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2024 | OriginalPaper | Chapter

1. Introductory Developments

Author : Valérie Mignon

Published in: Principles of Econometrics

Publisher: Springer Nature Switzerland

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Abstract

Econometrics is a discipline with a strong operational content. It enables us to quantify a phenomenon, establish a relationship between several variables, validate or invalidate a theory, evaluate the effects of an economic policy measure, etc. This introductory chapter gives some examples to illustrate in a simple way what econometrics can do. It presents the concepts of model and variable and offers some statistical reminders about the mean, variance, standard deviation, covariance, and linear correlation coefficient. A brief introduction to the concept of stationarity is also provided. Finally, this chapter lists the main databases in economics and finance and the most commonly used software packages.

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next chapter The Simple Regression Model
Footnotes
1
The series are expressed in real terms, i.e., they are deflated by the consumer price index of each country.
 
2
The data are extracted from the national statistical institutes of the two countries: Statistics Finland and the Italian National Institute of Statistics (Istat).
 
3
Strictly speaking, a reading of the General Theory suggests that the concave function seems closest to Keynes’ words; the affine form, however, is the most frequently chosen for practical reasons.
 
4
The circumflex (or hat) notation is a simple convention indicating that this is an estimate (and not an observed value). This convention will be adopted throughout the book.
 
5
Remember that an individual, or a statistical unit, is an element of the population studied.
 
6
A population is a set of elements, called statistical units or individuals, that we wish to study.
 
7
The division by \((T-1)\) instead of T comes from the loss of one degree of freedom since the empirical mean (and not the true population mean) is used in calculating the variance.
 
8
If more than two variables are studied, the concept of multiple correlation must be used (see below).
 
9
The logarithmic transformation is a special case of the Box-Cox transformation used to reduce the variability of a time series (see Box and Cox 1964, and Chap. 2) below.
 
Literature
Box, G.E.P. and D.R. Cox (1964),17 “An Analysis of Transformations” , Journal of the Royal Statistical Society, Series B, 26, pp. 211–243.
Feldstein, M. and C. Horioka (1980), “Domestic Saving and International Capital Flows”, Economic Journal, 90, pp. 314–329.CrossRef
Hoel, P.G. (1974), Introduction to Mathematical Statistics, John Wiley & Sons.
bib89 Intriligator, M.D. (1978), Econometric Models, Techniques and Applications, Prentice Hall.
Keynes, J.M. (1936), The General Theory of Employment, Interest, and Money, Macmillan.
Mood, A.M., Graybill, F.A. and D.C. Boes (1974), Introduction to the Theory of Statistics, McGraw-Hill.
Morgenstern, O. (1963), The Accuracy of Economic Observations, Princeton University Press.
Newbold, P. (1984), Statistics for Business and Economics, Prentice Hall.
Spanos, A. (1999), Probability Theory and Statistical Inference: Econometric Modeling with Observational Data, Cambridge University Press.
Metadata
Title
Introductory Developments
Author
Valérie Mignon
Copyright Year
2024
Book
Principles of Econometrics

Print ISBN: 978-3-031-52534-6

Electronic ISBN: 978-3-031-52535-3

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-52535-3_1

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