国际油价冲击对中国宏观经济的影响

 长期以来,伴随油价冲击的往往是国际经济和社会的剧烈动荡,这使得油价冲击对宏观经济的影响成为日益重要的研究课题。本文首次运用结构向量自回归（SVAR）模型,研究了国际油价波动对我国宏观经济所产生的动态冲击效应。实证研究发现：国际油价上涨确实对产出有逆向影响,但冲击后的产出变化在回归到零值后会越过零值继续上升;国际油价上涨对CPI有正向影响,但影响不显著,且CPI并不会在当期就对油价冲击做出响应,而是有一个相当的滞后期,然后在达到一个高点之后慢慢下降,逐渐回归到0值,但在达到0值后还会继续向下;国际油价上涨对一年期存款利率基本没有影响。针对造成这种实证结果的原因,本文最后给出了相应的解释和政策建议。     

Dynamic Asymmetries in U.S. Unemployment

We examine dynamic asymmetries in U.S. unemployment using nonlinear time series models and Bayesian methods. We find strong statistical evidence in favor of a two-regime threshold auto-regressive model. Empirical results indicate that, once we take into account both parameter and model uncertainty, there are economically interesting asymmetries in the unemployment rate. One finding of particular interest is that shocks that lower the unemployment rate tend to have a smaller effect than shocks that raise the unemployment rate. This finding is consistent with unemployment rises being sudden and falls gradual.     

Age Replacement Policy with a Safety Constraint via the Bayesian Method

Consider a system that is subject to shocks that arrive according to a non homogeneous Poisson process. As the shocks occur, the system has m + 1 failure modes including the following: (i) a non repairable failure (catastrophic) mode that calls for a replacement and (ii) m repairable failure (non catastrophic) modes that are rectified by minimal repairs. In this article, we propose an age-replacement model with minimal repair based on using the natural conjugate prior of Bayesian method. In addition, a safety constraint is considered to control the risk of occurring catastrophic failures in a specified time interval. The minimum-cost replacement policy is studied in terms of its existence and safety constraint. A numerical example is also presented to illustrate the proposed model.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

Small sample sensitivity analysis techniques for computer models.with an application to risk assessment

As modeling efforts expand to a broader spectrum of areas the amount of computer time required to exercise the corresponding computer codes has become quite costly (several hours for a single run is not uncommon). This costly process can be directly tied to the complexity of the modeling and to the large number of input variables (often numbering in the hundreds) Further, the complexity of the modeling (usually involving systems of differential equations) makes the relationships among the input variables not mathematically tractable. In this setting it is desired to perform sensitivity studies of the input-output relationships. Hence, a judicious selection procedure for the choic of values of input variables is required, Latin hypercube sampling has been shown to work well on this type of problem.

However, a variety of situations require that decisions and judgments be made in the face of uncertainty. The source of this uncertainty may be lack ul knowledge about probability distributions associated with input variables, or about different hypothesized future conditions, or may be present as a result of different strategies associated with a decision making process In this paper a generalization of Latin hypercube sampling is given that allows these areas to be investigated without making additional computer runs. In particular it is shown how weights associated with Latin hypercube input vectors may be rhangpd to reflect different probability distribution assumptions on key input variables and yet provide: an unbiased estimate of the cumulative distribution function of the output variable. This allows for different distribution assumptions on input variables to be studied without additional computer runs and without fitting a response surface. In addition these same weights can be used in a modified nonparametric Friedman test to compare treatments, Sample size requirements needed to apply the results of the work are also considered. The procedures presented in this paper are illustrated using a model associated with the risk assessment of geologic disposal of radioactive waste.     

粮食价格波动对物价水平的非对称影响——基于STR模型的实证分析

依据中国1998年1月-2011年6月的季度数据,运用平滑转换回归模型研究粮食价格波动对物价水平的影响,研究表明:粮食价格对物价水平存在单向Granger因果关系,其影响呈现非线性和不对称特征;当粮食价格处于高机制状态时,粮价波动对物价水平影响程度较大,CPI自我驱动能力较小;当粮食价格处于低机制状态时,粮价波动对物价水平影响程度微弱,CPI自我驱动能力较强.     

金融危机和地产调控冲击下地产板块和整体股市的波动性研究

本文采用股票指数数据，通过BEKK—GJR—GARCH模型考察了地产板块和整体股市之间的均值溢出和波动溢出效应，并在此基础之上，进一步考察了在金融危机发生的特定时间窗口下，两者之间的波动溢出效应。此外，本文在引入表征危机事件和地产调控冲击的虚拟变量之后，考察了冲击对地产板块和整体股市波动性的影响。本文的实证模型考虑了非对称性因素并采用广义误差分布（GED）处理“厚尾”问题，是对现有研究范式的有益探索。本文的实证结果认为，地产板块和整体股市之间存在着显著的波动溢出效应，均值溢出效应的存在不甚稳健，但两种溢出效应都存在明显的非对称性。地产板块对整体股市的波动溢出持续性很小，但冲击会加剧波动，反之整体股市对地产板块的则具备持续性，冲击更强烈。波动溢出在2008年金融危机和2015年股灾期间存在变化，尤其是一方对另一方的直接冲击作用都更弱了，可能由两市场联结减弱导致，但非对称性依旧突出。引入表征事件冲击的虚拟变量后，估计结果能够显示出危机和地产调控对于地产板块和整体股市的波动性存在明确影响：直接来看，2010年的房市调控影响幅度最大，超过2015年股灾和2008年金融危机，这一点值得房市调控政策制定者注意；间接来看，六次事件中的五次均对地产板块和整体股市之间的相关性有影响，普遍性很高，由此，风险监管层有必要关注不同冲击下股市内部相关性的变化。     

Max-Linear Competing Factor Models

Models incorporating “latent” variables have been commonplace in financial, social, and behavioral sciences. Factor model, the most popular latent model, explains the continuous observed variables in a smaller set of latent variables (factors) in a matter of linear relationship. However, complex data often simultaneously display asymmetric dependence, asymptotic dependence, and positive (negative) dependence between random variables, which linearity and Gaussian distributions and many other extant distributions are not capable of modeling. This article proposes a nonlinear factor model that can model the above-mentioned variable dependence features but still possesses a simple form of factor structure. The random variables, marginally distributed as unit Fréchet distributions, are decomposed into max linear functions of underlying Fréchet idiosyncratic risks, transformed from Gaussian copula, and independent shared external Fréchet risks. By allowing the random variables to share underlying (latent) pervasive risks with random impact parameters, various dependence structures are created. This innovates a new promising technique to generate families of distributions with simple interpretations. We dive in the multivariate extreme value properties of the proposed model and investigate maximum composite likelihood methods for the impact parameters of the latent risks. The estimates are shown to be consistent. The estimation schemes are illustrated on several sets of simulated data, where comparisons of performance are addressed. We employ a bootstrap method to obtain standard errors in real data analysis. Real application to financial data reveals inherent dependencies that previous work has not disclosed and demonstrates the model’s interpretability to real data. Supplementary materials for this article are available online.     

Some Sampling Properties of a Model for Income Distribution

A recently proposed model for describing the distribution of income over a population, based on the Burr distribution, has been shown to fit better than the commonly used lognormal or gamma distributions. The current article extends that analysis by deriving the large-sample properties of the maximum likelihood estimates for this three-parameter model. Consequently, resulting confidence intervals for some measures of income inequality (including the Gini index) are used to further test the model's validity, as well as to examine apparent trends in inequality over time. Since these properties depend on the way the income data are grouped and censored, implications for choosing data-report intervals can be analyzed. Specifically, a choice between two common methods of reporting the data is shown to have an important impact on Gini index estimates.     

Evaluating Value-at-Risk Models via Quantile Regression

This article is concerned with evaluating Value-at-Risk estimates. It is well known that using only binary variables, such as whether or not there was an exception, sacrifices too much information. However, most of the specification tests (also called backtests) available in the literature, such as Christoffersen (1998) and Engle and Manganelli (2004) are based on such variables. In this article we propose a new backtest that does not rely solely on binary variables. It is shown that the new backtest provides a sufficient condition to assess the finite sample performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker and Xiao 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series.     

Bayesian neural networks for nonlinear time series forecasting

In this article, we apply Bayesian neural networks (BNNs) to time series analysis, and propose a Monte Carlo algorithm for BNN training. In addition, we go a step further in BNN model selection by putting a prior on network connections instead of hidden units as done by other authors. This allows us to treat the selection of hidden units and the selection of input variables uniformly. The BNN model is compared to a number of competitors, such as the Box-Jenkins model, bilinear model, threshold autoregressive model, and traditional neural network model, on a number of popular and challenging data sets. Numerical results show that the BNN model has achieved a consistent improvement over the competitors in forecasting future values. Insights on how to improve the generalization ability of BNNs are revealed in many respects of our implementation, such as the selection of input variables, the specification of prior distributions, and the treatment of outliers.     

A Multivariate GARCH Model Incorporating the Direct and Indirect Transmission of Shocks

Theoretical models of contagion and spillovers allow for asset-specific shocks that can be directly transmitted from one asset to another, as well as indirectly transmitted across uncorrelated assets through some intermediary mechanism. Standard multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, however, provide estimates of volatilities and correlations based only on the direct transmission of shocks across assets. As such, spillover effects via an intermediary asset or market are not considered. In this article, a multivariate GARCH model is constructed that provides estimates of volatilities and correlations based on both directly and indirectly transmitted shocks. The model is applied to exchange rate and equity returns data. The results suggest that if a spillover component is observed in the data, the spillover augmented models provide significantly different volatility estimates compared to standard multivariate GARCH models.     

国外跨境电子商务税收冲击的经济效应：基于TSO-DSGE模型的研究

基于跨境电子商务的特点，本文构建了一个包含异质性贸易品生产商的三部门开放经济动态随机一般均衡模型，并在模型中引入了跨境电子商务出口贸易中介部门，定量分析了国外跨境电子商务税收冲击的经济效应。结果发现：①国外跨境电子商务税收冲击对我国产出的影响较为显著，抑制强度达71.3%，持续时间大致为10季；②对于厂商而言，税收冲击会提高产品价格，抑制国外居民消费，引发国内产出下降；为追求利润最大化，国内资本和劳动会转向传统贸易品和非贸易品；③提高跨境电子商务在出口贸易中的占比虽会导致收敛周期变长，但能更有效应对税收冲击；④跨境电子商务产品替代弹性越小，税收冲击的负面影响越小；且减少替代弹性能使税收冲击响应强度按64.22%的速度衰减，同时使产出波动周期平均缩短4.75季。据此，提出培育品牌卖家、增强消费者的品牌认同感，细化目标消费者、提高消费价格粘性，出台临时性出口补贴应对机制、落实解决出口退税难等对策。     

Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section

This article proposes a Bayesian estimation framework for a typical multi-factor model with time-varying risk exposures to macroeconomic risk factors and corresponding premia to price U.S. publicly traded assets. The model assumes that risk exposures and idiosyncratic volatility follow a break-point latent process, allowing for changes at any point on time but not restricting them to change at all points. The empirical application to 40 years of U.S. data and 23 portfolios shows that the approach yields sensible results compared to previous two-step methods based on naive recursive estimation schemes, as well as a set of alternative model restrictions. A variance decomposition test shows that although most of the predictable variation comes from the market risk premium, a number of additional macroeconomic risks, including real output and inflation shocks, are significantly priced in the cross-section. A Bayes factor analysis massively favors the proposed change-point model. Supplementary materials for this article are available online.     

Bayesian Model Selection and Averaging in Additive and Proportional Hazards Models

Although Cox proportional hazards regression is the default analysis for time to event data, there is typically uncertainty about whether the effects of a predictor are more appropriately characterized by a multiplicative or additive model. To accommodate this uncertainty, we place a model selection prior on the coefficients in an additive-multiplicative hazards model. This prior assigns positive probability, not only to the model that has both additive and multiplicative effects for each predictor, but also to sub-models corresponding to no association, to only additive effects, and to only proportional effects. The additive component of the model is constrained to ensure non-negative hazards, a condition often violated by current methods. After augmenting the data with Poisson latent variables, the prior is conditionally conjugate, and posterior computation can proceed via an efficient Gibbs sampling algorithm. Simulation study results are presented, and the methodology is illustrated using data from the Framingham heart study.     

Weighted-Average Least Squares Prediction

Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty from the selection procedure. This article proposes a weighted-average least squares (WALS) prediction procedure that is not conditional on the selected model. Taking both model and error uncertainty into account, we also propose an appropriate estimate of the variance of the WALS predictor. Correlations among the random errors are explicitly allowed. Compared to other prediction averaging methods, the WALS predictor has important advantages both theoretically and computationally. Simulation studies show that the WALS predictor generally produces lower mean squared prediction errors than its competitors, and that the proposed estimator for the prediction variance performs particularly well when model uncertainty increases.     

Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box–Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically estimated model of data revisions for U.S. output growth is used to investigate small-sample properties.     

大维变量选择、混频因子模型与新冠肺炎疫情冲击下的GDP现时预测

新冠肺炎疫情不仅对我国宏观经济造成了巨大冲击,也为准确预测我国宏观经济未来走势带来挑战。本文从新冠肺炎疫情冲击出发,将模型置信集检验与U-MIDAS模型组合,设计了一种在混频情形下利用预测变量的异质性波动从大维数据集中选取对GDP具有稳定预测效果变量的方法。通过利用选取出的稳定性变量构建多种形式的混频目标因子模型并与其他类型的混频因子模型对比,全面评估了不同模型在疫情前后对GDP进行高频现时预测的效果。研究发现,在疫情冲击前的平稳时期,利用覆盖范围较广的变量构建双因子MIDAS模型预测效果最优;利用稳定性变量构建的单因子U-MIDAS模型同样具有良好的预测效果。当经济从冲击中持续恢复时,利用部分稳定性变量构建的双因子U-MIDAS模型在捕捉到GDP的核心变化后率先对其连续做出准确的现时预测。经济稳定时,对预测变量设定较长的滞后阶数会提升预测效果;在冲击后的恢复期中则应减少滞后阶数,避免变量在冲击中出现的异常值对预测产生负面影响。本文也为当经济受到巨大外生冲击或处于冲击后的恢复期时其他宏观经济指标的预测提供了有价值的参考。     

中国经济向新常态转换的冲击影响机制研究

本文分析了中国自2007年以来逐步向新常态经济转换的冲击影响机制。我们应用SV-TVP-VAR模型分析了2001Q1~2015Q3间以技术冲击和投资冲击代表的供求冲击对中国经济波动的动态影响机制,结果表明这两种冲击的影响机制都在2007年左右发生了结构性的变化,具体表现为：首先从影响的方向来看,投资冲击的短期影响为正但波动性加大,中长期的影响则变为负值且影响逐步增强,而无论是从短期还是中长期来看技术冲击对中国经济增长的正面影响逐步增强,但从2014年以来其影响有所下降;其次从影响的数量来看,分时段的方差分解表明2007年之后投资冲击对产出波动的解释力度大幅上升,而技术冲击的影响比较平稳。这些结论说明中国经济向新常态的转换主要源于需求侧的不利冲击,但最近以来供求冲击都呈现了不利影响的趋势,为此我们也提出了相应的政策建议。     

外部冲击、需求管理与经济增长——基于中国数据的实证研究

通过建立外部冲击指数,研究外部冲击对中国宏观经济的影响。结果发现,外部冲击比国内政策能更好地解释中国宏观经济波动,且具有较强持续性;外部冲击对中国经济增长的影响主要表现在滞后1期和2期。当中国经济受到外部冲击时,采取货币政策和财政政策刺激需求,对于稳定宏观经济可以起到显著效果。