This paper proposes a new method for approximating vector autoregressions by a finite-state Markov chain. The method is more robust to the number of discrete values and tends to outperform the existing methods over a wide range of the parameter space, especially for highly persistent vector...

The Vector Autoregressive (VAR) model with zero coefficient restrictions canbe formulated as a Seemingly Unrelated Regression Equation (SURE) model. Boththe response vectors and the coefficient matrix of the regression equationscomprise columns from a Toeplitz matrix. Efficient numerical and...

This paper investigates the feedback relationship between stock market returns and economic fundamentals in an emerging market. Starting from an intertemporal consumption-based CAPM (CCAPM), we obtain a restricted VAR model for stock returns and macroeconomic variables. We then apply this model...

This paper gives a systematic application of maximum likelihood inference concerning cointegration vectors in non-stationary vector valued autoregressive time series models with Gaussian errors, where the model includes a constant term and seasonal dummies. The hypothesis of cointegration is...

The purpose of this paper is to give a systematic account of the maximum likelihood inference concerning cointegration vectors in non-stationary vector value autoregressive time series with Gaussian errors. The hypothesis of r cointegration vectors is given a simple parametric formulation in...