Standardized slowly varying regressors are shown to be <italic>L</italic>-approximable. This fact allows us to provide alternative proofs of asymptotic expansions of nonstochastic quantities and central limit results due to P.C.B. Phillips, under a less stringent assumption on linear processes. The recourse to...

For order q kernel density estimators we show that the constant bq in bias=bqhq+o(hq) can be made arbitrarily small, while keeping the variance bounded. A data-based selection of bq is presented and Monte Carlo simulations illustrate the advantages of the method.

We find the asymptotic distribution of the OLS estimator of the parameters [beta] and [rho] in the mixed spatial model with exogenous regressors Yn=Xn[beta]+[rho]WnYn+Vn. The exogenous regressors may be bounded or growing, like polynomial trends. The assumption about the spatial matrix Wn is...

We derive the asymptotics of the OLS estimator for a purely autoregressive spatial model. Only low-level conditions are used. As the sample size increases, the spatial matrix is assumed to approach a square-integrable function on the square (0,1)2. The asymptotic distribution is a ratio of two...