for fixed T in the specific cases where (i) T = 2, (ii) the AR parameter is 0 or 1, (iii) the variance of the individual effects is zero

Spatial Fixed Effects and Spatial Dependence

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification re- moves spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when the true DGP takes the form of a spatial lag or spatial error dependence. In addition, we also show that only in the special case where the dependence is group-wise, with all observations in the same group as neighbors of each other, do spatial fixed effects correctly remove spatial correlation.spatial autocorrelation, spatial econometrics, spatial externalities, spatial fixed effects, spatial interaction, spatial weights

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 effects'' 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 will 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.Fixed effects, Correlated effects, (Essentially) random effects, Conditional likelihood, Modified likelihood, GMM, Quasi likelihood, Unit root test, Cross-sectional dependence

Fixed effects selection in the linear mixed-effects model using adaptive ridge procedure for L0 penalty performance

This paper is concerned with the selection of fixed effects along with the estimation of fixed effects, random effects and variance components in the linear mixed-effects model. We introduce a selection procedure based on an adaptive ridge (AR) penalty of the profiled likelihood, where the covariance matrix of the random effects is Cholesky factorized. This selection procedure is intended to both low and high-dimensional settings where the number of fixed effects is allowed to grow exponentially with the total sample size, yielding technical difficulties due to the non-convex optimization problem induced by L0 penalties. Through extensive simulation studies, the procedure is compared to the LASSO selection and appears to enjoy the model selection consistency as well as the estimation consistency