[This paper considers a problem faced by players who are involved in a sequence of games: not necessarily the same games, not necessarily with the same opponents, and not necessarily under conditions of complete information. The players are assumed to act in response to stationary Markovian hypotheses which they form about the actions of their opponents. Conditions are explored which require that these hypotheses be correct on average and that the players actions be optimal in response to their hypotheses.]

Technical change in general milk processing is estimated within a homothetic frontier production function allowing neutrally variable scale elasticity. The results show that technical progress is characterized by a rapid increase in optimal scale and a small capital saving bias, increasing the marginal productivity of labour relative to capital. To characterize technical change, Salter's measures of bias and technical advance are utilized and interpreted within the framework of the efficiency cancepts of Farrell.(This abstract was borrowed from another version of this item.)

[We study a team with many processes; the output process depends on the resources allocated to it by the resource manager, on a local decision by the process manager, and on a (random) parameter of the process. We compare two communication patterns: (1) resource allocations are based on full information, but local decisions are based only on corresponding local information; (2) all decisions are based on full information. We show that, if the criterion is expected average output per process, and if the process parameters are independent and identically distributed, then (under certain regularity assumptions) for "large" teams the additional communication among process managers in (2) over (1) has approximately no value.]

[The purpose of this paper is to amend some of Hannan's [6] identification results for ARMAX models and to discuss identification in ARMAX models when identities are present. In addition, we briefly discuss how the ARMAX results can be adapted to obtain identification conditions for dynamic simultaneous equations models with autoregressive disturbances.]

A simple test for heteroscedastic disturbances in a linear regression model is developed using the framework of the Lagrangian multiplier test. For a wide range of heteroscedastic and random coefficient specifications, the criterion is given as a readily computed function of the OLS residuals. Some finite sample evidence is presented to supplement the general asymptotic properties of Lagrangian multiplier tests.

For a general vector linear time series model we prove the strong consistency and asymptotic normality of parameter estimates obtained by maximizing a particular time domain approximation to a Gaussian likelihood, although we do not assume that the observations are necessarily normally distributed. To solve the normal equations we set up a constrained Gauss-Newton iteration and obtain the properties of the iterates when the sample size is large. In particular we show that the iterates are efficient when the iteration begins with a VN- consistent estimator. We obtain similar results to the above for a frequency domain approximation to a Gaussian likelihood. We use the asymptotic estimation theory to obtain the asymptotic distribution of several familiar test statistics for testing nonlinear equality constraints.