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未知方差下的高斯模型选择AI 解读

Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean μ of Y by model selection. More precisely, we start with a collection $\mathcal{S}=\{S_{m},m\in\mathcal{M}\}$ of linear subspaces of ℝn and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estimator among these. Our first objective is to analyze the…

线性因子模型中风险溢价的检验AI 解读

We show that statistical inference on the risk premia in linear factor models that is based on the Fama-MacBeth (FM) and generalized least squares (GLS) two-pass risk premia estimators is misleading when the [beta]'s are small and/or the number of assets is large. We propose novel statistics, that are based on the maximum likelihood estimator of Gibbons [Gibbons, M., 1982. Multivariate tests of financial models: A new approach. Journal of Financial Economics 10, 3-27], which…

多元时间序列回归模型序贯程序的检测延迟AI 解读

We consider a multiple regression model in which the explanatory variables are specified by time series. To sequentially test for the stability of the regression parameters in time, we introduce a detector which is based on the first excess time of a CUSUM-type statistic over a suitably constructed threshold function. The aim of this paper is to study the delay time associated with this detector. As our main result, we derive the limit distribution of the delay time and…

基于近似 Bahadur 斜率比较结构突变检验的相对功效AI 解读

We compare the asymptotic relative efficiency of the Exp, Mean, and Sup functionals of the Wald, LM and LR tests for structural change analyzed by Andrews [Andrews, D.W.K., 1993. Tests for parameter instability and structural change with unknown change point. Econometrica 61, 821-856.] and Andrews and Ploberger [Andrews, D.W.K., Ploberger, W., 1994. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62, 1383-1414.]. We derive the…

股票收益与预期商业状况：半个世纪的直接证据AI 解读

Using survey data, we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a counter-cyclical fashion. Moreover, inclusion of expected business conditions in otherwise-standard predictive return regressions substantially reduce the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium,…

利用时变Copula刻画非齐次依赖关系AI 解读

Measuring dependence in multivariate time series is tantamount to modeling its dynamic structure in space and time. In risk management, the nonnormal behavior of most financial time series calls for non-Gaussian dependences. The correct modeling of non-Gaussian dependences is, therefore, a key issue in the analysis of multivariate time series. In this article we use copula functions with adaptively estimated time-varying parameters for modeling the distribution of returns.…

区分随机游走与均值变化备择AI 解读

We study test procedures that detect structural breaks in underlying data sequences. In particular, we wish to discriminate between different reasons for these changes, such as (1) shifting means, (2) random walk behavior, and (3) constant means but innovations switching from stationary to difference stationary behavior. Almost all procedures presently available in the literature are simultaneously sensitive to all three types of alternatives. The test statistics under…

用于开发动态治疗方案的筛选实验AI 解读

Dynamic treatment regimes are time-varying treatments that individualize sequences of treatments to the patient. The construction of dynamic treatment regimes is challenging because a patient will be eligible for some treatment components only if he has not responded (or has responded) to other treatment components. In addition there are usually a number of potentially useful treatment components and combinations thereof. In this article, we propose new methodology for…

利用贝叶斯平滑样条混合进行局部谱分析AI 解读

In many practical problems, time series are realizations of nonstationary random processes. These processes can often be modeled as processes with slowly changing dynamics or as piecewise stationary processes. In these cases, various approaches to estimating the time-varying spectral density have been proposed. Our approach in this article is to estimate the log of the Dahlhaus local spectrum using a Bayesian mixture of splines. The basic idea of our approach is to first…

方差、偏度与峰度时变的风险价值：NIG-ACD 模型AI 解读

A new model for financial returns with time varying variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously suggested NIG models are evaluated by their Value at Risk (VaR) forecasts on a long series of daily Standard and Poor's 500 returns. All three models perform very well compared with extant models and clearly outperform a Gaussian GARCH model. Moreover, the results show that only the new model…

Student-t扰动下马尔可夫转换门槛非对称GARCH模型的贝叶斯估计AI 解读

A Bayesian estimation of a regime-switching threshold asymmetric GARCH model is proposed. The specification is based on a Markov-switching model with Student-t innovations and K separate GJR(1,1) processes whose asymmetries are located at free non-positive threshold parameters. The model aims at determining whether or not: (i) structural breaks are present within the volatility dynamics; (ii) asymmetries (leverage effects) are present, and are different between regimes and…

线性协变量含测量误差的半参数变系数部分线性模型推断AI 解读

We study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square-based estimation procedures are developed for parametric and nonparametric components after we calibrate the error-prone covariates. Asymptotic properties of the proposed estimators are established. We also propose the profile least-square-based ratio test and Wald test to identify…

跳跃惩罚最小二乘估计的一致性与收敛速度AI 解读

We study the asymptotics for jump-penalized least squares regression aiming at approximating a regression function by piecewise constant functions. Besides conventional consistency and convergence rates of the estimates in L2([0, 1)) our results cover other metrics like Skorokhod metric on the space of càdlàg functions and uniform metrics on C([0, 1]). We will show that these estimators are in an adaptive sense rate optimal over certain classes of “approximation spaces.”…

非平稳时间序列的函数系数模型AI 解读

This paper studies functional coefficient regression models with nonstationary time series data, allowing also for stationary covariates. A local linear fitting scheme is developed to estimate the coefficient functions. The asymptotic distributions of the estimators are obtained, showing different convergence rates for the stationary and nonstationary covariates. A two-stage approach is proposed to achieve estimation optimality in the sense of minimizing the asymptotic mean…

具有单调功效的均值变化检验AI 解读

Several widely used tests for a changing mean exhibit nonmonotonic power in finite samples, due to "incorrect"Â estimation of nuisance parameters under the alternative. In this paper, we study the issue of nonmonotonic power in testing for changing mean. We investigate the asymptotic power properties of the tests, using a new framework where alternatives are characterized as having "large" changes. The asymptotic analysis provides a theoretical explanation to the power…

零假设与备择下均允许未知时点趋势突变的单位根检验AI 解读

Perron [Perron, P., 1989. The great crash, the oil price shock and the unit root hypothesis. Econometrica 57, 1361-1401] introduced a variety of unit root tests that are valid when a break in the trend function of a time series is present. The motivation was to devise testing procedures that were invariant to the magnitude of the shift in level and/or slope. In particular, if a change is present it is allowed under both the null and alternative hypotheses. This analysis was…

部分不稳定广义矩模型中的有效推断AI 解读

This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable…

历史数据对预测股票收益长期分布有多大用处？AI 解读

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The empirical results strongly reject ignoring structural change or…

股票价格中的市场基本面与理性泡沫：贝叶斯视角AI 解读

Abstract Using Bayesian Markov chain Monte Carlo methods, we decompose the log price‐dividend ratio into a market fundamentals component and a bubble component. The market fundamentals component depends on expectations of future dividend growth and required returns, while the bubble component is assumed to follow a Markov switching model that allows for the possibility of exploding and collapsing regimes. If prior beliefs allow for the possibility of persistent shocks to…

预测变量轨迹聚类间响应密度变化的贝叶斯推断AI 解读

In epidemiology, it is often of interest to assess how individuals with different trajectories over time in an environmental exposure or biomarker differ with respect to a continuous response. For ease in interpretation and presentation of results, epidemiologists typically categorize predictors prior to analysis. To extend this approach to time-varying predictors, one can cluster individuals by their predictor trajectory, with the cluster index included as a predictor in a…

重复测量分析中非参数变系数模型的变量选择AI 解读

Nonparametric varying-coefficient models are commonly used for analysis of data measured repeatedly over time, including longitudinal and functional responses data. While many procedures have been developed for estimating the varying-coefficients, the problem of variable selection for such models has not been addressed. In this article, we present a regularized estimation procedure for variable selection that combines basis function approximations and the smoothly clipped…

纵向数据协方差矩阵的半参数估计AI 解读

Estimation of longitudinal data covariance structure poses significant challenges because the data are usually collected at irregular time points. A viable semiparametric model for covariance matrices was proposed in Fan, Huang and Li (2007) that allows one to estimate the variance function nonparametrically and to estimate the correlation function parametrically via aggregating information from irregular and sparse data points within each subject. However, the asymptotic…

需求估计的另一种方法：含条件波动的高频航空旅客到达量神经网络回归AI 解读

In this paper we provide an alternative approach to analyze the demand for international tourism in the Balearic Islands, Spain, by using a neural network model that incorporates time-varying conditional volatility. We consider daily air passenger arrivals to Palma de Mallorca, Ibiza and Mahon, which are located in the islands of Mallorca, Ibiza and Menorca, respectively, as a proxy for international tourism demand for the Balearic Islands. Spain is a world leader in terms…

广义自回归条件相关模型AI 解读

This paper develops a generalized autoregressive conditional correlation (GARCC) model when the standardized residuals follow a random coefficient vector autoregressive process. As a multivariate generalization of the Tsay (1987, Journal of the American Statistical Association 82, 590–604) random coefficient autoregressive (RCA) model, the GARCC model provides a motivation for the conditional correlations to be time varying. GARCC is also more general than the Engle (2002,…