This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently...

An important issue in macroeconomic modelling using times series data centers around the question of whether the observed series is generated by a stationary or a non-stationary process. Recent research has shown that there is a seasonal cycle in the US economy that closely mirrors business...

This thesis addresses the issue of estimating persistence of economic shocks using time series models. First, it is shown that the log likelihood function for ARIMA models is not strictly quadratic with respect to the persistence estimate. This result explains why the persistence literature has...

One of the most important aspects in analyzing economic time series is to specify whether the observed series is generated by a stationary or non-stationary process, since most macroeconomic variables could be generated by a unit autoregressive root process. This determination as to whether or...

This study develops a framework for the fitting, analysis, and forecasting of linear and nonlinear time series models. Through Priestley's State Dependent Model and the Kalman filter algorithm, linear, nonlinear and nonstationary models have been fitted to the US unemployment rate series. The...