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Self-selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile. To assess the impact of self-selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work. Available estimation methods are either infeasible for a selection model with so many alternatives or place potentially severe restrictions on earnings and the selection process. This paper develops an alternative econometric methodology that combines Lee's (1983) parametric maximum order statistic approach to reduce the dimensionality of the error terms with more recent work on semiparametric estimation of selection models (e.g., Ahn and Powell (1993)). The resulting semiparametric correction is easy to implement and can be adapted to a variety of other polychotomous choice problems. The empirical work, which uses 1990 U.S. Census data, confirms the role of comparative advantage in mobility decisions. The results suggest that self-selection of higher educated individuals to states with higher returns to education generally leads to upward biases in OLS estimates of the returns to education in state-specific labor markets. While the estimated returns to a college education are significantly biased, correcting for the bias does not narrow the range of returns across states. Consistent with the finding that the corrected return to a college education differs across the U.S., the relative state-to-state migration flows of college- versus high school-educated individuals respond strongly to differences in the return to education and amenities across states. KEYWORDS: Selection bias, polychotomous choice, Roy model, return to education, migration.

This paper develops an empirical model of the relationship between road traffic accidents and traffic flows. The analysis focuses on the accident externality, which is determined mainly by the difference between the marginal and average risks. The model is estimated using a new data-set which combines hourly London traffic count data from automated vehicle recorders together with police records of road accidents. The accident-flow relationship is seen to vary considerably between different road classes and geographical areas. More importantly, even having controlled for these and other differences, the accident externality is shown to vary significantly with traffic flows. In particular, while the accident externality is typically close to zero for low to moderate traffic flows, it increases substantially at high traffic flows.

Protection against asymmetric shocks in global economies can be provided through a complex insurance scheme. This scheme, derived from a complex econometric formula based on historical data, is needed now that the exchange rate has been lost as a policy instrument due to the European monetary union and the euro currency. Simplifying the econometric formula makes the scheme less viable as a protective measure.

Single-equation error-correction models (ECMs) are in wide use for analysis of cointegrated variables. Diagnostic tests should be used to check the specification of ECMs. Monte Carlo evidence shows that the finite-sample significance levels of the tests can be sensitive to the method used to estimate long-run coefficients that give the error-correction term of the ECM. Based on autoregressive distributed lag (ADL) models, estimates of long-run coefficients should be made. There is a good indirect approach to asymptotically valid checks.

A simultaneous equation system presumes that endogenous explanatory variables are correlated with equation-specific structural disturbances. Our paper proposes estimation methods and tests for switching orthogonality of a subset of the endogenous variables over a sample subset when the switching point is presumed known. Dividing the full sample period into two sub-periods, the null hypothesis of switching orthogonality/endogeneity is that the subset of endogenous variables is correlated with the disturbance in one of the two periods only, whereas the alternative hypothesis of endogeneity is that the subset of variables is correlated with the disturbance in both periods.

A graph is better than a table for illustrating the empirical distribution functions of the probability values in certain economic experiments. Graphs provide information on all points in the distribution and can be interpreted more readily. P value plots, P value discrepancy plots and size power curves can be graphed. P is the probability that a specific value will occur.

An analysis of the effects of reputation in the decision making process of two long-run players involved in a repeated game reveals that patience with regards the overall strategy that one or both players is directly related to the equilibrium payoff. Commitment to a particular strategy results in a greater degree of attaining an average equilibrium payoff similar to the Stackelberg payoff.

An analysis of econometric tools for macroeconomic forecasting that compensates for input variations reveals that there is no single model that ensures immunity from predictive failure. The study also supports the adoption of a more robust forecasting approach instead of intercept corrections. Finally, error-correction is a possible field of further study since it has better potential applications in macroeconomic forecasting than co-integration models.

Through Monte Carlo experiments the effects of a feedback mechanism on the accuracy in finite samples of ordinary and bootstrap inference procedures are examined in stable first- and second-order autoregressive distributed-lag models with non-stationary weakly exogenous regressors. The Monte Carlo is designed to mimic situations that are relevant when a weakly exogenous policy variable affects (and is affected by) the outcome of agents' behaviour. In the parameterizations we consider, it is found that small-sample problems undermine ordinary first-order asymptotic inference procedures irrespective of the presence and importance of a feedback mechanism. We examine several residual-based bootstrap procedures, each of them designed to reduce one or several specific types of bootstrap approximation error. Surprisingly, the bootstrap procedure which only incorporates the conditional model overcomes the small sample problems reasonably well. Often (but not always) better results are obtained if the bootstrap also resamples the marginal model for the policymakers' behaviour., The accuracy of bootstraping statistical inference procedures in small samples is examined in autoregressive distributed-lag models having non-stationary regressors. A comparison of the finite sample consequences of the strongly and weakly exogenous regressors, in four varying bootstrap procedures, is made using the Monte Carlo method. A conditional model is involved in two of these procedures, which are found to surmount the small sample problems justifiably well. A resampling of the marginal model also provides good results.

Recent research has emphasized the poor finite-sample performance of the instrumental variables (IV) estimator when the instruments are weakly correlated with the regressors. We show how the canonical correlations between regressors and instruments can provide a measure of instrument relevance in the general multiple-instrument-multiple-regressor case. However, our simulation results indicate that any such relevance measure probably has little practical merit, as its use may actually exacerbate the poor finite-sample properties of the IV estimator.

Duration data analyses benefit from the use of discrete-time models. However, available econometric software are usually unable to account for the sampling method used, thereby raising the probability of sample selection bias. A method for using such software in estimating discrete-time models is proposed. It involves transforming the problem into a binary dependent variable regression by rewritting complex sequence likelihood functions into a non-sequence likelihood form. The method is generalized to cases with function dependent on survival between selection and interview periods.

Asymptotic equivalence of Aitken and feasible Aitken estimators in linear models with nonscalar identity error covariance matrices is usually established in a tedious case-by-case manner. Some general sufficient conditions for this equivalence exist, but there are problems with the extant conditions. These problems are discussed, and new widely applicable sufficient conditions are presented and applied to a variety of error structures.

Switching techniques in production models with produced inputs involves a discontinuous variation of a plurality of technical coefficients, not a continuous one as many assume. Introducing a continuum spectrum of techniques in a Sraffa-type production model demonstrates that the same wage-profit frontier is available by substituting one or more techniques for others requiring different inputs.

We consider the question, 'Under what conditions is the extremum of a random function over a random set itself a random object?' The answer is relevant to problems in both game theory and econometrics, as we illustrate with examples. Our purpose here is to bring the powerful tools of the theory of analytic sets as developed by Dellacherie and Meyer (1978) to the wider attention of the economics profession and to distill Dellacherie and Meyer's work in such a way as to provide some readily accessible theoretical results that will permit relatively easy treatment of economically or econometrically relevant applications.

This article develops tests of covariance restrictions after estimating by three-stage least squares a dynamic random effects model from panel data. The asymptotic distribution of covariance matrix estimates under non-normality is obtained. It is shown how minimum chi-square tests for interesting covariance restrictions can be calculated from a generalised linear regression involving the sample autocovariances and dummy variables. Asymptotic efficiency exploiting covariance restrictions can also be attained using a GLS estimator.

In econometric applications, often several hypothesis tests are carried out at once. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. This paper suggests a stepwise multiple testing procedure that asymptotically controls the familywise error rate. Compared to related single-step methods, the procedure is more powerful and often will reject more false hypotheses. In addition, we advocate the use of studentization when feasible. Unlike some stepwise methods, the method implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect false hypotheses. The methodology is presented in the context of comparing several strategies to a common benchmark. However, our ideas can easily be extended to other contexts where multiple tests occur. Some simulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.

This paper presents a review of non-experimental methods for the evaluation of social programmes. We consider matching and selection methods and analyse each for cross-section, repeated cross-section and longitudinal data. The methods are assessed drawing on evidence from labour market programmes in the UK and in the US. JEL classification: J38, H3, C2.