Quantitative Economics January 2019 is now online

TABLE OF CONTENTS, January 2019, Volume 10, Issue 1
Full Issue

Articles
Abstracts follow the listing of articles.

Uncertainty quantification and global sensitivity analysis for economic models
Daniel Harenberg, Stefano Marelli, Bruno Sudret, Viktor Winschel

Strong convergence and dynamic economic models
Robert L. Bray

Inference in dynamic discrete choice problems under local misspecification
Federico A. Bugni, Takuya Ura

Partial identification by extending subdistributions
Alexander Torgovitsky

Bayesian inference on structural impulse response functions
Mikkel Plagborg‐Møller

All over the map: A worldwide comparison of risk preferences
Olivier l'Haridon, Ferdinand M. Vieider

Eliciting risk preferences using choice lists
David J. Freeman, Yoram Halevy, Terri Kneeland

Incidence, salience, and spillovers: The direct and indirect effects of tax credits on wages
Ghazala Azmat

Hurdles and steps: Estimating demand for solar photovoltaics
Kenneth Gillingham, Tsvetan Tsvetanov

Recursive allocations and wealth distribution with multiple goods: Existence, survivorship, and dynamics
R. Colacito, M. M. Croce, Zhao Liu

Monetary policy switching and indeterminacy
Jean Barthélemy, Magali Marx

Discretionary monetary policy in the Calvo model
Willem Van Zandweghe, Alexander L. Wolman

Uncertainty quantification and global sensitivity analysis for economic models
Daniel Harenberg, Stefano Marelli, Bruno Sudret, Viktor Winschel

Abstract
We present a global sensitivity analysis that quantifies the impact of parameter uncertainty on model outcomes. Specifically, we propose variance‐decomposition‐based Sobol' indices to establish an importance ranking of parameters and univariate effects to determine the direction of their impact. We employ the state‐of‐the‐art approach of constructing a polynomial chaos expansion of the model, from which Sobol' indices and univariate effects are then obtained analytically, using only a limited number of model evaluations. We apply this analysis to several quantities of interest of a standard real‐business‐cycle model and compare it to traditional local sensitivity analysis approaches. The results show that local sensitivity analysis can be very misleading, whereas the proposed method accurately and efficiently ranks all parameters according to importance, identifying interactions and nonlinearities. Computational techniques uncertainty quantification sensitivity analysis polynomial chaos expansion C60 C63
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Strong convergence and dynamic economic models
Robert L. Bray

Abstract
Morton and Wecker (1977) stated that the value iteration algorithm solves a dynamic program's policy function faster than its value function when the limiting Markov chain is ergodic. I show that their proof is incomplete, and provide a new proof of this classic result. I use this result to accelerate the estimation of Markov decision processes and the solution of Markov perfect equilibria. Markov decision process Markov perfect equilibrium strong convergence relative value iteration dynamic discrete choice nested fixed point nested pseudo‐likelihood C01 C13 C15 C61 C63 C65
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Inference in dynamic discrete choice problems under local misspecification
Federico A. Bugni, Takuya Ura

Abstract
Single‐agent dynamic discrete choice models are typically estimated using heavily parametrized econometric frameworks, making them susceptible to model misspecification. This paper investigates how misspecification affects the results of inference in these models. Specifically, we consider a local misspecification framework in which specification errors are assumed to vanish at an arbitrary and unknown rate with the sample size. Relative to global misspecification, the local misspecification analysis has two important advantages. First, it yields tractable and general results. Second, it allows us to focus on parameters with structural interpretation, instead of “pseudo‐true” parameters.We consider a general class of two‐step estimators based on the K‐stage sequential policy function iteration algorithm, where K denotes the number of iterations employed in the estimation. This class includes Hotz and Miller (1993)'s conditional choice probability estimator, Aguirregabiria and Mira (2002)'s pseudo‐likelihood estimator, and Pesendorfer and Schmidt‐Dengler (2008)'s asymptotic least squares estimator.We show that local misspecification can affect the asymptotic distribution and even the rate of convergence of these estimators. In principle, one might expect that the effect of the local misspecification could change with the number of iterations K. One of our main findings is that this is not the case, that is, the effect of local misspecification is invariant to K. In practice, this means that researchers cannot eliminate or even alleviate problems of model misspecification by choosing K. Single‐agent dynamic discrete choice models estimation inference misspecification local misspecification C13 C61 C73
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Partial identification by extending subdistributions
Alexander Torgovitsky

Abstract
I show that sharp identified sets in a large class of econometric models can be characterized by solving linear systems of equations. These linear systems determine whether, for a given value of a parameter of interest, there exists an admissible joint distribution of unobservables that can generate the distribution of the observed variables. The joint distribution of unobservables is not required to satisfy any parametric restrictions, but can (if desired) be assumed to satisfy a variety of location, shape, and/or conditional independence restrictions. To prove sharpness of the characterization, I generalize a classic result in copula theory concerning the extendibility of subcopulas to show that related objects—termed subdistributions—can be extended to proper distribution functions. I describe this characterization argument as partial identification by extending subdistributions, or PIES. One particularly attractive feature of PIES is that it focuses directly on the sharp identified set for a parameter of interest, such as an average treatment effect, without needing to construct the identified set for the entire model. I apply PIES to univariate and bivariate binary response models. A notable product of the analysis is a method for characterizing the sharp identified set for the average treatment effect in Manski's (1975, 1985, 1988) semiparametric binary response model. Partial identification maximum score bivariate probit copulas linear programming discrete choice semiparametric endogeneity C14 C20 C51
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Bayesian inference on structural impulse response functions
Mikkel Plagborg‐Møller

Abstract
I propose to estimate structural impulse responses from macroeconomic time series by doing Bayesian inference on the Structural Vector Moving Average representation of the data. This approach has two advantages over Structural Vector Autoregressions. First, it imposes prior information directly on the impulse responses in a flexible and transparent manner. Second, it can handle noninvertible impulse response functions, which are often encountered in applications. Rapid simulation of the posterior distribution of the impulse responses is possible using an algorithm that exploits the Whittle likelihood. The impulse responses are partially identified, and I derive the frequentist asymptotics of the Bayesian procedure to show which features of the prior information are updated by the data. The procedure is used to estimate the effects of technological news shocks on the U.S. business cycle. Bayesian inference Hamiltonian Monte Carlo impulse response function news shock nonfundamental noninvertible partial identification structural vector autoregression structural vector moving average Whittle likelihood C11 C32
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All over the map: A worldwide comparison of risk preferences
Olivier l'Haridon, Ferdinand M. Vieider

Abstract
We obtain rich measurements of risk preferences for 2939 subjects across 30 countries, and use the data to paint a picture of the distribution of risk preferences across the globe using structural equation models. Reference‐dependence and likelihood‐dependence are found to be important everywhere. Model parameters in non‐Western countries differ systematically from those in Western countries, with poorer countries substantially more risk tolerant than rich countries on average. We qualify previous findings on gender effects and cognitive ability by showing how they mainly impact likelihood‐dependence. We further add novel evidence on the correlation between risk preferences and study major. Whereas we confirm previous results on observable characteristics of subjects explaining little of overall preference heterogeneity, a few macroeconomic indicators can explain a considerable part of the between‐country heterogeneity. Risk preferences cultural comparison prospect theory C93 D03 D80 O12
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Eliciting risk preferences using choice lists
David J. Freeman, Yoram Halevy, Terri Kneeland

Abstract
We study the effect of embedding pairwise choices between lotteries within a choice list on measured risk attitude. Using an experiment with online workers, we find that subjects choose the risky lottery rather than a sure payment significantly more often when responding to a choice list. This behavior can be rationalized by the interaction between nonexpected utility and the random incentive system, as suggested by Karni and Safra (1987). Random incentive system isolation independence axiom multiple price list reduction of compound lotteries preference reversals certainty effect C90 C91 D81 D90
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Incidence, salience, and spillovers: The direct and indirect effects of tax credits on wages
Ghazala Azmat

Abstract
Tax credits are a popular way to alleviate in‐work poverty. A common empirical assumption is that the benefit of the tax credit is borne solely by the claimant workers. However, economic theory suggests no particular reason why this should be the case. This paper investigates the impact of the Working Families' Tax Credit, introduced in the UK in 1999, on wages. Unlike similar tax credit policies, this tax credit was paid through employers rather than directly to workers, making it more salient to the employer. Using a novel identification strategy, we can separately identify the effect on wages associated with an increase in the amount of tax credit and that associated with the change in salience. We find evidence that: (1) through the salience mechanism the firm cuts the wage of claimant workers relative to similarly skilled nonclaimants by 30 percent of the tax credit, which is approximately 7 percent of the wage, and (2) there is a negative spillover effect onto the wages of claimant and nonclaimant workers of 1.7 percent, which is approximately 8 percent of the tax credit for claimant workers. Wages tax credits incidence salience I38 J30 H22 H23
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Hurdles and steps: Estimating demand for solar photovoltaics
Kenneth Gillingham, Tsvetan Tsvetanov

Abstract
This paper estimates demand for residential solar photovoltaic (PV) systems using a new approach to address three empirical challenges that often arise with count data: excess zeros, unobserved heterogeneity, and endogeneity of price. Our results imply a price elasticity of demand for solar PV systems of −0.65. Counterfactual policy simulations indicate that reducing state financial incentives in half would have led to 9% fewer new installations in Connecticut in 2014. Calculations suggest a subsidy program cost of $364/tCO2 assuming solar displaces natural gas. Our Poisson hurdle approach holds promise for modeling the demand for many new technologies. Count data hurdle model fixed effects instrumental variables Poisson energy policy C33 C36 Q42 Q48
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Recursive allocations and wealth distribution with multiple goods: Existence, survivorship, and dynamics
R. Colacito, M. M. Croce, Zhao Liu

Abstract
We characterize the equilibrium of a complete markets economy with multiple agents featuring a preference for the timing of the resolution of uncertainty. Utilities are defined over an aggregate of two goods. We provide conditions under which the solution of the planner's problem exists, and it features a nondegenerate invariant distribution of Pareto weights. We also show that perturbation methods replicate the salient features of our recursive risk‐sharing scheme, provided that higher‐order terms are included. Recursive preferences multiple agents equilibrium C62 F37
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Monetary policy switching and indeterminacy
Jean Barthélemy, Magali Marx

Abstract
This paper determines conditions for the existence of a unique rational expectations equilibrium—determinacy—in a monetary policy switching economy. We depart from the existing literature by providing such conditions considering all bounded equilibria. We then apply these conditions to a new Keynesian model with switching Taylor rules. First, deviation from the Taylor principle in one regime does not necessarily cause indeterminacy. Second, very different responses to inflation may trigger indeterminacy even if both regimes satisfy the Taylor principle. Determinacy thus results from the adequacy between monetary regimes rather than the determinacy of each of them taken in isolation. Markov‐switching indeterminacy monetary policy E31 E43 E52
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Discretionary monetary policy in the Calvo model
Willem Van Zandweghe, Alexander L. Wolman

Abstract
We study discretionary equilibrium in the Calvo pricing model for a monetary authority that chooses the money supply, producing three main contributions. First, price‐adjusting firms have a unique equilibrium price for a broad range of parameterizations, in contrast to earlier results for the Taylor pricing model. Second, a generalized Euler equation makes transparent how the monetary authority affects future welfare through its influence on the future state of the economy. Third, we provide global solutions, including welfare analysis, for the transitional dynamics that occur if the monetary authority gains or loses the ability to commit. Time‐consistent optimal monetary policy discretion Markov‐perfect equilibrium sticky prices relative price distortion E31 E52

Publication Date: 
Wednesday, February 13, 2019

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