Practical Methods in Multivariate Time Series Analysis (Causality, Arma, Box-Jenkins)

Box and Jenkins initiated the burgeoning interest in time series model building over a decade ago when they developed several specialized techniques used in model selection, estimation, and checking. These methods have been widely applied by researchers interested in lag structures, forecasts, and interrelationships of variables over time. The Box-Jenkins approach to time series analysis has proved to be quite effective when dealing with single time series, or, in certain cases, with pairs of time series. They did not, however, discuss guidelines for analysis when a bivariate or multivariate model exhibits feedback, or, in other word, when the causal relations are not unidirectional. The purpose of this thesis is to establish a workable methodology for building multivariate time series models where feedback is possible and to apply it over a range of economic data sets.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Hotopp, Steven Michael

• Economics, General

Identifier

(UMI)AAI8600211

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/70764