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持续且内生的微观结构效应与采样时长下证券协变矩阵的偏差校正AI 解读

I propose a bias-corrected non-parametric estimator of the covariation matrix of log security prices, designed as a convex combination of two realized kernels. The estimator is simple but possesses desirable statistical properties including consistency, asymptotic normality and the parametric rate of convergence in the presence of persistent, diurnally heteroskedastic and endogenous microstructure effects. It is robust to the asynchronous trading of multiple securities with…

无混杂条件下不可观测因素单调性的检验AI 解读

Monotonicity in a scalar unobservable is a common assumption when modeling heterogeneity in structural models. Among other things, it allows one to recover the underlying structural function from certain conditional quantiles of observables. Nevertheless, monotonicity is a strong assumption and in some economic applications unlikely to hold, e.g., random coefficient models. Its failure can have substantive adverse consequences, in particular inconsistency of any estimator…

面板结果的贝叶斯处理效应与变量选择：产假对收入的影响AI 解读

We propose two alternative Bayesian treatment effect modeling and inferential frameworks for panel outcomes to estimate dynamic earnings effects of a long maternity leave on mothers’ subsequent earnings. Modeling of the endogeneity of the treatment and the panel structure of the earnings are based on the modeling tradition of the Roy switching regression model and the shared factor approach, respectively. We implement stochastic variable selection to test, for example, for…

s 值：回归系数稳健程度的低情境依赖度量AI 解读

This paper proposes a context-minimal range of alternative regression models that is used to generate a range of alternative estimates. A prior distribution is assumed with a zero mean but an ambiguous covariance matrix. The choice of the prior covariance matrix is facilitated by transformation to standardized variables which makes the prior expected R2 equal to the sum of the prior variances. Three different ranges of the prior expected R2 are used to define three different…

广义经验似然估计框架中的固定平滑渐近理论AI 解读

This paper concerns the fixed-smoothing asymptotics for two commonly used estimators in the generalized empirical likelihood estimation framework for time series data, namely the continuous updating estimator and the maximum blockwise empirical likelihood estimator. For continuously updating generalized method of moments (GMM) estimator, we show that the results for the two-step GMM estimator in Sun (2014a) continue to hold under suitable assumptions. For continuous updating…

异方差二元响应模型中特殊回归变量的信息含量AI 解读

We quantify the informational content of special regressors in heteroskedastic binary response models with median-independent or conditionally symmetric errors. Based on Lewbel (1998), a special regressor is additively separable in the latent payoff and conditionally independent from the error term. We find that with median-independent errors a special regressor does not increase the identifying power by a criterion in Manski (1988) or lead to positive Fisher information for…

含依赖及共同协变量的风险率函数核估计AI 解读

We propose a hazard model where dependence between events is achieved by assuming dependence between covariates. This model allows for correlated variables specific to observations as well as macro variables which all observations share. This setup better fits many economic and financial applications where events are not independent. Nonparametric estimation of the hazard function is then studied. Kernel estimators proposed in Nielsen and Linton (1995) and Linton et al.…

面板数据的平滑分位数回归AI 解读

This paper studies fixed effects estimation of quantile regression models for panel data. Under an asymptotic framework where both the numbers of individuals and time periods grow at the same rate, we show that the fixed-effects estimator for the smoothed objective function has a limiting normal distribution with a bias in the mean, and provide the analytic form of the asymptotic bias. We propose a one-step bias correction estimator based on the analytic bias formula…

三角非可分模型识别的不连续性检验AI 解读

This paper presents a test for the validity of control variable approaches to identification in triangular nonseparable models. Assumptions commonly imposed to justify such methods include full independence of instruments and disturbances and existence of a reduced form that is strictly monotonic in a scalar disturbance. We show that if the data has a particular structure, namely that the distribution of the endogenous variable has a mass point at the lower (or upper)…

多元与多重置换检验AI 解读

In this article, we consider the use of permutation tests for comparing multivariate parameters from two populations. First, the underlying properties of permutation tests when comparing parameter vectors from two distributions P and Q are developed. Although an exact level α test can be constructed by a permutation test when the fundamental assumption of identical underlying distributions holds, permutation tests have often been misused. Indeed, permutation tests have…

随机系数面板模型中的统计推断AI 解读

This paper studies the asymptotics of the Weighted Least Squares (WLS) estimator of the autoregressive root in a panel Random Coefficient Autoregression (RCA). We show that, in an RCA context, there is no “unit root problem” : the WLS estimator is always asymptotically normal, irrespective of the average value of the autoregressive root, of whether the autoregressive coefficient is random or not, and of the presence and degree of cross dependence. Our simulations indicate…

爆炸性连续时间模型的双重渐近理论AI 解读

This paper establishes a double asymptotic theory for explosive continuous time Lévy-driven processes and the corresponding exact discrete time models. The double asymptotic theory assumes the sample size diverges because the sampling interval (h) shrinks to zero and the time span (N) diverges. Both the simultaneous and sequential double asymptotic distributions are derived. In contrast to the long-time-span asymptotics (N→∞ with fixed h) where no invariance principle…

波动率函数依赖关系的推断理论AI 解读

We develop inference theory for models involving possibly nonlinear transforms of the elements of the spot covariance matrix of a multivariate continuous-time process observed at high frequency. The framework can be used to study the relationship among the elements of the latent spot covariance matrix and processes defined on the basis of it such as systematic and idiosyncratic variances, factor betas and correlations on a fixed interval of time. The estimation is based on…

利用采购经理指数预测产出增长的多国方法AI 解读

This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of strong unobserved common factors can lead to an undetermined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability (as the panel dimensions N,T→j∞) to the infeasible…

面向高维模型的分层 Metropolis–Hastings 采样器AI 解读

Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the “dynamic striated Metropolis–Hastings (DSMH)” sampler. Grounded in the Metropolis–Hastings algorithm, it pools the strengths from the equi-energy and sequential Monte Carlo samplers while avoiding the weaknesses of the standard Metropolis–Hastings algorithm and those of importance sampling. In particular, the…

摘要暂缺

多元自回归指数模型的结构分析AI 解读

We address the issue of parameter dimensionality reduction in Vector Autoregressive models (VARs) for many variables by imposing specific reduced rank restrictions on the coefficient matrices that simplify the VARs into Multivariate Autoregressive Index (MAI) models. We derive the Wold representation implied by the MAIs and show that it is closely related to that associated with dynamic factor models. Then, the theoretical analysis is extended to the case of general rank…

大型贝叶斯 VARMA 模型AI 解读

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this…

多元 AR 与 ARMA 系统结构：常规、奇异及单频与混频情形AI 解读

This paper is concerned with the structure of multivariate AR and ARMA systems. The emphasis is on two “non-standard” cases: We deal with the structure of singular AR and ARMA systems which generate singular spectral densities and with identifiability of ARMA systems from mixed frequency data. In the mixed frequency case we show that, for the case where the MA order is smaller than or equal to the AR order, identifiability can be achieved generically. Furthermore, we…

混合单整与温和爆炸性回归变量下的稳健计量推断AI 解读

This paper explores in several prototypical models a convenient inference procedure for nonstationary variable regression that enables robust chi-square testing for a wide class of persistent and endogenous regressors. The approach uses the mechanism of self-generated instruments called IVX instrumentation developed by Magdalinos and Phillips (2009b). We first show that these methods remain valid for regressors with local unit roots in the explosive direction and mildly…

宏观建模中的 VARMA 识别：一种新方法AI 解读

This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon form and presents a fully automatic, strongly consistent, data driven approach to model specification. A novel feature of the inferential procedures developed here is that they work in terms of a canonical representation based on the Kronecker invariants in which the variables are expressed in the form of scalar dynamic structural…

TENET：尾部事件驱动的网络风险AI 解读

CoVaR is a measure for systemic risk of the networked financial system conditional on institutions being under distress. The analysis of systemic risk is the focus of recent econometric analyses and uses tail event and network based techniques. Here, in this paper we bring tail event and network dynamics together into one context. In order to pursue such joint efforts, we propose a semiparametric measure to estimate systemic interconnectedness across financial institutions…

动态预测组合：金融摩擦与预测表现AI 解读

We apply a novel methodology for estimating time-varying weights in linear prediction pools, which we call Dynamic Pools, and use it to investigate the relative forecasting performance of DSGE models with and without financial frictions for output growth and inflation from 1992 to 2011. We find strong evidence of time variation in the pool’s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress…

高斯混合向量自回归AI 解读

This paper proposes a new nonlinear vector autoregressive (VAR) model referred to as the Gaussian mixture vector autoregressive (GMVAR) model. The GMVAR model belongs to the family of mixture vector autoregressive models and is designed for analyzing time series that exhibit regime-switching behavior. The main difference between the GMVAR model and previous mixture VAR models lies in the definition of the mixing weights that govern the regime probabilities. In the GMVAR…