This interview with Michio Hatanaka is the first in this series given in the East, of which we are very proud. Hatanaka is a pioneer of econometrics in Japan. In the early 1950s he traveled to the United States to study as a graduate student at Vanderbilt University. That step was really unusual...

Let {<bold>X</bold>(<italic>t</italic>)} be a multivariate Gaussian stationary process with the spectral density matrix <italic>f</italic>₀(ω), where θ is an unknown parameter vector. Using a quasi-maximum likelihood estimator <private-char>null</private-char> of θ, we estimate the spectral density matrix <italic>f</italic>₀(ω) by <italic>f</italic><private-char>null</private-char>(ω). Then we derive asymptotic expansions of...

We compare the distributional properties of the four predictors commonly used in practice. They are based on the maximum likelihood, two types of the least squared, and the Yule-Walker estimators. The asymptotic expansions of the distribution, bias, and mean-squared error for the four predictors...

In this paper we consider the situation in which ordinary least squares (OLS) is used to estimate an ARMA (1,1) model with one exogenous variable. Applying Edgeworth expansion techniques, we examine the misspecification errors and the approximate distributions of the OLS estimator. Extensive...