Definition Econometrics is the application of statistical methods to economic data and is described as the branch of economics that aims to give empirical content to economic relations. Econometrics is an amalgam of economic theory, mathematical economics, economic statistics, and mathematical statistics. Economic theory makes statements or hypotheses that are mostly qualitative in nature; while, econometrics gives empirical content to most economic theory. For example, microeconomic theory states that, other things remaining the same, a reduction in the price of a commodity is expected to increase the quantity demanded of that commodity. Thus, economic theory postulates a negative or inverse relationship between the price and quantity demanded of a commodity. But the theory itself does not provide any numerical measure of the relationship between the two; that is, it does not tell by how much the quantity will go up or down as a result of a certain change in the price of the commodity. It is the job of the econometrician to provide such numerical estimates. Econometrics allows economists to sift through mountains of data to extract simple relationships. Econometricians formulate a statistical model, usually based on economic theory, confront it with the data, and try to come up with a specification that meets the required goals. Precisely, Econometrics is the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference. he first known use of the term "econometrics" (in cognate form) was by Polish economist Paweł Ciompa in 1910. Ragnar Frisch is credited with coining the term in the sense in which it is used today. Methodology Econometrics uses a fairly straightforward approach to economic analysis. Although there are several schools of thought on econometric methodology, the traditional or classical methodology, which still dominates empirical research in economics and other social and behavioural sciences proceeds as: 1) Statement of theory or hypothesis: The first step to econometric methodology is to look at a set of data and define a specific hypothesis that explains the nature and shape of the set. For example, Keynes postulated that the marginal propensity to consume (MPC), the rate of change of consumption for a unit (say, a dollar) change in income, is greater than zero but less than 1. 2) Specification of mathematical model of the theory: An effective model outlines a specific mathematical relationship between the explanatory variable and the dependent variable being tested. A model is simply a set of mathematical equations. A mathematical economist might suggest a precise form of the functional relationship regarding the phenomenon subjecting the hypothesis. For example, the functional form of the Keynesian consumption function be as: Y = β1 + β2X 0 < β2 < 1 where Y = consumption expenditure X = income, and β1 and β2 known as the parameters of the model, the intercept and slope coefficients. The slope coefficient β2 measures the MPC. 3) Specification of the statistical (or econometric) model: The purely mathematical is of limited interest to the econometrician, for it assumes that there is an exact or deterministic relationship between the explanatory variable and the dependent variable. But relationships between economic variables are generally inexact. To allow for the inexact relationships between economic variables, the econometrician would modify the deterministic mathematical model by including disturbance term (error term), which is a random
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(stochastic) variable having well‐defined probabilistic properties. The disturbance term may well represent all the effective factors that are not taken into account explicitly. For example, the econometric model of Keynesian postulate be as: Y = β1 + β2X + u where u is the error (disturbance) term Obtaining the data: To estimate the econometric model, that is, to obtain the numerical values of the parameters of the model, we need data. For example, we may use U.S. economic data for the period 1981–1996, from Economic Report of the President (1998), to estimate numeric values of β1 and β2. Estimation of the parameters of the econometric model: Having the data, the next task is to estimate the parameters of the model. The numerical estimates of the parameters give empirical content to the model. For example, using regression analysis technique and the data, we obtain the estimates of β1 and β2 as: −184.08 and 0.7064. Thus, the estimated consumption function is: Y= −184.08 + 0.7064Xi The hat on the Y indicates that it is an estimate Hypothesis testing: According to “positive” economists like Milton Friedman, a theory or hypothesis that is not verifiable by appeal to empirical evidence may not be admissible as a part of scientific enquiry. So, assuming that the fitted model is a reasonably good approximation of reality, we have to develop suitable criteria to find out whether the obtained estimates are in accord with the expectations of the theory that is being tested. Such confirmation or refutation of economic theories on the basis of sample evidence is known as hypothesis testing. For example, as noted earlier, Keynes expected the MPC to be positive but less than 1. In our example, we found the MPC to be about 0.70. But before we accept this finding as confirmation of Keynesian consumption theory, we must enquire whether this estimate is sufficiently below 1 to convince us that this is not a chance occurrence or peculiarity of the particular data we have used. In other words, is 0.70 statistically less than 1? If it is, it may support Keynes’ theory. Forecasting or prediction: If the chosen model does not refute the hypothesis or theory under consideration, we may use it to predict the future value(s) of the dependent variable (or, forecast variable) Y on the basis of known or expected future value(s) of the explanatory (or predictor) variable X. For example, putting the GDP value for 1997 (7269.8 billion dollars) on the obtained Keynesian model, we obtain: Y1997 = −184.0779 + 0.7064 (7269.8) = 4951.3167 Thus, given the value of the GDP, the average forecast consumption expenditure is about 4951 billion dollars. Using the model for control or policy purposes: It is possible, and of a great interest, to use an estimated model for control, or policy, purposes. By appropriate fiscal and monetary policy mix, the government can manipulate the control variable(s) to produce the desired level of the target variable.
