Quantitative Economics, January 2020, Volume 11, Issue 1, is now online
TABLE OF CONTENTS, January 2020, Volume 11, Issue 1
Full Issue
Articles
Abstracts follow the listing of articles.
Simple and honest confidence intervals in nonparametric regression
Timothy B. Armstrong, Michal Kolesár
Inference on breakdown frontiers
Matthew A. Masten, Alexandre Poirier
Indirect inference with(out) constraints
David T. Frazier, Eric Renault
Nonparametric estimation of triangular simultaneous equations models under weak identification
Sukjin Han
A persistence‐based Wold‐type decomposition for stationary time series
Fulvio Ortu, Federico Severino, Andrea Tamoni, Claudio Tebaldi
A dynamic model of personality, schooling, and occupational choice
Petra E. Todd, Weilong Zhang
Waiting for affordable housing in New York City
Holger Sieg, Chamna Yoon
Worker overconfidence: Field evidence and implications for employee turnover and firm profits
Mitchell Hoffman, Stephen V. Burks
The provision of wage incentives: A structural estimation using contracts variation
Xavier D'Haultfœuille, Philippe Février
Contracting under uncertainty: Groundwater in South India
Xavier Giné, Hanan G. Jacoby
On households and unemployment insurance
Sekyu Choi, Arnau Valladares‐Esteban
Simple and honest confidence intervals in nonparametric regression
Timothy B. Armstrong, Michal Kolesár
Abstract
We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we use critical values that take into account the possible bias of the estimator upon which the CIs are based. We show that this approach leads to CIs that are more efficient than conventional CIs that achieve coverage by undersmoothing or subtracting an estimate of the bias. We give sharp efficiency bounds of using different kernels, and derive the optimal bandwidth for constructing honest CIs. We show that using the bandwidth that minimizes the maximum mean‐squared error results in CIs that are nearly efficient and that in this case, the critical value depends only on the rate of convergence. For the common case in which the rate of convergence is n−2/5, the appropriate critical value for 95% CIs is 2.18, rather than the usual 1.96 critical value. We illustrate our results in a Monte Carlo analysis and an empirical application. Confidence intervals regression discontinuity nonparametric regression C14 C21
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Inference on breakdown frontiers
Matthew A. Masten, Alexandre Poirier
Abstract
Given a set of baseline assumptions, a breakdown frontier is the boundary between the set of assumptions which lead to a specific conclusion and those which do not. In a potential outcomes model with a binary treatment, we consider two conclusions: First, that ATE is at least a specific value (e.g., nonnegative) and second that the proportion of units who benefit from treatment is at least a specific value (e.g., at least 50%). For these conclusions, we derive the breakdown frontier for two kinds of assumptions: one which indexes relaxations of the baseline random assignment of treatment assumption, and one which indexes relaxations of the baseline rank invariance assumption. These classes of assumptions nest both the point identifying assumptions of random assignment and rank invariance and the opposite end of no constraints on treatment selection or the dependence structure between potential outcomes. This frontier provides a quantitative measure of the robustness of conclusions to relaxations of the baseline point identifying assumptions. We derive √N consistent sample analog estimators for these frontiers. We then provide two asymptotically valid bootstrap procedures for constructing lower uniform confidence bands for the breakdown frontier. As a measure of robustness, estimated breakdown frontiers and their corresponding confidence bands can be presented alongside traditional point estimates and confidence intervals obtained under point identifying assumptions. We illustrate this approach in an empirical application to the effect of child soldiering on wages. We find that sufficiently weak conclusions are robust to simultaneous failures of rank invariance and random assignment, while some stronger conclusions are fairly robust to failures of rank invariance but not necessarily to relaxations of random assignment. Nonparametric identification partial identification sensitivity analysis selection on unobservables rank invariance treatment effects directional differentiability C14 C18 C21 C25 C51
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Indirect inference with(out) constraints
David T. Frazier, Eric Renault
Abstract
Indirect Inference (I‐I) estimation of structural parameters θ requires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parameters β. The estimators of the instrumental parameters will encapsulate the statistical information used for inference about the structural parameters. As such, artificially constraining these parameters may restrict the ability of the auxiliary model to accurately replicate features in the structural data, which may lead to a range of issues, such as a loss of identification. However, in certain situations the parameters β naturally come with a set of q restrictions. Examples include settings where β must be estimated subject to q possibly strict inequality constraints g(β)>0, such as, when I‐I is based on GARCH auxiliary models. In these settings, we propose a novel I‐I approach that uses appropriately modified unconstrained auxiliary statistics, which are simple to compute and always exists. We state the relevant asymptotic theory for this I‐I approach without constraints and show that it can be reinterpreted as a standard implementation of I‐I through a properly modified binding function. Several examples that have featured in the literature illustrate our approach. Inequality restrictions constrained estimation parameters on the boundary indirect inference stochastic volatility C10 C13 C15
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Nonparametric estimation of triangular simultaneous equations models under weak identification
Sukjin Han
Abstract
This paper analyzes the problem of weak instruments on identification, estimation, and inference in a simple nonparametric model of a triangular system. The paper derives a necessary and sufficient rank condition for identification, based on which weak identification is established. Then nonparametric weak instruments are defined as a sequence of reduced‐form functions where the associated rank shrinks to zero. The problem of weak instruments is characterized as concurvity, which motivates the introduction of a regularization scheme. The paper proposes a penalized series estimation method to alleviate the effects of weak instruments and shows that it achieves desirable asymptotic properties. A data‐driven procedure is proposed for the choice of the penalization parameter. The findings of this paper provide useful implications for empirical work. To illustrate them, Monte Carlo results are presented and an empirical example is given in which the effect of class size on test scores is estimated nonparametrically. Triangular models nonparametric identification weak identification weak instruments series estimation regularization concurvity C13 C14 C36
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A persistence‐based Wold‐type decomposition for stationary time series
Fulvio Ortu, Federico Severino, Andrea Tamoni, Claudio Tebaldi
Abstract
This paper shows how to decompose weakly stationary time series into the sum, across time scales, of uncorrelated components associated with different degrees of persistence. In particular, we provide an Extended Wold Decomposition based on an isometric scaling operator that makes averages of process innovations. Thanks to the uncorrelatedness of components, our representation of a time series naturally induces a persistence‐based variance decomposition of any weakly stationary process. We provide two applications to show how the tools developed in this paper can shed new light on the determinants of the variability of economic and financial time series. Wold decomposition temporal aggregation persistence heterogeneity forecasting C18 C22 C50
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A dynamic model of personality, schooling, and occupational choice
Petra E. Todd, Weilong Zhang
Abstract
This paper develops a dynamic model of schooling and occupational choices that incorporates personality traits, as measured by the “big five” traits. The model is estimated using the HILDA dataset from Australia. Personality traits are found to play an important role in explaining education and occupation choices over the lifecycle. Results show that individuals with a comparative advantage in schooling and white‐collar work have, on average, higher cognitive skills and higher personality trait scores. Allowing personality traits to evolve with age and with schooling proves to be important to capturing the heterogeneity in how people respond to educational policies. The estimated model is used to evaluate two education policies: compulsory senior secondary school and a 50% college tuition subsidy. Both policies increase educational attainment and also affect personality traits. Personality traits and education policies occupational choice unobserved types human capital investment dynamic discrete choice D61 I26 J24
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Waiting for affordable housing in New York City
Holger Sieg, Chamna Yoon
Abstract
We develop a new dynamic equilibrium model with heterogeneous households that captures the most important frictions that arise in housing rental markets and explains the political popularity of affordable housing policies. We estimate the model using data collected by the New York Housing Vacancy Survey in 2011. We find that there are significant adjustment costs in all markets as well as serious search frictions in the market for affordable housing. Moreover, there are large queuing frictions in the market for public housing. Having access to rent‐stabilized housing increases household welfare by up to $65,000. Increasing the supply of affordable housing by 10% significantly improves the welfare of all renters in the city. Progressive taxation of higher‐income households that live in public housing can also be welfare improving. Urban housing policies excess demand housing supply moving costs rationing search frictions queuing C33 C83 D45 D58 H72 R31
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Worker overconfidence: Field evidence and implications for employee turnover and firm profits
Mitchell Hoffman, Stephen V. Burks
Abstract
Combining weekly productivity data with weekly productivity beliefs for a large sample of truckers over 2 years, we show that workers tend to systematically and persistently overpredict their productivity. If workers are overconfident about their own productivity at the current firm relative to their outside option, they should be less likely to quit. Empirically, all else equal, having higher productivity beliefs is associated with an employee being less likely to quit. To study the implications of overconfidence for worker welfare and firm profits, we estimate a structural learning model with biased beliefs that accounts for many key features of the data. While worker overconfidence moderately decreases worker welfare, it also substantially increases firm profits. Overconfidence biased learning turnover D03 J24 J41 M53
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The provision of wage incentives: A structural estimation using contracts variation
Xavier D'Haultfœuille, Philippe Février
Abstract
We address empirically the issues of the optimality of simple linear compensation contracts and the importance of asymmetries between firms and workers. For that purpose, we consider contracts between the French National Institute of Statistics and Economics (Insee) and the interviewers it hired to conduct its surveys in 2001, 2002, and 2003. To derive our results, we exploit an exogenous change in the contract structure in 2003, the piece rate increasing from 20.2 to 22.9 euros. We argue that such a change is crucial for a structural analysis. It allows us, in particular, to identify and recover nonparametrically some information on the cost function of the interviewers and on the distribution of their types. This information is used to select correctly our parametric restrictions. Our results indicate that the loss of using such simple contracts instead of the optimal ones is no more than 16%, which might explain why linear contracts are so popular. We also find moderate costs of asymmetric information in our data, the loss being around 22% of what Insee could achieve under complete information. Incentives asymmetric information optimal contracts nonparametric identification C14 D82 D86
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Contracting under uncertainty: Groundwater in South India
Xavier Giné, Hanan G. Jacoby
Abstract
To quantify contracting distortions in a real‐world market, we develop and structurally estimate a model of contracting under payoff uncertainty in the south Indian groundwater economy. Uncertainty arises from unpredictable fluctuations in groundwater supply during the agricultural dry season. Our model highlights the tradeoff between the ex post inefficiency of long‐term contracts and the ex ante inefficiency of spot contracts. We use unique data on both payoff uncertainty and relationship‐specific investment collected from a large sample of well‐owners in Andhra Pradesh to estimate the model's parameters. Our estimates imply that spot contracts entail a 3% efficiency loss due to hold‐up. Counterfactual simulations also reveal that the equilibrium contracting distortion reduces the overall gains from trade by about 4% and the seasonal income of the median borewell owner by 2%, with proportionally greater costs borne by smaller landowners. Hold‐up relationship‐specific investment subjective probabilities structural estimation L14 Q15
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On households and unemployment insurance
Sekyu Choi, Arnau Valladares‐Esteban
Abstract
We study unemployment insurance in a framework where the main source of heterogeneity among agents is the type of household they live in: some agents live alone while others live with their spouses as a family. Our exercise is motivated by the fact that married individuals can rely on spousal income to smooth labor market shocks, while singles cannot. We extend a version of the standard incomplete‐markets model to include two‐agent households and calibrate it to the US economy with special emphasis on matching differences in labor market transitions across gender and marital status as well as aggregate wealth moments. Our central finding is that changes to the current unemployment insurance program are valued differently by married and single households. In particular, a more generous unemployment insurance reduces the welfare of married households significantly more than that of singles and vice versa. We show that this result is driven by the amount of self‐insurance existing in married households, and thus, we highlight the interplay between self‐ and government‐provided insurance and its implication for policy. Households marriage family unemployment unemployment insurance worker flows heterogeneous agents D91 E24 J64 J65
