arXiv 30 Apr 2019 · Econometrics
arXiv:1904.13194 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the role of high-dimensional information sets in the context of Markov switching models with time varying transition probabilities. Markov switching models are commonly employed in empirical macroeconomic research and policy work. However, the information used to model the switching process is usually limited drastically to ensure stability of the model. Increasing the number of included variables to enlarge the information set might even result in decreasing precision of the model. Moreover, it is often not clear a priori which variables are actually relevant when it comes to informing the switching behavior. Building strongly on recent contributions in the field of factor analysis, we introduce a general type of Markov switching autoregressive models for non-linear time series analysis. Large numbers of time series are allowed to inform the switching process through a factor structure. This factor-augmented Markov switching (FAMS) model overcomes estimation issues that are likely to arise in previous assessments of the modeling framework. More accurate estimates of the switching behavior as well as improved model fit result. The performance of the FAMS model is illustrated in a simulated data example as well as in an US business cycle application.
appendix boundary found by appendix_command · 85% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kastner G (2019) Sparse Bayesian time-varying covariance estimation in many dimensions | 1.000 | 5 | 3 | 100% |
| 2 | Hamilton JD (1989) A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle | 0.928 | 4 | 3 | 100% |
| 3 | Kaufmann S (2015) K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inf… | 0.874 | 5 | 2 | 100% |
| 4 | Kaufmann S, and Schumacher C (2019) Bayesian estimation of sparse dynamic factor models with order-independent and ex-post mode identification | 0.737 | 3 | 2 | 100% |
| 5 | Frühwirth-Schnatter S (2006) Finite mixture and Markov switching models | 0.644 | 2 | 2 | 100% |
| 6 | Kim CJ, and Nelson CR (1999) State-space models with regime switching: Classical and Gibbs-sampling approaches with applications | 0.644 | 2 | 2 | 100% |
| 7 | Frühwirth-Schnatter S, and Frühwirth R (2010) Data augmentation and MCMC for binary and multinomial logit models, in Statistical modelling and regression structures, 111–132,… | 0.511 | 5 | 2 | 20% |
| 8 | Meligkotsidou L, and Dellaportas P (2011) Forecasting with non-homogeneous hidden Markov models | 0.511 | 2 | 1 | 100% |
| 9 | Bernanke BS, Boivin J, and Eliasz P (2005) Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach | 0.511 | 2 | 1 | 100% |
| 10 | Kastner G (2016) factorstochvol: Bayesian estimation of (sparse) latent factor stochastic volatility models | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 57 scored citations.