Estimation of the Parameters of Linear Time Series Models Subject to Nonlinear Restrictions

Abstract

Least squares estimators of the parameters of a linear time series model, where the parameters are constrained by a set of nonlinear restrictions, are studied. The model may contain lags of the dependent variable as regressors and the sums of squares of the explanatory variables may grow at different rates as the sample size increases. The estimation procedures can be applied to a regression model with an error process that satisfies either a stationary or a nonstationary autoregression.