Quantitative Economics, May 2020, Volume 11, Issue 2 is now online
TABLE OF CONTENTS, May 2020, Volume 11, Issue 2
Full Issue
Articles
Abstracts follow the listing of articles.
Identifying the discount factor in dynamic discrete choice models
Jaap H. Abbring, Øystein Daljord
Semiparametric estimation of structural functions in nonseparable triangular models
Victor Chernozhukov, Iván Fernández‐Val, Whitney Newey, Sami Stouli, Francis Vella
A competing risks model with time‐varying heterogeneity and simultaneous failure
Ruixuan Liu
Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects
Jungmo Yoon, Antonio F. Galvao
Inference in nonparametric/semiparametric moment equality models with shape restrictions
Yu Zhu
Household portfolios and financial preparedness for retirement
Rowena Crawford, Cormac O'Dea
Consumption insurance with advance information
Christian A. Stoltenberg, Swapnil Singh
Group lending, matching patterns, and the mystery of microcredit: Evidence from Thailand
Christian Ahlin
The price of polarization: Estimating task prices under routine‐biased technical change
Michael J. Böhm
A narrative approach to a fiscal DSGE model
Thorsten Drautzburg
Identifying the discount factor in dynamic discrete choice models
Jaap H. Abbring, Øystein Daljord
Abstract
Empirical research often cites observed choice responses to variation that shifts expected discounted future utilities, but not current utilities, as an intuitive source of information on time preferences. We study the identification of dynamic discrete choice models under such economically motivated exclusion restrictions on primitive utilities. We show that each exclusion restriction leads to an easily interpretable moment condition with the discount factor as the only unknown parameter. The identified set of discount factors that solves this condition is finite, but not necessarily a singleton. Consequently, in contrast to common intuition, an exclusion restriction does not in general give point identification. Finally, we show that exclusion restrictions have nontrivial empirical content: The implied moment conditions impose restrictions on choices that are absent from the unconstrained model.
Discount factor dynamic discrete choice empirical content identification C25 C61
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Semiparametric estimation of structural functions in nonseparable triangular models
Victor Chernozhukov, Iván Fernández‐Val, Whitney Newey, Sami Stouli, Francis Vella
Abstract
Triangular systems with nonadditively separable unobserved heterogeneity provide a theoretically appealing framework for the modeling of complex structural relationships. However, they are not commonly used in practice due to the need for exogenous variables with large support for identification, the curse of dimensionality in estimation, and the lack of inferential tools. This paper introduces two classes of semiparametric nonseparable triangular models that address these limitations. They are based on distribution and quantile regression modeling of the reduced form conditional distributions of the endogenous variables. We show that average, distribution, and quantile structural functions are identified in these systems through a control function approach that does not require a large support condition. We propose a computationally attractive three‐stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. These results establish the validity of the bootstrap for three‐stage estimators of structural functions, and lead to simple inference algorithms. We illustrate the implementation and applicability of all our methods with numerical simulations and an empirical application to demand analysis.
Structural functions nonseparable models control function quantile and distribution regression semiparametric estimation uniform inference C14 C31 C35 C51
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A competing risks model with time‐varying heterogeneity and simultaneous failure
Ruixuan Liu
Abstract
This paper proposes a new bivariate competing risks model in which both durations are the first passage times of dependent Lévy subordinators with exponential thresholds and multiplicative covariates effects. Our specification extends the mixed proportional hazards model, as it allows for the time‐varying heterogeneity represented by the unobservable Lévy processes and it generates the simultaneous termination of both durations with positive probability. We obtain nonparametric identification of all model primitives given competing risks data. A flexible semiparametric estimation procedure is provided and illustrated through the analysis of a real dataset.
Duration analysis competing risks first passage times nonparametric identification C14 C34 C41
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Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects
Jungmo Yoon, Antonio F. Galvao
Abstract
This study develops cluster robust inference methods for panel quantile regression (QR) models with individual fixed effects, allowing for temporal correlation within each individual. The conventional QR standard errors can seriously underestimate the uncertainty of estimators and, therefore, overestimate the significance of effects, when outcomes are serially correlated. Thus, we propose a clustered covariance matrix (CCM) estimator to solve this problem. The CCM estimator is an extension of the heteroskedasticity and autocorrelation consistent covariance matrix estimator for QR models with fixed effects. The autocovariance element in the CCM estimator can be substantially biased, due to the incidental parameter problem. Thus, we develop a bias‐correction method for the CCM estimator. We derive an optimal bandwidth formula that minimizes the asymptotic mean squared errors, and propose a data‐driven bandwidth selection rule. We also propose two cluster robust tests, and establish their asymptotic properties. We then illustrate the practical usefulness of the proposed methods using an empirical application.
Cluster robust standard errors quantile regression panel data heteroskedasticity and autocorrelation consistent covariance matrix estimation C21 C23
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Inference in nonparametric/semiparametric moment equality models with shape restrictions
Yu Zhu
Abstract
This paper studies the inference problem of an infinite‐dimensional parameter with a shape restriction. This parameter is identified by arbitrarily many unconditional moment equalities. The shape restriction leads to a convex restriction set. I propose a test of the shape restriction, which controls size uniformly and applies to both point‐identified and partially identified models. The test can be inverted to construct confidence sets after imposing the shape restriction. Monte Carlo experiments show the finite‐sample properties of this method. In an empirical illustration, I apply the method to ascending auctions held by the US Forest Service and show that imposing shape restrictions can significantly improve inference.
Nonparametric/semiparametric models partial identification shape restrictions unconditional moments C12 C14
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Household portfolios and financial preparedness for retirement
Rowena Crawford, Cormac O'Dea
Abstract
Using a lifecycle model of consumption, saving and portfolio choice combined with linked survey and administrative data on wealth and lifetime earnings we evaluate measures of retirement preparedness. We estimate heterogeneous discount factors for households and compare these estimates of their patience to their replacement rates—the simple measure often used to evaluate the adequacy of retirement savings. We find first that the specification of the model's asset structure matters quantitatively for preference parameter estimates—households appear to be much more patient when they are assumed to have access only to a risk‐free asset compared to when we account for the fact that much of their wealth is stored in higher‐return tax‐advantaged private pensions and in housing. Second, we find that only the most patient households achieve the replacement rates out of final earnings that are often recommended by policymakers and industry as sensible benchmarks for retirement preparedness. Notwithstanding this, we find that even quite impatient households in the population we study achieve high replacement rates out of lifetime average income—a more sensible summary measure of preparedness for retirement.
Lifecycle model wealth savings pensions patience discount factors D14 D31 D91 E21 H55
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Consumption insurance with advance information
Christian A. Stoltenberg, Swapnil Singh
Abstract
This paper investigates whether assuming that households possess advance information on their income shocks helps to overcome the difficulty of standard models to understand consumption insurance in the US. As our main result, we find that the quantitative relevance of advance information crucially depends on the structure of insurance markets. For a realistic amount of advance information, a complete markets model with endogenous solvency constraints due to limited commitment explains several key consumption insurance measures better than existing models without advance information. In contrast, when advance information is integrated into a standard incomplete markets model, it affects household consumption‐saving decisions too little to bridge the gap between the model and the data and can induce counterfactual correlations between current consumption growth and future income growth.
Advance information consumption insurance subjective expectations endogenous borrowing constraints limited commitment D31 D52 E21
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Group lending, matching patterns, and the mystery of microcredit: Evidence from Thailand
Christian Ahlin
Abstract
How has the microcredit movement managed to push financial frontiers? Theory shows that if borrowers vary in unobservable risk, then group‐based, joint liability contracts price for risk more accurately than individual contracts, provided that borrowers match with others of similar project riskiness (Ghatak (1999, 2000)). This more accurate risk‐pricing can attract safer borrowers and rouse an otherwise dormant credit market. We extend the theory to include correlated risk, and show that borrowers will match with partners exposed to similar shocks to lower their chances of facing liability for their partners. We use unique data on Thai microcredit borrowing groups to test for homogeneous matching by project riskiness and type of risk exposure. Evidence supports the theory, in that groups are more homogeneous in riskiness but less diversified in type of risk exposure than they would be under random matching. The results suggest that group lending is improving risk‐pricing by embedding a discount for safe borrowers, and can thus explain part of the unprecedented rise in financial intermediation among the world's poor; but that a potential pitfall of voluntary group formation is antidiversification, which points to strategies for lender intervention.
Microcredit matching group lending group formation C14 C57 C78 O13 O16
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The price of polarization: Estimating task prices under routine‐biased technical change
Michael J. Böhm
Abstract
This paper proposes a new approach to estimate task prices per efficiency unit of skill in the Roy model. I show how the sorting of workers into tasks and their associated wage growth can be used to identify changes in task prices under relatively weak assumptions. The estimation exploits the fact that the returns to observable talents will change differentially over time depending on the changes in prices of those tasks that they predict workers to sort into. In the generalized Roy model, also the average non‐pecuniary amenities in each task are identified. I apply this approach to the literature on routine‐biased technical change, a key prediction of which is that task prices should polarize. Empirical results for male workers in U.S. data indicate that abstract and manual tasks' relative prices indeed increased during the 1990s and 2000s.
Task prices Roy model routine‐biased technical change polarization wage distribution J23 J24 J31
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A narrative approach to a fiscal DSGE model
Thorsten Drautzburg
Abstract
Structural DSGE models are used for analyzing both policy and the sources of business cycles. Conclusions based on full structural models are, however, potentially affected by misspecification. A competing method is to use partially identified SVARs based on narrative shocks. This paper asks whether both approaches agree. Specifically, I use narrative data in a DSGE‐SVAR that partially identify policy shocks in the VAR and assess the fit of the DSGE model relative to this narrative benchmark. In developing this narrative DSGE‐SVAR, I develop a tractable Bayesian approach to proxy VARs and show that such an approach is valid for models with a certain class of Taylor rules. Estimating a DSGE‐SVAR based on a standard DSGE model with fiscal rules and narrative data, I find that the DSGE model identification is at odds with the narrative information as measured by the marginal likelihood. I trace this discrepancy to differences in impulse responses, identified historical shocks and policy rules. The results indicate monetary accommodation of fiscal shocks.
Fiscal policy monetary policy DSGE model Bayesian estimation narrative shocks Bayesian VAR C32 E32 E52 E62