What Is Econometrics?

1.1 Introduction

What is econometrics? A few definitions are given below:

The method of econometric research aims, essentially, at a conjunction of economic theory and actual measurements, using the theory and technique of statistical infer­ence as a bridge pier.

Trygve Haavelmo (1944)

Econometrics may be defined as the quantitative analysis of actual economic phe­nomena based on the concurrent development of theory and observation, related by appropriate methods of inference.

Samuelson, Koopmans and Stone (1954)

Econometrics is concerned with the systematic study of economic phenomena using observed data.

Aris Spanos (1986)

Broadly speaking, econometrics aims to give empirical content to economic relations for testing economic theories, forecasting, decision making, and for ex post deci- sion/policy evaluation.

J. Geweke, J. Horowitz, and M. H. Pesaran (2008)

For other definitions of econometrics, see Tintner (1953).

An econometrician has to be a competent mathematician and statistician who is an economist by training. Fundamental knowledge of mathematics, statistics and economic theory are a nec­essary prerequisite for this field. As Ragnar Frisch (1933) explains in the first issue of Econo- metrica, 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.

Ragnar Frisch is credited with coining the term ‘econometrics’ and he is one of the founders of the Econometrics Society, see Christ (1983). 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.

Lawrence R. Klein, the 1980 recipient of the Nobel Prize in economics “for the creation of econometric models and their application to the analysis of economic fluctuations and economic policies,”1 has always emphasized the integration of economic theory, statistical methods and practical economics. The exciting thing about econometrics is its concern for verifying or refuting economic laws, such as purchasing power parity, the life cycle hypothesis, the quantity theory of money, etc. These economic laws or hypotheses are testable with economic data. In fact, David F. Hendry (1980) emphasized this function of econometrics:

B. H. Baltagi, Econometrics, Springer Texts in Business and Economics, DOI 10.1007/978-3-642-20059-5_1, 3

© Springer-Verlag Berlin Heidelberg 2011

The three golden rules of econometrics are test, test and test; that all three rules are broken regularly in empirical applications is fortunately easily remedied. Rigorously tested models, which adequately described the available data, encompassed previous findings and were derived from well based theories would enhance any claim to be scientific.

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. What is the effect of raising the tax on a pack of cigarettes by 10% in reducing smoking? 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 about future interest rates, unemployment, or GNP growth. Lawrence Klein (1971) emphasized this last function of econometrics:

Econometrics had its origin in the recognition of empirical regularities and the sys­tematic attempt to generalize these regularities into “laws” of economics. In a broad sense, the use of such “laws” is to make predictions – about what might have or what will come to pass. Econometrics should give a base for economic prediction beyond experience if it is to be useful. In this broad sense it may be called the science of economic prediction.

Econometrics, while based on scientific principles, still retains a certain element of art. According to Malinvaud (1966), the art in econometrics is trying to find the right set of assumptions which are sufficiently specific, yet realistic to enable us to take the best possible advantage of the available data. 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. In some cases, the available data are proxies for variables that are either not observed or cannot be measured. Many published empirical studies find that economic data may not have enough variation to discriminate between two competing economic theories. Manski (1995, p. 8) argues that

Social scientists and policymakers alike seem driven to draw sharp conclusions, even when these can be generated only by imposing much stronger assumptions than can be defended. We need to develop a (greater tolerance for ambiguity. We must face up to the fact that we cannot answer all of the (questions that we ask.

To some, the “art” element in econometrics has left a number of distinguished economists doubt­ful of the power of econometrics to yield sharp predictions. In his presidential address to the American Economic Association, Wassily Leontief (1971, pp. 2-3) characterized econometrics work as:

an attempt to compensate for the glaring weakness of the data base available to us by the widest possible use of more and more sophisticated techniques. Alongside the mounting pile of elaborate theoretical models we see a fast growing stock of equally intricate statistical tools. These are intended to stretch to the limit the meager supply of facts.

Economic data can be of the cross-section type, for e. g., a sample of firms or households or countries at a particular point in time. An important data source is the Current Population Survey. This is a monthly survey of 50,000 households in the U. S. which is used to estimate the unemployment rate. Data can also be of the time-series type, for e. g., macroeconomic variables like Gross Domestic Product (GDP), Personal Disposable Income, Consumption, Government Expenditures, etc. for the U. S. observed over the last 40 years. These can be found in the Economic Report of the President. See Chapter 14 for some basic time-series methods in econo­metrics. Data can also be following a group of households, firms, or countries over time, i. e., Longitudinal data or panel data. The National Longitudinal Survey of Youth, 1979 consists of a nationally representative sample of 12686 young men and women who were 14-22 years old in 1979. These individuals were interviewed annually through 1994 and currently interviewed on a biennial basis. The list of variables include information on schooling and career transitions, marriage and fertility, training investments, child care usage and drug and alcohol use. See Chapter 12 for some basic panel data methods in econometrics.

Most of the time the data collected are not ideal for the economic question at hand because they were posed to answer legal requirements or comply to regulatory agencies. Griliches (1986, p. 1466) describes the situation as follows:

Econometricians have an ambivilant attitude towards economic data. At one level, the ‘data’ are the world that we want to explain, the basic facts that economists purport to elucidate. At the other level, they are the source of all our trouble. Their imperfections make our job difficult and often impossible… We tend to forget that these imperfections are what gives us our legitimacy in the first place… Given that it is the ‘badness’ of the data that provides us with our living, perhaps it is not all that surprising that we have shown little interest in improving it, in getting involved in the grubby task of designing and collecting original data sets of our own. Most of our work is on ‘found’ data, data that have been collected by somebody else, often for quite different purposes.

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:

The encounters between econometricians and data are frustrating and ultimately unsatisfactory both because econometricians want too much from the data and hence tend to be dissappointed by the answers, and 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.

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