Quantitative Economics January 2021, Volume 12, Issue 1 is now online

TABLE OF CONTENTS, January 2021, Volume 12, Issue 1
Full Issue

Articles
Abstracts follow the listing of articles.

Identification and inference with ranking restrictions
Pooyan Amir‐Ahmadi, Thorsten Drautzburg

The discretization filter: A simple way to estimate nonlinear state space models
Leland E. Farmer

Sensitivity analysis using approximate moment condition models
Timothy B. Armstrong, Michal Kolesár

Robust inference in deconvolution
Kengo Kato, Yuya Sasaki, Takuya Ura

Partial identification of the distribution of treatment effects with an application to the Knowledge is Power Program (KIPP)
Brigham R. Frandsen, Lars J. Lefgren

Teacher labor markets, school vouchers, and student cognitive achievement: Evidence from Chile
Michela M. Tincani

The welfare effects of asset mean‐testing income support
Felix Wellschmied

Controlling for presentation effects in choice
Yves Breitmoser

A notion of prominence for games with natural‐language labels
Alessandro Sontuoso, Sudeep Bhatia

Identification and inference with ranking restrictions
Pooyan Amir‐Ahmadi, Thorsten Drautzburg

Abstract

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

Structural VAR set‐identification sign restrictions ranking restrictions heterogeneity posterior bounds Bayesian inference sampling methods productivity news C32 C53 E32

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The discretization filter: A simple way to estimate nonlinear state space models
Leland E. Farmer

Abstract

Existing methods for estimating nonlinear dynamic models are either highly computationally costly or rely on local approximations which often fail adequately to capture the nonlinear features of interest. I develop a new method, the discretization filter, for approximating the likelihood of nonlinear, non‐Gaussian state space models. I establish that the associated maximum likelihood estimator is strongly consistent, asymptotically normal, and asymptotically efficient. Through simulations, I show that the discretization filter is orders of magnitude faster than alternative nonlinear techniques for the same level of approximation error in low‐dimensional settings and I provide practical guidelines for applied researchers. It is my hope that the method's simplicity will make the quantitative study of nonlinear models easier for and more accessible to applied researchers. I apply my approach to estimate a New Keynesian model with a zero lower bound on the nominal interest rate. After accounting for the zero lower bound, I find that the slope of the Phillips Curve is 0.076, which is less than 1/3 of typical estimates from linearized models. This suggests a strong decoupling of inflation from the output gap and larger real effects of unanticipated changes in interest rates in post Great Recession.

Nonlinear filtering discretization regime switching state space models DSGE models zero lower bound C11 C13 E40 E50

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Sensitivity analysis using approximate moment condition models
Timothy B. Armstrong, Michal Kolesár

Abstract

We consider inference in models defined by approximate moment conditions. We show that near‐optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias and, therefore, differs from the one that is optimal under correct specification. To formally show the near‐optimality of these CIs, we develop asymptotic efficiency bounds for inference in the locally misspecified GMM setting. These bounds may be of independent interest, due to their implications for the possibility of using moment selection procedures when conducting inference in moment condition models. We apply our methods in an empirical application to automobile demand, and show that adjusting the weighting matrix can shrink the CIs by a factor of 3 or more.

Sensitivity analysis confidence intervals misspecification generalized method of moments semiparametric efficiency C12 C13 C52

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Robust inference in deconvolution
Kengo Kato, Yuya Sasaki, Takuya Ura

Abstract

Kotlarski's identity has been widely used in applied economic research based on repeated‐measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. Our approach is robust in that we do not require the completeness. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results.

Deconvolution measurement error robust inference uniform confidence band C14 C57

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Partial identification of the distribution of treatment effects with an application to the Knowledge is Power Program (KIPP)
Brigham R. Frandsen, Lars J. Lefgren

Abstract

We bound the distribution of treatment effects under plausible and testable assumptions on the joint distribution of potential outcomes, namely that potential outcomes are mutually stochastically increasing. We show how to test the empirical restrictions implied by those assumptions. The resulting bounds substantially sharpen bounds based on classical inequalities. We apply our method to estimate bounds on the distribution of effects of attending a Knowledge is Power Program (KIPP) charter school on student achievement, and find that a substantial majority of students' math achievement benefited from attendance, especially those who would have fared poorly in a traditional classroom.

Program evaluation treatment effects distributional effects charter schools C14 C21 C26

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Teacher labor markets, school vouchers, and student cognitive achievement: Evidence from Chile
Michela M. Tincani

Abstract

I use administrative and survey data from Chile and a structural model to evaluate teacher policies in a market‐based school system. The model accommodates equilibrium effects on parental sorting across school sectors (public or private), on the self‐selection of individuals into teaching and across school sectors, and on teacher wages in private schools. I use the estimated model to simulate a reform that is planned to be implemented in Chile in 2023. Tying public school teacher wages to teacher skills and introducing minimum competency requirements for teaching is predicted to increase student test scores by 0.30 standard deviations and decrease the achievement gap between the poorest and richest 25% of students by a third. These impacts are ten times as large as the impact of a flat wage increase in public schools, and over twice as large as the impact of only introducing minimum competency requirements. The key driver of policy outcomes is an improvement in the pool of teachers, amplified by equilibrium effects on teacher wages in private schools. The equilibrium effects are large, accounting for 70% of estimated policy impacts.

Teacher labor markets equilibrium effects rigid pay merit pay teacher entry teacher sorting achievement gaps parental sorting I24 J24 J31 J38

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The welfare effects of asset mean‐testing income support
Felix Wellschmied

Abstract

This paper studies the savings and employment effects of the asset means‐test in US income support programs using a structural life‐cycle model with productivity, disability, and unemployment risk. An asset means‐test incentivizes low‐income households to hold few financial assets making them vulnerable to predictable and unpredictable income changes. Moreover, it incentivizes relatively productive households that happen to have few financial assets to leave the labor force. However, it allows for relative generous transfers to households in most need. Moreover, it counteracts relatively productive households leaving the labor force after the age of 50. In terms of the welfare of an unborn household, the asset means‐test that optimally trades off these effects is $150,000, and abolishing it is close to optimal.

Means‐tested programs public insurance incomplete markets D91 I38 J26

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Controlling for presentation effects in choice
Yves Breitmoser

Abstract

Experimenters make theoretically irrelevant decisions concerning user interfaces and ordering or labeling of options. Reanalyzing dictator games, I first show that such decisions may drastically affect comparative statics and cause results to appear contradictory across experiments. This obstructs model testing, preference analyses, and policy predictions. I then propose a simple model of choice incorporating both presentation effects and stochastic errors, and test the model by reanalyzing the dictator game experiments. Controlling for presentation effects, preference estimates become consistent across experiments and predictive out‐of‐sample. This highlights both the necessity and the possibility to control for presentation in economic experiments.

Presentation effects utility estimation counterfactual predictions laboratory experiment C10 C90

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A notion of prominence for games with natural‐language labels
Alessandro Sontuoso, Sudeep Bhatia

Abstract

We study games with natural‐language labels (i.e., strategic problems where options are denoted by words), for which we propose and test a measurable characterization of prominence. We assume that—ceteris paribus—players find particularly prominent those strategies that are denoted by words more frequently used in their everyday language. To operationalize this assumption, we suggest that the prominence of a strategy‐label is correlated with its frequency of occurrence in large text corpora, such as the Google Books corpus (“n‐gram” frequency). In testing for the strategic use of word frequency, we consider experimental games with different incentive structures (such as incentives to and not to coordinate), as well as subjects from different cultural/linguistic backgrounds. Our data show that frequently‐mentioned labels are more (less) likely to be selected when there are incentives to match (mismatch) others. Furthermore, varying one's knowledge of the others' country of residence significantly affects one's reliance on word frequency. Overall, the data show that individuals play strategies that fulfill our characterization of prominence in a (boundedly) rational manner.

Focal points salience coordination hide‐and‐seek culture language C72 C91

 

 

Publication Date: 
Thursday, January 14, 2021

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