基于中国实时数据的产出缺口估计

文章基于1994Q1～2008Q4的数据并分别利用三次趋势和HP滤波两种模型方法估计了我国的实时、准实时和最终产出缺口。分析表明,这一时期我国的产出缺口遭受了较大而且高度持续的修正,说明我国的实时产出缺口和基于事后修正数据估计的产出缺口有很大不同。由于产出缺口是货币政策决策的重要依据,而货币政策决策总发生在"实时",不能等待后来产出缺口等数据信息的修正。因此,区分实时产出缺口和基于事后修正数据估计的产出缺口,对政策制定者而言十分重要。     

中国工资和价格下调刚性下的货币政策非对称性研究

为考察货币政策的非对称性效应,本文将工资下调刚性与价格下调刚性特征构建于宏观结构模型,并利用中国宏观经济季度数据,采用模拟矩方法对上述结构模型中的参数进行估计,最后利用估计得到的参数对模型进行随机动态模拟分析。模型参数的估计结果表明,我国工资和价格的变动具有明显的下调刚性特征。基于估计得到的参数对模型的动态模拟分析说明,信贷政策、存款准备金率政策以及存贷利差政策都在执行方向和力度上对经济系统产生了不同类型的非对称性效应,并且不同种类的货币政策工具对经济产生了不同的影响效果。本文的研究对我国中央银行根据经济状况选择恰当的货币政策工具,以及确定政策工具在数量上的执行力度都有着参考意义。     

中国新凯恩斯主义菲利普斯曲线的经验研究

 本文构建了附加利率的新凯恩斯主义菲利普斯曲线模型,并运用广义矩估计的研究方法,运用中国1997 -2008年的季度数据对所构建的模型进行了估计与检验。经验结果表明,利率作为通货膨胀的驱动因素在统计和经济意义上都具有显著性,并且较国外许多国家更明显;当期通货膨胀动态变化受通货膨胀惯性和预期的共同影响,而预期起主导作用;厂商的定价行为既有前瞻性,又有后顾性,但后顾性处于主导地位。     

产出缺口、产出增长率与泰勒规则

传统的泰勒规则是以产出缺口作为反映经济活动的指标,由于产出缺口的不可观测性直接导致了利率反应函数估计精度的下降。以产出增长率缺口替代产出缺口,重新对泰勒规则进行估计,实证结果表明:使用GDP增长率缺口替代产出缺口,显著地改善了利率反应函数的估计效果;从预测误差来看,后顾性利率反应函数的预测结果更接近于实际名义利率,而前瞻性模型的预测能力相对较差。这说明中国利率调整政策更多地依赖于以往的经验而前瞻性预期相对较弱,这也充分表明了中国货币政策的调整是谨慎的,在一定程度上是可预见的。     

考虑资产价格波动的前瞻性泰勒规则及实证检验

文章在借鉴国外研究的基础上,结合中国国情对泰勒规则进行合理扩展,构建把资产价格纳入框架的前瞻性货币政策利率反应模型,并基于2000—2009年经济金融数据进行实证检验。J0hansen协整检验和GMM反应函数估计表明考虑资产价格波动的前瞻性泰勒规则对市场利率模拟更好,发现资产价格对市场利率存在正向影响,利率调整对预期通胀缺口的反应不足。因此建议将资产价格作为内生性影响因素纳入央行利率规则之中,并提高货币政策前瞻性。     

住房价格与中国货币政策规则

 住房价格高涨背景下我国货币政策的操作一直以来颇受关注。本文构建含有房产的动态随机一般均衡模型,对我国货币政策规则是否关注住房价格进行了检验。此外,本文通过数值模拟比较了将房价纳入货币政策规则与否对宏观经济波动所产生的影响。本文的主要结论有：央行在2003-2010年的实际操作中已将住房价格波动纳入修订泰勒规则中;将住房价格波动纳入货币政策规则对调控房价上涨有较好效果,但代价是调控过程中通胀率的持续上升,以及产出水平和家庭消费负向偏离稳态;家庭住房贷款首付比例能够有效降低稳态水平下的住房价格。     

中国可变价格型货币政策研究

当前主流的货币政策理论分析框架(DSGE模型)只考虑不变的政策规则,而大量的实证研究表明央行普遍采取可变的政策规则.本文首次将马尔科夫区制转换货币政策规则纳入理性预期研究框架,在灵活价格设定下,构建马尔科夫区制转换理性预期(MSRE)模型,用待定系数法求得MSV解析解,在三种“平稳”定义下讨论均衡确定性条件,利用马尔科夫区制转换状态空间模型对相关结构参数进行极大似然估计,最后对我国可变价格型货币政策调控通货膨胀率的效果进行定量分析,并剥离出经济主体预期未来政策变动的经济效果.     

我国季度GDP实时数据预测与评价

传统实证研究中使用的当期特定数据存在滞后信息和噪音信息缺陷,导致模型估计结果存在偏误。应用宏观经济实时数据可以有效的剔除造成模型偏误的滞后信息和噪音信息,得到更为准确的估计结果。MIDAS模型可将低频的关键经济数据与高频数据同时估计,较好的解决了应用一般模型存在的高频数据信息损失问题。本文应用M-MIDAS-DL模型与季度GDP实时数据建立我国季度GDP预测模型,实证表明,应用实时数据与组合预测方法,能及时准确预测出2008年以来中国经济增长率的下滑与反弹走势,能起到较好的提前预警作用,是当前较为有效的经济预测手段之一。     

中国银行业结构特征对银行信贷渠道的影响

选取中国57家商业银行1998—2010年的面板数据,建立动态面板模型,运用广义矩估计方法就中国银行业结构特征对货币政策银行信贷渠道的影响进行实证检验,着重考察中国银行业资本充足率、资本规模和流动性等结构特征对货币政策银行信贷传导渠道的影响效应。研究表明：在这三个结构特征指标中,资本充足率对货币政策传导的有着显著且最强烈的影响,其次为规模特征,最后是流动性。同时发现在资本管制下的外资银行并没有表现出与中资银行在货币政策传导效果上的差异。     

基于影子利率期限结构的货币政策效应分析

文章首先估计并分析了影子利率期限结构模型,然后基于该模型得到了一种新的衡量货币政策趋势的政策利率,最后利用政策利率分析了货币政策对宏观经济状态的影响效应.研究结果表明,基于影子利率模型得到的宏观经济状态对于政策利率冲击所产生的响应结果为货币政策效应的计量分析提供了一个新的视角.因此中央银行在制定货币政策时,不应只关注表面的实际利率值,同时也应注重潜在影子利率值的波动情况,以便实现预期政策目标.     

Bayesian Bandwidth Estimation in Nonparametric Time-Varying Coefficient Models

Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this article, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time-varying coefficients in time series models. We establish a large sample theory for the proposed bandwidth estimator and Bayesian estimators of the unknown parameters involved in the error density. A Monte Carlo simulation study shows that (i) the proposed Bayesian estimators for bandwidth and parameters in the error density have satisfactory finite sample performance; and (ii) our proposed Bayesian approach achieves better performance in estimating the bandwidths than the normal reference rule and cross-validation. Moreover, we apply our proposed Bayesian bandwidth estimation method for the time-varying coefficient models that explain Okun’s law and the relationship between consumption growth and income growth in the U.S. For each model, we also provide calibrated parametric forms of the time-varying coefficients. Supplementary materials for this article are available online.     

Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications that are natural extensions to certain existing models, one of which allows for time-varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time-varying correlations, heavy-tailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the best specifications are those that allow for time-varying correlation coefficients.     

Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications that are natural extensions to certain existing models, one of which allows for time-varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time-varying correlations, heavy-tailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the best specifications are those that allow for time-varying correlation coefficients.     

Estimation and inference of the joint conditional distribution for multivariate longitudinal data using nonparametric copulas

In this paper we study estimating the joint conditional distributions of multivariate longitudinal outcomes using regression models and copulas. For the estimation of marginal models, we consider a class of time-varying transformation models and combine the two marginal models using nonparametric empirical copulas. Our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Empirical copulas combined with time-varying transformation models may allow quite flexible modelling for the joint conditional distributions for multivariate longitudinal data. We derive the asymptotic properties for the copula-based estimators of the joint conditional distribution functions. For illustration we apply our estimation method to an epidemiological study of childhood growth and blood pressure.     

函数型变量倾斜分位回归模型及其应用

本文考虑函数型数据的结构特征,针对两类函数型变量分位回归模型（函数型因变量对标量自变量和函数型因变量对函数型自变量）,基于函数型倾斜分位曲线的定义构建新型函数型倾斜分位回归模型。对于第二类模型,本文分别考虑样条基函数对模型系数展开和函数型主成分基函数对函数型自变量展开,得到倾斜分位回归模型的基本形式。参数估计采用成分梯度Boosting算法最小化加权非对称损失函数,提高计算效率。在理论上证明了倾斜分位回归模型的系数估计量均服从渐近正态分布。模拟和实证研究结果显示,倾斜分位回归模型比已有的逐点分位回归模型具有更好的拟合效果。根据积分均方预测误差准则,本文提出的模型有一致较好的预测能力。     

Local Box–Cox transformation on time-varying parametric models for smoothing estimation of conditional CDF with longitudinal data

Nonparametric estimation and inferences of conditional distribution functions with longitudinal data have important applications in biomedical studies, such as epidemiological studies and longitudinal clinical trials. Estimation approaches without any structural assumptions may lead to inadequate and numerically unstable estimators in practice. We propose in this paper a nonparametric approach based on time-varying parametric models for estimating the conditional distribution functions with a longitudinal sample. Our model assumes that the conditional distribution of the outcome variable at each given time point can be approximated by a parametric model after local Box–Cox transformation. Our estimation is based on a two-step smoothing method, in which we first obtain the raw estimators of the conditional distribution functions at a set of disjoint time points, and then compute the final estimators at any time by smoothing the raw estimators. Applications of our two-step estimation method have been demonstrated through a large epidemiological study of childhood growth and blood pressure. Finite sample properties of our procedures are investigated through a simulation study. Application and simulation results show that smoothing estimation from time-variant parametric models outperforms the existing kernel smoothing estimator by producing narrower pointwise bootstrap confidence band and smaller root mean squared error.     

基于带跳时变系数模型的PPI与CPI相关性研究

2008年以来,我国PPI与CPI走势出现了多次背离与分化,从整体上看,两者相关性很弱。但从动态视角来看,由于相关关系可能会因时而变,整体相关性有可能被关系本身的方向和强弱变化所削弱甚至掩盖。为准确反映两者相关性的动态变化,本文放宽时变系数函数的光滑性假设,提出了带跳时变系数模型,并给出一种非参数三步估计方法：首先,估计系数函数中跳点的位置和个数;然后,基于估计的跳点和Bootstrap方法选择的窗宽给出系数函数的最终估计;最后,利用蒙特卡洛模拟评价本文提出的非参数估计和窗宽选择方法的有限样本性质。通过对2008年1月至2017年12月我国PPI和CPI月度同比数据的实证分析,我们发现该模型能较好地刻画PPI与CPI相关性的时变和带跳特征,进而也验证了该模型的应用价值。     

时变三因子模型风险系数的动态研究

由Fama和French提出的三因子模型能够较好地解释股票的收益率风险溢价。文章以状态空间模型为框架,将风险因子系数作为状态变量,市场风险溢价作为观测变量,构建时变三因子模型来应对股票市场价格的时变特征。研究结果显示,利用卡尔曼滤波来估计时变风险因子系数,增强了估计结果的准确性与连贯性;风险因子系数变化规律与中国A股市场政策和环境影响相吻合,消除非理性噪声后的时变三因子模型更具有解释力度。     

Multivariate Stochastic Volatility: A Review

The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation, and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely, (i) asymmetric models, (ii) factor models, (iii) time-varying correlation models, and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and model comparison are also reviewed.     

Multivariate Stochastic Volatility: A Review

The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation, and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely, (i) asymmetric models, (ii) factor models, (iii) time-varying correlation models, and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and model comparison are also reviewed.