Technological Revolutions and the Three Great Slumps: A Medium-Run Analysis

Cao, Dân; L’Huillier, Jean-Paul

doi:10.2139/ssrn.2871324

Cited by 14 publications

(16 citation statements)

References 26 publications

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“…As a …nal exercise, we produce the power spectra from the wavelet decomposition for real GDP growth, unemployment, and the federal funds rate in Figures 7-9. 14 The wavelet-based spectra are akin to classical spectra in that they decompose a time series into frequencies and measure the contribution of each frequency to the overall behavior of a time series. 15 1 2 This interpretation is consistent both with the learning and inherent in ‡ation persistence story in Primiceri (2006) or Sargent, Williams, and Zha (2006), the in ‡ation misperception argument in Lubik and Matthes (2016), as well as a number of recent papers on evolving private sector beliefs, for instance, in Bianchi (2013). 1 3 The results for the 3-month rate are almost identical to those for the FFR.…”

Section: Baseline Resultssupporting

confidence: 63%

Assessing U.S. Aggregate Fluctuations Across Time and Frequencies

Matthes¹,

Lubik²,

Verona³

2019

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We study the behavior of key macroeconomic variables in the time and frequency domain. For this purpose, we decompose U.S. time series into various frequency components. This allows us to identify a set of stylized facts: GDP growth is largely a high-frequency phenomenon whereby in ‡ation and nominal interest rates are characterized largely by low-frequency components. In contrast, unemployment is a mediumterm phenomenon. We use these decompositions jointly in a structural VAR where we identify monetary policy shocks using a sign restriction approach. We …nd that monetary policy shocks a¤ect these key variables in a broadly similar manner across all frequency bands. Finally, we assess the ability of standard DSGE models to replicate these …ndings. While the models generally capture low-frequency movements via stochastic trends and business-cycle ‡uctuations through various frictions, they fail at capturing the medium-term cycle.

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Section: Baseline Resultssupporting

confidence: 63%

Assessing U.S. Aggregate Fluctuations Across Time and Frequencies

Matthes¹,

Lubik²,

Verona³

2019

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“…Similar theoretical results were established in Cao and L'Huillier () for a small open economy RBC model without capital and inelastic labor.…”

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confidence: 84%

Where is the GE? Consumption Dynamics in DSGEs

L’Huillier¹,

Yoo²

2018

J of Money Credit Banking

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We offer a partial equilibrium perspective on the behavior of consumption in dynamic stochastic general equilibrium (DSGE) models. We consider a benchmark dynamic general equilibrium model and show that a standard calibration implies that the real interest rate is essentially fixed. One manifestation of this feature is that, with separable preferences, the reaction of consumption to total factor productivity (TFP) shocks is flat: the randomwalk permanent income hypothesis holds almost exactly, pretty much as in a partial equilibrium consumption-savings problem. These results help explain the prominent role of aggregate demand, and how it is achieved, in modern DSGE analysis. MODERN DSGES ARE VIEWED AS the epitome of modern general equilibrium macroeconomic analysis. Complex dynamic-programming problems are solved by a number of interacting optimizing agents, leading to equilibrium relations immune to the Lucas critique. These relations balance general equilibrium (GE) forces coming from the supply-side effects of exogenous technological change and from the demand-side effects of exogenous changes in spending, together with the effects of other shocks. These forces are amplified (or moderated) by a host of frictions as nominal rigidities, collateral constraints, balance-sheet effects, etc. There

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“…There is growing recent evidence that market economies are subject to endogenous boom-bust cycles, with times of expansion "sowing the seeds" for the slump that follows (see Beaudry et al, 2017Beaudry et al, , 2020. In fact, the Great Recession, the Great Depression, and the Japanese slump of the 1990s were all preceded by periods of major technological innovation (Cao and L'Huillier, 2018), and it is easy to find similar evidence for the case of sudden-stops in emerging markets (Boz, 2009). But these episodes were not just preceded by positive technological innovation.…”

Section: Introductionmentioning

confidence: 93%

Technology Shocks and Predictable Minsky Cycles

L’Huillier

Phelan

Wieman

2021

Preprint

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Big technological improvements in a new, secondary sector lead to a period of excitement about the future prospects of the overall economy, generating boom-bust dynamics propagating through credit markets. Increased future capital prices relax collateral constraints today, leading to a boom before the realization of the shock.But reallocation of capital toward the secondary sector when the shock hits leads to a bust going forward. These cycles are perfectly foreseen in our model, making them markedly different from the typical narrative about unexpected financial shocks used to explain crises. In fact, these cycles echo Minsky's original narrative for financial cycles, according to which "financial trauma occur as normal functioning event in a capitalistic economy." (Minsky, 1980)

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Technological Revolutions and the Three Great Slumps: A Medium-Run Analysis

Cited by 14 publications

References 26 publications

Assessing U.S. Aggregate Fluctuations Across Time and Frequencies

Assessing U.S. Aggregate Fluctuations Across Time and Frequencies

Where is the GE? Consumption Dynamics in DSGEs

Technology Shocks and Predictable Minsky Cycles

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