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on Econometric Time Series |
| By: | Ioannis Papageorgiou; Ioannis Kontoyiannis |
| Abstract: | A hierarchical Bayesian framework is introduced for developing rich mixture models for real-valued time series, along with a collection of effective tools for learning and inference. At the top level, meaningful discrete states are identified as appropriately quantised values of some of the most recent samples. This collection of observable states is described as a discrete context-tree model. Then, at the bottom level, a different, arbitrary model for real-valued time series - a base model - is associated with each state. This defines a very general framework that can be used in conjunction with any existing model class to build flexible and interpretable mixture models. We call this the Bayesian Context Trees State Space Model, or the BCT-X framework. Efficient algorithms are introduced that allow for effective, exact Bayesian inference; in particular, the maximum a posteriori probability (MAP) context-tree model can be identified. These algorithms can be updated sequentially, facilitating efficient online forecasting. The utility of the general framework is illustrated in two particular instances: When autoregressive (AR) models are used as base models, resulting in a nonlinear AR mixture model, and when conditional heteroscedastic (ARCH) models are used, resulting in a mixture model that offers a powerful and systematic way of modelling the well-known volatility asymmetries in financial data. In forecasting, the BCT-X methods are found to outperform state-of-the-art techniques on simulated and real-world data, both in terms of accuracy and computational requirements. In modelling, the BCT-X structure finds natural structure present in the data. In particular, the BCT-ARCH model reveals a novel, important feature of stock market index data, in the form of an enhanced leverage effect. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.00913&r=ets |
| By: | Miguel Herculano; Punnoose Jacob |
| Abstract: | We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62% of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample. |
| Keywords: | Financial Conditions Index, Mixed-Frequency, Bayesian Methods |
| JEL: | C11 C32 C52 C53 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2023-42&r=ets |
| By: | Yoshibumi Makabe (Bank of Japan); Yosuke Matsumoto (Bank of Japan); Wataru Hirata (Bank of Japan) |
| Abstract: | This paper considers a vertical production chain in an otherwise canonical sticky price model, and estimates the New Keynesian Phillips Curve with the vertical production stages (PS-NKPC), using the commodity-flow-based U.S. price data. We employ a Bayesian VAR-GMM method and compare the PS-NKPC with the canonical NKPC based on a quasi-marginal likelihood criterion, which is robust under weakly identified parameters. Thus our result adds to the empirical relevance of the so-called ``pipeline price pressures'' incurred by upstream stages of production. Our estimates suggest that (i) the PS-NKPC performs better than the canonical New Keynesian Phillips Curve in terms of quasi-marginal likelihood-based model comparison, and (ii) pipeline price pressures have non-negligible impacts on consumer price inflation as well as producer price inflation. |
| Keywords: | New Keynesian Phillips curve; VAR-GMM; Bayesian method; Production chain; Pipeline pressure |
| JEL: | C11 C26 C52 E31 |
| Date: | 2023–08–21 |
| URL: | http://d.repec.org/n?u=RePEc:boj:bojwps:wp23e13&r=ets |