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含外生变量的删失自回归模型的拟似然估计AI 解读

Maximum likelihood estimation of a censored autoregressive model with exogenous variables (CARX) requires computing the conditional likelihood of blocks of data of variable dimensions. As the random block dimension generally increases with the censoring rate, maximum likelihood estimation becomes quickly numerically intractable with increasing censoring. We introduce a new estimation approach using the complete-incomplete data framework with the complete data comprising the…

业绩费与资产收益可预测性的渐近推断AI 解读

In this article, we provide analytical, simulation, and empirical evidence on a test of equal economic value from competing predictive models of asset returns. We define economic value using the concept of a performance fee—the amount an investor would be willing to pay to have access to an alternative predictive model used to make investment decisions. We establish that this fee can be asymptotically normal under modest assumptions. Monte Carlo evidence shows that our test…

货币政策效应的半参数估计：重新审视“推绳子”理论AI 解读

We develop flexible semiparametric time series methods for the estimation of the causal effect of monetary policy on macroeconomic aggregates. Our estimator captures the average causal response to discrete policy interventions in a macrodynamic setting, without the need for assumptions about the process generating macroeconomic outcomes. The proposed estimation strategy, based on propensity score weighting, easily accommodates asymmetric and nonlinear responses. Using this…

一般趋势与横截面相关的固定 T 面板单位根推断AI 解读

This article proposes a new panel unit root test based on the generalized method of moments approach for panels with a possibly small number of time periods, T, and a large number of cross-sectional units, N. In the model that we consider the deterministic trend function is essentially unrestricted and the errors obey a multifactor structure that allows for rich forms of unobserved heterogeneity. In spite of these allowances, the GMM estimator considered is shown to be…

动态因子模型的谱域 EM 算法AI 解读

We make two complementary contributions to efficiently estimate dynamic factor models: a frequency domain EM algorithm and a swift iterated indirect inference procedure for ARMA models with no asymptotic efficiency loss for any finite number of iterations. Although our procedures can estimate such models with many series without good initial values, near the optimum we recommend switching to a gradient method that analytically computes spectral scores using the EM principle.…

自回归过程中的非因果阶数误设AI 解读

This paper examines noncausal order misspecification in noncausal and mixed processes. We consider the constrained maximum likelihood (ML) estimators of autoregressive parameters obtained when noncausal order s is fixed and potentially different from the true order s0. The effect of such noncausal order misspecification on the constrained ML estimators of the autoregressive parameters is examined by means of the binding function. We find that, surprisingly, the misspecified…

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…

均值结构断点估计的新分布理论AI 解读

Based on the Girsanov theorem, this paper obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model. The exact distribution is asymmetric and tri-modal, indicating that the estimator is biased. These two properties are also found in the finite sample distribution of the least squares (LS) estimator of structural break point in the discrete time model, suggesting the classical long-span asymptotic theory is…

Itô半鞅短时变动分布的精确贝叶斯矩推断AI 解读

We modify the Gallant and Tauchen (1996) efficient method of moments (EMM) method to perform exact Bayesian inference, where exact means no reliance on asymptotic approximations. We use this modification to evaluate the empirical plausibility of recent predictions from high frequency financial theory regarding the small-time movements of an Itô semimartingale. The theory indicates that the probability distribution of the small moves should be locally stable around the…

条件自回归与内蕴自回归的边界修正AI 解读

This paper discusses edge correction for a large class of conditional and intrinsic autoregressions on two-dimensional finite regular arrays. The proposed method includes a novel reparameterization, retains the simple neighbourhood structure, ensures the nonnegative definiteness of the precision matrix, and enables scalable matrix-free statistical computation. The edge correction provides new insight into how higher-order differencing enters into the precision matrix of a…

含噪高频数据中误差分布的频域分析AI 解读

Data observed at a high sampling frequency are typically assumed to be an additive composite of a relatively slow-varying continuous-time component, a latent stochastic process or smooth random function, and measurement error. Supposing that the latent component is an Itô diffusion process, we propose to estimate the measurement error density function by applying a deconvolution technique with appropriate localization. Our estimator, which does not require equally-spaced…

波动率泛函的局部非参数 bootstrapAI 解读

Volatility functionals are widely used in financial econometrics. In the literature, they are estimated with realized volatility functionals using high-frequency data. In this paper we introduce a nonparametric local bootstrap method that resamples the high-frequency returns with replacement in local windows shrinking to zero. While the block bootstrap in time series (Hall et al., 1995) aims to reduce correlation, the local bootstrap is intended to eliminate the…

利用自距离相关矩阵检验多元时间序列的独立性AI 解读

SUMMARYWe introduce the matrix multivariate auto-distance covariance and correlation functions for time series, discuss their interpretation and develop consistent estimators for practical implementation. We also develop a test of the independent and identically distributed hypothesis for multivariate time series data and show that it performs better than the multivariate Ljung–Box test. We discuss computational aspects and present a data example to illustrate the method.

近似贝叶斯计算估计量的渐近效率AI 解读

Many statistical applications involve models for which it is difficult to evaluate the likelihood, but from which it is relatively easy to sample. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such cases. We present results on the asymptotic variance of estimators obtained using approximate Bayesian computation in a large data limit. Our key assumption is that the data are summarized by a fixed-dimensional summary…

Summary Methodology is proposed to uncover structural breaks in functional data that is ‘fully functional’ in the sense that it does not rely on dimension reduction techniques. A thorough asymptotic theory is developed for a fully functional break detection procedure as well as for a break date estimator, assuming a fixed break size and a shrinking break size. The latter result is utilized to derive confidence intervals for the unknown break date. The main results highlight…

非平稳时间序列 M 估计中的梯度结构变化检测AI 解读

We consider structural change testing for a wide class of time series M-estimation with nonstationary predictors and errors. Flexible predictor-error relationships, including exogenous, state-heteroscedastic and autoregressive regressions and their mixtures, are allowed. New uniform Bahadur representations are established with nearly optimal approximation rates. A CUSUM-type test statistic based on the gradient vectors of the regression is considered. In this paper, a simple…

依赖数据半参数回归的渐近分布无关检验AI 解读

This article proposes a new general methodology for constructing nonparametric and semiparametric Asymptotically Distribution-Free (ADF) tests for semiparametric hypotheses in regression models for possibly dependent data coming from a strictly stationary process. Classical tests based on the difference between the estimated distributions of the restricted and unrestricted regression errors are not ADF. In this article, we introduce a novel transformation of this difference…

时间序列分位数回归的平滑分块自助法AI 解读

Quantile regression allows for broad (conditional) characterizations of a response distribution beyond conditional means and is of increasing interest in economic and financial applications. Because quantile regression estimators have complex limiting distributions, several bootstrap methods for the independent data setting have been proposed, many of which involve smoothing steps to improve bootstrap approximations. Currently, no similar advances in smoothed bootstraps…

大规模面板高频数据中的系统性与特质性波动率AI 解读

In this paper, we separate the integrated (spot) volatility of an individual Itô process into integrated (spot) systematic and idiosyncratic volatilities, and estimate them by aggregation of local factor analysis (localization) with large-dimensional high-frequency data. We show that, when both the sampling frequency $n$ and the dimensionality $p$ go to infinity and $p\geq C\sqrt{n}$ for some constant $C$, our estimators of High dimensional Itô process; common driving…

波动率变化检验AI 解读

In this paper, we propose a consistent U‐statistic test with good sampling properties to detect changes in volatility. We show that the test has a limiting standard normal distribution under the null hypothesis, and that it is powerful compared with various alternatives. A Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural changes in volatility. An empirical example is examined to demonstrate the…

非平稳波动下的自适应野生自助法单位根检验AI 解读

Recent research has emphasized that permanent changes in the innovation variance (caused by structural shifts or an integrated volatility process) lead to size distortions in conventional unit root tests. It has been shown how these size distortions can be resolved using the wild bootstrap. In this paper, we first derive the asymptotic power envelope for the unit root testing problem when the non‐stationary volatility process is known. Next, we show that under suitable…

时变信息下的有效估计与新凯恩斯主义菲利普斯曲线AI 解读

Empirical evidence suggests that many macroeconometric and financial models are subject to both instability and identification problems. We address both issues under the unified framework of time-varying information, which includes changes in instrument strength, in second moment of instruments, and in the variance of moment conditions. Our new estimation method exploits these changes for increased efficiency of the estimates of the (stable) structural parameters. We also…

Cholesky-GARCH模型与时变条件贝塔的渐近理论AI 解读

This paper proposes a new model with time-varying slope coefficients. Our model, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition of the conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinal data. We derive stationarity and invertibility conditions and prove consistency and asymptotic normality of the Full and equation-by-equation QML estimators of this model. We then show that this class of models is useful to…

大横截面下事后风险溢价估计与资产定价检验：回归校准法AI 解读

We propose a modification of the two-pass cross-sectional regression approach for estimating ex-post risk premia in linear asset pricing models, suitable for the case of large cross sections and short time series. Employing the regression-calibration method, we provide a beta correction method, which deals with the error-in-variables problem, based on which we construct an N-consistent estimator of ex-post risk premia and develop associated novel asset pricing tests.…