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稳定分布潜在因子模型的间接推断估计AI 解读

Cross-sections of financial returns are characterized by common underlying factors and exhibit fat tails that may be captured by α-stable distributions. This paper focuses on estimating factor models with independent latent factors and idiosyncratic noises featuring a multivariate α-stable distribution constant over time (static factor models) or a time-varying conditional multivariate α-stable distribution (GARCH factor models). Although the simulation of such a…

大型条件异方差因子模型的间接估计：道琼斯30只股票应用AI 解读

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for large-scale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small…

α 稳定分布与过程的间接估计AI 解读

The α-stable family of distributions constitutes a generalization of the Gaussian distribution, allowing for asymmetry and thicker tails. Its practical usefulness is coupled with a marked theoretical appeal, as it stems from a generalized version of the central limit theorem in which the assumption of the finiteness of the variance is replaced by a less restrictive assumption concerning a somehow regular behaviour of the tails. Estimation difficulties have however hindered…

受约束的间接估计AI 解读

We develop generalized indirect estimation procedures that handle equality and inequality constraints on the auxiliary model parameters by extracting information from the relevant multipliers, and compare their asymptotic efficiency to maximum likelihood. We also show that, regardless of the validity of the restrictions, the asymptotic efficiency of such estimators can never decrease by explicitly considering the multipliers associated with additional equality constraints.…

含多元 t 创新的条件异方差动态回归模型：极大似然估计与推断AI 解读

Hessian, and information matrix of conditionally heteroscedastic dynamic regression models when the conditional distribution is multivariate t. We also derive one-sided and two-sided Lagrange multiplier tests for multivariate normality versus multivariate t based on the first two moments of the squared norm of the standardized innovations evaluated at the Gaussian pseudo-maximum likelihood estimators of the conditional mean and variance parameters. Finally, we illustrate our…

利用控制变量降低间接推断方差：连续时间利率模型AI 解读

Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for estimating stochastic differential equations. They adjust for the bias (inconsistency) caused by discretization of the underlying stochastic process, which is in continuous time. The price to be paid is an increased variance of the estimated parameters. The variance suffers from an additional component, which depends on the stochastic simulation involved in the…

解析导数与 GARCH 估计的计算AI 解读

In the context of univariate GARCH models we show how analytic first and second derivatives of the log-likelihood can be successfully employed for estimation purposes. Maximum likelihood GARCH estimation usually relies on the numerical approximation to the log-likelihood derivatives, on the grounds that an exact analytic differentiation is much too burdensome. We argue that this is not the case and that the computational benefit of using the analytic derivatives (first and…

精确全信息最大似然与工具变量之间的一个特殊结果AI 解读

The iterative application of an instrumental variable method to a system of simultaneous equations may exactly produce FIML, upon convergence. Instruments achieving this target do not need to be either uncorrelated with the error terms or correlated as much as possible with the replaced explanatory variables. This curious mathematical result, which contradicts some common wisdom and intuition, is proved in our paper. Our proof also provides a unified scheme that covers the…

计量模型随机模拟中误差过程的替代设定AI 解读

Abstract This paper analyses the stochastic simulation of econometric models using three different methods for specifying the probability distribution of the structural error terms. The impact of these different assumptions on the simulation bias and model variance is explored empirically. Monte Carlo variance reduction techniques are used to distinguish the effects of the different specifications.

全信息最大似然协方差矩阵的替代估计量：蒙特卡洛研究AI 解读

On cherche a exposer aux utilisateurs des techniques d'estimation par le maximum de vraisemblance a information complete. Pourquoi le choix de l'estimateur de la matrice de covariance des coefficients n'est pas aussi neutre qu'on peut l'esperer

On propose une methode analytique simplifiee qui autorise le calcul, meme pour des systemes de dimension moyenne

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计量经济模型中事后预测方差的一则札记AI 解读

IT IS WELL KNOWN that in an econometric model, under general assumptions, the ex-post forecast error (conditional on predetermined variables) can be decomposed into the sum of two independent terms; the former is the component due to error in estimated coefficients, while the latter depends on the random error terms. The asymptotic covariance matrix of the first component can be analytically derived through intermediate computation of the covariance matrix of restricted…

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计量经济模型的随机模拟程序AI 解读

model. Many results (observed value, deterministic solution, computed mean among the replications, minimum and maximum of the stochastic solution, first relative differences of the observed, deterministic and mean stochastic values) are displayed and several empirical indicators of goodness of fit are computed: (1) the mean over the simulation period of the actual, deterministic and mean stochastic values; (2) the root mean square error (RMSE) of the deterministic and mean…