中国价格型货币政策的系统性与非系统性时变效应研究

本文建立兼具随机波动率和时变参数的VAR模型,刻画经济系统中结构冲击和传导机制的时变性,并在同一框架内分析价格型货币政策的系统性和非系统性效应。研究结果显示：（1）对应于货币政策冲击,货币政策的非系统性效应在大波动时期存在“价格之谜”现象,在大稳定时期则出现政策冲击波动以及经济活动对其同向响应程度的双重下降现象,甚至在有些时段出现负向响应,其平抑经济波动的作用得到一定程度的体现。（2）系统性效应显示货币政策对于通货膨胀的响应强度整体呈消极特征,但存在一种往积极方向转变的动态学习模式,而且这种转变呈现不同状态的频繁转换。（3）反事实分析显示货币政策系统性和非系统性效应虽然有所改善,但这并不是宏观经济从大波动向大稳定转变的主要原因。     

基于SVAR模型的中国货币与财政政策动态效应研究

货币政策和财政政策是国家宏观经济调控的两大基本政策手段。通过采用结构式向量自回归（SVAR）模型,引入5个分别反映产出、通货膨胀率、货币政策、财政收入、财政支出的变量,建立相应的联立方程,以对货币政策与财政政策传导机制进行研究。研究结果表明：2002年至今,货币政策在调节宏观经济运行方面的效果要强于财政政策;货币政策与财政政策对需求都有一定程度的冲击;同时货币政策对需求波动的影响也更为明显;增加财政支出会对货币供给量产生一定的正向冲击。     

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。     

理性预期下宏观经济政策真的无效吗

本文在对理性预期模型评述的基础上,通过将宏观经济政策的有效性分为前期有效、当期有效与后期有效后发现,即便是在理性预期理论框架下,宏观经济政策总体上都是有效的,不同的是,在理性预期理论框架下,宏观经济政策仅表现为前期有效。在经验证据上,本文分别以我国和美国的相关实际数据为样本,利用实证分析的方法检验了货币政策的有效性。分析结果显示,我国的货币政策不仅是前期有效的,而且后期也是有效的,相比而言,美国的货币政策却表现出前期有效、后期无效的特点。究其原因,两者的差异主要在于政策信号传递以及公众对货币政策预期形成方式不同,但无论公众对宏观经济政策的预期符合什么形式,它都会对宏观经济经济运行产生影响,经济政策都是不可或缺的。     

财政政策与货币政策对国民收入的影响分析

 内容提要：财政政策和货币政策同属政府干预宏观经济最重要、使用最频繁的两大工具,可依宏观经济调控要求进行合理搭配。本文在人民币一篮子货币汇率制度框架下,用修正的M-F模型深入分析我国财政与货币政策对国民经济的综合影响。结果显示扩张的财政政策辅以扩张的货币政策是目前较理想的操作方式。用2005年7月至2010年6月的月度数据进行的实证研究结果佐证了上述理论。最后提出相关政策建议。     

货币政策转型背景下汇率调整对宏观经济的影响——基于开放经济DSGE模型的研究

在货币政策转型背景下研究人民币汇率调整对宏观经济波动的影响,通过构建一个符合中国实际情况的开放经济DSGE模型,对比分析数量型和价格型的货币政策规则对烫平人民币汇率调整导致的宏观经济波动的效果。研究结果表明:当人民币汇率冲击导致经济波动时,数量型规则比价格型规则对烫平经济波动的作用更有效,且能够更好地减小社会福利损失。因此,在货币政策逐渐从数量型向价格型转变的过程中,仍然不能放弃数量型工具的使用,综合运用数量和价格两种调控手段对于转型中的中国而言是比较合适的选择。     

基于多重尝试Metropolis算法的非线性DSGE模型贝叶斯推断

本文提出一种改进的多重尝试Metropolis算法,用于非线性动态随机一般均衡模型的贝叶斯参数估计和模型选择。多重尝试策略通过每次迭代抽取多个尝试点的方法来提高算法的混合速率,新方法中提出使用近似的方法提高计算速度,并通过接收概率调整偏差。数值实验表明新方法在相同的计算时间内具有更高的估计效率。最后,本文比较了具有不同货币政策设定的模型对中国经济数据的拟合效果,发现中国数据更加支持具有时变通胀目标的模型。     

影子银行对货币政策有效性的影响研究

影子银行体系的扩张削弱了货币政策宏观调控的效果。基于影子银行资金运作模式以及有限理性假设下的投资者偏好理论,从货币的流向入手,结合改进的货币乘数方程分析了影子银行对货币政策有效性的影响。理论分析表明:影子银行通过对现金漏损率、存款准备金率以及活期存款转化为定期存款比率的影响间接进行信用创造;有限理性视角下,投资者存在偏好反转的可能,从而影子银行能够通过影响投资者资产配置决策改变货币乘数,进而弱化货币政策的有效性。实证分析发现,影子银行体系的扩张对货币供应量、利率水平、货币乘数等货币政策中介变量产生了显著的影响,进而影响了货币政策的有效性。据此,提出扩大法定存款准备金的计提范围、加快完善利率市场化制度、创新货币政策工具和稳定投资者信心等建议。     

Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators

This article proposes a group bridge estimator to select the correct number of factors in approximate factor models. It contributes to the literature on shrinkage estimation and factor models by extending the conventional bridge estimator from a single equation to a large panel context. The proposed estimator can consistently estimate the factor loadings of relevant factors and shrink the loadings of irrelevant factors to zero with a probability approaching one. Hence, it provides a consistent estimate for the number of factors. We also propose an algorithm for the new estimator; Monte Carlo experiments show that our algorithm converges reasonably fast and that our estimator has very good performance in small samples. An empirical example is also presented based on a commonly used U.S. macroeconomic dataset.     

中国利率期限结构与货币政策联合建模的实证研究

本文采用卡尔曼滤波和极大似然函数估计方法对中国无套利利率期限结构与货币政策联合建模进行估计,实证结果显示通货膨胀目标值对利率期限结构的冲击是扩张性和持续性的,对于所有到期期限都是持续上升的;货币政策冲击对利率期限结构冲击的效应则是递减的,对利率期限结构曲线的斜率影响较显著;通货膨胀冲击对利率期限结构曲线的曲度影响较显著。模型样本外预测大大优于VAR模型。研究结果表明,本文构建模型一方面有助于对利率期限结构的预测,另一方面有助于央行制定前瞻有效的货币政策。     

Estimation of finite population mean in stratified sampling using scrambled responses in the presence of measurement errors

This study focuses on the estimation of population mean of a sensitive variable in stratified random sampling based on randomized response technique (RRT) when the observations are contaminated by measurement errors (ME). A generalized estimator of population mean is proposed by using additively scrambled responses for the sensitive variable. The expressions for the bias and mean square error (MSE) of the proposed estimator are derived. The performance of the proposed estimator is evaluated both theoretically and empirically. Results are also applied to a real data set.     

Forecasting and conditional projection using realistic prior distributions

This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied t o 10 macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variable responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates

We provide unconditional forecasts as of 1982:12 and 1983:3. We also describe how a model such as this can be used to make conditional projections and to analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982: 12

Although no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, information that may help in evaluating causal hypotheses without containing any such hypotheses.     

Efficient estimation in a regression model with missing responses

This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses are missing at random. We show how the asymptotic variance is affected by the estimator of the regression parameter, and by the imputation method. To estimate the regression parameter, the ordinary least squares is efficient only if the error distribution happens to be normal. If the errors are not normal, then we propose a one step improvement estimator or a maximum empirical likelihood estimator to efficiently estimate the parameter.To investigate the imputation’s impact on the estimation of the mean response, we compare the listwise deletion method and the propensity score method (which do not use imputation at all), and two imputation methods. We demonstrate that listwise deletion and the propensity score method are inefficient. Partial imputation, where only the missing responses are imputed, is compared to full imputation, where both missing and non-missing responses are imputed. Our results reveal that, in general, full imputation is better than partial imputation. However, when the regression parameter is estimated very poorly, the partial imputation will outperform full imputation. The efficient estimator for the mean response is the full imputation estimator that utilizes an efficient estimator of the parameter.     

The Time-Varying-Parameter Model for Modeling Changing Conditional Variance: The Case of the Lucas Hypothesis

The main econometric issue in testing the Lucas (1973) hypothesis in a time series context is estimation of the forecast-error variance conditional on past information. The conditional variance may vary through time as monetary policy evolves and agents are obliged to infer its present state. Under the assumption that a monetary policy regime is continuously changing, a time-varying-parameter model is proposed for the monetary-growth function. Based on Kalman-filtering estimation of recursive forecast errors and their conditional variances, the Lucas hypothesis is tested for the U.S. economy (1964:1–1985:4) using monetary growth as aggregate demand variable. The Lucas hypothesis is rejected in favor of Friedman's (1977) hypothesis—the conditional variance of monetary growth affects real output directly, not through the coefficients on the forecast-error term in the Lucas-type output equation.     

Double robust estimation in longitudinal marginal structural models

In this article we consider estimation of causal parameters in a marginal structural model for the discrete intensity of the treatment specific counting process (e.g. hazard of a treatment specific survival time) based on longitudinal observational data on treatment, covariates and survival. We define three estimators: the inverse probability of treatment weighted (IPTW) estimator, the maximum likelihood estimator (MLE), and a double robust (DR) estimator. The DR estimator is obtained by following a general methodology for constructing double robust estimating functions in censored data models as described in van der Laan and Robins (Unified Methods for Censored Longitudinal Data and Causality, 2002). The double-robust estimator is consistent and asymptotically linear when either the treatment mechanism or the partial likelihood of the observed data is consistently estimated. We illustrate the superiority of the DR estimator relative to the IPTW and ML estimators in a simulation study. The proposed methodology is also applied to estimate the causal effect of exercise on physical functioning in a longitudinal study of seniors in Sonoma County.     

Improved estimation methods for unrelated question randomized response techniques

The randomized response technique (RRT) is an important tool, commonly used to avoid biased answers in survey on sensitive issues by preserving the respondents’ privacy. In this paper, we introduce a data collection method for survey on sensitive issues combining both the unrelated-question RRT and the direct question design. The direct questioning method is utilized to obtain responses to a non sensitive question that is related to the innocuous question from the unrelated-question RRT. These responses serve as additional information that can be used to improve the estimation of the prevalence of the sensitive behavior. Furthermore, we propose two new methods for the estimation of the proportion of respondents possessing the sensitive attribute under a missing data setup. More specifically, we develop the weighted estimator and the weighted conditional likelihood estimator. The performances of our estimators are studied numerically and compared with that of an existing one. Both proposed estimators are more efficient than the Greenberg's estimator. We illustrate our methods using real data from a survey study on illegal use of cable TV service in Taiwan.     

Asymptotically efficient estimation in response adaptive trials

We examine the issue of asymptotic efficiency of estimation for response adaptive designs of clinical trials, from which the collected data set contains a dependency structure. We establish the asymptotic lower bound of exponential rates for consistent estimators. Under certain regularity conditions, we show that the maximum likelihood estimator achieves the asymptotic lower bound for response adaptive trials with dichotomous responses. Furthermore, it is shown that the maximum likelihood estimator of the treatment effect is asymptotically efficient in the Bahadur sense for response adaptive clinical trials.     

SVARMA模型的设定、方差分解分析和应用

本文首先研究了传统凯恩斯主义IS-LM-PC模型的SVARMA模型表示,为宏观经济计量分析建立SVARMA模型提供了模型设定依据;其次,建立了VARMA/SVARMA模型方差分解分析方法;另外,基于SVARMA模型对中国宏观经济政策的动态效应进行了实证分析,实证分析发现（1）SVARMA模型与SVAR模型的分析结果存在重要的区别;（2）在政策实施6-7期前后,财政政策和货币政策对抑制通货膨胀的效果发生逆转;（3）宏观经济的价格水平存在粘性;（4）货币供给冲击对通货膨胀率的变化具有滞后的正向影响,对实际产出的影响不明显等。     

Estimation of the average causal effect via multiple propensity score stratification

Suppose we are interested in estimating the average causal effect (ACE) for the population mean from observational study. Because of simplicity and ease of interpretation, stratification by a propensity score (PS) is widely used to adjust for influence of confounding factors in estimation of the ACE. Appropriateness of the estimation by the PS stratification relies on correct specification of the PS. We propose an estimator based on stratification with multiple PS models by clustering techniques instead of model selection. If one of them correctly specifies, the proposed estimator removes bias and thus is more robust than the standard PS stratification.     

Forecast Error Variance Decompositions with Local Projections

Abstract

We propose and study properties of an estimator of the forecast error variance decomposition in the local projections framework. We find for empirically relevant sample sizes that, after being bias-corrected with bootstrap, our estimator performs well in simulations. We also illustrate the workings of our estimator empirically for monetary policy and productivity shocks. KEYWORDS: Forecast error variance decomposition; Local projections.