Hours per capita and productivity: evidence from correlated unobserved components models

Basistha, Arabinda

doi:10.1002/jae.1013

Cited by 14 publications

(10 citation statements)

References 40 publications

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“…There is considerable potential for future research drawing on this paper’s accomplishments. Particularly, it seems promising to extend multivariate approaches as in Cochrane (1994), Morley (2007), Basistha (2009), or Sinclair (2009). Based on the current methodology, further time series containing valuable information on macroeconomic trends and cycles should be employed.…”

Section: Summary and Discussionmentioning

confidence: 99%

Analyzing U.S. Output and the Great Moderation by Simultaneous Unobserved Components

Weber

2011

Journal of Money, Credit and Banking

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This paper seeks to determine the causal interaction between structural trend and cycle innovations in an unobserved components framework of aggregate output. For the purpose of identification, I propose allowing for shifts in volatility. This strategy provides good estimation precision when applied to U.S. industrial production. In the early 1980s, predominance of cycle shocks gives way to strong negative spillovers of trend impulses, consistent with real business cycle theories. The coincident reduction of macroeconomic volatility was mainly caused by pronounced dampening of transitory disturbances. This is in accordance with an important role of macroeconomic policy in explaining the Great Moderation.

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Section: Summary and Discussionmentioning

confidence: 99%

Analyzing U.S. Output and the Great Moderation by Simultaneous Unobserved Components

Weber

2011

Journal of Money, Credit and Banking

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“…For convenience, the prior for λ ηy ,y was a truncated normal distributions on (−∞, 0) with mean −0.5 and variance equal to 1. This prior is based on the MLE estimates and on results of previous studies [for example, Morley et al (2003), Basistha (2009), Sinclair (2009), or Morley and Singh (2016]. This restriction is also consistent with impulse responses from studies that use VECM models.…”

Section: Appendix B: Bayesian Estimationmentioning

confidence: 64%

What Explains the Recent Jobless Recoveries?

Panovska

2016

Macroecon. Dynam.

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This paper considers a correlated unobserved-components model for output, employment, and hours in order to disentangle the causes for the last three jobless recoveries. The composition of the structural shocks during recessions and the periods immediately following recessions has changed, as have the responses of employment and hours to those shocks. Recessions before 1984 were followed by recoveries driven by positive permanent shocks to output, whereas post-1984 recessions were followed by weak recoveries in demand. Employment is more sensitive to demand shocks post-1984, leading to weak recoveries in employment. In addition, hours and employment were complements before 1984, but are treated as substitutes after 1984. Much of the initial decrease in demand is now absorbed on the intensive margin.

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“…Roberts (2001) also assumed that α = 1 but allowed for correlations between innovations. Basistha (2007Basistha ( , 2009, on the other hand, allowed α < 1; he also allowed for correlated trend-cycle innovations. 2 Stella and Stock (2016) take a different approach and specify the inflation trend as a unit root process, similar to the unemployment rate trend in the GDP-unemployment rate bivariate UC model of Section 3.2, as follows: 3…”

Section: Bivariate Uc Model: Gdp and The Inflation Ratementioning

confidence: 99%

When Can Trend-Cycle Decompositions Be Trusted?

González‐Astudillo

Roberts

2016

FEDS

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In this paper, we examine the results of GDP trend-cycle decompositions from the estimation of bivariate unobserved components models that allow for correlated trend and cycle innovations. Three competing variables are considered in the bivariate setup along with GDP: the unemployment rate, the inflation rate, and gross domestic income. We find that the unemployment rate is the best variable to accompany GDP in the bivariate setup to obtain accurate estimates of its trend-cycle correlation coefficient and the cycle. We show that the key feature of unemployment that allows for precise estimates of the cycle of GDP is that its nonstationary component is "small" relative to its cyclical component. Using quarterly GDP and unemployment rate data from 1948:Q1 to 2015:Q4, we obtain the trend-cycle decomposition of GDP and find evidence of correlated trend and cycle components and an estimated cycle that is about 2 percent below its trend at the end of the sample.

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Hours per capita and productivity: evidence from correlated unobserved components models

Cited by 14 publications

References 40 publications

Analyzing U.S. Output and the Great Moderation by Simultaneous Unobserved Components

Analyzing U.S. Output and the Great Moderation by Simultaneous Unobserved Components

What Explains the Recent Jobless Recoveries?

When Can Trend-Cycle Decompositions Be Trusted?

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