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含吸收态的状态切换ARMA贝叶斯推断：事前实际利率动态AI 解读

One goal of this article is to develop an efficient Metropolis–Hastings (MH) algorithm for estimating an ARMA model with a regime-switching mean, by designing a new efficient proposal distribution for the regime-indicator variable. Unlike the existing algorithm, our algorithm can achieve reasonably fast convergence to the posterior distribution even when the latent regime-indicator variable is highly persistent or when there exist absorbing states. Another goal is to…

含内生解释变量的马尔可夫转换模型Ⅱ：两步极大似然估计AI 解读

This paper proposes a two-step maximum likelihood estimation (MLE) procedure to deal with the problem of endogeneity in Markov-switching regression models. A joint estimation procedure provides us with an asymptotically most efficient estimator, but it is not always feasible, due to the 'curse of dimensionality' in the matrix of transition probabilities. A two-step estimation procedure, which ignores potential correlation between the latent state variables, suffers less from…

马尔可夫切换与贝弗里奇—纳尔逊分解：美国产出持续性是否改变？AI 解读

We show that, for a class of univariate and multivariate Markov-switching models, exact calculation of the Beveridge-Nelson (BN) trend/cycle components is possible. The key to exact BN trend/cycle decomposition is to recognize that the latent first-order Markov-switching process in the model has an AR(1) representation, and that the model can be cast into a state-space form. Given the state-space representation, we show that impulse-response function analysis can be…

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…

股权溢价的结构突变AI 解读

This article uses Bayesian marginal likelihood analysis to compare univariate models of the stock return behavior and test for structural breaks in the equity premium. The analysis favors a model that relates the equity premium to Markov-switching changes in the level of market volatility and accommodates volatility feedback. For this model, there is evidence of a one-time structural break in the equity premium in the 1940s, with no evidence of additional breaks in the…

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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…

商业周期转折点、新同步指数与持续期依赖：状态转换动态因子模型AI 解读

The synthesis of the dynamic factor model of Stock and Watson (1989) and the regime-switching model of Hamilton (1989) proposed by Diebold and Rudebusch (1996) potentially encompasses both features of the business cycle identified by Burns and Mitchell (1946): (1) comovement among economic variables through the cycle and (2) nonlinearity in its evolution. However, maximum-likelihood estimation has required approximation. Recent advances in multimove Gibbs sampling…

短暂市场风潮与 1987 年股市崩盘AI 解读

Using a fad model with Markov-switching heteroscedasticity in both the fundamental and fad components (UC-MS model), this paper examines the possibility that the 1987 stock market crash was an example of a short-lived fad. While we usually think of fads as speculative bubbles, what the UC-MS model seems to be picking up is unwarranted pessimism which the market exhibited with the OPEC oil shock and the '87 crash. Furthermore, the conditional variance implied by the UC-MS…

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含马尔可夫转换异方差的不可观测成分时间序列模型：状态变化与通胀不确定性AI 解读

In this article, I first extend the standard unobserved-component time series model to include Hamilton's Markov-switching heteroscedasticity. This will provide an alternative to the unobserved-component model with autoregressive conditional heteroscedasticity, as developed by Harvey, Ruiz, and Sentana and by Evans and Wachtel. I then apply a generalized version of the model to investigate the link between inflation and its uncertainty (U.S. data, gross national product…

The main econometric issue in testing the Lucas (1973) hypothesis in a time series context is estimation of the forecast-error variance conditional on past information. The conditional variance may vary through time as monetary policy evolves and agents are obliged to infer its present state. Under the assumption that a monetary policy regime is continuously changing, a time-varying-parameter model is proposed for the monetary-growth function. Based on Kalman-filtering…