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动态均衡经济：比较模型与数据的框架AI 解读

We propose a constructive, multivariate framework for assessing agreement between (generally misspecified) dynamic equilibrium models and data, which enables a complete second-order comparison of the dynamic properties of models and data. We use bootstrap algorithms to evaluate the significance of deviations between models and data, and we use goodness-of-fit criteria to produce estimators that optimize economically-relevant loss functions. We provide a detailed illustrative…

非对称损失下的最优预测AI 解读

Prediction problems involving asymmetric loss functions arise routinely in many fields, yet the theory of optimal prediction under asymmetric loss is not well developed. We study the optimal prediction problem under general loss structures and characterize the optimal predictor. We compute the optimal predictor analytically in two leading tractable cases and show how to compute it numerically in less tractable cases. A key theme is that the conditionally optimal forecast is…

非对称损失下预测与模型选择的进一步结果AI 解读

We make three related contributions. First, we propose a new technique for solving prediction problems under asymmetric loss using piecewise-linear approximations to the loss function, and we establish existence and uniqueness of the optimal predictor. Second, we provide a detailed application to optimal prediction of a conditionally heteroscedastic process under asymmetric loss, the insights gained from which are broadly applicable. Finally, we incorporate our results into…

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断点内生选择下的结构稳定性检验：解析与自助法的尺寸比较AI 解读

We compare the performance of two alternative approximations to the finite-sample distributions of test statistics for structural change, one based on asymptotics and one based on the bootstrap. We focus on tests acknowledging that the breakpoint is selected endogenously — in particular, the ‘supremum’ tests of Andrews (1993). We explore a variety of issues of interest in applied work, focusing particularly on smaller samples and persistent dynamics. The bootstrap…

预测准确性的比较AI 解读

We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample tests are proposed, evaluated,…

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结构时间序列分析与建模软件包评述AI 解读

Abstract This package performs specialized tasks related to specification, estimation, prediction and diagnostic checking in the context of a particular class of unobserved‐components models. It is menu‐driven and easy to use. Although the present (first) release suffers from a number of limitations, omissions and bugs, it is nevertheless a useful tool in the hands of a knowledgeable user.

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汇率波动的动态：多变量潜在因子 ARCH 模型AI 解读

Abstract We study temporal volatility patterns in seven nominal dollar spot exchange rates, all of which display strong evidence of autoregressive conditional heteroskedasticity (ARCH). We first formulate and estimate univariate models, the results of which are subsequently used to guide specification of a multivariate model. The key element of our multivariate approach is exploitation of factor structure, which facilitates tractable estimation via a substantial reduction in…

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序列相关与预测组合AI 解读

It is shown that regression-based methods of forecast combination lead to serially correlated combined prediction errors. The form of the serial correlation is characterized, and specification, estimation, and prediction are treated. A fully optimal combined predictor, which exploits the serial correlation, is developed and compared with existing regression-based methods in a numerical example, leading to decreases in mean squared prediction error.

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