估计MS-DSGE模型脉冲响应函数的EM算法及其应用

研究目标：考察不同区制下外生冲击对中国宏观经济的非对称性效应。研究方法：引入两状态的Markov区制转换过程建立MS-DSGE模型,并基于MS-DSGE模型的Markov区制转换动态因子模型的表示提出了估计MS-DSGE模型脉冲响应函数的极大似然估计EM算法。研究发现：本文提出的估计方法具有良好的有限样本性质和收敛性,参数估计量具有渐近正态分布。实证分析发现,应持续施行扩张性政策以刺激经济稳定增长,对冲挤占效应以及稳定物价水平。尤其,当经济处于“衰退”区制时,政府应实施及时有效的调控政策刺激经济运行区制的转移。研究创新：与Bayesian分析方法比较,本文提出的估计方法避免了对数线性化MS-DSGE模型的随机奇异性以及对先验分布的设定和观测变量选取的非稳健性。研究价值：提出了一种估计MS-DSGE模型脉冲响应函数的方法。     

一个估计DSGE模型脉冲响应函数的方法

本文获得国家自然基金“具有Markov体制转换的动态因子模型建模方法及其应用研究”(71271142)、天津财经大学研究生科研资助计划“DSGE模型的估计方法研究——基于动态因子模型的视角”(2014TCB04)的资助。     

收入冲击、偏好冲击与中国经济波动——基于DSGE方法的数值分析

文章基于包含消费习惯与借贷约束的RBC模型，尝试将收入冲击与偏好冲击纳入到该模型中，并采用随机动态一般均衡（DSGE）方法解释中国经济波动。研究发现：（1）模型能够解释实际产出、消费、投资与资本波动的92.6%、77.8%、84.5%、87.6%。（2）收入冲击与偏好冲击对我国实际产出、投资、就业和资本的影响均有明显的持续性，而对消费的影响均表现为短期性。（3）收入冲击对消费的影响较小，而对就业有较大影响；消费偏好冲击对两者的影响与收入冲击恰恰相反。     

外生冲击沿部门传导的作用机制和影响研究——基于中国七部门DSGE模型的数值模拟北大核心CSSCI

本文基于七部门DSGE模型的脉冲响应分析,认为外生冲击沿部门传导的作用机制主要包括推动作用、拉动作用和替代作用。其中推动作用和拉动作用是一个部门受到正向冲击带动其他部门产出上升的作用机制,而替代作用则可能使其他部门产出下降。多部门DSGE模型相比其他投入产出模型的优势在于通过引入消费者的最优决策较好地刻画了替代作用。模拟结果显示,对制造业的经济刺激政策对各部门产出的带动作用最大,而对建筑业和房地产业的经济刺激政策对各部门产出的带动作用则相对较小。     

成本推动冲击和中国经济波动

本文立足近年来国内经济频繁波动的现实背景,通过构建一个包含成本推动冲击、消费偏好冲击、技术冲击以及货币政策冲击的DSGE模型,运用脉冲响应分析、方差分解等方法对中国经济波动的外生冲击因素进行考察。研究发现：产出波动是由多种冲击共同推动,其中成本推动冲击贡献最大;通货膨胀波动主要是由成本推动冲击和货币政策冲击引致,消费偏好冲击和技术冲击的影响有限。进一步地,反事实模拟分析也证实了这一结论。这也揭示出近年来中国经济高速增长依附高昂成本,且经济增长方式是粗放式的典型化事实。     

Markov区制转换模型在行业CAPM分析中的应用

整体经济环境的变化导致了股票收益和风险的时变性,从而使得行业板块系统风险β系数也表现出时变特征。本文以沪深股市中的24个行业板块为研究对象,运用Markov区制转换方法客观划分股市高、低波动状态,建立了一个二状态Markov区制转换CAPM模型,对我国股市行业β系数的动态进行了实证分析。结果表明,就大多数行业而言,在高、低两种波动状态下CAPM模型均得以成立,且Markov区制转换CAPM模型显著优于传统的CAPM模型。     

我国SHIBOR市场利率波动的实证研究

以SHIBOR(上海银行间同业拆借利率)市场利率为数据样本,构建了带有混合高斯分布的Markov状态转换GARCH(1,1)模型,分析表明我国SHIBOR市场利率变动的时间序列具有尖峰、后尾和波动聚集等现象。对比普通的GARCH(1,1)模型、Markov状态转换GARCH(1,1)模型和带混合高斯分布的Markov状态转换GARCH(1,1)模型的贝叶斯后验参数估计值发现,第三种模型对我国SHIBOR市场利率波动的拟合效果最好,普通GARCH(1,1)模型的拟合效果最差。研究说明我国SHOBOR市场发展正在逐步健全起来。     

非线性时间序列分析STAR模型及其在经济学中的应用

20世纪90年代末以来，非线性时间序列模型两个主要的研究方向是混沌论模型（chaos model）和机制转换模型（switching regime models），而后者考虑了各种不同形式的机制转换行为（switching regime behavior），通常被认为由三个最常见的机制转换模型组成。平滑转换自回归模型（STAR）由于能在某种程度上捕捉到机制转换过程中时间序列的动态过程，因而成为近年国外计量经济学前沿领域追踪的热点之一。本文将主要对平滑转换自回归模型（STAR）的特征、估计、检验方法以及在经济领域的应用做深入的探讨。     

沪、深、港股市信息溢出效应与动态相关性——基于DCC-（BV）EGARCH-VAR的检验

本文将收益与波动作为刻画股市信息的代理变量，将沪市（深市）受到的收益和波动冲击分解为来自自身的“本地因素”，来自深市（沪市）的“区域因素”（作为内生变量引入）和来自港市的“世界因素”（作为外生变量引入），首次将DCC-（BV）EGARCH-VAR的方法引入对沪、深、港三地股票市场收益和波动溢出效应与动态相关性研究。     

货币供应量对我国房地产市场的溢出效应——基于DSGE模型的仿真分析

随着房地产市场与资本市场以及实体经济的关联度愈发紧密,探究货币供应量对房地产市场的溢出效应对于未来提高货币政策传导效率、促进经济平稳运行有着现实意义。通过构建包含房地产中间厂商的DSGE模型,探求货币供应量对房地产市场的影响机制与影响程度,为此在模型中引入货币冲击的外生冲击变量,以更好地模拟现实经济运行机制。仿真分析结果发现,货币供应量不直接影响房地产市场,主要通过利率水平下降与物价水平上升等传导路径对房地产市场产生正向冲击,并且这一间接影响期限短、程度深。为了促进房地产市场的健康发展,以研究结论为现实指导,从控制货币供应量、开发多样化金融产品和加强境外资金流入管理三方面提出针对房地产市场的监管策略。     

PRACTICAL TOOLS FOR POLICY ANALYSIS IN DSGE MODELS WITH MISSING SHOCKS

In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors that feed into the structural shocks of the DSGE model to create correlated disturbances. We estimate the factor structure by either matching impulse responses of the augmented DSGE model to those generated by an auxiliary model or by using Bayesian techniques. We apply this methodology to track the effects of oil shocks and housing demand shocks in models without energy or housing sectors. Copyright © 2014 John Wiley & Sons, Ltd.     

NON‐LINEAR DSGE MODELS AND THE CENTRAL DIFFERENCE KALMAN FILTER

This paper introduces a quasi maximum likelihood approach based on the central difference Kalman filter to estimate non‐linear dynamic stochastic general equilibrium (DSGE) models with potentially non‐Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. These properties are verified in a Monte Carlo study for a DSGE model solved to second and third order with structural shocks that are Gaussian, Laplace distributed, or display stochastic volatility. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluating Point and Density Forecasts of DSGE Models

This paper investigates the accuracy of forecasts from four dynamic stochastic general equilibrium (DSGE) models for inflation, output growth and the federal funds rate using a real‐time dataset synchronized with the Fed's Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation are the model forecasts dominated by the Greenbook projections. A comparison with forecasts from Bayesian vector autoregressions shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Selecting structural innovations in DSGE models

Dynamic stochastic general equilibrium (DSGE) models are typically estimated assuming the existence of certain structural shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are “nonexistent” and propose a method to select the economic shocks driving macroeconomic uncertainty. Forcing these nonexisting shocks in estimation produces a downward bias in the estimated internal persistence of the model. We show how these distortions can be reduced by using priors for standard deviations whose support includes zero. The method allows us to accurately select shocks and estimate model parameters with high precision. We revisit the empirical evidence on an industry standard medium‐scale DSGE model and find that government and price markup shocks are innovations that do not generate statistically significant dynamics.     

A Note on the Identification of Dynamic Economic Models with Generalized Shock Processes

Dynamic stochastic general equilibrium (DSGE) models with generalized shock processes, such as shock processes which follow a vector autoregression (VAR), have been an active area of research in recent years. Unfortunately, the structural parameters governing DSGE models are not identified when the driving process behind the model follows an unrestricted VAR. This finding implies that parameter estimates derived from recent attempts to estimate DSGE models with generalized driving processes should be treated with caution, and that there always exists a tradeoff between identification and the risk of model misspecification. However, these results also make it easier to address the issue of model misspecification by making it computationally easier to check the validity of cross‐equation restrictions.     

The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models vs. Simple Linear Econometric Models*

In this paper we construct output gap and inflation predictions using a variety of dynamic stochastic general equilibrium (DSGE) sticky price models. Predictive density accuracy tests related to the test discussed in Corradi and Swanson [Journal of Econometrics (2005a), forthcoming] as well as predictive accuracy tests due to Diebold and Mariano [Journal of Business and Economic Statistics (1995) , Vol. 13, pp. 253–263]; and West [Econometrica (1996) , Vol. 64, pp. 1067–1084] are used to compare the alternative models. A number of simple time‐series prediction models (such as autoregressive and vector autoregressive (VAR) models) are additionally used as strawman models. Given that DSGE model restrictions are routinely nested within VAR models, the addition of our strawman models allows us to indirectly assess the usefulness of imposing theoretical restrictions implied by DSGE models on unrestricted econometric models. With respect to predictive density evaluation, our results suggest that the standard sticky price model discussed in Calvo [Journal of Monetary Economics (1983), Vol. XII, pp. 383–398] is not outperformed by the same model augmented either with information or indexation, when used to predict the output gap. On the other hand, there are clear gains to using the more recent models when predicting inflation. Results based on mean square forecast error analysis are less clear‐cut, although the standard sticky price model fares best at our longest forecast horizon of 3 years, it performs relatively poorly at shorter horizons. When the strawman time‐series models are added to the picture, we find that the DSGE models still fare very well, often outperforming our forecast competitions, suggesting that theoretical macroeconomic restrictions yield useful additional information for forming macroeconomic forecasts.     

Exact likelihood computation for nonlinear DSGE models with heteroskedastic innovations

Phenomena such as the Great Moderation have increased the attention of macroeconomists towards models where shock processes are not (log-)normal. This paper studies a class of discrete-time rational expectations models where the variance of exogenous innovations is subject to stochastic regime shifts. We first show that, up to a second-order approximation using perturbation methods, regime switching in the variances has an impact only on the intercept coefficients of the decision rules. We then demonstrate how to derive the exact model likelihood for the second-order approximation of the solution when there are as many shocks as observable variables. We illustrate the applicability of the proposed solution and estimation methods in the case of a small DSGE model.     

Endogenous business cycle propagation and the persistence problem: The role of labor-market frictions

Contrasting sharply with a recent trend in DSGE modeling, we propose a business cycle model where frictions and shocks are chosen with parsimony. The model emphasizes a few labor-market frictions and shocks to monetary policy and technology. The model, estimated from U.S. quarterly postwar data, accounts well for important differences in the serial correlation of the growth rates of aggregate quantities, the size of aggregate fluctuations and key comovements, including the correlation between hours and labor productivity. Despite its simplicity, the model offers an answer to the persistence problem (Chari et al., 2000) that does not rely on multiple frictions and adjustment lags or ad hoc backward-looking components. We conclude modern DSGE models need not embed large batteries of frictions and shocks to account for the salient features of postwar business cycles.     

What inventories tell us about aggregate fluctuations—A tractable approach to (S,s) policies

We estimate a DSGE model with (S,s) inventory policies. We find that (i) taking inventories into account can significantly improve the empirical fit of DSGE models in matching the standard business-cycle moments (in addition to explaining inventory fluctuations); (ii) (S,s) inventory policies can significantly amplify aggregate output fluctuations, in contrast to the findings of the recent general-equilibrium inventory literature; and (iii) aggregate demand shocks become more important than technology shocks in explaining the business cycle once inventories are incorporated into the model. An independent contribution of our paper is that we develop a solution method for analytically solving (S,s) inventory policies in general equilibrium models with heterogeneous firms and a large aggregate state space, and we illustrate how standard log-linearization methods can be used to solve various versions of our inventory model, generate impulse response functions, and estimate the model׳s deep structural parameters.     

Evaluation of dynamic stochastic general equilibrium models based on distributional comparison of simulated and historical data

We take as a starting point the existence of a joint distribution implied by different dynamic stochastic general equilibrium (DSGE) models, all of which are potentially misspecified. Our objective is to compare “true” joint distributions with ones generated by given DSGEs. This is accomplished via comparison of the empirical joint distributions (or confidence intervals) of historical and simulated time series. The tool draws on recent advances in the theory of the bootstrap, Kolmogorov type testing, and other work on the evaluation of DSGEs, aimed at comparing the second order properties of historical and simulated time series. We begin by fixing a given model as the “benchmark” model, against which all “alternative” models are to be compared. We then test whether at least one of the alternative models provides a more “accurate” approximation to the true cumulative distribution than does the benchmark model, where accuracy is measured in terms of distributional square error. Bootstrap critical values are discussed, and an illustrative example is given, in which it is shown that alternative versions of a standard DSGE model in which calibrated parameters are allowed to vary slightly perform equally well. On the other hand, there are stark differences between models when the shocks driving the models are assigned non-plausible variances and/or distributional assumptions.