[In the classical linear regression model the conflict between the W, LR, and LM tests is due to the tests not having the correct significance level. This paper shows that the probability of conflict can be substantial when the three tests are based on the asymptotic chi-square critical value. For this model some computable correction factors for the chi-square critical values are examined, including those derived from a second-order Edgeworth approximation to the exact distributions. It is shown that the probability of conflict between the Edgeworth size-corrected tests is of no practical importance over a wide range of conditions.]

[This paper investigates the distribution of the least squares estimator of the coefficient α in the model @c"t = @a@c"t -"1 + @?"t where the @?"t where the @?"t are independently distributed N (O, @s extasciicircum2). The exact finite sample and limiting distributions are calculated when α ≥ 1 and finite sample distributions when α extless 1. These distributions are used to compute the power functions of tests of the random walk hypothesis α = 1 as well as the hypotheses.]

Recent years have seen increased demand from institutional investors for passive replication products that track the performance of hedge fund strategies using liquid investable assets such as futures contracts. In practice, linear replication methods suffer from poor tracking performance and high turnover. We propose a model combination approach to index replication that pools information from a diverse set of pre-specified factor models. Compared with existing methods, the pooled clone strategies yield consistently lower tracking errors, generate less severe portfolio drawdowns, and require substantially smaller trading volume. The pooled hedge fund clones also provide economic benefits in a portfolio allocation context.

[This paper investigates whether agents can learn how to form rational expectations using standard econometric techniques in the case of a linear stochastic supply and demand model with a production lag. This model has a unique rational expectations equilibrium in which the expected price is a linear function of an observable exogenous random variable. Outside of rational expectations equilibrium agents predict the price by using a regression of past prices on the exogenous random variable where the regression is estimated by either ordinary least squares or Bayesian methods. If the agents are Bayesians, they may have diverse prior beliefs on the mean of the estimated parameter, but all have the same precision. This estimation procedure would be appropriate for an outside observer estimating the parameters of the model in rational expectations equilibrium the coefficient of the equation relating the mathematical conditional expectation of the price to the exogenous variable is constant through time. Outside rational expectations equilibrium this coefficient, which changes each time new data change the regression coefficient. The data are generated by a time-varying parameter model where the varying parameter is determined by past data and the estimation procedure. Agents fail to take this feedback into account and so are estimating a misspecific model.]

Model selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.