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差分带来的差异：观测冲击的局部投影应使用水平还是差分？AI 解读

ABSTRACT We show there are large finite‐sample estimation improvements from estimating local projections (LP) in a cumulated differences (long‐differenced) specification vs. a specification in levels when the impulse response of interest is to an externally identified (“observed”) shock. The long‐differenced specification substantially reduces, and in many cases eliminates, estimation bias, as well as significantly improves confidence interval coverage. These improvements…

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…

具有内生状态转换的马尔可夫状态转换回归模型估计AI 解读

Following Hamilton [1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357-384], estimation of Markov regime-switching regressions typically relies on the assumption that the latent state variable controlling regime change is exogenous. We relax this assumption and develop a parsimonious model of endogenous Markov regime-switching. Inference via maximum likelihood estimation is possible with relatively minor…

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…

商业周期定年方法实时表现的比较AI 解读

We evaluate the ability of formal rules to establish U.S. business cycle turning point dates in real time. We consider two approaches, a nonparametric algorithm and a parametric Markov-switching dynamic-factor model. Using a new “real-time” dataset of coincident monthly variables, we find that both approaches would have accurately identified the NBER business cycle chronology had they been in use over the past 30 years, with the Markov-switching model most closely matching…

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…

波动减弱的美国经济AI 解读

Using a Bayesian model comparison strategy, we search for a volatility reduction in U.S. real gross domestic product (GDP) growth within the postwar sample. We find that aggregate real GDP growth has been less volatile since the early 1980s, and that this volatility reduction is concentrated in the cyclical component of real GDP.Sales and production growth in many of the components of real GDP display similar reductions in volatility, suggesting the aggregate volatility…

马尔可夫状态转换与单位根检验AI 解读

We investigate the power and size performance of unit-root tests when the data undergo Markov regime switching. All tests, including those robust to a single break in trend growth rate, have low power against a process with a Markov-switching trend. Under the null hypothesis, we find that previously documented size distortions in Dickey-Fuller-type tests caused by a single break in trend growth rate or variance do not generalize to most parameterizations of Markov switching…