Statistical Tools for Program Evaluation pp 137-187 | Cite as

# Econometric Analysis

## Abstract

Econometrics encompasses several multivariate tools for testing a theory or hypothesis, quantifying it and providing indications about the evolution of an outcome of interest. This chapter gives the basic knowledge on how those tools work, with examples and the corresponding R-CRAN codes. The first step is dedicated to simple and multiple linear regression models and their estimation by the method of ordinary least squares (Sects. 5.1 and 5.2). The classical assumptions (e.g., linearity, normality of residuals, homoscedasticity, non-autocorrelation) underlying the method are exposed (Sect. 5.3). An important step in conducting an econometric analysis is model specification as it determines the validity of the regression analysis (Sect. 5.4). Another issue is the choice of the functional form that best fits the data, i.e. whether the variables are expressed or not in a non-linear form (Sect. 5.5). Several tests also exist to detect potential misspecifications (Jarque-Bera, Breusch-Pagan, and Durbin-Watson tests) which are fully detailed (Sect. 5.6). Last, the selection of the final model relies on a meticulous examination of regression outputs, e.g., whether the variables sufficiently explain the outcome of interest (Sect. 5.7). Methods are then extended to the case where the latter is binary (Sect. 5.8), the so-called logit and probit models.

## Keywords

Regression Ordinary least squares Residuals Misspecification Logit Probit## References

- Benini, R. (1907). Sull’uso delle formole empiriche a nell’economia applicata.
*Giornale degli economisti, 2nd series, 35*, 1053–1063.Google Scholar - Eatwell, J., Milgate, M., & Newman, P. (1990).
*Econometrics*. Springer.Google Scholar - Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics.
*Philosophical Transactions of the Royal Society of London, 222*, 309–368.CrossRefGoogle Scholar - Frisch, R. (1934).
*Statistical confluence analysis by means of complete regression systems*. Oslo: University Institute of Economics.Google Scholar - Galton, F. (1886). Regression towards mediocrity in hereditary stature.
*The Journal of the Anthropological Institute of Great Britain and Ireland, 15*, 246–263.CrossRefGoogle Scholar - Green, S. B. (1991). How many subjects does it take to do a regression analysis?
*Multivariate Behavioral Research, 26*, 499–510.CrossRefGoogle Scholar - Greene, W. H. (2011).
*Econometric analysis*(7th ed.). Hoboken, NJ: Pearson.Google Scholar - Gujarati, D. N., & Porter, D. C. (2009).
*Basic econometrics*. McGraw-Hill Irwin.Google Scholar - Moore, H. L. (1914).
*Economic cycles: Their law and cause*. New York: Macmillan Press.Google Scholar - Pearson, K. (1894). Contribution to the mathematical theory of evolution.
*Philosophical Transactions of the Royal Society of London Series A, 185*, 71–110.CrossRefGoogle Scholar - Verbeek, M. (2012).
*A guide to modern econometrics*(4th ed.). Chichester: Wiley.Google Scholar