Explore the vast range of titles available.

This paper shows that the consumption-based capital asset pricing model (C-CAPM) with low-probability disaster risk rationalizes pricing errors. We find that implausible estimates of risk aversion and time preference are not puzzling if market participants expect a future catastrophic change in fundamentals, which just happens not to occur in the sample (a \"peso problem\"). A bias in structural parameter estimates emerges as a result of pricing errors in quiet times. While the bias essentially removes the pricing error in the simple models when risk-free rates are constant, time-variation may also generate large and persistent estimated pricing errors in simulated data. We also show analytically how the problem of biased estimates can be avoided in empirical research by resolving the misspecification in moment conditions.

A model of growth with endogenous innovation and distortionary taxes is presented. Since innovation is the only source of volatility, any variable that influences innovation directly affects volatility and growth. This joint endogeneity is illustrated by working out the effects through which economies with different tax levels differ in their volatility and growth process. We obtain analytical measures of macro volatility based on cyclical output and on output growth rates for plausible parametric restrictions. This analysis implies that controls for taxes should be included in the standard growth-volatility regressions. Our estimates show that the conventional Ramey-Ramey coefficient is affected sizeably. In addition, tax levels do indeed appear to affect volatility in our empirical application.

This paper explores the ability of the New-Keynesian (NK) model to explain the recent periods of quiet and stable inflation at near-zero nominal interest rates. We show how (conventional and unconventional) monetary policy shocks enlarge the ability to explain the facts, such that the theory supports both a negative and a positive response of inflation. Central to our finding is that monetary policy shocks may have temporary and/or permanent components. We find that the NK model can explain the recent episodes, even if one considers an active role of monetary policy and restrict ourselves to the regions of (local) determinacy. We also show that a new global solution, capturing highly nonlinear dynamics, is necessary to generate a prolonged period of near-zero interest rates as a policy choice.

This paper explores the ability of the New-Keynesian (NK) model to explain the recent periods of quiet and stable inflation at near-zero nominal interest rates. We show how (conventional and unconventional) monetary policy shocks enlarge the ability to explain the facts, such that the theory supports both a negative and a positive response of inflation. Central to our finding is that monetary policy shocks may have temporary and/or permanent components. We find that the NK model can explain the recent episodes, even if one considers an active role of monetary policy and restrict ourselves to the regions of (local) determinacy. We also show that a new global solution, capturing highly nonlinear dynamics, is necessary to generate a prolonged period of near-zero interest rates as a policy choice.

In this paper, we revisit the fiscal theory of the price level (FTPL) within the New Keynesian (NK) model. We show in which cases the average maturity of government debt matters for the transmission of policy shocks. The central task of this paper is to shed light on the theoretical predictions of the maturity structure on macro dynamics with an emphasis on model-implied expectations. In particular, we address the transmission channels of monetary and fiscal policy shocks on the interest rate and inflation dynamics. Our results illustrate the role of the maturity of existing debt in the wake of skyrocketing debt-to-GDP ratios and increasing government expenditures. We highlight our results by quantifying the effects of the large-scale US fiscal packages (CARES) and predict a surge in inflation if the deficits are not sufficiently backed by future surpluses.

This paper shows that the consumption-based capital asset pricing model (C-CAPM) with low-probability disaster risk rationalizes pricing errors. We find that implausible estimates of risk aversion and time preference are not puzzling if market participants expect a future catastrophic change in fundamentals, which just happens not to occur in the sample (a 'peso problem'). A bias in structural parameter estimates emerges as a result of pricing errors in quiet times. While the bias essentially removes the pricing error in the simple models when risk-free rates are constant, time-variation may also generate large and persistent estimated pricing errors in simulated data. We also show analytically how the problem of biased estimates can be avoided in empirical research by resolving the misspecification in moment conditions.

This paper shows that non-linearities imposed by a neoclassical production function alone can generate time-varying and asymmetric risk premia over the business cycle. These (empirical) key features become relevant, and asset market implications improve substantially when we allow for non-normalities in the form of rare disasters. We employ analytical solutions of dynamic stochastic general equilibrium models, including a novel solution with endogenous labor supply, to obtain closed-form expressions for the risk premium in production economies. In contrast to endowment economies, the curvature of the policy functions affects the risk premium through controlling the individual's effective risk aversion.

In this paper we use the property that certainty equivalence, as implied by a first-order approximation to the solution of stochastic discrete-time models, breaks in its equivalent continuous-time version. We study the extent to which a first-order approximated solution built by perturbation methods accounts for risk. We show that risk matters economically in a real business cycle (RBC) model with habit formation and capital adjustment costs and that neglecting risk leads to substantial pricing errors. A first-order approximation in continuous time reduces pricing errors by 90 percent relative to the certainty equivalent linear solution.

We study the statistical properties of heterogeneous agent models. Using aBewley-Hugget-Aiyagari model we compute the density function of wealth and in-come and use it for likelihood inference. We study the finite sample properties of themaximum likelihood estimator (MLE) using Monte Carlo experiments on artificialcross-sections of wealth and income. We propose to use the Kullback-Leibler diver-gence to investigate identification problems that may affect inference. Our resultssuggest that the unrestricted MLE leads to considerable biases of some parameters.Calibrating weakly identified parameters allows to pin down the other unidentifiedparameter without compromising the estimation of the remaining parameters. Weillustrate our approach by estimating the model for the U.S. economy using wealthand income data from the Survey of Consumer Finances.

We study the statistical properties of heterogeneous agent models. Using a Bewley-Hugget-Aiyagari model we compute the density function of wealth and income and use it for likelihood inference. We study the finite sample properties of the maximum likelihood estimator (MLE) using Monte Carlo experiments on artificial cross-sections of wealth and income. We propose to use the Kullback-Leibler divergence to investigate identification problems that may affect inference. Our results suggest that the unrestricted MLE leads to considerable biases of some parameters. Calibrating weakly identified parameters allows to pin down the other unidentified parameter without compromising the estimation of the remaining parameters. We illustrate our approach by estimating the model for the U.S. economy using wealth and income data from the Survey of Consumer Finances.