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通过敏感性分析评估不完美工具变量推断的可信度AI 解读

Abstract Consistent instrumental variables (IV) estimation requires instruments uncorrelated with model errors, but this assumption is usually both suspect and untestable. Here the asymptotic sampling distribution of the IV parameter estimator is derived for any specified instrument‐error covariance vector. This result makes it possible to quantify the sensitivity of any particular IV inference result to instrument‐error correlations, allowing one to assess the robustness of…

经济时间序列中测量误差的度量AI 解读

A spectral decomposition method is described for obtaining an upper bound on the amount of measurement error in a time series. The method is applied to generated data and to M1b, real GNP, and the CPI. The bounds provide insight into both the amount of measurement error in these series and the stochastic specification of the errors.

解释变量的次优预测如何得到最优利用AI 解读

Ashley (1983) gave a simple condition for determining when a forecast of an explanatory variable (Xt ) is sufficiently inaccurate that direct replacement of Xt by the forecast yields worse forecasts of the dependent variable than does respecification of the equation to omit Xt . Many available macroeconomic forecasts were shown to be of limited usefulness in direct replacement. Direct replacement, however, is not optimal if the forecast's distribution is known. Here optimal…