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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 Cochrane–Orcutt estimation ： ウィキペディア英語版 Cochrane–Orcutt estimation Cochrane–Orcutt estimation is a procedure in econometrics, which adjusts a linear model for serial correlation in the error term. It is named after statisticians Donald Cochrane and Guy Orcutt. ==Theory== Consider the model :$y\_t\; =\; \backslash alpha\; +\; X\_t\; \backslash beta+\backslash varepsilon\_t,\backslash ,$ where $y\_$ is the value of the dependent variable of interest at time ''t'', $\backslash beta$ is a column vector of coefficients to be estimated, $X\_$ is a row vector of explanatory variables at time ''t'', and $\backslash varepsilon\_t$ is the error term at time ''t''. If it is found via the Durbin–Watson statistic that the error term is serially correlated over time, then standard statistical inference as normally applied to regressions is invalid because standard errors are estimated with bias. To avoid this problem, the residuals must be modeled. If the process generating the residuals is found to be a stationary first-order autoregressive structure, , with the errors being white noise, then the Cochrane–Orcutt procedure can be used to transform the model by taking a quasi-difference: :$y\_t\; -\; \backslash rho\; y\_\; =\; \backslash alpha(1-\backslash rho)+\backslash beta(X\_t\; -\; \backslash rho\; X\_)\; +\; e\_t.\; \backslash ,$ In this specification the error terms are white noise, so statistical inference is valid. Then the sum of squared residuals (the sum of squared estimates of $e\_t^2$) is minimized with respect to $(\backslash alpha,\backslash beta)$, conditional on $\backslash rho$. 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「Cochrane–Orcutt estimation」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.