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时变协整模型的贝叶斯推断AI 解读

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper, we develop a new time varying parameter model which permits cointegration. We use a…

非线性与时变时间序列模型参数推断的灵活方法AI 解读

Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible modeling approach which can accommodate virtually any of these specifications. We build on earlier work showing the relationship between flexible functional forms and random variation in parameters. Our contribution is based around the use of priors on the time variation that is developed from considering a hypothetical…

模型不确定性下利用英国货币总量进行实时预测AI 解读

A popular account for the demise of the U.K.’s monetary targeting regime in the 1980s blames the fluctuating predictive relationships between broad money and inflation and real output growth. Yet ex post policy analysis based on heavily revised data suggests no fluctuations in the predictive content of money. In this paper, we investigate the predictive relationships for inflation and output growth using both real-time and heavily revised data. We consider a large set of…

This paper develops a new approach to change-point modelling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes that regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time-varying parameter (TVP) model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable…

平滑系数模型的半参数贝叶斯推断AI 解读

We describe procedures for Bayesian estimation and testing in cross sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between…

多方程模型中的半参数贝叶斯推断AI 解读

Abstract This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components. The approach treats the points on each nonparametric regression line as unknown parameters and uses a prior on the degree of smoothness of each line to ensure valid posterior inference despite the fact that the number of parameters…

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This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss how these simulation algorithms can also be used to select the…

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认识教育回报的异质性AI 解读

Abstract Using data from the National Longitudinal Survey of Youth (NLSY) we introduce and estimate various Bayesian hierarchical models that investigate the nature of unobserved heterogeneity in returns to schooling. We consider a variety of possible forms for the heterogeneity, some motivated by previous theoretical and empirical work and some new ones, and let the data decide among the competing specifications. Empirical results indicate that heterogeneity is present in…

内生延迟门限模型的贝叶斯分析AI 解读

autoregressive models: endogenous delay threshold. We apply our methods to the commonly used sunspot data set and find strong evidence in favor of the Endogenous Delay Threshold Autoregressive (EDTAR) model over linear and traditional threshold autoregressions.

含非期望产出的多产出生产模型AI 解读

Many production processes yield both good outputs and undesirable ones (e.g., pollutants). In this article we develop a generalization of a stochastic frontier model that is appropriate for such technologies. We discuss efficiency analysis and, in particular, define technical and environmental efficiency in the context of our model. We develop methods for carrying out Bayesian inference and apply them to a panel data set of Dutch dairy farms, where excess nitrogen production…

Models of search in labor markets are potentially of great use for policy analy-sis since their parameters are structural. However, a common feature of these models is that an assumption of optimal behavior on the part of agents is necessary to achieve identifica-tion. From a classical econometric perspective, this means the assumption of optimality is untestable and, if optimality is not imposed, it is impossible to learn about the unidentified parameters. This paper argues…

Many modelling issues and policy debates in macroeconomics depend on whether macroeconomic time series are best characterized as linear or nonlinear. If departures from linearity exist, it is important to know whether these are endogenously generated (as in, e.g. a threshold autoregressive model) or whether they merely reflect changing structure over time. In this paper, we discuss a model comparison methodology which addresses these issues. We advocate a Bayesian approach…

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贝叶斯分析、计算与交流软件 BACCAI 解读

Bayesian Analysis, Computation and Communication (BACC) is a new Bayesian software package which is linked to Gauss and takes the form of a set of Gauss commands. In this review, I outline a list of qualities that a Bayesian software package should have. I then discuss whether BACC has these qualities in the context of a brief description and an extended example.

美国失业率的动态非对称性AI 解读

The authors examine dynamic asymmetries in U.S. unemployment using nonlinear time series models and Bayesian methods. They find strong statistical evidence in favor of a two-regime threshold autoregressive model. Empirical results indicate that, once parameter and model uncertainty are both taken into account, there are economically interesting asymmetries in the unemployment rate. One finding of particular interest is that shocks that lower the unemployment rate tend to…

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