Advanced Time Series Data Analysis

Preface

It is well‐known that forecasting is one of the best inputs for decision‐making. However, we never know what type of model gives a perfect forecast values beyond the sample period, since there are a lot of possible models that can be developed to forecast any selected endogenous time series that are acceptable in the statistical sense. In addition, in‐sample forecast values are highly dependent on the data that happens to be selected by or available to researchers.

This book presents many alternative multiple regression models of a monthly, quarterly, and annual endogenous time series with specific growth patterns, starting with the simplest up to the most advanced time series models so that those models can show their differential in‐sample forecast values of the endogenous variable. Hence, the main objectives of this book are to present (i) various general specific equation of forecast models, which in fact are multiple time series regression models; (ii) various illustrative statistical results based on selected specific equations, with special notes and comments; and (iii) comparative studies between a set of special type of models using the same set of variables, such as additive models, interaction models, and heterogeneous regression models, without trend and with various alternative trends. The best possible fit forecasting model of an endogenous time series, in the statistical sense, is presented based on alternative specific growth cures of the time series. Furthermore, ...