论文检索

趋势—周期分解误设的简易修正及自然利率估计应用AI 解读

We propose a simple correction for misspecification in trend-cycle decompositions when the stochastic trend is assumed to be a random walk process but the estimated trend displays some serial correlation in first differences. Possible sources of misspecification that would otherwise be hard to detect and correct for include a small amount of measurement error, omitted variables, or minor approximation errors in model dynamics when estimating trend. Our proposed correction is…

住房市场繁荣与衰退期间边际消费倾向是否改变？AI 解读

Summary We extend a widely used semi‐structural model to identify and estimate dynamic consumption elasticities with respect to transitory income shocks. Applying our model to household survey data, we find a structural break in marginal propensities to consume following the end of the housing market boom, with the average across households increasing significantly. There is important heterogeneity by different household balance sheet characteristics, and the increase in the…

产出缺口的即时预测AI 解读

We propose a way to directly nowcast the output gap using the Beveridge–Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of potential, or the HP filter. We find that within-quarter nowcasts for the output gap are more reliable than for output growth, with monthly indicators for a credit risk spread, consumer…

利用大型贝叶斯向量自回归估计并分解产出缺口AI 解读

Summary We consider how to estimate the trend and cycle of a time series, such as real gross domestic product, given a large information set. Our approach makes use of the Beveridge–Nelson decomposition based on a vector autoregression, but with two practical considerations. First, we show how to determine which conditioning variables span the relevant information by directly accounting for the Beveridge–Nelson trend and cycle in terms of contributions from different…

基于似然比的结构突变时点置信集AI 解读

We propose the use of likelihood-ratio-based confidence sets for the timing of structural breaks in parameters from time series regression models. The confidence sets are valid for the broad setting of a system of multivariate linear regression equations under fairly general assumptions about the error and regressors, and allowing for multiple breaks in mean and variance parameters. In our asymptotic analysis, we determine the critical values for a likelihood ratio test of a…

状态切换过程的趋势—周期分解AI 解读

We present a new approach to trend/cycle decomposition of time series that follow regime-switching processes. The proposed approach, which we label the "regime-dependent steady-state" (RDSS) decomposition, is motivated as the appropriate generalization of the Beveridge and Nelson decomposition [Beveridge, S., Nelson, C.R., 1981. A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the…

大缓和来源的贝叶斯反事实分析AI 解读

Abstract We use counterfactual experiments to investigate the sources of the large volatility reduction in US real GDP growth in the 1980s. Contrary to an existing literature that conducts counterfactual experiments based on classical estimation and point estimates, we consider Bayesian analysis that provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. Using Blanchard and Quah's ( 1989 ) structural VAR model of output…

非线性与衰退的永久影响AI 解读

Abstract This paper presents a new nonlinear time series model that captures a post‐recession ‘bounce‐back’ in the level of aggregate output. While a number of studies have examined this type of business cycle asymmetry using recession‐based dummy variables and threshold models, we relate the ‘bounce‐back’ effect to an endogenously estimated unobservable Markov‐switching state variable. When the model is applied to US real GDP, we find that the Markov‐switching regimes are…