nep-ets 2009-05-02 papers

on Econometric Time Series

Issue of 2009‒05‒02
three papers chosen by
Yong Yin
SUNY at Buffalo

Bayesian Model Selection in the Analysis of Cointegration By Justyna Wróblewska
Pitfalls in Estimating Asymmetric Effects of Energy Price Shocks By Kilian, Lutz; Vigfusson, Robert J.
Optimal Dimension of Transition Probability Matrices for Markov Chain Bootstrapping By Roy Cerqueti; Paolo Falbo; Cristian Pelizzari

Bayesian Model Selection in the Analysis of Cointegration

By:	Justyna Wróblewska (Cracow University of Economics)
Abstract:	In this paper we present the Bayesian model selection procedure within the class of cointegrated processes. In order to make inference about the cointegration space we use the class of Matrix Angular Central Gaussian distributions. To carry out posterior simulations we use an alorithm based on the collapsed Gibbs sampler. The presented methods are applied to the analysis of the price - wage mechanism in the Polish economy.
Keywords:	cointegration, Bayesian analysis, Grassmann manifold, Stiefel manifold, posterior probability
JEL:	C11 C32 C52
Date:	2009–03–22
URL:	http://d.repec.org/n?u=RePEc:wse:wpaper:32&r=ets

Pitfalls in Estimating Asymmetric Effects of Energy Price Shocks

By:	Kilian, Lutz; Vigfusson, Robert J.
Abstract:	A common view in the literature is that the effect of energy price shocks on macroeconomic aggregates is asymmetric in energy price increases and decreases. We show that widely used asymmetric vector autoregressive models of the transmission of energy price shocks are misspecified, resulting in inconsistent parameter estimates, and that the implied impulse responses have been routinely computed incorrectly. As a result, the quantitative importance of unanticipated energy price increases for the U.S. economy has been exaggerated. In response to this problem, we develop alternative regression models and methods of computing responses to energy price shocks that yield consistent estimates regardless of the degree of asymmetry. We also introduce improved tests of the null hypothesis of symmetry in the responses to energy price increases and decreases. An empirical study reveals little evidence against the null hypothesis of symmetry in the responses to energy price shocks. Our analysis also has direct implications for the theoretical literature on the transmission of energy price shocks and for the debate about policy responses to energy price shocks.
Keywords:	Asymmetry; Energy price; Impulse response; Net increase; Oil price; Propagation; Shock; Transmission; Vector autoregression
JEL:	C32 E37 Q43
Date:	2009–04
URL:	http://d.repec.org/n?u=RePEc:cpr:ceprdp:7284&r=ets

Optimal Dimension of Transition Probability Matrices for Markov Chain Bootstrapping

By:	Roy Cerqueti (Univesity of Macerata); Paolo Falbo (University of Brescia); Cristian Pelizzari (University of Brescia)
Abstract:	<p> </p><p align="left"><font size="1">While the large portion of the literature on Markov chain (possibly of order<br />higher than one) bootstrap methods has focused on the correct estimation of<br />the transition probabilities, little or no attention has been devoted to the<br />problem of estimating the dimension of the transition probability matrix.<br />Indeed, it is usual to assume that the Markov chain has a one-step memory<br />property and that the state space could not to be clustered, and coincides<br />with the distinct observed values. In this paper we question the opportunity<br />of such a standard approach.<br />In particular we advance a method to jointly estimate the order of the Markov<br />chain and identify a suitable clustering of the states. Indeed in several real<br />life applications the "memory" of many<br />processes extends well over the last observation; in those cases a correct<br />representation of past trajectories requires a significantly richer set than<br />the state space. On the contrary it can sometimes happen that some distinct<br />values do not correspond to really "different<br />states of a process; this is a common conclusion whenever,<br />for example, a process assuming two distinct values in t is not affected in<br />its distribution in t+1. Such a situation would suggest to reduce the<br />dimension of the transition probability matrix.<br />Our methods are based on solving two optimization problems. More specifically<br />we consider two competing objectives that a researcher will in general pursue<br />when dealing with bootstrapping: preserving the similarity between the<br />observed and the bootstrap series and reducing the probabilities of getting a<br />perfect replication of the original sample. A brief axiomatic discussion is<br />developed to define the desirable properties for such optimal criteria. Two<br />numerical examples are presented to illustrate the method.</font></p><p align="left"> </p>
Keywords:	order of Markov chains,similarity of time series,transition probability matrices,multiplicity of time series,partition of states of Markov chains,Markov chains,bootstrap methods
JEL:	C14 C15 C61
Date:	2009–04
URL:	http://d.repec.org/n?u=RePEc:mcr:wpdief:wpaper00053&r=ets

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