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Econometrics is the use of quantitative statistical methods and mathematics to forecast or predict future trends on the basis of historical data. It tests the real world data with many statistical tools and then compares it with a theory or hypothesis to determine whether the result confirms the theory or hypothesis or not. Econometrics can be divided into two broad categories — theoretical and applied econometrics.
A Little More on What is Econometrics
Today, econometrics is not only used as an academic subject. Rather, it is used by traders, policy makers, students, and economists. The field of econometrics was introduced by Simon Kuznets, R. Frisch and L. Klein. They were awarded Nobel Prize in economics in 1971 for their contribution in the field of economics.
Econometricians use quantitative tools and methods to analyses historical data for testing or developing theory or model etc. Econometricians may use different quantitative tools such as probability distribution, covariance, correlation, simple regression, time series analysis, frequency distribution, probability distribution and more methods.
Simples Scenario of Econometric Testing
Let’s assume econometrician want to test income effect. An economist may record publicly available data. He may then develop a hypothesis that an increase in a person’s income will lead to an increase in spending. He may use regression or correlation to determine their relationship. Regression will measure how strongly income affects consumption and will determine whether their relationship is significant or not.
If you want to analyze the impacts of investment on GDP, you would obtain historical data of investment rates and the corresponding level of GDP. The investment would be your independent variable whereas GDP is a dependent variable. In most cases linear relationship is used where change in one variable causes a change in other variable. You may want to use many explanatory variables in your regression model. A simple regression model is used to analyze the relationship between the dependent and an explanatory variable. You would use multiple regression models to explain the relationship between the dependent and multiple explanatory variables.
References for Econometrics
Academic Research on Econometrics
- ● Introduction to the Theory and Practice of Econometrics., Judge, G. G., Hill, R. C., Griffiths, W., Lutkepohl, H., & Lee, T. C. (1982).
- ● Estimation and inference in econometrics, Davidson, R., & MacKinnon, J. G. (1993). OUP Catalogue. This paper explores the techniques of estimation. The paper makes use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. The author goes on to show how estimation can be taken from a mere algebraic process to a standard software package.
- ● Spurious regressions in econometrics, Granger, C. W., & Newbold, P. (1974). Journal of econometrics, 2(2), 111-120.
- ● Specification tests in econometrics, Hausman, J. A. (1978). Econometrica: Journal of the econometric society, 1251-1271. Using the result that under the null hypothesis of no misspecification an asymptotically efficient estimator must have zero asymptotic covariance with its difference from a consistent but asymptotically inefficient estimator, specification tests are devised for a number of model specifications in econometrics. Local power is calculated for small departures from the null hypothesis. An instrumental variable test as well as tests for a time series cross section model and the simultaneous equation model are presented. An empirical model provides evidence that unobserved individual factors are present which are not orthogonal to the included right-hand-side variable in a common econometric specification of an individual wage equation.
- ● Introduction to econometrics, Maddala, G. S., & Lahiri, K. (2009). Wiley.
- ● The biggest myth in spatial econometrics, LeSage, J. P., & Pace, R. K. (2014). Econometrics, 2(4), 217-249. This paper investigates the belief that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. The author wishes to test the validity of this belief using theoretical basis. Results show that this belief is a common myth, which may have arisen from past applied work that incorrectly interpreted the model coefficients as if they were partial derivatives, or from use of misspecified models.
- ● The Lagrange multiplier test and its applications to model specification in econometrics, Breusch, T. S., & Pagan, A. R. (1980). The Review of Economic Studies, 47(1), 239-253.
- ● The economics and econometrics of active labor market programs, Heckman, J. J., LaLonde, R. J., & Smith, J. A. (1999). In Handbook of labor economics (Vol. 3, pp. 1865-2097). Elsevier. This paper examines the belief that public sector-sponsored employment and training programs and other active labor market policies as tools for integrating the unemployed and economically disadvantaged into the workforce. It goes on to show the impacts of active labor market policies, such as job training, job search assistance, and job subsidies, and the methods used to evaluate their effectiveness. The aim of this paper is to show the impact of these policies on different firms, and the reason for the different results.
- ● Long memory processes and fractional integration in econometrics, Baillie, R. T. (1996). Journal of econometrics, 73(1), 5-59. This paper provides a survey and review of the major econometric work on long memory processes, fractional integration, and their applications in economics and finance. Some of the definitions of long memory are reviewed, together with previous work in other disciplines.
- ● Applied econometrics, Hayo, B. (1971).
- ● Recent developments in the econometrics of program evaluation, Imbens, G. W., & Wooldridge, J. M. (2009). Journal of economic literature, 47(1), 5-86. This paper emphasizes on the different questions surrounding the casual effects of programs or policies. This theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. In this review, the authors discuss some of the recent developments. This research focuses primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.
- ● Understanding spurious regressions in econometrics, Phillips, P. C. (1986). Journal of econometrics, 33(3), 311-340. This paper provides an analytical study of linear regressions involving the levels of economic time series. An asymptotic theory is developed for regressions that relate quite general integrated random processes. Another theory is developed for the regression coefficients and for conventional significance tests. The limiting behavior of regression diagnostics such as the Durbin–Watson statistic, the coefficient of determination and the Box–Pierce statistic is also analyzed. The theoretical results that we present explain many of the earlier simulation findings of Granger and Newbold, 1974, Granger and Newbold, 1977.