A Tax-Based Estimate of the Elasticity of Intertemporal Substitution

By Jonathan Gruber. 2013

231 citations recorded by the source corpus.

Subfields: Accounting, Economics and Econometrics, Gender Studies

View Paper: https://www.semanticscholar.org/paper/c4228031d2600616d5beb31f4962a04c6c17f848

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Citations in the corpus

Uncertainty Shocks and Balance Sheet Recessions (2017, background): “Gruber (2013) estimates an EIS of 2 on the basis of variation across individuals in the capital income tax rate, while Mulligan (2002) finds an EIS considerably higher.”
A Model of the Consumption Response to Fiscal Stimulus Payments (2011, background): “To deal with endogeneity, Gruber (2006) uses cross-individual differences in after-tax real interest rates that derive from arguably exogenous differences in capital income tax rates and estimates an elasticity around 2.”
How Far are We from the Slippery Slope? The Laffer Curve Revisited (2009, unknown): “R̃t = (1− τ̃ t )(θ ỹt k̃t−1 − δ) + 1 (23)”
Uninsured Idiosyncratic Investment Risk (2005, unknown): “This strategy, which is employed by Mulligan (2002) for macro data and by Gruber (2005) for micro data, delivers consistently high point estimates—indeed closer to 2.”
Cross-Country Heterogeneity in Intertemporal Substitution (2015, background): “Gruber (2006) stresses that studies using micro data should include year fixed effects for the identification to come from cross-sectional variation and not from time series variation correlated with consumption.”
Fiscal Stimulus in a Monetary Union: Evidence from U.S. Regions (2014, unknown): “Hall (1988) estimates the IES to be close to zero, while Bansal and Yaron (2004), Gruber (2006) and Nakamura, et al. (2011) argue for values above 1.”
MEASURING INTERTEMPORAL SUBSTITUTION: THE IMPORTANCE OF METHOD CHOICES AND SELECTIVE REPORTING (2015, background): “Gruber (2006) notes that in the latter group of studies the identi cation of the elasticity comes from time series variation correlated with consumption, which may lead to a downward bias.”
Growth-Rate and Uncertainty Shocks in Consumption: Cross-Country Evidence (2012, unknown): “However, Hansen and Jagannathan (1991) show that σ(Mt)R f t ≥ E(Re t )/σ(Re t ), where Mt is the level of the stochastic discount factor and R e is the excess return on the stock market.”
Can Structural Reforms Help Europe (2013, unknown): “Although several authors provide evidence in support of our calibration (Hansen and Singleton, 1983; Summers, 1984; Attanasio and Weber, 1989; Rotemberg and Woodford, 1997; Gruber, 2006), other macroeconomists would consider such a value for σ as a low estimate for this parameter (Hall, 1988).”
Explaining International Fertility Differences (2009, background): “Gruber (2006) discusses the biases associated with the different estimation strategies.”
Credit Risk and Disaster Risk (2013, unknown): “Most direct estimates using aggregate data nd low numbers (e.g. Hall (1988)), but this view has been challenged by several authors (see among others Bansal and Yaron (2004), Gruber (2006), Mulligan (2004), Vissing-Jorgensen (2002)).”
An Empirical Evaluation of the Long-Run Risks Model for Asset Prices (2009, background): “…and Weber (1989), Beaudry and van Wincoop (1996), Vissing-Jorgensen (2002), Attanasio and Vissing-Jorgensen (2003), Mulligan (2004), Gruber (2006), Guvenen (2006), Hansen, Heaton, Lee, and Roussanov (2007), Engegelhardt and Kumar (2008)) show that the IES is large and indeed greater than one.”
Estimating the Elasticity of Intertemporal Substitution Using Mortgage Notches (2018, methodology): “…in Jappelli & Pistaferri (2007) to elasticities of about 1 in Dunsky & Follain (2000) and 1.5-3.5 in Follain & Dunsky 1A methodological exception is Gruber (2013) who uses cross-sectional and time series variation in capital income tax rates to identify the EIS and obtains very large estimates of…”
Expectation Shocks and Learning as Drivers of the Business Cycle (2011, unknown): “There is large uncertainty on the value of the intertemporal elasticity of substitution coefficient σ. Values used in macro studies, as well as estimates from micro data, range from values very close to 0 to values substantially above 1 (e.g., Hall, 1988, Gruber, 2006, Woodford, 2003).”