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利用矩条件对时变参数进行观测驱动滤波AI 解读

We develop a new and flexible semi-parametric approach for time-varying parameter models when the true dynamics are unknown. The time-varying parameters are estimated using a recursive updating scheme that is driven by the influence function of a conditional moments-based criterion. We show that the updates ensure local improvements of the conditional criterion function in expectation. The dynamics are observation driven, which yields a computationally efficient methodology…

得分驱动模型的最大似然估计AI 解读

We establish strong consistency and asymptotic normality of the maximum likelihood estimator for stochastic time-varying parameter models driven by the score of the predictive conditional likelihood function. For this purpose, we formulate primitive conditions for global identification, invertibility, strong consistency, and asymptotic normality both under correct specification and misspecification of the model. A detailed illustration is provided for a conditional…

非线性非高斯状态空间模型的数值加速重要性抽样AI 解读

We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector and a low-dimensional signal…

广义自回归得分模型及其应用AI 解读

SUMMARY We propose a class of observation‐driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time‐varying parameters in a wide class of nonlinear models. The GAS model encompasses other well‐known models such as the generalized autoregressive conditional…

美国违约计数的动态因子模型：宏观、潜在脆弱性与行业效应及2008年信贷危机AI 解读

We develop a high-dimensional, nonlinear, and non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into latent components for (1) macroeconomic/financial risk, (2) autonomous default dynamics (frailty), and (3) industry-specific effects. We analyze discrete U.S. corporate default counts together with macroeconomic and financial variables in one unifying framework. We find that approximately 35% of default rate variation is due to…

利用多种宏观协变量建模潜在脆弱性相关违约AI 解读

We propose a novel time series panel data framework for estimating and forecasting time-varying corporate default rates subject to observed and unobserved risk factors. In an empirical application for a U.S. dataset, we find a large and significant role for a dynamic frailty component even after controlling for more than 80% of the variation in more than 100 macro-financial covariates and other standard risk factors. We emphasize the need for a latent component to prevent a…

独立与相依观测下崩溃点的综合定义AI 解读

Summary We provide a new definition of breakdown in finite samples, with an extension to asymptotic breakdown. Previous definitions centre on defining a critical region for either the parameter or the objective function. If for a particular outlier configuration the critical region is entered, breakdown is said to occur. In contrast with the traditional approach, we leave the definition of the critical region implicit. Our proposal encompasses previous definitions of…

摘要暂缺

存在加性异常值时的 ARCH 检验AI 解读

In this paper we investigate the properties of the Lagrange Multiplier (LM) test for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliers (AOs). We show analytically that both the asymptotic size and power are adversely affected if AOs are neglected: the test rejects the null hypothesis of homoscedasticity too often when it is in fact true, while the test has difficulty detecting genuine GARCH effects. Several…

存在加性异常值时的 ARCH 检验AI 解读

In this paper we investigate the properties of the Lagrange Multiplier (LM) test for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliers (AOs). We show analytically that both the asymptotic size and power are adversely affected if AOs are neglected: the test rejects the null hypothesis of homoscedasticity too often when it is in fact true, while the test has difficulty detecting genuine GARCH effects. Several…

存在异常值时的平滑转换非线性检验AI 解读

Regime-switching models, like the smooth transition autoregressive (STAR) model, are typically applied to time series of moderate length. Hence, the nonlinear features that these models intend to describe may be reflected in only a few observations. Conversely, neglected outliers in a linear time series of moderate length may incorrectly suggest STAR (or other) type(s of) nonlinearity. In this article, the authors propose outlier robust tests for STAR-type nonlinearity.…

协整分析中的异常值检测AI 解读

Standard unit-root and cointegration tests are sensitive to atypical events such as outliers and structural breaks. In this article, the authors use outlier-robust estimation techniques to examine the impact of these events on cointegration analysis. Their outlier-robust cointegration test provides a new diagnostic tool for signaling when standard cointegration results might be driven by a few aberrant observations. A main feature of the authors' approach is that the…

利用伪似然比检验进行协整检验AI 解读

This paper considers pseudomaximum likelihood estimators for vector autoregressive models. These estimators are used to determine the cointegration rank of a multivariate time series process using pseudolikelihood ratio tests. The asymptotic distributions of these tests depend on nuisance parameters if the pseudolikelihood is non-Gaussian. This even holds if the likelihood is correctly specified. The nuisance parameters have a natural interpretation and can be consistently…

摘要暂缺

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…

基于 M 估计量的单位根检验AI 解读

This paper considers unit root tests based on M estimators. The asymptotic theory for these tests is developed. It is shown how the asymptotic distributions of the tests depend on nuisance parameters and how tests can be constructed that are invariant to these parameters. It is also shown that a particular linear combination of a unit root test based on the ordinary least-squares (OLS) estimator and on an M estimator converges to a normal random variate. The interpretation…