Springer Texts in Business and Economics
What is Econometrics?
This chapter emphasizes that an econometrician has to be a competent mathematician and statistician who is an economist by training. It is the unification of statistics, economic theory and mathematics that constitutes econometrics. Each view point, by itself is necessary but not sufficient for a real understanding of quantitative relations in modern economic life, see Frisch (1933).
Econometrics aims at giving empirical content to economic relationships. The three key ingredients are economic theory, economic data, and statistical methods. Neither ‘theory without measurement’, nor ‘measurement without theory’ are sufficient for explaining economic phenomena. It is as Frisch emphasized their union that is the key for success in the future development of econometrics.
Econometrics provides tools for testing economic laws, such as purchasing power parity, the life cycle hypothesis, the wage curve, etc. These economic laws or hypotheses are testable with economic data. As Hendry (1980) emphasized “The three golden rules of econometrics are test, test and test.”
Econometrics also provides quantitative estimates of price and income elasticities of demand, returns to scale in production, technical efficiency in cost functions, wage elasticities, etc. These are important for policy decision making. Raising the tax on a pack of cigarettes by 10%, how much will that reduce consumption of cigarettes? How much will it generate in tax revenues? What is the effect of raising minimum wage by $1 per hour on unemployment? What is the effect of raising beer tax on motor vehicle fatality?
Econometrics also provides predictions or forecasts about future interest rates, unemployment, orGNP growth. As Klein (1971) emphasized: “Econometrics should give a base for economic prediction beyond experience if it is to be useful.”
Data in economics are not generated under ideal experimental conditions as in a physics laboratory. This data cannot be replicated and is most likely measured with error. Most of the time the data collected are not ideal for the economic question at hand. Griliches (1986, p. 1466) describes economic data as the world that we want to explain, and at the same time the source of all our trouble. The data’s imperfections makes the econometrician’s job difficult and sometimes impossible, yet these imperfections are what gives econometricians their legitimacy.
B. H. Baltagi, Solutions Manual for Econometrics, Springer Texts 1
in Business and Economics, DOI 10.1007/978-3-642-54548-1—1,
© Springer-Verlag Berlin Heidelberg 2015
Even though economists are increasingly getting involved in collecting their data and measuring variables more accurately and despite the increase in data sets and data storage and computational accuracy, some of the warnings given by Griliches (1986, p. 1468) are still valid today:
econometricians want too much from the data and hence tend to be disappointed by the answers, because the data are incomplete and imperfect. In part it is our fault, the appetite grows with eating. As we get larger samples, we keep adding variables and expanding our models, until on the margin, we come back to the same insignificance levels.
Pesaran (1990, pp. 25-26) also summarizes some of the limitations of econometrics:
There is no doubt that econometrics is subject to important limitations, which stem largely from the incompleteness of the economic theory and the nonexperimental nature of economic data. But these limitations should not distract us from recognizing the fundamental role that econometrics has come to play in the development of economics as a scientific discipline. It may not be possible conclusively to reject economic theories by means of econometric methods, but it does not mean that nothing useful can be learned from attempts at testing particular formulations of a given theory against (possible) rival alternatives. Similarly, the fact that econometric modelling is inevitably subject to the problem of specification searches does not mean that the whole activity is pointless. Econometric models are important tools for forecasting and policy analysis, and it is unlikely that they will be discarded in the future. The challenge is to recognize their limitations and to work towards turning them into more reliable and effective tools. There seem to be no viable alternatives.
Econometrics have experienced phenomenal growth in the past 50 years. There are six volumes of the Handbook of Econometrics, most of it dealing with post 1960s research. A lot of the recent growth reflects the rapid advances in computing technology. The broad availability of micro data bases is a major advance which facilitated the growth of panel data methods (see Chap. 12) and microeconometric methods especially on sample selection and discrete choice (see Chap. 13) and that also lead to the award of the Nobel Prize in Economics to James Heckman and Daniel McFadden in 2000. The explosion in research in time series econometrics which lead to the development of ARCH and GARCH and cointegration (see Chap. 14) which also lead to the award of the Nobel Prize in Economics to Clive Granger and Robert Engle in 2003.
The challenge for the twenty-first century is to narrow the gap between theory and practice. Many feel that this gap has been widening with theoretical research growing more and more abstract and highly mathematical without an application in sight or a motivation for practical use. Heckman (2001) argues that econometrics is useful only if it helps economists conduct and interpret empirical research on economic data. He warns that the gap between econometric theory and empirical practice has grown over the past two decades. Theoretical econometrics becoming more closely tied to mathematical statistics. Although he finds nothing wrong, and much potential value, in using methods and ideas from other fields to improve empirical work in economics, he does warn of the risks involved in uncritically adopting the methods and mind set of the statisticians:
Econometric methods uncritically adapted from statistics are not useful in many research activities pursued by economists. A theorem-proof format is poorly suited for analyzing economic data, which requires skills of synthesis, interpretation and empirical investigation. Command of statistical methods is only a part, and sometimes a very small part, of what is required to do first class empirical research.
Geweke et al. (2008) in the The New Palgrave Dictionary provide the following recommendations for the future:
Econometric theory and practice seek to provide information required for informed decision-making in public and private economic policy. This process is limited not only by the adequacy of econometrics, but also by the development of economic theory and the adequacy of data and other information. Effective progress, in the future as in the past, will come from simultaneous improvements in econometrics, economic theory, and data. Research that specifically addresses the effectiveness of the interface between any two of these three in improving policy — to say nothing of all of them — necessarily transcends traditional subdisciplinary boundaries within economics. But it is precisely these combinations that hold the greatest promise for the social contribution of academic economics.
For a world wide ranking of econometricians as well as academic institutions in the field of econometrics, see Baltagi (2007).
References
Frisch, R. (1933), “Editorial,” Econometrica, 1: 1-14.
Geweke, J., J. Horowitz, and M. H. Pesaran (2008), “Econometrics,” The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan.
Heckman, J. J. (2001), “Econometrics and Empirical Economics,” Journal of Econometrics, 100: 3-5.
Hendry, D. F. (1980), “Econometrics - Alchemy or Science?” Economica, 47: 387-406.
Klein, L. R. (1971), “Whither Econometrics?” Journal of the American Statistical Association, 66: 415-421.
