A Portmanteau Test for Serially Correlated Errors in Fixed Effects Models

We propose a portmanteau test for serial correlation of the error term in a fixed effects model. The test is derived as a conditional Lagrange multiplier test, but it also has a straightforward Wald test interpretation. In Monte Carlo experiments, the test displays good size and power properties.

Estimation of linear fixed-effects models with individual-specific slopes in Stata

Fixed-effects regression is considered a powerful method for estimating causal effects with survey data. However, in the linear model, the conventional technique of time-demeaning does not yield consistent estimates of the parameters when unobserved heterogeneity is not time-constant. Jeffrey M. Wooldridge (2002, Econometric Analysis of Cross Section and Panel Data [MIT Press], 317–322) derived a general model for the situation where unobserved and observed characteristics of individuals interact to produce the outcome. The fixed-effects model with individual constants and slopes (FEIS) is a remedy for coefficients that are biased due to, for example, maturation or learning where unobserved traits affect individual growth curves differently for treated and controls. The Stata xtfeis command implements the FEIS estimator in Mata, allowing for individual constants and (potentially many) slopes. Without specifying slope variables, the model collapses to the conventional model estimated by xtreg, fe that accounts for individual constants only. xtfeis implements standard errors that are robust to serial correlation or heteroskedasticity of unknown form. Estimates of the slope parameters are available optionally. The command requires panel data with at least J + 1 observations per unit, where J is the number of individual-specific slope variables (usually, but not necessarily, also including the individual-specific constant). I will present results for the effect of marriage on male wages based on real data (GSOEP and NLSY) to demonstrate the practical relevance of the method. I will use simulation results to assess robustness of the estimator to autocorrelation, measurement error, and misspecification of functional form.

Why Do Fixed-Effects Models Perform So Poorly? The Case of Academic Salaries

A large and growing line of research has used longitudinal data to eliminate unobservable individual effects that may bias cross-section parameter estimates. The resulting estimates, though unbiased, are generally quite imprecise. This study shows that the imprecision can arise from the measurement error that commonly exists in the data used to represent the dependent variable in these studies. The example of economists' salaries, which are administrative data free of measurement error, demonstrates that estimates based on changes in longitudinal data can be precise. The results indicate the importance of improving the measurement of the variables to which the increasingly high-powered techniques designed to analyze panel data are applied. The estimates also indicate that the payoff to citations to scholarly work is not an artifact of unmeasured individual effects that could be biasing previous estimates of the determinants of academic salaries.

Is distance dying at last? Falling home bias in fixed effects models of patent citations

We examine the “home bias” of international knowledge spillovers as measured by the speed of patent citations (i.e. knowledge spreads slowly over international boundaries). We present the first compelling econometric evidence that the geographical localization of knowledge spillovers has fallen over time, as we would expect from the dramatic fall in communication and travel costs. Our proposed estimator controls for correlated fixed effects and censoring in duration models and we apply it to data on over two million citations between 1975 and 1999. Home bias declines substantially when we control for fixed effects: there is practically no home bias for the more “modern” sectors such as pharmaceuticals and information/communication technologies

Penguin island closure feasibility study analysis results update: random effects models applied to both Western and Eastern Cape islands

This Addendum updates results from previous papers using random effects instead of fixed effects models for the year factors. Results are now given for Bird and St Croix islands in addition to those for Dassen and Robben islands. The results from random effects models are qualitatively unchanged from those given earlier from the fixed effects models, except that the periods required to obtain statistically significant results are extended somewhat as a result of the removal of the negative bias in the residual variance estimates for the earlier fixed effects models

Is distance dying at last? Falling home bias in fixed effects models of patent citations

We examine the "home bias" of knowledge spillovers (the idea that knowledge spreads more slowly over international boundaries than within them) as measured by the speed of patent citations. We present econometric evidence that the geographical localization of knowledge spillovers has fallen over time, as we would expect from the dramatic fall in communication and travel costs. Our proposed estimator controls for correlated fixed effects and censoring in duration models and we apply it to data on over two million patent citations between 1975 and 1999. Home bias is exaggerated in models that do not control for fixed effects. The fall in home bias over time is weaker for the pharmaceuticals and information/communication technology sectors where agglomeration externalities may remain strong.

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations

We examine the home bias of international knowledge spillovers as measured by the speed of patent citations (i.e. knowledge spreads slowly over international boundaries). We present the first compelling econometric evidence that the geographical localization of knowledge spillovers has fallen over time, as we would expect from the dramatic fall in communication and travel costs. Our proposed estimator controls for correlated fixed effects and censoring in duration models and we apply it to data on over two million citations between 1975 and 1999. Home bias declines substantially when we control for fixed effects: there is practically no home bias for the more modern sectors such as pharmaceuticals and information/communication technologies.

Is Distance Dying at Last? Falling Home Bias in Fixed Effects Models of Patent Citations

We examine the "home bias" of international knowledge spillovers as measured by the speed of patent citations (i.e. knowledge spreads slowly over international boundaries). We present the first compelling econometric evidence that the geographical localization of knowledge spillovers has fallen over time, as we would expect from the dramatic fall in communication and travel costs. Our proposed estimator controls for correlated fixed effects and censoring in duration models and we apply it to data on over two million citations between 1975 and 1999. Home bias declines substantially when we control for fixed effects: there is practically no home bias for the more "modern" sectors such as pharmaceuticals and information/communication technologies.Fixed effects, home bias, patent citations, knowledge spillovers