This paper develops an exact maximum likelihood technique for estimating regression equation with general p'th order autoregressive disturbances. Recent expression of the analytic inverse of the covariance matrix of a stationary AR(p) process provide the basis for an iterative, modified...

This paper applies the recent theoretical method of Richmond (1982) on estimating joint confidence intervals to the case of Lorenz curves. Using the results of Beach and Davidson (1982) on the asymptotic distribution of a vector of Lorenz curve ordinates, the paper provides joint confidence...

Maximum likelihood estimation of equation systems with first-order autocorrelation should, in principle, take into account the first observation and associated stationarity condition. In the general case, this leads to computational difficulties compared with conventional procedures, which...

This paper advances a new method for analyzing short-run cyclical changes in income concentration in the size distribution of income. Rather than focussing on Gini coefficients or variance, this approach involves analyzing fluctuations in individual quantile income levels for each of seven age...

This paper develops a technique for estimating linear models with second-order autoregressive errors, which utilizes the full set of observations, and explicitly constrains the estimates of the error process to satisfy a priori stationarity conditions. A nonlinear solution technique which is new...