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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Econometrics ： ウィキペディア英語版 Econometrics Econometrics is the application of mathematics, statistical methods, and computer science, to economic data and is described as the branch of economics that aims to give empirical content to economic relations.〔M. Hashem Pesaran (1987). "Econometrics," ''The New Palgrave: A Dictionary of Economics'', v. 2, p. 8 (8-22 ). Reprinted in J. Eatwell ''et al.'', eds. (1990). ''Econometrics: The New Palgrave'', (p. 1 ) (1-34 ). (Abstract ) (2008 revision by J. Geweke, J. Horowitz, and H. P. Pesaran).〕 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."〔P. A. Samuelson, T. C. Koopmans, and J. R. N. Stone (1954). "Report of the Evaluative Committee for ''Econometrica''," ''Econometrica'' 22(2), p. 142. , as described and cited in Pesaran (1987) above.〕 An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships."〔Paul A. Samuelson and William D. Nordhaus, 2004. ''Economics''. 18th ed., McGraw-Hill, p. 5.〕 The first known use of the term "econometrics" (in cognate form) was by Polish economist Paweł Ciompa in 1910.〔http://www.dziejekrakowa.pl/biogramy/index.php?id=516〕 Ragnar Frisch is credited with coining the term in the sense in which it is used today.〔• 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. )〕 ==Basic econometric models: linear regression== The basic tool for econometrics is the 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.〔Greene (2012), 12.〕 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. 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 ($\backslash Delta\backslash \; \backslash text$) is a function of an intercept ($\backslash beta\_0$), a given value of GDP growth multiplied by a slope coefficient $\backslash beta\_1$ and an error term, $\backslash varepsilon$: :$\backslash Delta\backslash \; \backslash text\; =\; \backslash beta\_0\; +\; \backslash beta\_1\backslash text\; +\; \backslash varepsilon.$ The unknown parameters $\backslash beta\_0$ and $\backslash beta\_1$ can be estimated. Here $\backslash beta\_1$ is estimated to be −1.77 and $\backslash beta\_0$ 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 $\backslash beta\_1$ 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Econometrics」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.