Is it one break or ongoing permanent shocks that explains U.S. real GDP?

Luo, Sukun; Startz, Richard

doi:10.1016/j.jmoneco.2014.04.016

Cited by 42 publications

(31 citation statements)

References 12 publications

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“…First, allowing for a nonzero correlation between the permanent and transitory shocks substantially improves model fit. This is in line with the finding in Morley, Nelson, and Zivot () who report an estimate of −0.9 for the correlation parameter, but in contrast to the posterior mean of 0.18 reported in Luo and Startz () under their preferred model. Second, the correlated unobserved components model of Morley, Nelson, and Zivot () dominates any deterministic trend models with or without a break in trend growth.…”

supporting

confidence: 92%

“…Consequently, there are notable differences between the results of the two approaches. The second is Luo and Startz (), who compare three models using Bayesian methods: the model of Morley, Nelson, and Zivot () with or without a break and a model with an unknown break date. Their preferred model is the model of Morley, Nelson, and Zivot () with a break at 2006Q1.…”

mentioning

confidence: 99%

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A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output

Grant¹,

Chan²

2017

J of Money Credit Banking

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We compare a number of widely used trend‐cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions—different decompositions have broad implications for the relative importance of real versus nominal shocks in explaining variations in output. Using U.S. quarterly real GDP, we find that the overall best model is an unobserved components model with two features: (i) a nonzero correlation between trend and cycle innovations and (ii) a break in trend output growth in 2007. The annualized trend output growth decreases from about 3.4% to 1.2%–1.5% after the break. The results also indicate that real shocks are more important than nominal shocks. The slowdown in trend output growth is robust when we expand the set of models to include bivariate unobserved components models.

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supporting

confidence: 92%

mentioning

confidence: 99%

A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output

Grant¹,

Chan²

2017

J of Money Credit Banking

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“…1 This permits sudden changes in the slope occurring occasionally at dates that need not be pre-specified but which are the outcome of the smoothed trend estimate. Notably, Luo and Startz (2013) recently confirmed Perron and Wada's (2009) finding using a Bayesian methodology. While a number of previous studies have considered allowing for a change in the slope of the trend within the context of UC models, e.g., Mitra and Sinclair (2012) for the G7 countries, in our view allowing for the possibility of changes in only the slope of the trend 1 function is insufficient.…”

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confidence: 69%

Measuring business cycles with structural breaks and outliers: Applications to international data

Perron

Wada

2016

Research in Economics

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This paper first generalizes the trend-cycle decomposition framework of Perron and Wada (2009) based on unobserved components models with innovations having a mixture of normals distribution, which is able to handle sudden level and slope changes to the trend function as well as outliers. We investigate how important are the differences in the implied trend and cycle compared to the popular decomposition based on the Hodrick and Prescott (HP) (1997) filter. Our results show important qualitative and quantitative differences in the implied cycles for both real GDP and consumption series for the G7 countries. Most of the differences can be ascribed to the fact that the HP filter does not handle well slope changes, level shifts and outliers, while our method does so. Then, we reassess how such different cycles affect some socalled "stylized facts" about the relative variability of consumption and output across countries.JEL Classification: C22, E32.

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“…In related research, provides evidence for breaks in labor productivity in 1973: Q2, 1995:Q3, and 2003:Q1, and links the latter two to developments in the IT sector. From a Bayesian perspective, Luo and Startz (2014) calculate the posterior probability of a single break and find the most likely break date to be 2006:Q1 for the full postwar sample and 1973:Q1 for a 7 The same result holds for an AR(2) specification. In both cases, stability of the autoregressive coe cients cannot be rejected, whereas stability of the variance is rejected at the 1%-level.…”

mentioning

confidence: 77%

Tracking the Slowdown in Long-Run GDP Growth

Antolín-Díaz

Drechsel

Petrella

2017

The Review of Economics and Statistics

121

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Using a dynamic factor model that allows for changes in both the longrun growth rate of output and the volatility of business cycles, we document a significant decline in long-run output growth in the United States. Our evidence supports the view that most of this slowdown occurred prior to the Great Recession. We show how to use the model to decompose changes in long-run growth into its underlying drivers. At low frequencies, a decline in the growth rate of labor productivity appears to be behind the recent slowdown in GDP growth for both the US and other advanced economies. When applied to realtime data, the proposed model is capable of detecting shifts in long-run growth in a timely and reliable manner.

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Is it one break or ongoing permanent shocks that explains U.S. real GDP?

Cited by 42 publications

References 12 publications

A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output

A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output

Measuring business cycles with structural breaks and outliers: Applications to international data

Tracking the Slowdown in Long-Run GDP Growth

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