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Analysing Economic DataRegression

Analysing Economic Data: Regression [Regressions are introduced as straight lines fitted through a scatterplot. The calculation of a regression as the ‘line of best fit’, obtained by minimising the sum of squared vertical deviations about the line (the least squares approach), is developed. This provides the least squares formulae for estimating the intercept and slope, and the interpretation of the regression line is discussed. The links between correlation, causation, reverse regression and partial correlation are investigated. Further issues involving regressions, such as how to deal with non-linearity, the use of time trends and lagged dependent variables as regressors, and the computation of elasticities, are all developed and illustrated using various economic examples.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Analysing Economic DataRegression

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Publisher
Palgrave Macmillan UK
Copyright
© Palgrave Macmillan, a division of Macmillan Publishers Limited 2014
ISBN
978-1-349-48656-4
Pages
83 –99
DOI
10.1057/9781137401908_6
Publisher site
See Chapter on Publisher Site

Abstract

[Regressions are introduced as straight lines fitted through a scatterplot. The calculation of a regression as the ‘line of best fit’, obtained by minimising the sum of squared vertical deviations about the line (the least squares approach), is developed. This provides the least squares formulae for estimating the intercept and slope, and the interpretation of the regression line is discussed. The links between correlation, causation, reverse regression and partial correlation are investigated. Further issues involving regressions, such as how to deal with non-linearity, the use of time trends and lagged dependent variables as regressors, and the computation of elasticities, are all developed and illustrated using various economic examples.]

Published: Oct 26, 2015

Keywords: Income Growth; Phillips Curve; Dependent Variable Model; Reverse Regression; Aggregate Time Series

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