Measuring the business cycle effects of permanent and transitory shocks in cointegrated time series

Centoni, Marco; Cubadda, Gianluca

doi:10.1016/s0165-1765(03)00060-0

Cited by 30 publications

(24 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, over both short and long time horizons, we find supply shocks explain the bulk of the variations in per capita incomes for the G7 economies, implying the importance of productivity shocks. Our findings, while consistent with those of Ahmed et al (1993) are in sharp contrast to Keating and Nye (1999), Gavosto and Pellegrini (1999), Centoni and Cubadda (2003) and Hartley and Walsh (2003). This difference in results could be due to our methodological innovation.…”

Section: Discussionsupporting

confidence: 90%

“…However, in the case of Japan, they found most of the variations in output for short time horizons were due to supply shocks. Centoni and Cubadda (2003) examine the relative importance of permanent and transitory shocks on the US business cycles by modelling per capita gross domestic product (GDP), per capita investment and per capita consumption in a multivariate framework over the period January 1974 through April 2001. They find that demand shocks explain the bulk of the variations in GDP (82%) and investment (86%), but supply shocks explain the bulk of the variations (57%) in consumption.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Common Trends and Common Cycles in Per Capita GDP: The Case of the G7 Countries, 1870–2001

Narayan

2008

Int Adv Econ Res

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Common Trends and Common Cycles in Per Capita GDP: The Case of the G7 Countries, 1870–2001

Narayan

2008

Int Adv Econ Res

View full text Add to dashboard Cite

show abstract

“…We can then measure the role each of these plays. We use the permanent‐transitory decomposition of Centoni and Cubadda (2003) 14 . Given this decomposition, we can calculate the fraction of the total variance of the forecast error at horizon h , which is attributable to the permanent component (and the fraction attributable to transitory shocks is one minus this) 15 .…”

Section: Resultsmentioning

confidence: 99%

Re‐Examining the Consumption–Wealth Relationship: The Role of Model Uncertainty

Koop¹,

Potter²,

Strachan³

2008

J of Money Credit Banking

View full text Add to dashboard Cite

This paper discusses the consumption-wealth relationship. Following the recent in ‡uential work of Lettau and Ludvigson [e.g. Lettau and Ludvigson (2001), (2004)], we use data on consumption, assets and labor income and a vector error correction framework. Key …ndings of their work are that consumption does respond to permanent changes in wealth in the expected manner, but that most changes in wealth are transitory and have no e¤ect on consumption. We investigate the robustness of these results to model uncertainty and argue for the use of Bayesian model averaging. We …nd that there is model uncertainty with regards to the number of cointegrating vectors, the form of deterministic components, lag length and whether the cointegrating residuals a¤ect consumption and income directly. Whether this uncertainty has important empirical implications depends on the researcher's attitude towards the economic theory used by Lettau and Ludvigson. If we work with their model, our …ndings are very similar to theirs. However, if we work with a broader set of models and let the data speak, we obtain somewhat di¤erent results. In the latter case, we …nd that the exact magnitude of the role of permanent shocks is hard to estimate precisely. Thus, although some support exists for the view that their role is small, we cannot rule out the possibility that they have a substantive role to play.

show abstract

“…This suggests the evidence in support of stationary real interest rates has gradually strengthened over time, while the evidence that unemployment is stationary has weakened. Next, we compute the permanent-transitory variance decomposition for in ‡ation using the approach of Centoni and Cubadda (2003). This variance decomposition is a function of the parameters in the VECM.…”

Section: Bayesian Inference In the Time Varying Cointegration Modelmentioning

confidence: 99%

Bayesian inference in a time varying cointegration model

Koop

Leon-Gonzalez

Strachan

2011

Journal of Econometrics

View full text Add to dashboard Cite

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a speci…cation which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for in ‡ation.

show abstract

Measuring the business cycle effects of permanent and transitory shocks in cointegrated time series

Cited by 30 publications

References 7 publications

Common Trends and Common Cycles in Per Capita GDP: The Case of the G7 Countries, 1870–2001

Common Trends and Common Cycles in Per Capita GDP: The Case of the G7 Countries, 1870–2001

Re‐Examining the Consumption–Wealth Relationship: The Role of Model Uncertainty

Bayesian inference in a time varying cointegration model

Contact Info

Product

Resources

About