Quantitative Economics November 2018 is now online
TABLE OF CONTENTS, November 2018, Volume 9, Issue 3
Full Issue
Articles
Abstracts follow the listing of articles.
Inference for VARs identified with sign restrictions
Eleonora Granziera, Hyungsik Roger Moon, Frank Schorfheide
A method for solving and estimating heterogeneous agent macro models
Thomas Winberry
Joint analysis of the discount factor and payoff parameters in dynamic discrete choice models
Tatiana Komarova, Fabio Sanches, Daniel Silva Junior, Sorawoot Srisuma
Estimation of dynastic life‐cycle discrete choice models
George‐Levi Gayle, Limor Golan, Mehmet A. Soytas
Global identification of linearized DSGE models
Andrzej Kocięcki, Marcin Kolasa
Quasi‐Bayesian model selection
Atsushi Inoue, Mototsugu Shintani
A new model for interdependent durations
Bo E. Honoré, Áureo de Paula
Heterogeneous treatment effects with mismeasured endogenous treatment
Takuya Ura
Specification testing in random coefficient models
Christoph Breunig, Stefan Hoderlein
A scale‐free transportation network explains the city‐size distribution
Marcus Berliant, Axel H. Watanabe
Meaning and credibility in experimental cheap‐talk games
Ernest K. Lai, Wooyoung Lim
Household debt and crises of confidence
Thomas Hintermaier, Winfried Koeniger
Inference for VARs identified with sign restrictions
Eleonora Granziera, Hyungsik Roger Moon, Frank Schorfheide
Abstract
There is a fast growing literature that set‐identifies structural vector autoregressions (SVARs) by imposing sign restrictions on the responses of a subset of the endogenous variables to a particular structural shock (sign‐restricted SVARs). Most methods that have been used to construct pointwise coverage bands for impulse responses of sign‐restricted SVARs are justified only from a Bayesian perspective. This paper demonstrates how to formulate the inference problem for sign‐restricted SVARs within a moment‐inequality framework. In particular, it develops methods of constructing confidence bands for impulse response functions of sign‐restricted SVARs that are valid from a frequentist perspective. The paper also provides a comparison of frequentist and Bayesian coverage bands in the context of an empirical application—the former can be substantially wider than the latter. Bayesian inference frequentist inference set‐identified models sign restrictions structural VARs C1 C32
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A method for solving and estimating heterogeneous agent macro models
Thomas Winberry
Abstract
I develop a computational method for solving and estimating heterogeneous agent macro models with aggregate shocks. The main challenge is that the aggregate state vector contains the distribution of agents, which is typically infinite‐dimensional. I approximate the distribution with a flexible parametric family, reducing its dimensionality to a finite set of endogenous parameters, and solve for the dynamics of these endogenous parameters by perturbation. I implement the method in Dynare and show that it is fast, general, and easy to use. As an illustration, I use the method to perform a Bayesian estimation of a heterogeneous firm model with aggregate shocks to neutral and investment‐specific productivity. I find that the behavior of investment at the firm level quantitatively shapes inference about the aggregate shock processes, suggesting an important role for micro data in estimating DSGE models. Heterogeneous agents computational economics estimation lumpy investment C63 E22 E32
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Joint analysis of the discount factor and payoff parameters in dynamic discrete choice models
Tatiana Komarova, Fabio Sanches, Daniel Silva Junior, Sorawoot Srisuma
Abstract
Most empirical and theoretical econometric studies of dynamic discrete choice models assume the discount factor to be known. We show the knowledge of the discount factor is not necessary to identify parts, or even all, of the payoff function. We show the discount factor can be generically identified jointly with the payoff parameters. On the other hand, it is known the payoff function cannot be nonparametrically identified without any a priori restrictions. Our identification of the discount factor is robust to any normalization choice on the payoff parameters. In IO applications, normalizations are usually made on switching costs, such as entry costs and scrap values. We also show that switching costs can be nonparametrically identified, in closed‐form, independently of the discount factor and other parts of the payoff function. Our identification strategies are constructive. They lead to easy to compute estimands that are global solutions. We illustrate with a Monte Carlo study and the dataset used in Ryan (). Discount factor dynamic discrete choice problem identification estimation switching costs C14 C25 C51
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Estimation of dynastic life‐cycle discrete choice models
George‐Levi Gayle, Limor Golan, Mehmet A. Soytas
Abstract
This paper explores the estimation of a class of life‐cycle discrete choice dynastic models. It provides a new representation of the value function for these class of models. It compare a multistage conditional choice probability (CCP) estimator based on the new value function representation with a modified version of the full solution maximum likelihood estimator (MLE) in a Monte Carlo study. The modified CCP estimator performs comparably to the MLE in a finite sample but greatly reduces the computational cost. Using the proposed estimator, we estimate a dynastic model and use the estimated model to conduct counterfactual simulations to investigate the role Nature versus Nurture in intergenerational mobility. We find that Nature accounts for 20 percent of the observed intergenerational immobility at the bottom of income distribution. That means that 80 percent of mobility at the bottom of the income distribution is explained by economic decision and economic/institutional constraints. Discrete choice models dynastic models intergenerational mobility nature versus nurture C13 J13 J22 J62
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Global identification of linearized DSGE models
Andrzej Kocięcki, Marcin Kolasa
Abstract
This paper introduces a computational framework to analyze global identification of linearized DSGE models. A formal identification condition is established that relies on the restrictions linking the observationally equivalent state space representations and on the inherent constraints imposed by the model solution on the deep parameters. This condition is next used to develop an algorithm that checks global identification by searching for observationally equivalent model parametrizations. The algorithm is efficient as the identification conditions it employs shrink considerably the space of candidate deep parameter points and the model does not need to be solved at each of these points. The working of the algorithm is demonstrated with two examples. Global identification DSGE models state‐space representation C13 C51 E32
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Quasi‐Bayesian model selection
Atsushi Inoue, Mototsugu Shintani
Abstract
In this paper, we establish the consistency of the model selection criterion based on the quasi‐marginal likelihood (QML) obtained from Laplace‐type estimators. We consider cases in which parameters are strongly identified, weakly identified and partially identified. Our Monte Carlo results confirm our consistency results. Our proposed procedure is applied to select among New Keynesian macroeconomic models using US data. Impulse response function matching Laplace‐type estimators quasi‐marginal likelihood C11 C32 C52
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A new model for interdependent durations
Bo E. Honoré, Áureo de Paula
Abstract
This paper introduces a bivariate version of the generalized accelerated failure time model. It allows for simultaneity in the econometric sense that the two realized outcomes depend structurally on each other. Another feature of the proposed model is that it will generate equal durations with positive probability. Our approach takes a stylized economic model that leads to a univariate generalized accelerated failure time model as a starting point. In this model, agents decide when to transition from an initial state to a new one, and the covariates influence the difference in the utility flow in the two states. We introduce simultaneity by allowing the utility flow to depend on the status of the other person. The econometric model is then completed by assuming that the observed outcome is the Nash bargaining solution in that simple economic model. The advantage of this approach is that it includes independent realizations from the generalized accelerated failure time model as a special case, and deviations from this special case can be given an economic interpretation. We established identification under assumptions that are similar to those in the literature on nonparametric estimation of duration models. We illustrate the model by studying the joint retirement decisions in married couples using the Health and Retirement Study. In that example, it seems reasonable to allow for the possibility that each partner's optimal retirement time depends on the retirement time of the spouse. Moreover, the data suggest that the wife and the husband retire at the same time for a nonnegligible fraction of couples. The main empirical finding is that the simultaneity is economically important. In our preferred specification, the indirect utility associated with being retired increases by approximately when one's spouse retires. Interdependent durations joint retirement interactions and simultaneity J26 C41 C3
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Heterogeneous treatment effects with mismeasured endogenous treatment
Takuya Ura
Abstract
This paper studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treatment is mismeasured and endogenous. Using a binary instrumental variable, I characterize the sharp identified set for the local average treatment effect under the exclusion restriction of an instrument and the deterministic monotonicity of the true treatment in the instrument. Even allowing for general measurement error (e.g., the measurement error is endogenous), it is still possible to obtain finite bounds on the local average treatment effect. Notably, the Wald estimand is an upper bound on the local average treatment effect, but it is not the sharp bound in general. I also provide a confidence interval for the local average treatment effect with uniformly asymptotically valid size control. Furthermore, I demonstrate that the identification strategy of this paper offers a new use of repeated measurements for tightening the identified set. Local average treatment effect instrumental variable nonclassical measurement error endogenous measurement error partial identification C21 C26
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Specification testing in random coefficient models
Christoph Breunig, Stefan Hoderlein
Abstract
In this paper, we suggest and analyze a new class of specification tests for random coefficient models. These tests allow to assess the validity of central structural features of the model, in particular linearity in coefficients, generalizations of this notion like a known nonlinear functional relationship, or degeneracy of the distribution of a random coefficient, that is, whether a coefficient is fixed or random, including whether an associated variable can be omitted altogether. Our tests are nonparametric in nature, and use sieve estimators of the characteristic function. We provide formal power analysis against global as well as against local alternatives. Moreover, we perform a Monte Carlo simulation study, and apply the tests to analyze the degree of nonlinearity in a heterogeneous random coefficients demand model. While we find some evidence against the popular QUAIDS specification with random coefficients, it is not strong enough to reject the specification at the conventional significance level. Nonparametric specification testing random coefficients unobserved heterogeneity sieve estimation characteristic function consumer demand C12 C14
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A scale‐free transportation network explains the city‐size distribution
Marcus Berliant, Axel H. Watanabe
Abstract
Zipf's law is one of the best known empirical regularities in urban economics. There is extensive research on the subject, where each city is treated symmetrically in terms of the cost of transactions with other cities. Recent developments in network theory facilitate the examination of an asymmetric transport network. In a scale‐free network, the chance of observing extremes in network connections becomes higher than the Gaussian distribution predicts and, therefore, it explains the emergence of large clusters. The city‐size distribution shares the same pattern. This paper decodes how accessibility of a city to other cities on the transportation network can boost its local economy and explains the city‐size distribution as a result of its underlying transportation network structure. We confirm our model predictions with US and Belgian data. Finally, we discuss the endogenous evolution of transport networks. Zipf's law city‐size distribution scale‐free network L14 R12 R40
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Meaning and credibility in experimental cheap‐talk games
Ernest K. Lai, Wooyoung Lim
Abstract
We design experimental games to evaluate the predictive power of the first cheap‐talk refinement, neologism‐proofness. In our first set of treatments designed to evaluate the refinement with its usual emphasis on literal meanings, we find that a fully revealing equilibrium that is neologism‐proof is played more often; senders deviate from an equilibrium in a way that can be predicted by the credibility of the neologism; and receivers' behavior indicates that they understand senders' deviating incentives. Our second set of treatments evaluates neologism‐proofness from an evolutionary perspective in the absence of a common language. We find that the proportion of observations in which the meaning of a neologism evolves to disrupt a prevailing fully revealing equilibrium is lower when the equilibrium is neologism‐proof. Our findings shed light on the capabilities and limitations of the refinement concept in predicting laboratory behavior under different language environments. Neologism‐proofness cheap talk equilibrium refinement evolution of meanings laboratory experiment C72 C92 D82 D83
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Household debt and crises of confidence
Thomas Hintermaier, Winfried Koeniger
Abstract
This paper develops a notion of consumer confidence within a dynamic competitive equilibrium framework. In any situation where multiple equilibrium prices on next‐period spot markets are equally supported by the state of the economy, confidence is encoded in the subjective probabilities consumers attach to these multiple future outcomes. Our approach characterizes the set of all equilibrium‐consistent subjective probabilities, and thereby endogenizes the extent of uncertainty faced by consumers. We use the structure of an economy with collateralized household debt and housing markets to develop and illustrate this concept. Our approach determines the specific range of debt levels at which this economy is vulnerable to crises of confidence, as well as the debt‐level‐specific extent of confidence‐driven house price fluctuations. Consumer confidence asset price expectations household debt collateral constraints D84 E32 E44 G01
