Eco-520

Model 1- Static Regression
C(t)=a+b*y(t)+u(t)
Our first model has the log of consumption as the dependent variable and the log of income as the independent variable and used 29 observations. The intercept is 0.1171 and that’s our best prediction for consumption in case income is zero. The slope reveals that for a $1 billion increase in income, consumption would go up by 0.8247 billion and from the elasticity we observe that for a 1% increase in income there will be an 82% increase in consumption, so as one would expect a higher level of income would lead to a higher consumption level. The estimated variance is 0.00047696 and the standard error is 0.01288. Using the t-statistic of a which is 1.96 we accept the null that the intercept is zero and the t-statistic for b is 115.01 allows us to reject the null about the slope. The Fvalue of 13227.1 allows us to assume a strong relationship between consumption and income. The DW statistic is used to test for the presence of both positive and negative correlation in the residuals. The Null Hypothesis (Ho) is that there is no significant correlation. Here in our first model we get a DW statistic 0.4754, and checking at the 5% significance points of d1 and du for DW test, we find that for one independent variable our DW statistic is between 0 and d1, that’s the region of rejection, this tells us that we reject the Null Hypothesis that there is no significant correlation in the residuals, and there’s a positive autocorrelation. The short run income elasticity is 0.8247 and the long run income elasticity is also 0.8247 since this is a simple regression model and there are no dynamic elements.

Model 2- Univariate Time Series 1 c (t)=a+b*c(t-1)+u(t)
Our second model has the log of consumption as the dependent variable and the lag of the log of consumption as the independent variable and used 28 observations. The