Bayesian Analysis of an Unobserved-Component Time Series Model of GDP With Markov-Switching and Time-Varying Growths

Luginbuhl, Rob; Vos, Aart de

doi:10.1080/07350015.1999.10524833

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Evidence of such regimes has been documented in short-term interest rates (Gray, 1996, Ang and Bekaert, 2002, Smith, 2002, GDP or GNP (Hamilton, 1989, Goodwin, 1993, Luginbuhl and de Vos, 1999, Lam, 2004, inflation (Kim, 1993, Kumar andOkimoto, 2007), and market turbulence (Chow, Jacquier, Kritzman, and Lowry, 1999, Kritzman, Lowry, and Van Royen, 2001, and Kritzman and Li, 2010. Billio, Getmansky, Lo, and Pelizzon (2010) independently applied principal components analysis to determine the extent to which several financial industries became more unified across two separate regimes.…”

Section: Part I: Literature Reviewmentioning

confidence: 99%

Principal Components as a Measure of Systemic Risk

Kritzman¹,

Page

Rigobón

2010

SSRN Journal

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Section: Part I: Literature Reviewmentioning

confidence: 99%

Principal Components as a Measure of Systemic Risk

Kritzman¹,

Page

Rigobón

2010

SSRN Journal

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“…7). However, Bayesian approaches, especially MCMC techniques, are also a welldeveloped field in the literature (Frühwirth-Schnatter 2006;Kim and Kang 2019;Luginbuhl and de Vos 1999). In the Bayesian estimation of UC models, the state vector and additional parameters are drawn from a joint distribution, rather than treating the unknown parameters as fixed when maximizing the likelihood numerically.…”

Section: Robustness and Discussionmentioning

confidence: 99%

“…In the Bayesian estimation of UC models, the state vector and additional parameters are drawn from a joint distribution, rather than treating the unknown parameters as fixed when maximizing the likelihood numerically. As a consequence, the Bayesian approach bears the advantage that the posteriors incorporate any underlying uncertainty regarding the additional parameters (Luginbuhl and de Vos 1999). Moreover, in order for the Kim filter to account for all possible regime permutations, M 2 individual predictions need to be produced in a single time period for a model with M states.…”

Section: Robustness and Discussionmentioning

confidence: 99%

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

Haimerl

Hartl

2023

Econometrics

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The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions, and public behavior.

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“…The unobserved components models have been used in different economic applications, for estimating the natural level of the labor supply (Bull and Frydman [1983]), for modeling credibility of the monetary authority (Weber [1991]), for analyzing the GDP (Luginbuhl and Vos [1999], Morley et al [2003]), the Purchasing Power Parity (PPP) (Kleijn and van Dijk [2001]), consumption (Elwood [1998]), unemployment (Chung and Harvey [2000], Berger and Everaert [2009]), for modeling tax revenues (Koopman and Ooms [2003]), cycles (Chambers and McGarry [2002]) and for analyzing financial series (Cowan and Joutz [2006]), among others.…”

Section: Methodologies Of Estimation Of the Nirmentioning

confidence: 99%

Estimations of the natural rate of interest in Colombia

González-Molano¹,

Melo‐Velandia²,

Rojas³

et al. 2010

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Three methodologies to estimate the natural interest rate, NIR, are implemented for the Colombian economy. Two methods are statistical filters and the third involves some economic theory. The first method is based on unobserved components decomposition of the real interest rate and explores the statistical characteristics of the data. The second is a multivariate version of the Hodrick-Prescott filter augmented by an economic relationship, HPMV. The NIR in both cases is defined as the trend component of the market real interest rate; then, the NIR may be considered as a long-run real interest rate anchor for monetary policy. The third method consists in estimating a semi-structural model for a small open economy. In this case the NIR is defined as the interest rate that does not affect the output dynamics in the short run and assures output and inflation convergence to their long run equilibriums. This implies that the NIR is a medium-run anchor for monetary policy. Three features are observed in the dynamics of the NIR estimates for the period 2000-2009. The first part of the sample (2000)(2001)(2002)(2003) shows a downward trend, followed by a period of stabilization and upward trend (2004)(2005)(2006)(2007)(2008) and at the end of the sample the NIR start decreasing again. The NIR in the last quarter of the sample, 2009Q2, is around 3.1 in average.

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Bayesian Analysis of an Unobserved-Component Time Series Model of GDP With Markov-Switching and Time-Varying Growths

Cited by 6 publications

References 10 publications

Principal Components as a Measure of Systemic Risk

Principal Components as a Measure of Systemic Risk

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

Estimations of the natural rate of interest in Colombia

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