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对模型选择全过程进行自助法推断：一个计量经济学例子AI 解读

Abstract If a researcher has mined the data (i.e. selected an empirical model based on a series of trial estimates), inferences based on the final set of results are in general incorrect. This note treats the entire data mining process as an estimator and shows how a bootstrapping technique may improve the quality of inference. The method is applied to an empirical example on the deterrent effects of capital punishment.

It is shown that selecting regressors based on the prediction error sum of squares is asymptotically related to hypothesis testing with White's (1980) heteroscedasticity-consistent covariance matrix. A simulation experiment suggest that this asymptotic relation may be useful. Illustrative examples are also given.

使用 PRESS 与 White t 统计量选择预测回归变量AI 解读

It is shown that selecting regressors based on the prediction error sum of squares is asymptotically related to hypothesis testing with White's (1980) heteroscedasticity-consistent covariance matrix. A simulation experiment suggest that this asymptotic relation may be useful. Illustrative examples are also given.

摘要暂缺

未来需求的概率分布AI 解读

The bootstrapping technique of Efron (1979) is used to estimate the probability distribution of future electricity demand for Hydro Quebec. The application follows the regression approach of Freedman and Peters (1984a,b) but also allows for serially correlated disturbances and uncertainty in the independent variable forecasts.

理性预期假说约束的 Wald 检验构造AI 解读

Abstract In this paper, we examine the small sample properties of alternative formulations of Wald tests of non‐linear restrictions implied by the rational expectations hypothesis. A Monte Carlo analysis is presented as well as an example using Canadian aggregate time series data. The evidence indicates that Wald test results are extremely sensitive in small samples to the way in which the non‐linear restrictions in such models are parameterized, with a multiplicative form…

非线性约束的 Wald 检验构造AI 解读

On montre a partir de l'evidence de Monte Carlo que les differences dans la forme fonctionnelle des restrictions non lineaires sont importantes dans les petites tailles d'echantillons