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

By Dimitrios Asteriou

ISBN-10: 0230506402

ISBN-13: 9780230506404

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

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Extra resources for Applied Econometrics: A Modern Approach Using Eviews and Microfit Revised Edition

Example text

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. The changes are measured relative to the value in a base date (which may be revised from time to time). Common examples of indices are the consumer price index (CPI) and the JSE all-share price index. many prices in one series (the all-share index is comprised of many individual companies' share prices).

Hesis testing and confidence intervals a 1 a-a [·; aa r. • r. e. a normal distribution with 0 mean and variance 1). If we replace the unknown aa and ag by their estimates sa and sg this is no longer true. However, it is relatively easy (the proof of this, however, is r :: I Under the assumptions of the CLRM, we know that the estimators and p obtained by OLS follow a normal distribution with means a and {3 and variances a? and a? a {3 respectively. J 40 . l·:j_: l~~. l follow the student's t-distribution with n - 2 degrees of freedom.

1-. '; where t = 1 for 1990 and t = T = 13 for 2002. Because past events can influence future events and lags in behaviour are prevalent in social sciences, time is a very important dimension in time series data sets. __i_~}agged one period will be denoted as Yt-l and obviously when it is lagged s ·• ·- ·- • • . ~c. -.. :- '1:: -~ i l_,_; The Structure of Economic Data r-! __ j r ' ~~4 LJ Il \. ( l I •, 9 A key feature of time series data, that makes it more difficult to analyse than crosssectional data, is the fact that economic observations are commonly dependent across time.

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