Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mengheng Li ◽  
Ivan Mendieta-Muñoz
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Estimation ◽  
Phillips Curve ◽  
Estimation Procedure ◽  
Structural Representation ◽  
Unobserved Components ◽  
Structural Interaction ◽  
Structural Interpretation ◽  
The Us ◽  
Unobserved Components Model

Abstract We propose a structural representation of the correlated unobserved components model, which allows for a structural interpretation of the interactions between trend and cycle shocks. We show that point identification of the full contemporaneous matrix which governs the structural interaction between trends and cycles can be achieved via heteroskedasticity. We develop an efficient Bayesian estimation procedure that breaks the multivariate problem into a recursion of univariate ones. An empirical implementation for the US Phillips curve shows that our model is able to identify the magnitude and direction of spillovers of the trend and cycle components both within-series and between-series.

A Bayesian Analysis Of The Unit Root Hypothesis Within An Unobserved Components Model

1994 ◽  
Vol 10 (3-4) ◽  
pp. 552-578 ◽  
Author(s):  
Eric Zivot
Keyword(s):  
Bayesian Analysis ◽  
Unit Root ◽  
Odds Ratio ◽  
Unit Roots ◽  
Ratio Test ◽  
Structural Representation ◽  
Unobserved Components ◽  
Posterior Odds ◽  
Posterior Odds Ratio ◽  
Unobserved Components Model

In this paper we extend some of Phillips's [4] results to nonlinear unobserved components models and develop a posterior odds ratio test of the unit root hypothesis based on flat and Jeffreys priors. In contrast to the analysis presented by Schotman and van Dijk [9], we utilize a nondegenerate structural representation of the components model that allows us to determine well-behaved Jeffreys priors, posterior densities under flat priors and Jeffreys priors, and posterior odds ratios for the unit root hypothesis without a proper prior for the level parameter. The analysis highlights the importance of the treatment of initial values for inference concerning stationarity and unit roots.

scholarly journals Bayesian Analysis of Interleaved Learning and Response Bias in Behavioral Experiments

2007 ◽  
Vol 97 (3) ◽  
pp. 2516-2524 ◽  
Author(s):  
Anne C. Smith ◽  
Sylvia Wirth ◽  
Wendy A. Suzuki ◽  
Emery N. Brown
Keyword(s):  
Bayesian Analysis ◽  
State Space ◽  
State Space Model ◽  
Estimation Procedure ◽  
Ideal Observer ◽  
Model Parameters ◽  
Behavioral Experiments ◽  
Actual Object ◽  
Space Model ◽  
Multiple Tasks

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood–based approaches in the analysis of a simulated conditioned T-maze task and of an actual object–place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

scholarly journals A Note on ‘What Drives Share Prices in the Middle East?’

2008 ◽  
Author(s):  
Panos Priftakis ◽  
M. Ishaq Bhatti
Keyword(s):  
Middle East ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Predictive Ability ◽  
Oil Prices ◽  
Unobserved Components ◽  
Market Prices ◽  
Share Prices ◽  
Unobserved Components Model

There are several hypotheses suggesting that some properties of oil prices make it interesting to focus on the predictive ability of oil prices for stock returns. This paper reviews some models recently used in the literature and selects the most suitable one for measuring the relationships and/or linkages of oil prices to the stock markets of the selected five oil producing countries in the Middle East. In particular, the paper uses two methodologies to test for the presence of a cointegrating relationship between the two variables and an unobserved components model to find a relationship between the two variables. The results rejects convincingly that there is no linkage between the prices of oil and the stock market prices in these oil-based economies.  

scholarly journals Inflation and the steeplechase between economic activity variables: evidence for G7 countries

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Jaromír Baxa ◽  
Miroslav Plašil ◽  
Bořek Vašíček
Keyword(s):  
Economic Activity ◽  
Phillips Curve ◽  
Model Averaging ◽  
Real Economy ◽  
G7 Countries ◽  
Real Activity ◽  
The Great Recession ◽  
The Us ◽  
Activity Measures ◽  
Dynamic Model Averaging

AbstractA sharp increase in unemployment accompanied by a relatively muted response of inflation during the Great Recession and a consecutive inflationless recovery cast further doubts on the very existence of the Phillips curve as a systemic relation between real activity and inflation. With the aid of dynamic model averaging, this paper aims to highlight that this relation resurfaces if (i) inflationary pressures are captured by a richer set of real activity measures, and (ii) one accounts for the existence of a non-linear response of inflation to the driving variable. Based on data for the US and other G7 countries, our results show that the relation between economic activity and inflation is quite sturdy when one allows for more complex assessment of the former. We find that measures of economic activity describe inflation developments to a varying degree across time and space. This can blur the picture of inflation–real economy comovements in models where only a single variable of economic activity is considered. The output gap is often outperformed by unemployment-related variables. Our results also confirm a weakening of the inflation–activity relationship (i.e. a flattening of the Phillips curve) in the last decade that is robust both across activity measures and across countries.

scholarly journals OUTPUT FLUCTUATIONS IN THE G-7: AN UNOBSERVED COMPONENTS APPROACH

2011 ◽  
Vol 16 (3) ◽  
pp. 396-422 ◽  
Author(s):  
Sinchan Mitra ◽  
Tara M. Sinclair
Keyword(s):  
Structural Breaks ◽  
Previous Literature ◽  
Unobserved Components ◽  
The Real ◽  
Real Gdp ◽  
Output Fluctuations ◽  
Transitory Shocks ◽  
Unobserved Components Model ◽  
Explicit Correlation

This paper proposes a multivariate unobserved-components model to simultaneously decompose the real GDP for each of the G-7 countries into its respective trend and cycle components. In contrast to previous literature, our model allows for explicit correlation between all the contemporaneous trend and cycle shocks. We find that all the G-7 countries have highly variable stochastic permanent components for output, even once we allow for structural breaks. We also find that common restrictions on the correlations between trend and cycle shocks are rejected by the data. In particular, we find that correlations across permanent and transitory shocks are important both within and across countries.

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