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误设 ARCH 模型的滤波与预测：第二篇AI 解读

A companion paper (Nelson, 1992) showed that in data observed at high frequencies, an ARCH model may perform well in estimating the conditional variance of a process, even when the ARCH model is severely misspecified. While such models may perform reasonably well at filtering (i.e., at estimating unobserved instantaneous conditional variances), they may perform disastrously at medium- and long-term forecasting of the process and its volatility. In this paper, we develop…

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理解计量经济学检验的三种方式AI 解读

Recent methodological disputes amongst econometricians are shown to depend critically upon a sequence of differing conceptions of the empirical process, although they are nowhere spelled out in detail. The third conception of critical postmodernism is further described, comparing practices across physics, psychology, and economics using both narratives from recent philosophy of science and the technique of Birge ratios.

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高频外汇市场数据结构模型的非参数估计AI 解读

Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method…

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对异常值稳健的单位根检验及其扩展 Nelson–Plosser 数据应用AI 解读

This paper considers unit root tests based on robust estimators with a high breakdown point and high efficiency. The asymptotic distribution of these tests is derived. Critical values for the test are obtained via simulation. It is found that the size of the classical OLS based Dickey-Fuller test breaks down if the time series contains additive outliers For innovative outliers the size of the robust test is less stable, while its size-adjusted power properties are better. An…

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异方差样本选择模型的两步估计AI 解读

This paper considers two-step estimation of a sample selection model in which there is heteroskedasticity of unknown form in the latent errors. We propose an estimator which uses recent developments in nonparametric regression estimation involving series approximations. The estimator is shown to be consistent and asymptotically normally distributed under reasonable conditions. A small Monte Carlo experiment demonstrates the usefulness of the estimator and highlights the bias…