Consistency of elementwise-weighted total least squares estimator in a multivariate errors-in-variables model <Emphasis Type="Bold">AX=B</Emphasis>

A multivariate measurement error model AXB is considered. The errors in [A,B] are rowwise independent, but within each row the errors may be correlated. Some of the columns are observed without errors, and in addition the error covariance matrices may differ from row to row. The total covariance structure of the errors is supposed to be known up to a scalar factor. The fully weighted total least squares estimator of X is studied, which in the case of normal errors coincides with the maximum likelihood estimator. We give mild conditions for weak and strong consistency of the estimator, when the number of rows in A increases. The results generalize the conditions of Gallo given for a univariate homoscedastic model (where B is a vector), and extend the conditions of Gleser given for the multivariate homoscedastic model. We derive the objective function for the estimator and propose an iteratively reweighted numerical procedure.Acknowledgements.A. Kukush is supported by a postdoctoral research fellowship of the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe. S. Van Huffel is a full professor with the Katholieke Universiteit Leuven. This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction (IUAP Phase V-22), initiated by the Belgian State, Prime Ministers Office-Federal Office for Scientific, Technical and Cultural Affairs, of the Concerted Research Action (GOA) projects of the Flemish Government MEFISTO-666 (Mathematical Engineering for Information and Communication Systems Technology), of the IDO/99/03 project (K.U. Leuven) Predictive computer models for medical classification problems using patient data and expert knowledge, of the FWO projects G.0200.00, G.0078.01 and G.0270.02. The scientific responsibility is assumed by its authors. The authors would like to thank Maria Luisa Rastello and Amedeo Premoli for bringing the EW-TLS problem to their attention. The authors are grateful to two anonymous referees for the valuable comments.     

Keynesian theorising during hard times: stock-flow consistent models as an unexplored 'frontier' of Keynesian macroeconomics

This paper argues that the stock-flow consistent approach tomacroeconomic modelling (SFCA) is a natural outcome of the pathtaken by Keynesian macroeconomic thought in the 1960s and 1970s,a theoretical ‘frontier’ that remained largely unexploredwith the end of Keynesian academic hegemony. It does so in twosteps. First, it phrases the representative views of Davidson,Godley, Minsky and Tobin as different ‘closures’of the same (SFC) accounting framework, calling attention totheir similarities and logical implications. Second, it discussesunresolved issues within this approach and how it differs from‘modern’ theorising.     

地方政府、行为一致性与农民负担决定——以山西省1994—2000年的面板数据为例

本文的切入点是影响山西省农民负担决定的因素，分析的重点是既有文献中暗含的“高级别地方政府无关假定”。以城市为分析单位，使用计量经济学中面板数据的分析方法，本文的研究结果证实了已有的两种主要理论观点（非农收入理论与财政支出理论）的稳健性；这就意味着“高级别地方政府无关假定”是与事实不符的假定，这样的假定会局限人们对农民负担问题的深入理解。同时，这样的发现还意味着在农民负担决定问题上，各级地方政府的行为存在着某种事实上的一致性，从而为分析财政分权体制下政府竞争或串谋存在的范围提供了一个重要的案例。     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

基于宏观资产负债表的居民债务问题及其风险研究

本文从宏观资产负债表的编制出发,对比世界主要经济体的居民债务演变,有效度量中国居民部门的债务杠杆水平。通过宏观部门经济间的关联,构造基于存量-流量一致的部门结构化均衡模型,剖析居民部门债务风险的传导机制,分析货币、财政和地产政策在居民债务风险防范中的作用。结果表明:中国居民部门金融资产负债率和偿债比例偏高,偿债压力逐步加大。但适中的资产负债率和较高的潜在经济增速为我国居民部门债务风险的化解提供了足够的空间。紧缩性货币政策有利于居民部门去杠杆化,但由此引发的消费减速和经济下行将导致企业和政府部门债务攀升;减税较政府直接支出更能激发消费内需、降低居民和企业债务杠杆,同时,长期经济的好转利于政府债务风险稳定。紧缩性地产政策虽然有助于长期经济结构的调整,但需防范短期经济下滑。