Econometrics - wikipedia Econometrics Economics A supply and demand diagram, illustrating the effects of an increase in demand. 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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. More precisely, it is `` the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference ''. An introductory economics textbook describes econometrics as allowing economists `` to sift through mountains of data to extract simple relationships ''. The first known use of the term `` econometrics '' ( in cognate form ) was by Polish economist Paweł Ciompa in 1910. Jan Tinbergen is considered by many to be one of the founding fathers of econometrics. Ragnar Frisch is credited with coining the term in the sense in which it is used today. The basic tool for econometrics is the multiple linear regression model. Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods. Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency. Applied econometrics uses theoretical econometrics and real - world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting. Contents 1 Basic models : linear regression 2 Theory 3 Methods 4 Example 5 Journals 6 Limitations and criticisms 7 See also 8 Notes 9 Further reading 10 External links Basic models : linear regression The basic tool for econometrics is the multiple linear regression model. In modern econometrics, other statistical tools are frequently used, but linear regression is still the most frequently used starting point for an analysis. Estimating a linear regression on two variables can be visualized as fitting a line through data points representing paired values of the independent and dependent variables. Okun 's law representing the relationship between GDP growth and the unemployment rate. The fitted line is found using regression analysis. For example, consider Okun 's law, which relates GDP growth to the unemployment rate. This relationship is represented in a linear regression where the change in unemployment rate ( Δ Unemployment ( displaystyle Delta ( \text ( Unemployment ) ) ) ) is a function of an intercept ( β 0 ( displaystyle \beta _ ( 0 ) ) ), a given value of GDP growth multiplied by a slope coefficient β 1 ( displaystyle \beta _ ( 1 ) ) and an error term, ε ( displaystyle \varepsilon ) : Δ Unemployment = β 0 + β 1 Growth + ε. ( displaystyle Delta ( \text ( Unemployment ) ) = \beta _ ( 0 ) + \beta _ ( 1 ) ( \text ( Growth ) ) + \varepsilon. ) The unknown parameters β 0 ( displaystyle \beta _ ( 0 ) ) and β 1 ( displaystyle \beta _ ( 1 ) ) can be estimated. Here β 1 ( displaystyle \beta _ ( 1 ) ) is estimated to be − 1.77 and β 0 ( displaystyle \beta _ ( 0 ) ) is estimated to be 0.83. This means that if GDP growth increased by one percentage point, the unemployment rate would be predicted to drop by 1.77 points. The model could then be tested for statistical significance as to whether an increase in growth is associated with a decrease in the unemployment, as hypothesized. If the estimate of β 1 ( displaystyle \beta _ ( 1 ) ) were not significantly different from 0, the test would fail to find evidence that changes in the growth rate and unemployment rate were related. The variance in a prediction of the dependent variable ( unemployment ) as a function of the independent variable ( GDP growth ) is given in polynomial least squares. Theory See also : Estimation theory Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods. Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency. An estimator is unbiased if its expected value is the true value of the parameter ; it is consistent if it converges to the true value as sample size gets larger, and it is efficient if the estimator has lower standard error than other unbiased estimators for a given sample size. Ordinary least squares ( OLS ) is often used for estimation since it provides the BLUE or `` best linear unbiased estimator '' ( where `` best '' means most efficient, unbiased estimator ) given the Gauss - Markov assumptions. When these assumptions are violated or other statistical properties are desired, other estimation techniques such as maximum likelihood estimation, generalized method of moments, or generalized least squares are used. Estimators that incorporate prior beliefs are advocated by those who favor Bayesian statistics over traditional, classical or `` frequentist '' approaches. Methods Main article : Methodology of econometrics Applied econometrics uses theoretical econometrics and real - world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting. Econometrics may use standard statistical models to study economic questions, but most often they are with observational data, rather than in controlled experiments. In this, the design of observational studies in econometrics is similar to the design of studies in other observational disciplines, such as astronomy, epidemiology, sociology and political science. Analysis of data from an observational study is guided by the study protocol, although exploratory data analysis may be useful for generating new hypotheses. Economics often analyzes systems of equations and inequalities, such as supply and demand hypothesized to be in equilibrium. Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous - equation models. These methods are analogous to methods used in other areas of science, such as the field of system identification in systems analysis and control theory. Such methods may allow researchers to estimate models and investigate their empirical consequences, without directly manipulating the system. One of the fundamental statistical methods used by econometricians is regression analysis. Regression methods are important in econometrics because economists typically can not use controlled experiments. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments. Observational data may be subject to omitted - variable bias and a list of other problems that must be addressed using causal analysis of simultaneous - equation models. Example A simple example of a relationship in econometrics from the field of labor economics is : ln ⁡ ( wage ) = β 0 + β 1 ( years of education ) + ε. ( displaystyle ln ( ( \text ( wage ) ) ) = \beta _ ( 0 ) + \beta _ ( 1 ) ( ( \text ( years of education ) ) ) + \varepsilon. ) This example assumes that the natural logarithm of a person 's wage is a linear function of the number of years of education that person has acquired. The parameter β 1 ( displaystyle \beta _ ( 1 ) ) measures the increase in the natural log of the wage attributable to one more year of education. The term ε ( displaystyle \varepsilon ) is a random variable representing all other factors that may have direct influence on wage. The econometric goal is to estimate the parameters, β 0 and β 1 ( displaystyle \beta _ ( 0 ) ( mbox ( and ) ) \beta _ ( 1 ) ) under specific assumptions about the random variable ε ( displaystyle \varepsilon ). For example, if ε ( displaystyle \varepsilon ) is uncorrelated with years of education, then the equation can be estimated with ordinary least squares. If the researcher could randomly assign people to different levels of education, the data set thus generated would allow estimation of the effect of changes in years of education on wages. In reality, those experiments can not be conducted. Instead, the econometrician observes the years of education of and the wages paid to people who differ along many dimensions. Given this kind of data, the estimated coefficient on Years of Education in the equation above reflects both the effect of education on wages and the effect of other variables on wages, if those other variables were correlated with education. For example, people born in certain places may have higher wages and higher levels of education. Unless the econometrician controls for place of birth in the above equation, the effect of birthplace on wages may be falsely attributed to the effect of education on wages. The most obvious way to control for birthplace is to include a measure of the effect of birthplace in the equation above. Exclusion of birthplace, together with the assumption that ε ( displaystyle epsilon ) is uncorrelated with education produces a misspecified model. Another technique is to include in the equation additional set of measured covariates which are not instrumental variables, yet render β 1 ( displaystyle \beta _ ( 1 ) ) identifiable. An overview of econometric methods used to study this problem were provided by Card ( 1999 ). Journals The main journals that publish work in econometrics are Econometrica, the Journal of Econometrics, the Review of Economics and Statistics, Econometric Theory, the Journal of Applied Econometrics, Econometric Reviews, the Econometrics Journal, Applied Econometrics and International Development, and the Journal of Business & Economic Statistics. Limitations and criticisms See also : Criticisms of econometrics Like other forms of statistical analysis, badly specified econometric models may show a spurious relationship where two variables are correlated but causally unrelated. In a study of the use of econometrics in major economics journals, McCloskey concluded that some economists report p - values ( following the Fisherian tradition of tests of significance of point null - hypotheses ) and neglect concerns of type II errors ; some economists fail to report estimates of the size of effects ( apart from statistical significance ) and to discuss their economic importance. She also argues that some economists also fail to use economic reasoning for model selection, especially for deciding which variables to include in a regression. In some cases, economic variables can not be experimentally manipulated as treatments randomly assigned to subjects. In such cases, economists rely on observational studies, often using data sets with many strongly associated covariates, resulting in enormous numbers of models with similar explanatory ability but different covariates and regression estimates. Regarding the plurality of models compatible with observational data - sets, Edward Leamer urged that `` professionals... properly withhold belief until an inference can be shown to be adequately insensitive to the choice of assumptions ''. See also Wikimedia Commons has media related to Econometrics. Augmented Dickey -- Fuller test Choice modelling Cowles Foundation Econometric software Financial modeling Financial econometrics Granger causality Important publications in econometrics Macroeconomic model Methodological individualism Predetermined variables Single equation methods ( econometrics ) Spatial econometrics Unit root Notes Jump up ^ M. Hashem Pesaran ( 1987 ). `` Econometrics, '' The New Palgrave : A Dictionary of Economics, v. 2, p. 8 ( pp. 8 -- 22 ). Reprinted in J. Eatwell et al., eds. ( 1990 ). Econometrics : The New Palgrave, p. 1 ( pp. 1 -- 34 ). Abstract ( 2008 revision by J. Geweke, J. Horowitz, and H.P. Pesaran ). Jump up ^ P.A. Samuelson, T.C. Koopmans, and J.R.N. Stone ( 1954 ). `` Report of the Evaluative Committee for Econometrica, '' Econometrica 22 ( 2 ), p. 142. ( p p. 141 - 146 ), as described and cited in Pesaran ( 1987 ) above. Jump up ^ Paul A. Samuelson and William D. Nordhaus, 2004. Economics. 18th ed., McGraw - Hill, p. 5. Jump up ^ http://www.dziejekrakowa.pl/biogramy/index.php?id=516 Jump up ^ http://www.elsevierweekblad.nl/Economie/achtergrond/2015/10/1969---Jan-Tinbergen-Nobelprijs-economie-2700626W/?masterpageid=5573 Jump up ^ Magnus, Jan & Mary S. Morgan ( 1987 ) The ET Interview : Professor J. Tinbergen in : ' Econometric Theory 3, 1987, 117 - 142. Jump up ^ Willlekens, Frans ( 2008 ) International Migration in Europe : Data, Models and Estimates. New Jersey. John Wiley & Sons : 117. Jump up ^ H.P. Pesaran ( 1990 ), `` Econometrics, '' Econometrics : The New Palgrave, p. 2, citing Ragnar Frisch ( 1936 ), `` A Note on the Term ' Econometrics ', '' Econometrica, 4 ( 1 ), p. 95. Aris Spanos ( 2008 ), `` statistics and economics, '' The New Palgrave Dictionary of Economics, 2nd Edition. Abstract. ^ Jump up to : Greene, William ( 2012 ). `` Chapter 1 : Econometrics ''. Econometric Analysis ( 7th ed. ). Pearson Education. pp. 47 -- 48. ISBN 9780273753568. Ultimately, all of these will require a common set of tools, including, for example, the multiple regression model, the use of moment conditions for estimation, instrumental variables ( IV ) and maximum likelihood estimation. With that in mind, the organization of this book is as follows : The first half of the text develops fundamental results that are common to all the applications. The concept of multiple regression and the linear regression model in particular constitutes the underlying platform of most modeling, even if the linear model itself is not ultimately used as the empirical specification. ^ Jump up to : Greene, William ( 2012 ). Econometric Analysis ( 7th ed. ). Pearson Education. pp. 34, 41 -- 42. ISBN 9780273753568. ^ Jump up to : Wooldridge, Jeffrey ( 2012 ). `` Chapter 1 : The Nature of Econometrics and Economic Data ''. Introductory Econometrics : A Modern Approach ( 5th ed. ). South - Western Cengage Learning. p. 2. ISBN 9781111531041. Jump up ^ Clive Granger ( 2008 ). `` forecasting, '' The New Palgrave Dictionary of Economics, 2nd Edition. Abstract. Jump up ^ Wooldridge, Jeffrey ( 2013 ). Introductory Econometrics, A modern approach. South - Western, Cengage learning. ISBN 978 - 1 - 111 - 53104 - 1. Jump up ^ Herman O. Wold ( 1969 ). `` Econometrics as Pioneering in Nonexperimental Model Building, '' Econometrica, 37 ( 3 ), pp. 369 - 381. Jump up ^ For an overview of a linear implementation of this framework, see linear regression. Jump up ^ Edward E. Leamer ( 2008 ). `` specification problems in econometrics, '' The New Palgrave Dictionary of Economics. Abstract. Jump up ^ Pearl, Judea ( 2000 ). Causality : Model, Reasoning, and Inference. Cambridge University Press. ISBN 0521773628. Jump up ^ Card, David ( 1999 ). `` The Causal Effect of Education on Earning ''. In Ashenfelter, O. ; Card, D. Handbook of Labor Economics. Amsterdam : Elsevier. pp. 1801 -- 1863. ISBN 0444822895. Jump up ^ `` The Econometrics Journal -- Wiley Online Library ''. Wiley.com. Retrieved 8 October 2013. Jump up ^ McCloskey ( May 1985 ). `` The Loss Function has been mislaid : the Rhetoric of Significance Tests ''. American Economic Review. 75 ( 2 ). Jump up ^ Stephen T. Ziliak and Deirdre N. McCloskey ( 2004 ). `` Size Matters : The Standard Error of Regressions in the American Economic Review, '' Journal of Socio - economics, 33 ( 5 ), pp. 527 - 46 ( press + ). Jump up ^ Leamer, Edward ( March 1983 ). `` Let 's Take the Con out of Econometrics ''. American Economic Review. 73 ( 1 ) : 34. JSTOR 1803924. Jump up ^ Leamer, Edward ( March 1983 ). `` Let 's Take the Con out of Econometrics ''. American Economic Review. 73 ( 1 ) : 43. JSTOR 1803924. Further reading Econometric Theory book on Wikibooks Giovannini, Enrico Understanding Economic Statistics, OECD Publishing, 2008, ISBN 978 - 92 - 64 - 03312 - 2 External links Wikimedia Commons has media related to Econometrics. Look up econometrics in Wiktionary, the free dictionary. Journal of Financial Econometrics Econometric Society The Econometrics Journal Econometric Links Teaching Econometrics ( Index by the Economics Network ( UK ) ) Applied Econometric Association The Society for Financial Econometrics The interview with Clive Granger -- Nobel winner in 2003, about econometrics Economics Economic theory Econometrics Applied economics Macroeconomics Adaptive expectations Aggregate demand Balance of payments Business cycle Capacity utilization Capital flight Central bank Consumer confidence Currency Demand shock Depression ( Great Depression ) DSGE Economic growth Economic indicator Economic rent Effective demand General Theory of Keynes Hyperinflation Inflation Interest Interest rate Investment IS -- LM model Microfoundations Monetary policy Money NAIRU National accounts PPP Rate of profit Rational expectations Recession Saving Shrinkflation Stagflation Supply shock Unemployment Macroeconomics publications Microeconomics Aggregation problem Budget set Consumer choice Convexity Cost -- benefit analysis Deadweight loss Distribution Duopoly Economic equilibrium Economic shortage Economic surplus Economies of scale Economies of scope Elasticity Expected utility hypothesis Externality General equilibrium theory Indifference curve Intertemporal choice Marginal cost Market failure Market structure Monopoly Monopsony Non-convexity Oligopoly Opportunity cost Preference Production set Profit Public good Returns to scale Risk aversion Scarcity Social choice theory Sunk costs Supply and demand Theory of the firm Trade Transaction cost Value Uncertainty Utility Microeconomics publications Applied fields Agricultural Business Demographic Development Economic history Education Environmental Financial Health Industrial organization International Knowledge Labour Law and economics Monetary Natural resource Public Service Urban Welfare Methodology Behavioral economics Computational economics Econometrics Economic systems Experimental economics Mathematical economics Methodological publications Economic thought Ancient economic thought Austrian school of economics Chicago school of economics Classical economics Feminist economics Heterodox economics Institutional economics Keynesian economics Mainstream economics Marxian economics Neoclassical economics Post-Keynesian economics Schools overview Notable economists and thinkers within economics Kenneth Arrow Gary Becker Francis Ysidro Edgeworth Milton Friedman Ragnar Frisch Friedrich Hayek Harold Hotelling John Maynard Keynes Tjalling Koopmans Paul Krugman Robert Lucas Jr. Jacob Marschak Alfred Marshall Karl Marx John von Neumann Vilfredo Pareto David Ricardo Paul Samuelson Joseph Schumpeter Amartya Sen Herbert A. 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Question: what is the meaning of blue in econometrics
Answer: "best linear unbiased estimator" (where "best" means most efficient, unbiased estimator)