书名 Switching-Models-Workbook
计量经济学高级时间序列分析:主要介绍各种先端结构变换模型的推导和在时间序列分析中的应用。适合金融、计量经济学、统计学专业。
关键词:门槛模型  门槛自回归 门槛回归与协整 马尔可夫转移模型
书本具体介绍如下：
This workbook is based upon the content of the RATS e-course on SwitchingModels and Structural Breaks, offered in fall of 2010. It covers a broad rangeof topics for models with various types of breaks or regime shifts.In some cases, models with breaks are used as diagnostics for models withfixed coefficients. If the fixed coefficient model is adequate, we would expect toreject a similar model that allows for breaks, either in the coefficients or in thevariances. For these uses, the model with the breaks isn’t being put forward asa model of reality, but simply as an alternative for testing purposes. Chapters 2and 3 provide several examples of these, with Chapter 2 looking at “fluctuationtests” and Chapter 3 examining parametric tests.Increasingly, however, models with breaks are being put forward as a description of the process itself. There are two broad classes of such models: thosewith observable regimes and those with hidden regimes. Models with observable criteria for classifying regimes are covered in Chapters 4 (Threshold Autoregressions), 5 (Threshold VAR and Cointegration) and 6 (Smooth ThresholdModels). In all these models, there is a threshold trigger which causes a shiftof the process from one regime to another, typically when an observable series moves across an (unknown) boundary. There are often strong economicargument for such models (generally based upon frictions such as transactionscosts), which must be overcome before an action is taken. Threshold modelsare generally used as an alternative to fixed coefficient autoregressions andVAR’s. As such, the response of the system to shocks is one of the more usefulways to examine the behavior of the model. However, as the models are nonlinear, there is no longer a single impulse response function which adequatelysummarizes this. Instead, we look at ways to compute two main alternatives:the eventual forecast function, and the generalized impulse response function(GIRF).The remaining seven chapters cover models with hidden regimes, that is models where there is no observable criterion which determines to which regimea data point belongs. Instead, we have a model which describes the behaviorof the observables in each regimes, and a second model which describes the(unconditional) probabilities of the regimes, which we combine using Bayesrule to infer the posterior probability of the regimes. Chapter 7 starts offwith the simple case of time independence of the regimes, while the remainder use the (more realistic) assumption of Markov switching. The sequenceof chapters 8 to 11 look at increasingly complex models based upon linear regressions, from univariate, to systems, to VAR’s with complicated restrictions.viPreface viiAll of these demonstrate the three main methods for estimating these types ofmodels: maximum likelihood, EM and Bayesian MCMC.The final two chapters look at Markov switching in models where exact likelihoods can’t be computed, requiring approximations to the likelihood. Chapter12 examines state-space models with Markov switching, while Chapter 13 isdevoted to switching ARCH and GARCH models.We use bold-faced Courier (for instance, DLM) for any use of RATS instructionor procedure names within the main text, and non-bolded Courier (%SCALAR)for any other pieces of code, such as function and variable names. For easyreference, the full text of each example is included. The running examples arealso available as separate files