Minimum Distance Estimation and Testing of DSGE Models from Structural VARs*

The aim of this paper is to complement the minimum distance estimation–structural vector autoregression approach when the weighting matrix is not optimal. In empirical studies, this choice is motivated by stochastic singularity or collinearity problems associated with the covariance matrix of impulse response functions. Consequently, the asymptotic distribution cannot be used to test the economic model's fit. To circumvent this difficulty, we propose a simple simulation method to construct critical values for the test statistics. An empirical application with US data illustrates the proposed method.     

Large time-varying parameter VARs

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.     

Infinite-dimensional VARs and factor models

This paper proposes a novel approach for dealing with the ‘curse of dimensionality’ in the case of infinite-dimensional vector autoregressive (IVAR) models. It is assumed that each unit or variable in the IVAR is related to a small number of neighbors and a large number of non-neighbors. The neighborhood effects are fixed and do not change with the number of units (N), but the coefficients of non-neighboring units are restricted to vanish in the limit as N tends to infinity. Problems of estimation and inference in a stationary IVAR model with an unknown number of unobserved common factors are investigated. A cross-section augmented least-squares (CALS) estimator is proposed and its asymptotic distribution is derived. Satisfactory small-sample properties are documented by Monte Carlo experiments. An empirical illustration shows the statistical significance of dynamic spillover effects in modeling of US real house prices across the neighboring states.     

Time varying VARs with inequality restrictions

In many applications involving time-varying parameter VARs, it is desirable to restrict the VAR coefficients at each point in time to be non-explosive. This is an example of a problem where inequality restrictions are imposed on states in a state space model. In this paper, we describe how existing MCMC algorithms for imposing such inequality restrictions can work poorly (or not at all) and suggest alternative algorithms which exhibit better performance. Furthermore, we show that previous algorithms involve an approximation relating to a key prior integrating constant. Our algorithms are exact, not involving this approximation. In an application involving a commonly used U.S. data set, we present evidence that the algorithms proposed in this paper work well.     

Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors—for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 min on a standard desktop computer. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.     

几类非线性协整模型研究综述

协整分析方法经过20多年的发展成为计量经济学界的一个前沿工具,在经济与金融领域得到了广泛的应用。线性协整分析已经成熟,而非线性协整的理论与方法仍在持续研究中。本文回顾了最近20年非线性协整的发展历史,其中包括结构变化、门限非线性、马尔可夫转换和平滑转换等几类非线性协整模型,强调了这些非线性机制的本质区别,总结了已取得的一些重要研究成果,最后对该问题的最新发展动向加以概括。     

Cointegrating rank selection in models with time-varying variance

Reduced rank regression (RRR) models with time varying heterogeneity are considered. Standard information criteria for selecting cointegrating rank are shown to be weakly consistent in semiparametric RRR models in which the errors have general nonparametric short memory components and shifting volatility provided the penalty coefficient _Cn→∞$C_n\to \infty$ and _Cn/n→0$C_n/n\to 0$ as n→∞$n\to \infty$. The AIC criterion is inconsistent and its limit distribution is given. The results extend those in Cheng and Phillips (2009a) and are useful in empirical work where structural breaks or time evolution in the error variances is present. An empirical application to exchange rate data is provided.     

Forecasting economic and financial variables with global VARs

This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.     

Inference in Cointegrating Models: UK M1 Revisited

The paper addresses the practical determination of cointegration rank. This is difficult for many reasons: deterministic terms play a crucial role in limiting distributions, and systems may not be formulated to ensure similarity to nuisance parameters; finite-sample critical values may differ from asymptotic equivalents; dummy variables alter critical values, often greatly; multiple cointegration vectors must be identified to allow inference; the data may be I(2) rather than I(1), altering distributions; and conditioning must be done with care. These issues are illustrated by an empirical application of multivariate cointegration analysis to a small model of narrow money, prices, output and interest rates in the UK.     

VARs,common factors and the empirical validation of equilibrium business cycle models

Equilibrium business cycle models have typically less shocks than variables. As pointed out by Altug (1989) International Economic Review 30 (4) 889–920 and Sargent (1989) The Journal of Political Economy 97 (2) 251–287, if variables are measured with error, this characteristic implies that the model solution for measured variables has a factor structure. This paper compares estimation performance for the impulse response coefficients based on a VAR approximation to this class of models and an estimation method that explicitly takes into account the restrictions implied by the factor structure. Bias and mean-squared error for both factor- and VAR-based estimates of impulse response functions are quantified using, as data-generating process, a calibrated standard equilibrium business cycle model. We show that, at short horizons, VAR estimates of impulse response functions are less accurate than factor estimates while the two methods perform similarly at medium and long run horizons.     

Efficient Estimation Of Cointegrating Vectors and Testing for Causality in Vector Autoregressions

This paper surveys various methods of estimating cointegrating vectors and testing for causality in cointegrated VARs, and draws some implications for the applied researcher. In a single equation framework a number of estimators can be used, whose asymptotic efficiency depends on the extent to which they correct for possible endogeneity and serial correlation of the regressors. Such estimates are asymptotically equivalent to those obtained using full system methods, even if the cointegration space is multidimensional, provided there are no cross-equation restrictions. Using the triangular representation proposed by Phillips (1988), we show that one can employ in the context of an ECM a least squares estimator if weak exogeneity holds. If not, the alternatives are augmenting it by the leads of the regressors as in Stock and Watson (1993), or using the fully modified (FM) estimator due to Phillips and Hansen (1990). Other possibilities are the nonparametric approach developed by Bierens (1997), or the ARDL formulation due to Pesaran and Shin (1995). As for causality testing, we argue that it should be conducted within an ECM rather than a VAR formulation, as the limit distributions are much more likely to be standard in the former case. Alternatively, one can carry out statistical tests in the context of a VAR in levels estimated either by using the FM-VAR method as in Phillips (1995), or by augmenting the VAR as in Toda and Yamamoto (1995). Other, computationally easier tests have been introduced by Dolado and Lutkepohl (1996) and Saikkonen and Lütkepohl (1996).     

似无关动态协整模型中的水平扭曲与校正

本文在解析似无关动态协整模型及其动态最小二乘估计的基础上,从理论上揭示了关于协整参数的假设检验存在严重的水平扭曲,即对协整参数约束的Wald检验统计量的渐近卡方分布存在严重的有限样本扭曲。进一步,本文应用自举抽样技术对水平扭曲进行了有效校正。基于本文的发现,我们建议在对似无关动态协整模型中的参数进行假设检验时,为保证结论的准确性,应使用自举抽样推断技术产生统计量值并由此来形成检验结论。     

制度系统的结构分析

对整个制度系统（注意,不是指单项制度）的演化成或变迁过程进行分析,是当前制度经济学的前沿课题。为了使分析能够有效进行,就必须对制度系统的结构,也就是一个制度系统中的各项具体制度的关系进行考察,本文对制度系统的结构进行了初步分析.     

扶壁式桥台结构稳定性分析

笔者根据扶壁式挡墙的计算模式,摸索出扶壁式桥台的构造及计算过程,并在实际工程中应用,为广大桥梁设计者提供一个同类工程的设计思路。     

二自由度云台的结构设计与分析

云台作为支撑相机的运动组件,在工业巡检机器人、安防机器人或警车监控等领域应用非常广泛。二自由度云台能够实现水平和俯仰两个方向的运动,其通过搭载机器人头部或相机等实现类人化的运动及空间大范围的实时监控。本文通过对一款二自由度云台的结构设计与分析,从整体方案的设计到基础参数的确定,再到机构方案的布局和建模分析,最终实现对云台的细节结构设计,实现对二自由度云台的机械产品设计,并利用机械建模软件solidworks完成云台的结构建模,为云台的后续模块化运用提供技术支撑。     

产业链内涵及结构分析

基于理论融合的观点,通过对专家的研究进行剖析,从不同的角度来理解产业链的内涵,并对产业链的结构进行分析,为相关研究提供一个合理的理论分析框架。     

Block Structural Path Analysis: Applications to Structural Changes in the Indonesian Economy

The compilation of several social accounting matrices for Indonesia has enabled the analysis of some important aspects of the structure of the Indonesian economy. In the present paper, the analysis that has been conducted with structural path methods will be enhanced through the identification of a block structure for the technique. This block structural path analysis is offered as a complement to—not a replacement for—the traditional applications of this method. A new form of triple decomposition of the social accounting matrix inverse is offered to assist in identifying important changes in the structure of the Indonesian economy for 1975, 1980 and 1985.     

基于结构可靠度矿柱稳定性分析

本文在单轴压缩实验的基础上,利用FLAC3D对规则的单一矿柱-顶板系统进行模拟。用强度折减法,根据折减系数不同的取值对最大主应力云图、塑性区分布、位移云图进行分析。在强度折减法的基础上进行可靠度计算,根据可靠度计算结果和折减系数法计算结果,对矿柱稳定性进行分析。     

混凝土结构裂缝及控制分析

文章时混凝土结构常见裂缝的种类、成因进行了分析,并结合工程实践经验提出预控措施.