Testing the lucas critique: A review

Claims that the parameters of an econometric model are invariant under changes in either policy rules or expectations processes entail super exogeneity and encompassing implications. Super exogeneity is always potentially refutable, and when both implications are involved, the Lucas critique is also refutable. We review the methodological background; the applicability of the Lucas critique; super exogeneity tests; the encompassing implications of feedback and feedforward models; and the role of incomplete information. The approach is applied to money demand in the u.S.A. to examine constancy, exogeneity, and encompassing, and reveals the Lucas critique to be inapplicable to the model under analysis.     

Testing the lucas critique: A review

Claims that the parameters of an econometric model are invariant under changes in either policy rules or expectations processes entail super exogeneity and encompassing implications. Super exogeneity is always potentially refutable, and when both implications are involved, the Lucas critique is also refutable. We review the methodological background; the applicability of the Lucas critique; super exogeneity tests; the encompassing implications of feedback and feedforward models; and the role of incomplete information. The approach is applied to money demand in the u.S.A. to examine constancy, exogeneity, and encompassing, and reveals the Lucas critique to be inapplicable to the model under analysis.     

Generalizing the Bayesian Vector Autoregression Approach for Regional Interindustry Employment Forecasting

The Bayesian vector autoregression (BVAR) employment-forecasting approach is generalized using data for the state of Georgia. This study advances previous regional BVAR approaches by (a) incorporating regional input-output coefficients instead of national coefficients, (b) using the coefficients both to specify the prior means in one model and to weight the variances of a Minnesota-type prior in a second model, and (c) including final-demand effects and links to national and world economies. Out-of-sample forecasts produced by the generalized BVAR models are compared to forecasts produced from an autoregressive model, an unconstrained VAR model, and a Minnesota BVAR model.     

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.     

信心如何影响中国通货膨胀

在Galí and Gertler（1999）的基础上,本文通过假设成本加成定价幅度是信心的函数,构造了基于信心的新凯恩斯菲利普斯曲线,并通过VAR模型和BVAR模型实证研究了信心是否能够影响中国通货膨胀。研究发现,企业家信心指数能够影响中国通胀,而消费者信心指数无法影响中国通胀;企业家信心指数对通货膨胀的影响机制类似于总需求冲击影响机制;VAR模型和BVAR模型的结论基本一致,这证实了本文结论的稳健性。     

Common Drifting Volatility in Large Bayesian VARs

The general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar. We propose two models in which conditional volatilities feature comovement and study them using U.S. macroeconomic data. The first model specifies the conditional volatilities as driven by a single common unobserved factor, plus an idiosyncratic component. We label this model BVAR with general factor stochastic volatility (BVAR-GFSV) and we show that the loss in terms of marginal likelihood from assuming a common factor for volatility is moderate. The second model, which we label BVAR with common stochastic volatility (BVAR-CSV), is a special case of the BVAR-GFSV in which the idiosyncratic component is eliminated and the loadings to the factor are set to 1 for all the conditional volatilities. Such restrictions permit a convenient Kronecker structure for the posterior variance of the VAR coefficients, which in turn permits estimating the model even with large datasets. While perhaps misspecified, the BVAR-CSV model is strongly supported by the data when compared against standard homoscedastic BVARs, and it can produce relatively good point and density forecasts by taking advantage of the information contained in large datasets.     

Vector autoregression modelling and forecasting growth of South Korea

In this paper, we have estimated vector autoregression (VAR), Bayesian vector autoregression (BVAR) and vector error-correction models (VECMs) using annual time-series data of South Korea for 1950-94. We find evidence supporting the view that growth of real per-capita income has been aided by income, investment and export growth, as well as government spending and exchange rate policies. The VECMs provide better forecasts of growth than do the VAR and BVAR models for both short-term and long-term predictions.     

中国改革开放政策有效性的定量研究

 1978年改革开放政策执行前后,中国经济发生了巨大的变化,居民的生活水平得到实质性提高。本文对Lucas模型进行扩展得到使用居民福利变动来量化政策有效性的理论模型,并基于中国1952-2007年的居民消费数据从波动和增长福利效应的视角分阶段定量研究改革开放政策的有效性。结果显示,从波动的福利效应视角来看,改革开放后第一阶段居民福利下降,第二阶段居民福利提高,总体而言居民福利收益相当于永久地每年给每一个居民21元;从增长的福利效应视角来看,第一阶段居民福利提高,第二阶段居民福利也提高,总体而言居民福利收益相当于永久地每年给每一个居民1503元。因此,无论是从波动还是从增长的福利效应视角来看,中国的改革开放政策都是非常有效的,进一步深化改革开放是继续有效提高居民福利水平的一个重要政策。     

对偶法核算全要素生产率

 在Griliches、Jorgenson（1967）和Hsieh（1999,2002）的基础上,结合卢卡斯（1988）的人力资本模型发展了一种基于人力资本的对偶法测度全要素生产率。以改革开放以来的上海为研究对象,同时运用对偶法和传统测度方法测度上海市全要素生产率,验证了经济增长率的变动更多来自于全要素生产率而不是投入要素的变动,同时发现两种方法测度结果差异显著。经过进一步分析,发现这种差异来源于直接资本价格与间接资本价格之间存在着高度的不一致性。通过对两种方法的适用性进行讨论,可以认为对偶法是传统测度方法一种有益的补充。     

通货膨胀的宏观经济影响因素分析

本文首先利用贝叶斯向量自回归（BVAR）模型,分析了通货膨胀对宏观经济的冲击响应及其剧烈程度。然后,利用门限模型验证了通货膨胀在不同变量作为门限变量情况下的门限效应。结果表明：通货膨胀对六个因素冲击的反应程度各异,其中,对流动性过剩的反应程度最强,对股票价格、产出缺口和国际油价的反应适度,对实际有效汇率和房价的反应较弱;股票价格、汇率和国际油价具有明显的门限特征,它们分别将通货膨胀分为高低两种区制状态。以上结果有利于我们更好地认识通货膨胀的反应机制,采用合理的经济政策应对通货膨胀。     

Forecasting With Bayesian Vector Autoregressions—Five Years of Experience

The results obtained in five years of forecasting with Bayesian vector autoregressions (BVAR's) demonstrate that this inexpensive, reproducible statistical technique is as accurate, on average, as those used by the best known commercial forecasting services. This article considers the problem of economic forecasting, the justification for the Bayesian approach, its implementation, and the performance of one small BVAR model over the past five years.     

A Note on Change-Over Designs

A change-over model with correlated errors is discussed. In particular the results of Patterson (1950) and Lucas (1951) for balanced change-over designs are extended to more than four treatments and more general variance matrices. A connection with recent work on neighbour models for field trials is made.     

中国经济增长动力追溯及阶段性演化特征

基于扩展的卢卡斯内生增长模型,运用Bai-Perron多重结构突变模型找出中国经济增长结构突变点,Prais-Winsten AR(1)和OLS对突变点前后不同增长阶段进行回归,并解析结构变化、制度变迁对经济增长的影响,以追溯新中国成立以来中国经济增长动力阶段性演化特征。研究结果表明:驱动中国经济增长的主动力因素是资本、劳动、对外开放和城镇化,阻碍经济增长表现为结构性、制度性因素不断增多增强;科技进步、结构优化、制度创新将成为增长新动力。     

中国金融风险周期监测与央行货币政策非对称性效果识别

本文采用时变参数因子增广向量自回归模型（TVP-FAVAR）,并基于动态模型平均法测度了金融子系统变量的时变权重,通过加权计算得到我国金融风险周期指数（FRI）。然后改进构建了分区制货币政策模型系统,并采用逻辑平滑转换向量自回归模型（LST-VAR）研究了高低区制下,价格型和数量型货币政策对金融风险的传导效应,在此基础上,设计并测度了我国货币政策的时滞效应。研究发现,我国金融风险具有明显的两区制特征,样本区间内FRI大多时期处于低风险区制,而高风险区制的时间段与国内外重大金融风险事件相吻合。两种紧缩的货币政策均有利于抑制金融风险及其波动,且在高风险区制的抑制效应更为显著。对于货币政策时滞效应,数量型货币政策对金融风险的抑制作用大于价格型货币政策,但2008、2012和2015年三个高金融风险期的时滞效应均较小。最后,根据研究结果提出相应政策建议。     

Estimating monotonic change in the rate and dependence parameters of INAR(1) process (Case study: IP counts data)

In this article, we consider a first-order integer-valued autoregressive (INAR(1)) model. Then, we propose change point estimators for the rate and dependence parameters in INAR(1) model using maximum likelihood estimation method when the type of change belongs to a family of monotonic changes. To monitor the process, a combined EWMA and c control chart is considered. The results show that the proposed change point estimators provide efficient estimates of the change time. At the end, to illustrate the application of the proposed estimators, a real case related to IP counts data is investigated.     

财政性高等教育投入的外溢效应研究

政府财政性投入是高等教育经费来源的主渠道,其投入规模对国家经济增长具有重要影响。在建立中国财政对高等教育投入因素的卢卡斯教育资源外溢效应模型的基础上,研究表明：财政性高等教育投入对经济增长的作用明显,而其与人力资源的共同作用却对经济增长具有微弱的反向作用。因此,深化财政体制改革、完善财政性教育经费管理和评估、建立高校运行成本核算、提高高等教育资源使用效率等措施,是增强财政性高等教育投入外溢效应和促进经济又好又快增长的重要手段。     

中国木材价格波动的动态均衡模型及实证分析

对传统蛛网模型作了改进,把第t期商品的供给函数考虑为t-1,t-2,…,1期价格和随机扰动的函数,考虑到价格调节政策、投资投机、调控政策、物价涨跌等因素对木材需求的影响,在需求函数中加入修正系数,以此建立以木材价格调节为中心的木材供需动态模型,并对模型进行了稳定性分析和供给变动对价格变动的影响分析,得到了均衡价格及其存在的稳定性条件和供给变动对价格变动的影响乘数。在上述理论模型的基础上,利用1993-2006年中国流通市场中的木材供需数据进行实证分析,对中国木材价格波动进行定量分析,并为判断木材价格的稳定性,解决木材供需矛盾提供了理论依据和现实操作方法。     

经济政策不确定性如何影响金融市场间的流动性协同运动？——基于中国金融周期的视角

金融市场间流动性出现高协同运动是发生危机传染的重要表现之一，因此，针对流动性动态联动效应的研究显得极为重要。本文基于中国金融市场数据测算了2003-2018年间我国股市、债市流动性，并对Colacito等（2011）的混频数据抽样动态条件相关系数模型（DCC-MIDAS）进行了扩展，同时从金融周期视角出发，运用扩展后的模型考察了经济不确定性在不同时间区间内对于流动性波动率和相关性是否存在不同的作用效果。研究结果表明，相较于单因子混频模型，引入经济政策不确定性的多因子混频模型可以更好地捕捉我国股债两市相关性的动态变化；同时，经济政策不确定性的提高会降低股债两市流动性的正相关性，但这一作用效果会在金融周期的拐点处转为加强两者的正相关性。本文不仅为讨论股债两市联动效应提供了流动性的新视角，也为金融市场风险监管提供了重要的参考依据。     

传统产业转型升级的政策变迁及实施效果——中国纺织产业的个案研究

从"政策变迁"和"实施效果"两个视角切入,在全面梳理中国纺织产业转型升级已有政策的基础上,基于文本计量分析法从四个维度研究中国纺织产业转型升级政策的历史变迁及主要特征,应用"目标达成"模式并结合模糊优选模型对政策效果进行评价。研究表明:中国纺织产业转型升级政策表现出比较明显的"周期性"、"专用性"、"治理性"和"约束性",而"行业整合"和"产业培育"类政策相对较少,并且指导性和可操作性亟待加强;中国纺织产业转型升级政策颁布的前五年实际效果一般,但是近年来稳步上升;不同政策工具对具体指标的影响程度、实际效果及持续性存在差异。     

A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models

Abstract. Real‐world phenomena are frequently modelled by Bayesian hierarchical models. The building‐blocks in such models are the distribution of each variable conditional on parent and/or neighbour variables in the graph. The specifications of centre and spread of these conditional distributions may be well motivated, whereas the tail specifications are often left to convenience. However, the posterior distribution of a parameter may depend strongly on such arbitrary tail specifications. This is not easily detected in complex models. In this article, we propose a graphical diagnostic, the Local critique plot, which detects such influential statistical modelling choices at the node level. It identifies the properties of the information coming from the parents and neighbours (the local prior) and from the children and co‐parents (the lifted likelihood) that are influential on the posterior distribution, and examines local conflict between these distinct information sources. The Local critique plot can be derived for all parameters in a chain graph model.