Download Applied Econometrics: A Modern Approach Using Eviews and by Dimitrios Asteriou PDF

By Dimitrios Asteriou

This new econometrics textual content bargains particularly with using econometric software program. The textual content takes the reader from many of the sorts of econometric info (time sequence, move sectional and panel), via their formatting in digital media (eg ASCII) to their move to and use in known software program packages--Excel, Microfit and Eviews. such a lot economics levels now require scholars to take advantage of proper software program to check econometric types and this article illustrates basically how this can be to be performed.

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Additional info for Applied Econometrics: A Modern Approach Using Eviews and Microfit Revised Edition

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Time series equations almost always generate higher R2 values than cross-section equations. This arises because cross- sectional data contain a great deal of random variation (usually called 'noise') which makes ESS small relative to TSS. 999 for the spurious regression reasons presented in point 1 above. Therefore, comparisons of time series and cross-sectional equations using R2 are not possible. S Low R2 does not mean wrong choice of Xt. Low values of R2 are not necessarily the result of using a wrong explanatory variable.

L The Classical Linear Regression Model -, ... J Estimators (BLUE). In order to do that, we first have to decompose the regression coefficients estimated under OLS into their random and non-random components. As a starting point note that Y1 has a non-random component (a+ f3Xt ), as well as a random component which is captured by the residuals u1 . 29) + Cov(X, u) l"l However, because a and f3 are constants we have that Cov(X, a) · = 0 and that Cov(X,{JX) = {3Cov(X,X) = pVar(X). 30) \ ·j . 31) )l which says that the OLS coefficient [3 estimated from any sample has a non-random component, {3, and a random component which depends on the Cov(X1 , u 1 ).

G. due to a business cycle); 3 seasonal (within-year pattern seen in weekly, monthly or quarterly data); or I] 4 irregular (random component; can be subdivided into episodic [unpredictable but identifiable] and residual [unpredictable and unidentifiable]). LJ ! Note that not all time series have all four components, although tqe irregular component is present in every series. As we shall see later, various techniques are· available for removing one or more components from a time series. g. price or guantity) from one period to another.

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