If the data show that such an association is present, a regression analysis can then be conducted to understand the strength of the relationship between income and consumption and whether or not that relationship is statistically significant—that is, it appears to be unlikely that it is due to chance alone. The first step to econometric methodology is to obtain and analyze a set of data and define a specific hypothesis that explains the nature and shape of the set.
This data may be, for example, the historical prices for a stock index, observations collected from a survey of consumer finances, or unemployment and inflation rates in different countries. Here, you want to test the idea that higher unemployment leads to lower stock market prices.
Stock market price is thus your dependent variable and the unemployment rate is the independent or explanatory variable. The most common relationship is linear, meaning that any change in the explanatory variable will have a positive correlation with the dependent variable, in which case a simple regression model is often used to explore this relationship, which amounts to generating a best-fit line between the two sets of data and then testing to see how far each data point is, on average, from that line.
Note that you can have several explanatory variables in your analysis—for example, changes to GDP and inflation in addition to unemployment in explaining stock market prices. When more than one explanatory variable is used, it is referred to as multiple linear regression , the model that is the most commonly used tool in econometrics.
Several different regression models exist that are optimized depending on the nature of the data being analyzed and the type of question being asked. The most common example is the ordinary least-squares OLS regression, which can be conducted on several types of cross-sectional or time-series data. If you're interested in a binary yes-no outcome—for instance, how likely you are to be fired from a job based on your productivity—you can use a logistic regression or a probit model.
Today, there are hundreds of models that an econometrician has at his disposal. These software packages can also easily test for statistical significance to provide support that the empirical results produced by these models are not merely the result of chance.
R-squared, t-tests, p-values, and null-hypothesis testing are all methods used by econometricians to evaluate the validity of their model results. Econometrics is sometimes criticized for relying too heavily on the interpretation of raw data without linking it to established economic theory or looking for causal mechanisms.
It is crucial that the findings revealed in the data are able to be adequately explained by a theory, even if that means developing your own theory of the underlying processes. Regression analysis also does not prove causation, and just because two data sets show an association, it may be spurious. For example, drowning deaths in swimming pools increase with GDP. Intriligator eds , Handbook of Econometrics Vol.
Haavelmo, T. Heckman, J. Hendry, D. Keynes, J. Klein, L. Koopmans, T. Marschak , eds. Learner, E. Leontief, W. Lucas, R. Brunner and A. Meltzer, eds. Maddala, G. Rao and R. Szekeley, eds. Try to get old econometrics exams from exam banks, libraries, or former students. These are particularly useful if the same economics professor has taught the course for many years.
Talk to former students of the course. They'll know the examination style of the professor and may be able to provide useful tips. Econometrics originally came from statistics. In general statistics is more general than econometrics , since while econometrics focuses in Statistical Inference, Statistics also deals with other important fields such as Design of Experiments and Sampling techiniques.
Econometrics, which seek to apply statistical and mathematical methods to economic analysis, is widely considered the third core area. Where microeconomics is concerned with individual units in the economy, such as a consumer or company, macroeconomics is an aggregate analysis of the economy as a whole. Math skills aren't the only skills that matter when studying economics, but math is part of the curriculum. Many applicants have completed a course in real analysis.
This means that undergraduates thinking about graduate school in economics should take mathematics courses each semester. Math and statistics are used in economics, but at the undergraduate degree level, the math and statistics are certainly not overwhelming. Economics majors are usually required to take one statistics course and one math course usually an introductory calculus course.
Both are good subjects. Economics will help you pursue a career in financial research, equity research, financial journalism whereas Statistics will give you option of pursuing a career in a range of Data Analytics related field which is in demand now a days. Financial Math offers an engaging, scaffolded curriculum that introduces key topics and principles necessary to financial literacy.
Tintner suggested reasons as to why he preferred the definition of econometrics as a combination of economics, mathematics and statistics, which are the three pillars or components of econometrics as recognized universally.
He argued that while the definition of econometrics was of some importance, it was to a certain extent arbitrary, but then he described econometrics as being related to economics in the same manner as psychometrics is related to psychology, sociometrics to sociology, and biometrics to biology.
Typically, when it comes to defining econometrics, the tendency is to identify the sub-divisions or branches of the discipline, its pillars or components , and the tasks functions that can be executed by using econometric techniques. Econometrics has become a household term that can be found as an entry in the Collins Dictionary , where it is emphasized that the term is singular www.
This brief definition overlaps with the definitions of related disciplines using quantitative techniques such as mathematical economics and operational research. However, it is typical that a description of what econometrics is all about gives rise to the problem of distinguishing econometrics from similar and close disciplines such as mathematical economics, statistics, economic statistics, mathematical statistics, statistical economics, quantitative economics, analytical economics, empirical economics, empirical econometrics, and perhaps operational research.
For example, Baltagi starts section 1. A large number of definitions can be found in the academic and professional literature — these definitions have common elements and they shed some light on the nature of econometrics.
For example, the popular website Investopedia www. Econometrics is the application of statistical and mathematical theories to economics for the purpose of testing hypotheses and forecasting future trends. Econometrics takes economic models and tests them through statistical trials. The results are then compared and contrasted against real-life examples.
Econometrics can be subdivided into two major categories: theoretical and applied. Two characteristics of econometrics are embodied in this definition. The first is that econometrics consists of econometric methods theoretical econometrics and applied econometrics. Theoretical econometrics is about the development of estimation, testing and model evaluation procedures, whereas applied econometrics is about the application of econometric methods to economic issues.
We will see later that econometric methods and applications evolved contemporaneously. The second characteristic is that econometric methods are designed to deal with either hypothesis testing or forecasting, although the two functions are related. This definition therefore identifies the branches of econometrics methods and applications or theoretical and applied and the functions that can be executed by using econometric techniques hypothesis testing and forecasting.
Another definition of econometrics, which can be found on lexicon. Econometrics is the art and science of using data to test various economic theories. More specifically, econometrics can be viewed as the use of mathematics and sophisticated statistical modelling to test economic or financial theories as well as forecast the effects of changes in economic or financial factors under various scenarios….
Econometrics is an inter-disciplinary effort to understand economic and financial behaviour through the use of data, economic theory, mathematics, statistical methods, and other quantitative techniques.
This definition describes econometrics as art and science, but it does not tell us whether it is more like art or science.
This is an important issue that will be examined later on when it is argued that neither econometrics nor economics is a science — at least not in the same sense as physics is a science. The definition also points to the main functions of hypothesis testing and forecasting and identifies the components of econometrics as economic theory, mathematics and statistics.
For example, some of the quantitative operational research techniques used to solve management problems include scheduling, transportation and network analysis. These techniques typically fall out of the realm of econometrics, which means that not all of quantitative techniques are used by econometricians.
This is another issue that will be dealt with later. In an editorial of the first issue of Econometrica , Frisch a wrote the following:. A word of explanation regarding the term econometrics may be in order. Its main object shall be to promote studies that aim at a unification of the theoretical-quantitative and the empirical-quantitative approach to economic problems. This definition identifies the components of econometrics as economic theory, mathematics and statistics.
However, Frisch argued that each of the three components was a necessary, but not by itself a sufficient condition for a real understanding of quantitative relations in modern economic life. Hence, he concluded that it was the unification of the three components that constituted econometrics. Several definitions come from recognized econometricians. Spanos defined it as the systematic study of economic phenomena using observed data.
In a comprehensive survey of the discipline, Geweke et al. A detailed description of econometrics can be found in the International Encyclopaedia of the Social Sciences www. Succinctly defined, econometrics is the study of economic theory in its relations to statistics and mathematics. The essential premise is that economic theory lends itself to mathematical formulation, usually as a system of relationships which may include random variables.
Economic observations are generally regarded as a sample drawn from a universe described by the theory. Using these observations and the methods of statistical inference, the econometrician tries to estimate the relationships that constitute the theory.
Next, these estimates may be assessed in terms of their statistical properties and their capacity to predict further observations. The quality of the estimates and the nature of the prediction errors may in turn feed back into a revision of the very theory by which the observations were organized and on the basis of which the numerical characteristics of the universe postulated were inferred. Thus, there is a reciprocating relationship between the formulation of theory and empirical estimation and testing.
The salient feature is the explicit use of mathematics and statistical inference. Nonmathematical theorizing and purely descriptive statistics are not part of econometrics. This extended definition identifies the components of econometrics as economic theory, statistics and mathematics and recognizes the nature of observations data used for the purpose of estimating and testing economic functional relations.
Non-mathematical theory is excluded from econometrics, implying that mathematical economic theory mathematical economics is part of it. The implication of this definition is that the presentation of an economic theory by using diagrams and confronting it with observed data do not constitute econometric analysis. It is not clear if the implication of this description is that descriptive economic theory and economic statistics are not sophisticated enough, which makes it useless.
A large number of econometricians and mathematical economists seem to think along these lines. Perhaps the best way to understand what econometrics is all about is to describe it in terms of its branches econometric methods and applied econometrics , functions hypothesis testing and forecasting and components economic theory, mathematics and statistics. But even this description has significant overlapping with related disciplines as we are going to see later.
The evolution of econometrics has led to a shift of emphasis in favour of econometric methods, as the development of new methods has become the end rather than the means to an end, and away from economic theory to pure mathematics and statistics.
An Australian university is probably the only university in the world that has a separate department of econometrics, a department where the academics often brag about not using real data.
If this is the case, where is measurement in econometrics? Appendix Table 1A. The precursor to econometrics was quantitative research in economics, the origins of which can be traced at least as far back as the work of the 16th-century political arithmeticians who analysed data in their studies of taxation, money and international trade. Geweke et al.
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