This paper describes some techniques for proving asymptotic normality of statistics defined by maximization of random criterion function. The techniques are based on a combination of recent results from the theory of empirical processes and a method of Huber for the study of maximum likelihood estimators under nonstandard conditions.

In this paper the testing and estimation problems are discussed in the case of serial correlation. Various models are particular cases of the general framework considered: the nonlinear simultaneous equations models, the probit models, the tobit models, the disequilibrium models, the frontier models, etc. In this context, it is shown that the score test can be written explicitly and that the statistic obtained is a generalization of that of Durbin and Watson; moreover, the maximum likelihood estimation procedure is shown to be robust with respect to serial correlation.

Linear rational expectations models generally have a large number of solutions. It is thus important to describe them exhaustively in order to study their properties and subsequently estimate which solution best fits the data. In this paper, a global approach is suggested allowing a simultaneous treatment of all possible cases. The fundamental concepts are the revision processes appearing in the procedure of updating expectations. It isfound that the set of solutions is completely described by using a limitednumber of these processes. We show how the method may be applied to determine the set of stationary solutions admitting an infinite moving-average representation. We give a natural parametrization of this set and discuss the exact number of independent parameters.

This paper is concerned with derivation of a new efficient algorithm for computing the exact Gaussian likelihood for structural parameters in nonstationary higher-order continuous-time dynamic models and with its application in the estimation of these parameters. The algorithm completely avoids the computation of the covariance matrix of the observations and is applicable to a system of any order with mixed stock and flow data. It is used as the basis for an iterative procedure in which the structural parameters and the initial state vector are estimated alternately.

Forecasts from a univariate autoregressive model estimated by OLS are unbiased, irrespective of whether the model fitted has the correct order; this property only requires symmetry of the distribution of the innovations. In this paper, this result is generalized to vector autoregressions and a wide class of multivariate stochastic processes (which include Gaussian stationary multivariate stochastic processes) is described for which unbiasedness of predictions holds: specifically, if a vector autoregression of arbitrary finite order is fitted to a sample from any process in this class, the fitted model will produce unbiased forecasts, in the sense that the prediction errors have distributions symmetric about zero. Different numbers of lags may be used for each variable in each autoregression and variables may even be missing, without unbiasedness being affected. This property is exact in finite samples. Similarly, the residuals from the same autoregressions have distributions symmetric about zero.

A size correction to adjust the critical values of asymptotic χ2 tests in a SUR context has been developed by Rothenberg and verified by Phillips. The adjustment centers the distribution of the observed test statistic so that it coincides more closely with its hypothetical distribution. The correction itself is an approximation to O(T−1), and while the adjustment should be superior to the asymptotic test (no adjustment) in small-sample situations, its performance can be expected to deteriorate as the sample size decreases. The Edgeworth corrections, using formulas provided by Phillips, are calculated for the Wald, likelihood ratio, and Lagrange multiplier tests in the context of symmetry testing in demand analysis using the Laitinen–Meisner simulated data set. The sample consists of 31 observations and the corrections were “adequate to useful” in the context of 5, 8, and 11 equations. However, in the extreme case of 14 equations the corrections typically only took up 60% of the adjustment required. The computational cost of calculating the Edgeworth correction for large SUR systems with a large number of restrictions also turns out to be quite heavy. The conclusion reached is that while Edgeworth corrections are not totally satisfactory, they are easy to include in a program and provide a useful critical value correction.

The estimation of K (K ≥ 3) location parameters is considered under quadratic loss when the coordinates of the best invariant estimators are spherically symmetrically distributed. Under these stochastic mechanisms traditional Stein estimators are evaluated for finite samples and shown to have a risk performance superior to some conventional rules.