Jan F. Kiviet

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An attempt is made to set rules for a fair and fruitful competition between alternative inference methods based on their performance in simulation experiments. This leads to a list of eight methodologic aspirations. Against their background we criticize aspects of many simulation studies that have been used in the past to compare competing estimators for(More)
In practice structural equations are often estimated by least-squares, thus neglecting any simultaneity. This paper reveals why this may often be justi…able and when. Assuming data stationarity and existence of the …rst four moments of the disturbances we …nd the limiting distribution of the ordinary least-squares (OLS) estimator in a linear simultaneous(More)
The asymptotic efficiency of OLS and IV estimators is examined in a simple dynamic structural model with a constant and two explanatory variables: the lagged dependent variable and another autoregressive variable, which may also include lagged or instantaneous feedbacks from the dependent variable. The parameter values are such that all variables are(More)
In designing Monte Carlo simulation studies for analyzing …nite sample properties of econometric inference methods, one can use either IID drawings in each replication for any series of exogenous explanatory variables or condition on just one realization of these. The results will usually di¤er, as do their interpretations. Conditional and unconditional(More)
An approximation to order T 2 is obtained for the bias of the full vector of leastsquares estimates in general stable but not necessarily stationary ARX(1) models with normal disturbances. This yields generalizations, allowing for various forms of initial conditions, of Kendall’s and White’s classic results for stationary AR(1) models. The accuracy of(More)
In simple static linear simultaneous equation models the empirical distributions of IV and OLS are examined under alternative sampling schemes and compared with their first-order asymptotic approximations. We demonstrate that the limiting distribution of consistent IV is not affected by conditioning on exogenous regressors, whereas that of inconsistent OLS(More)
A conversion of standard ordinary least-squares results into inference which is robust under endogeneity of some regressors has been put forward in Ashley and Parmeter, Economics Letters, 137 (2015) 70-74. However, their conversion is based on an incorrect (though by accident conservative) asymptotic approximation and entails a neglected but avoidable(More)
In dynamic regression models the least-squares coe±cient estimators are biased in ̄nite samples, and so are the usual estimators for the disturbance variance and for the variance of the coe±cient estimators. By deriving the expectation of the initial terms in an expansion of the usual expression for the asymptotic coe±cient variance estimator and by(More)