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均值与方差可预测性的新检验方法AI 解读

We propose parametric tests for serial correlation in levels and squares that exploit the non-normality of financial returns. Our tests are robust to distributional misspecification. Furthermore, our mean predictability tests can be robustified against time-varying volatility. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess their finite sample reliability. We apply our tests to quarterly returns on the five Fama–French…

多变量半非参数GARCH滤波Copula模型的有效估计AI 解读

This paper considers estimation of semi-nonparametric GARCH filtered copula models in which the individual time series are modeled by semi-nonparametric GARCH and the joint distributions of the multivariate standardized innovations are characterized by parametric copulas with nonparametric marginal distributions. The models extend those of Chen and Fan (2006) to allow for semi-nonparametric conditional means and volatilities, which are estimated via the method of sieves. The…

增广因子模型：市场风险因子验证与债券风险溢价预测AI 解读

We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama–French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the…

利用含噪神经测量评估消费者需求AI 解读

Recent studies have used the random utility framework to examine whether neural data can assess and predict demand for consumer products, both within and across individuals. However the effectiveness of this methodology has been limited by the large degree of measurement error in neural data. The resulting “error-in-variables” problem severely biases the estimates of the relationship between neural measurements and choice behaviour, thus limiting the role such data can play…

误设模型的广义聚合：资产定价应用AI 解读

We propose a generalized aggregation approach for model averaging. The entropy-based optimal criterion is a natural choice for aggregating information from many “globally” misspecified models as it adapts better to the underlying model uncertainty and obtains more robust approximations. Unlike almost all other approaches in the existing literature, we do not require a “reference model,” or a true data generation process contained in the set of models — neither implicitly nor…

带正则化的最大线性回归模型AI 解读

Motivated by the newly developed max-linear competing copula factor models and max-stable nonlinear time series models, we propose a new class of max-linear regression models to take advantages of easy interpretable features embedded in linear regression models. It can be seen that linear relation is a special case of max-linear relation. We develop an EM algorithm based maximum likelihood estimation procedure. The consistency and asymptotics of the estimators for parameters…

日本股票市场的尾部风险与收益可预测性AI 解读

This paper studies the predictability of the Japanese equity market, focusing on the forecasting power of nonparametric volatility and tail risk measures obtained from options data on the S&P 500 and Nikkei 225 market indices. The Japanese market is notoriously difficult to forecast using standard predictive indicators. We confirm that country-specific regressions for Japan – contrary to existing evidence for other national equity indices – produce insignificant…

随机系数多项 Logit 模型中的惩罚似然参数选择AI 解读

This paper is about estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters, some of which may be zero. The paper gives conditions under which, with probability approaching 1 as the sample size increases, penalized maximum likelihood (PML) estimation with the adaptive LASSO (AL) penalty distinguishes correctly between zero and non-zero parameters. The paper also gives conditions under which PML…

高维协变量观察性研究中的重叠性AI 解读

Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument,…

得分驱动波动率模型的连续时间极限AI 解读

We provide general conditions under which a class of discrete-time volatility models driven by the score of the conditional density converges in distribution to a stochastic differential equation as the interval between observations goes to zero. We show that the form of the diffusion limit depends on: (i) the link function, (ii) the conditional second moment of the score, (iii) the normalization of the score. Interestingly, the properties of the stochastic differential…

扩散Copula：识别与估计AI 解读

We propose a new semiparametric approach for modelling nonlinear univariate diffusions, where the observed process is a nonparametric transformation of an underlying parametric diffusion (UPD). This modelling strategy yields a general class of semiparametric Markov diffusion models with parametric dynamic copulas and nonparametric marginal distributions. We provide primitive conditions for the identification of the UPD parameters together with the unknown transformations…

含主导单元的空间模型估计与推断AI 解读

In spatial econometrics literature estimation and inference are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of units, n, in the network. This paper relaxes this restriction and allows for one or more units to have pervasive effects in the network. The linear–quadratic central limit theorem of Kelejian and Prucha (2001) is generalized to allow for such dominant units, and the asymptotic…

近单位根交互效应面板中的因子分析方法AI 解读

In a recent study, Bai (2013) proposes a new factor analytical (FA) method for estimation of stationary dynamic panel data models with fixed effects. Our interest in this method originates with the fact it does not require explicit demeaning of the data, a practice that is known to cause problems of bias and low power in near unit root panels. The purpose is to study the properties of FA when applied to such panels when the common component admits to a interactive effects…

观测驱动时间序列模型中的缺失观测AI 解读

We argue that existing methods for the treatment of missing observations in time-varying parameter observation-driven models lead to inconsistent inference. We provide a formal proof of this inconsistency for a Gaussian model with time-varying mean. A Monte Carlo simulation study supports this theoretical result and illustrates how the inconsistency problem extends to score-driven and, more generally, to observation-driven models, which include well-known models for…

大型面板模型中广泛影响单元的检测AI 解读

The importance of units that influence a large number of other units in a network has become increasingly recognized in the literature. In this paper we propose a new method to detect such pervasive units by basing our analysis on unit-specific residual error variances subject to suitable adjustments due to the multiple testing issues involved. Accordingly, a sequential multiple testing (SMT) procedure is proposed, which allows identification of pervasive units (if any)…

重新审视中国FDI区位：异质冲击面板方法AI 解读

Foreign Direct Investment (FDI) is viewed as a primary driving force in shaping the global economy and receives particular attention in empirical studies. In this paper, we argue that many of the existing studies ignore endogeneities that arise from shocks in source and destination countries. To address this endogeneity issue, we take the “controlling through estimating” idea from the econometric literature and propose using panel data models with heterogeneous shocks to…

具有相关随机效应的空间动态面板数据模型AI 解读

In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by…

变随机系数模型AI 解读

This paper analyzes unobserved heterogeneity when observed characteristics are modeled nonlinearly. The proposed model builds on varying random coefficients (VRC) that are determined by nonlinear functions of observed regressors and additively separable unobservables. This paper proposes a novel estimator of the VRC density based on weighted sieve minimum distance. The main example of sieve bases are Hermite functions which yield a numerically stable estimation procedure.…

椭圆分布及其推广下的高维协方差矩阵检验AI 解读

We develop tests for high-dimensional covariance matrices under a generalized elliptical model. Our tests are based on a central limit theorem for linear spectral statistics of the sample covariance matrix based on self-normalized observations. For testing sphericity, our tests neither assume specific parametric distributions nor involve the kurtosis of data. More generally, we can test against any non-negative definite matrix that can even be not invertible. As an…

大维动态因子模型：含一阶单整协整因子的脉冲响应估计AI 解读

We study a large-dimensional Dynamic Factor Model where: (i) the vector of factors Ft is I(1) and driven by a number of shocks that is smaller than the dimension of Ft; and, (ii) the idiosyncratic components are either I(1) or I(0). Under (i), the factors Ft are cointegrated and can be modeled as a Vector Error Correction Model (VECM). Under (i) and (ii), we provide consistent estimators, as both the cross-sectional size n and the time dimension T go to infinity, for the…

非参数部分识别下部分线性工具变量模型的稳健与最优估计AI 解读

This paper studies robust and optimal estimation of the slope coefficients in a partially linear instrumental variables model with nonparametric partial identification. We establish the root-n asymptotic normality of a penalized sieve minimum distance estimator of the slope coefficients. We show that the asymptotic normality holds regardless of whether the nonparametric function is point identified or only partially identified. However, in the presence of nonparametric…

动态面板空间向量自回归估计：稳定性与空间多变量协整AI 解读

This paper introduces dynamic panel spatial vector autoregressive models. We study features of dynamics and spatial interactions that an SVAR model can generate and classify the model into stable or unstable cases by partitioning parameter spaces. For stable, spatial cointegration, and mixed cointegration cases, we investigate identification and QML estimation of the models to take into account simultaneity and correlated relationships. Asymptotic properties and…

贝叶斯不等式检验的频率学派性质AI 解读

Bayesian and frequentist criteria fundamentally differ, but often posterior and sampling distributions agree asymptotically. For the corresponding single-draw experiment, we characterize the frequentist size of a certain Bayesian hypothesis test of (possibly nonlinear) inequalities. If the null hypothesis is that the parameter lies in a specified half-space, then the Bayesian test’s size equals α; if the null hypothesis is a subset of a half-space, then size is above α;…

非平稳自回归模型中的多重断点估计AI 解读

Chong (1995) and Bai (1997) proposed a sample-splitting method to estimate a multiple-break model. However, their studies focused on stationary time series models, in which the identification of the first break depends on the magnitude and the duration of the break, and a testing procedure is needed to assist the estimation of the remaining breaks in subsamples split by the break points found earlier. In this paper, we focus on nonstationary multiple-break autoregressive…