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Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalmanfilter, transfer-function and intervention models, unit root tests,cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M,Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterlytime 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 uniqueinteraction between GDP and GCEGI in both the short-run and the long-run andprovided policy makers with some suggestions. For example in the short run, GDPresponded positively and very significantly (0.00248) to GCEGI, while GCEGIreacted 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 pastGCEGI, while current GCEGI reacted negatively yet temporarily (0.29821) to ashock in past GDP. Therefore, policy makers should not adjust current GCEGIbased merely on the condition of current and past GDP. Although increasingGCEGI does help GDP in the short-term, significantly abrupt increase in GCEGImight not be good to the long-term health of GDP. Instead, a balanced,sustainable, and economically viable solution is recommended, so that theshort-term benefits to the current economy from increasing GCEGI often largelysecured by the long-term loan outweigh or at least equal to the negative effectto the future economy from the long-term debt incurred by the loan. Finally, Ifound that non-normally distributed volatility models generally perform betterthan normally distributed ones. More specifically, TARCH-GED performs the bestin the group of non-normally distributed, while GARCH-M does the best in thegroup of normally distributed.

Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013