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金融收益条件相关性对市场波动率的依赖建模AI 解读

Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns and several studies have shown that the market volatility is a major determinant of the correlations. We extend some models to include explicitly the dependence of the correlations on the market volatility. The models differ by the way—linear or nonlinear, direct or indirect—in which the volatility influences the correlations. Using a wide set…

通过辅助样本向研究样本倾斜法（AST）有效估计数据组合模型AI 解读

We propose a locally efficient estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems with data missing at random (of which the average treatment effect (ATE) estimand is a special case). Our estimator also possesses a double robustness property. Our procedure may be used to…

分布 Granger 因果关系的非参数检验及其金融传染应用AI 解读

This article introduces a kernel-based nonparametric inferential procedure to test for Granger causality in distribution. This test is a multivariate extension of the kernel-based Granger causality test in tail event. The main advantage of this test is its ability to examine a large number of lags, with higher-order lags discounted. In addition, our test is highly flexible because it can be used to identify Granger causality in specific regions on the distribution supports,…

未知共同因子的识别：引领者与跟随者AI 解读

This article has the following contributions. First, this article develops a new criterion for identifying whether or not a particular time series variable is a common factor in the conventional approximate factor model. Second, by modeling observed factors as a set of potential factors to be identified, this article reveals how to easily pin down the factor without performing a large number of estimations. This allows the researcher to check whether or not each individual…

In fuzzy regression discontinuity (FRD) designs, the treatment effect is identified through a discontinuity in the conditional probability of treatment assignment. We show that when identification is weak (i.e., when the discontinuity is of a small magnitude), the usual t-test based on the FRD estimator and its standard error suffers from asymptotic size distortions as in a standard instrumental variables setting. This problem can be especially severe in the FRD setting…

This article proposes a class of weighted differences of averages (WDA) statistics to test and estimate possible change-points in variance for time series with weakly dependent blocks and dependent panel data without specific distributional assumptions. We derive the asymptotic distributions of the test statistics for testing the existence of a single variance change-point under the null and local alternatives. We also study the consistency of the change-point estimator.…

We develop a finite-sample procedure to test the mean-variance efficiency and spanning hypotheses, without imposing any parametric assumptions on the distribution of model disturbances. In so doing, we provide an exact distribution-free method to test uniform linear restrictions in multivariate linear regression models. The framework allows for unknown forms of nonnormalities as well as time-varying conditional variances and covariances among the model disturbances. We…

This article develops a framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology deals with several important issues prevalent in policy and program evaluation, including application and approval stages, nonrandom treatment assignment, endogeneity, and discrete outcomes. This article presents a computationally efficient estimation algorithm and techniques for model comparison and treatment effects. The framework is…

This article provides a strategy to identify the existence and direction of a causal effect in a generalized nonparametric and nonseparable model identified by instrumental variables. The causal effect concerns how the outcome depends on the endogenous treatment variable. The outcome variable, treatment variable, other explanatory variables, and the instrumental variable can be essentially any combination of continuous, discrete, or “other” variables. In particular, it is…

In this article, we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time-varying stochastic volatilities. We use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results. First, we evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how…

The late-2000s financial crisis stressed the need to understand the world financial system as a network of countries, where cross-border financial linkages play a fundamental role in the spread of systemic risks. Financial network models, which take into account the complex interrelationships between countries, seem to be an appropriate tool in this context. To improve the statistical performance of financial network models, we propose to generate them by means of…

序贯条件独立下中介效应估计量的有限样本表现AI 解读

Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric estimators of (natural) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers…

非同步含噪资产价格的平顶已实现核二次协变差估计AI 解读

This article develops a general multivariate additive noise model for synchronized asset prices and provides a multivariate extension of the generalized flat-top realized kernel estimators, analyzed earlier by Varneskov (2014), to estimate its quadratic covariation. The additive noise model allows for α-mixing dependent exogenous noise, random sampling, and an endogenous noise component that encompasses synchronization errors, lead-lag relations, and diurnal…

医疗支出价格弹性的删失分位数工具变量估计AI 解读

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to…

门限双重自回归模型的检验与估计AI 解读

This article first proposes a score-based test for a double autoregressive model against a threshold double autoregressive (AR) model. It is an asymptotically distribution-free test and is easy to implement in practice. The article further studies the quasi-maximum likelihood estimation of a threshold double autoregressive model. It is shown that the estimated threshold is n-consistent and converges weakly to a functional of a two-sided compound Poisson process and the…

利用非伪因子预测美国宏观经济时间序列AI 解读

This study examines the practical implications of the fact that structural changes in factor loadings can produce spurious factors (or irrelevant factors) in forecasting exercises. These spurious factors can induce an overfitting problem in factor-augmented forecasting models. To address this concern, we propose a method to estimate nonspurious factors by identifying the set of response variables that have no structural changes in their factor loadings. Our theoretical…

低频时间序列的高频拆分：方法、信息损失与诊断AI 解读

Duk B. Juna, Jihwan Moonb & Sungho Parkca KAIST, College of Business, Seoul, Republic of Korea b Warrington College of Business, University of Florida, Gainesville, FL 32611 c W.P. Carey School of Business, Arizona State University, Tempe, AZ 85287 This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to…

有序响应模型中的单变量阈值效应：收入与幸福感梯度估计的应用AI 解读

This short article extends well-known threshold models to the ordered response setting. We consider the case where the sample is endogenously split to estimate regime-dependent coefficients for one variable of interest, while keeping the other coefficients and auxiliary parameters constant across the threshold. We use Monte Carlo methods to examine the behavior of the model. In addition, we derive the formulae for the partial effects associated with the model. We apply our…

时间序列模型中的非线性、结构突变与长程依赖AI 解读

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in…

利用协变量数据的时间序列模拟型密度估计AI 解读

This article proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

样本选择与不依从下的处理效应界：Job Corps的工资影响AI 解读

Randomized and natural experiments are commonly used in economics and other social science fields to estimate the effect of programs and interventions. Even when employing experimental data, assessing the impact of a treatment is often complicated by the presence of sample selection (outcomes are only observed for a selected group) and noncompliance (some treatment group individuals do not receive the treatment while some control individuals do). We address both of these…

用于处理效应估计的协变量选择准则AI 解读

We study how to select or combine estimators of the average treatment effect (ATE) and the average treatment effect on the treated (ATT) in the presence of multiple sets of covariates. We consider two cases: (1) all sets of covariates satisfy the unconfoundedness assumption and (2) some sets of covariates violate the unconfoundedness assumption locally. For both cases, we propose a data-driven covariate selection criterion (CSC) to minimize the asymptotic mean squared errors…

含吸收态的状态切换ARMA贝叶斯推断：事前实际利率动态AI 解读

One goal of this article is to develop an efficient Metropolis–Hastings (MH) algorithm for estimating an ARMA model with a regime-switching mean, by designing a new efficient proposal distribution for the regime-indicator variable. Unlike the existing algorithm, our algorithm can achieve reasonably fast convergence to the posterior distribution even when the latent regime-indicator variable is highly persistent or when there exist absorbing states. Another goal is to…

利用期权价格数据估计随机波动率模型参数AI 解读

This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral…