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奇异向量自回归AI 解读

This paper develops methods for the empirical analysis of singular processes, particularly in macroeconomics. A strong rationale, a well-developed theoretical framework and, as we show, empirical support exist for multivariate time series with a singular spectral density. A singular spectral density is consistent with economic theory underlying, for example, DSGE models in which the number of variables is greater than the number of structural shocks. This assumption…

大型时变参数 VAR 的状态空间降维AI 解读

This paper proposes a new approach to estimating high dimensional time varying parameter structural vector autoregressive models (TVP-SVARs) by taking advantage of an empirical feature of TVP-(S)VARs. TVP-(S)VAR models are rarely used with more than 4–5 variables. However recent work has shown the advantages of modelling VARs with large numbers of variables and interest has naturally increased in modelling large dimensional TVP-VARs. A feature that has not yet been utilized…

静态因子模型中的不变推断与高效计算AI 解读

Factor models are used in a wide range of areas. Two issues with Bayesian versions of these models are a lack of invariance to ordering of and scaling of the variables and computational inefficiency. This article develops invariant and efficient Bayesian methods for estimating static factor models. This approach leads to inference that does not depend upon the ordering or scaling of the variables, and we provide arguments to explain this invariance. Beginning from identified…

向量误差修正模型中的随机搜索变量选择及英国宏观应用AI 解读

SUMMARY This paper develops methods for stochastic search variable selection (currently popular with regression and vector autoregressive models) for vector error correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK…

时变维度模型AI 解读

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this article proposes several Time Varying Dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different…

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…

对《增长决定因素的联合性》的评论AI 解读

Abstract Doppelhofer and Weeks ( 2009 ) present a statistic designed to indicate the probability that pairs of regressors appear together or individually in a Bayesian model averaged linear regression. This comment presents an alternative measure that is designed to overcome some of the limitations of Doppelhofer and Weeks' statistic. Copyright © 2009 John Wiley & Sons, Ltd.

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