This thesis is concerned with simulation output analysis. In particular, we are inter-ested in estimating the variance parameter of a steady-state output process. The estimationof the variance parameter has immediate applications in problems involving (i) the precisionof the sample mean as a...

Motivated by the problem of setting prediction intervals in time seriesanalysis, this investigation is concerned with recovering a regression functionm(X_t) on the basis of noisy observations taking at random design pointsX_t.It is presumed that the corresponding observations are corrupted by...

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. This estimator does not require that disturbances are normally distributed, but if they are, the proposed estimator is asymptotically equivalent to the maximum likelihood...

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The VC estimates are moments...

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The VC estimator is a moments...

This paper discusses a practical estimation issue for time-varying transition probability (TVTP) Markov switching models. Time-varying transition probabilities allow researchers to capture important economic behavior that may be missed using constant (or fixed) transition probabilities. Despite...