“Chow test”的概念、定义、翻译、参考文献-科学参考

Chow test

Statistics

The null hypothesis is that the response variable Y is related to k explanatory variables by the multiple regression model E(Y)=Xβ, for which the residual sum of squares (see ANOVA) is R. The data consists of two sub-samples (1 and 2; of sizes n₁ and n₂) and the alternative hypothesis is that in sub-sample j (j=1,2) the model is E(Y)=Xβ_j with residual sum of squares R_j. The Chow test, introduced in 1960, assumes independent normal errors and has test statistic F, given by $Chow test$ The test statistic is compared with an F-distribution having k and (n₁+n₂−2k) degrees of freedom.

Economics

A test for the equality of the estimated coefficients in two linear regressions on two different data samples. The null hypothesis is that the coefficients in the two regressions are the same, and the alternative is that the coefficient on at least one explanatory variable is different. The test statistic involves the sums of squared residuals from three regressions: two separate regressions on two samples and one regression on a pooled sample. Under the null hypothesis the test statistic has an F-distribution. In time series the test is used to assess the stability of coefficients, that is, to assess whether there is a structural break in the model; the potential break point is assumed to be exogenous and must be specified ex ante. The test is not valid when the timing of the break depends on the data, or is endogenous.

单词	Chow test
释义	Chow test Statistics The null hypothesis is that the response variable Y is related to k explanatory variables by the multiple regression model E(Y)=Xβ, for which the residual sum of squares (see ANOVA) is R. The data consists of two sub-samples (1 and 2; of sizes n₁ and n₂) and the alternative hypothesis is that in sub-sample j (j=1,2) the model is E(Y)=Xβ_j with residual sum of squares R_j. The Chow test, introduced in 1960, assumes independent normal errors and has test statistic F, given by $Chow test$ The test statistic is compared with an F-distribution having k and (n₁+n₂−2k) degrees of freedom. Economics A test for the equality of the estimated coefficients in two linear regressions on two different data samples. The null hypothesis is that the coefficients in the two regressions are the same, and the alternative is that the coefficient on at least one explanatory variable is different. The test statistic involves the sums of squared residuals from three regressions: two separate regressions on two samples and one regression on a pooled sample. Under the null hypothesis the test statistic has an F-distribution. In time series the test is used to assess the stability of coefficients, that is, to assess whether there is a structural break in the model; the potential break point is assumed to be exogenous and must be specified ex ante. The test is not valid when the timing of the break depends on the data, or is endogenous.
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