Comprehensive TimeSeries Regression Models Using GRETL  U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013
Abstract
Using Gretl, I apply ARMA, Vector ARMA, VAR, statespace model with a Kalman filter, transferfunction and intervention models, unit root tests, cointegration test, volatility models (ARCH, GARCH, ARCHM, GARCHM, TaylorSchwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly time series of GDP and Government Consumption Expenditures & Gross Investment (GCEGI) from 1980 to 2013. The article is organized as: (I) Definition; (II) Regression Models; (III) Discussion. Additionally, I discovered a unique interaction between GDP and GCEGI in both the shortrun and the longrun and provided policy makers with some suggestions. For example in the short run, GDP responded positively and very significantly (0.00248) to GCEGI, while GCEGI reacted positively but not too significantly (0.08051) to GDP. In the long run, current GDP responded negatively and permanently (0.09229) to a shock in past GCEGI, while current GCEGI reacted negatively yet temporarily (0.29821) to a shock in past GDP. Therefore, policy makers should not adjust current GCEGI based merely on the condition of current and past GDP. Although increasing GCEGI does help GDP in the shortterm, significantly abrupt increase in GCEGI might not be good to the longterm health of GDP. Instead, a balanced, sustainable, and economically viable solution is recommended, so that the shortterm benefits to the current economy from increasing GCEGI often largely secured by the longterm loan outweigh or at least equal to the negative effect to the future economy from the longterm debt incurred by the loan. Finally, I found that nonnormally distributed volatility models generally perform better than normally distributed ones. More specifically, TARCHGED performs the best in the group of nonnormally distributed, while GARCHM does the best in the group of normally distributed.
 Publication:

arXiv eprints
 Pub Date:
 December 2014
 arXiv:
 arXiv:1412.5397
 Bibcode:
 2014arXiv1412.5397S
 Keywords:

 Economics  General Economics;
 Statistics  Applications
 EPrint:
 82 Pages with Gretl codes included