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A central limit theorem is proved for dependent stochastic processes. Global heterogeneity of the distribution of the terms is permitted, including asymptotically unbounded moments. The approach is to adapt a CLT for martingale differences due to McLeish and show that suitably defined Bernstein blocks satisfy the required conditions.
Let … be a moving average process of infinite order where the innovations ε(k) are in the domain of attraction of a stable law with index α ε (0, 2) and the parameter sequence decreases at a polynomial or exponential rate. These and similar processes have recently received increased attention both in the econometrics and statistics/probability literature. The present paper studies almost sure uniform rates of convergence of the empirical distribution function. Applications of these infinite variance processesin econometrics are mentioned.
Under general conditions the distribution function of the first few terms in a stochastic expansion of an econometric estimator or test statistic provides an asymptotic approximation to the distribution function of the original estimator or test statistic with an error of order less than that of the limiting normal or chi-square approximation. This can be used to establish the validity of several refined asymptotic methods, including the comparison of Nagar-type moments and the use of formal Edgeworth or Edgeworth-type approximations.
We introduce a semiparametric estimator for the censored linear regression model. It is based on the regression version of Huber's  M-estimator. It includes Powell's  censored least absolute deviations estimator as a special case and is related to Powell's  symmetrically censored least-squares estimator. We prove strong consistency and derive its asymptotic distribution which is √n-consistent with an easily computable covariance matrix. A small-scale simulation study shows that it works quite well in various cases.
This paper presents an overview of the Cowles Commission effort in the area of econometric theory and a critical review of the Brookings Project, but flaws are noted in both efforts. The Brookings part reflects a view of the project as seen by one of the younger members of the coordinating team; it is a view shared to some extent by other members. The paper deals with the research work stimulated by both projects and contains a brief but frank discussion of the emergence of a commercial econometric services industry. Finally, promising areas for future research are noted.
A well-known result in the method of moments literature is that the efficient instruments for the estimation of a model are functions of the conditional expectation of its gradient. Some recent studies have suggested the nonparametric estimation of these instruments when they are of unknown functional form. When these instruments in turn depend on the unknown parameters it has been suggested that these be replaced by preliminary consistent estimates. It is shown here that solving the sample moment equations simultaneously over the instruments and the residuals of the model will generally produce the same asymptotic efficiency and avoid the disadvantages inherent with the use of preliminary estimates.