We consider the finite sample power of various tests against serial correlation in the disturbances of a linear regression when these disturbances follow a stationary long memory process. It emerges that the power depends on the form of the regressor matrix and that, for the Durbin-Watson test...

In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step...

In this note we propose model selection criteria (MSC) for unconditional moment models using empirical likelihood (EL) statistics in the construction of the MSC The use of EL-statistics in lieu of the more common J-statistics leads to a much more transparent interpretation of the MSC by...

When alternatives are compared using an estimated criterion function, this may introduce a discrepancy between the true and the estimated criterion. In this paper, we consider a situation where a preordering (ranking) of stochastic sequences is defined from expected loss/gain, using a parametric...

Starting from an information process governed by a geometric Brownian motion we show that asset returns are predictable if the elasticity of the pricing kernel is not constant. Declining [Increasing] elasticity of the pricing kernel leads to mean reversion and negatively autocorrelated asset...

Properties of a specification test for the parametric form of the variance function in diffusion processes dXt = b (t,Xt) dt + sigma (t,Xt) dWt are discussed. The test is based on the estimation of certain integrals of the volatility function. If the volatility function does not depend on the...

Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity....