Quantitative Economics, July 2021, Volume 12, Issue 3 is now online

TABLE OF CONTENTS, July 2021, Volume 12, Issue 3
Full Issue

Articles
Abstracts follow the listing of articles.

Specification tests for non‐Gaussian maximum likelihood estimators
Gabriele Fiorentini, Enrique Sentana

Inference on semiparametric multinomial response models
Shakeeb Khan, Fu Ouyang, Elie Tamer

A unified framework for efficient estimation of general treatment models
Chunrong Ai, Oliver Linton, Kaiji Motegi, Zheng Zhang

Rationalizing rational expectations: Characterizations and tests
Xavier D'Haultfoeuille, Christophe Gaillac, Arnaud Maurel

A generalized approach to indeterminacy in linear rational expectations models
Francesco Bianchi, Giovanni Nicolò

Saddle cycles: Solving rational expectations models featuring limit cycles (or chaos) using perturbation methods
Dana Galizia

Average crossing time: An alternative characterization of mean aversion and reversion
John B. Donaldson, Rajnish Mehra

Bullying among adolescents: The role of skills
Miguel Sarzosa, Sergio Urzúa

Peer effects on the United States Supreme Court
Richard Holden, Michael Keane, Matthew Lilley

Bandits in the lab
Johannes Hoelzemann, Nicolas Klein

Specification tests for non‐Gaussian maximum likelihood estimators
Gabriele Fiorentini, Enrique Sentana

Abstract

We propose generalized DWH specification tests which simultaneously compare three or more likelihood‐based estimators in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Vars and multivariate regressions. We determine the rank of the differences between the estimators' asymptotic covariance matrices under correct specification, and take into account that some parameters remain consistently estimated under distributional misspecification. We provide finite sample results through Monte Carlo simulations. Finally, we analyze a structural Var proposed to capture the relationship between macroeconomic and financial uncertainty and the business cycle.

Durbin–Wu–Hausman tests partial adaptivity semiparametric estimators singular covariance matrices uncertainty and the business cycle C12 C14 C22 C32 C52
---
Inference on semiparametric multinomial response models
Shakeeb Khan, Fu Ouyang, Elie Tamer

Abstract

 

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set.

Multinomial response rank estimation dynamic panel data C14 C23 C35
---
A unified framework for efficient estimation of general treatment models
Chunrong Ai, Oliver Linton, Kaiji Motegi, Zheng Zhang

Abstract

 

This paper presents a weighted optimization framework that unifies the binary, multivalued, and continuous treatment—as well as mixture of discrete and continuous treatment—under a unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile, and asymmetric least squares causal effect of treatment as special cases. For this general framework, we first derive the semiparametric efficiency bound for the causal effect of treatment, extending the existing bound results to a wider class of models. We then propose a generalized optimization estimator for the causal effect with weights estimated by solving an expanding set of equations. Under some sufficient conditions, we establish the consistency and asymptotic normality of the proposed estimator of the causal effect and show that the estimator attains the semiparametric efficiency bound, thereby extending the existing literature on efficient estimation of causal effect to a wider class of applications. Finally, we discuss estimation of some causal effect functionals such as the treatment effect curve and the average outcome. To evaluate the finite sample performance of the proposed procedure, we conduct a small‐scale simulation study and find that the proposed estimation has practical value. In an empirical application, we detect a significant causal effect of political advertisements on campaign contributions in the binary treatment model, but not in the continuous treatment model.

Causal effect entropy maximization treatment effect semiparametric efficiency sieve method stabilized weights C14 C21
---
Rationalizing rational expectations: Characterizations and tests
Xavier D'Haultfoeuille, Christophe Gaillac, Arnaud Maurel

Abstract

 

In this paper, we build a new test of rational expectations based on the marginal distributions of realizations and subjective beliefs. This test is widely applicable, including in the common situation where realizations and beliefs are observed in two different data sets that cannot be matched. We show that whether one can rationalize rational expectations is equivalent to the distribution of realizations being a mean‐preserving spread of the distribution of beliefs. The null hypothesis can then be rewritten as a system of many moment inequality and equality constraints, for which tests have been recently developed in the literature. The test is robust to measurement errors under some restrictions and can be extended to account for aggregate shocks. Finally, we apply our methodology to test for rational expectations about future earnings. While individuals tend to be right on average about their future earnings, our test strongly rejects rational expectations.

Rational expectations test subjective expectations data combination C12 D84 E24
---
A generalized approach to indeterminacy in linear rational expectations models
Francesco Bianchi, Giovanni Nicolò

Abstract

 

We propose a novel approach to deal with the problem of indeterminacy in linear rational expectations models. The method consists of augmenting the original state space with a set of auxiliary exogenous equations to provide the adequate number of explosive roots in presence of indeterminacy. The solution in this expanded state space, if it exists, is always determinate, and is identical to the indeterminate solution of the original model. The proposed approach accommodates determinacy and any degree of indeterminacy, and it can be implemented even when the boundaries of the determinacy region are unknown. Thus, the researcher can estimate the model using standard software packages without restricting the estimates to the determinacy region. We combine our solution method with a novel hybrid Metropolis–Hastings algorithm to estimate the New–Keynesian model with rational bubbles by Galí (2021) over the period 1982:Q4–2007:Q3. We find that the data support the presence of two degrees of indeterminacy, implying that the central bank was not reacting strongly enough to the bubble component.

Indeterminacy general equilibrium solution method Bayesian methods C19 C51 C62 C63
---
Saddle cycles: Solving rational expectations models featuring limit cycles (or chaos) using perturbation methods
Dana Galizia

Abstract

 

Unlike linear ones, nonlinear business cycle models can generate sustained fluctuations even in the absence of shocks (e.g., via limit cycles/chaos). A popular approach to solving nonlinear models is perturbation methods. I show that, as typically implemented, these methods are incapable of finding solutions featuring limit cycles or chaos. Fundamentally, solutions are only required not to explode, while standard perturbation algorithms seek solutions that meet the stronger requirement of convergence to the steady state. I propose a modification to standard algorithms that does not impose this overly strong requirement.

Dynamic equilibrium economies computational methods nonlinear solution methods limit cycles chaos C63 C68 E37
---
Average crossing time: An alternative characterization of mean aversion and reversion
John B. Donaldson, Rajnish Mehra

Abstract

 

This study compares and contrasts the multiple characterizations of mean reversion in financial time series as regards the restrictions they imply. This is accomplished by translating them into statements about an alternative measure, the “Average Crossing Time” or ACT. We argue that the ACT measure, per se, provides not only a useful benchmark for the degree of mean reversion/aversion, but also an intuitive, and easily quantified sense of one time series being “more strongly mean‐reverting/averting” than another. We conclude our discussion by deriving the ACT measure for a wide class of stochastic processes and detailing its statistical characteristics. Our analysis is principally undertaken within a class of well‐understood production based asset pricing models.

Mean aversion mean reversion average crossing time time series asset pricing C13 C53 E3 E44 E47 G1 G12
---
Bullying among adolescents: The role of skills
Miguel Sarzosa, Sergio Urzúa

Abstract

 

Bullying cannot be tolerated as a normal social behavior portraying a child's life. This paper quantifies its negative consequences allowing for the possibility that victims and nonvictims differ in unobservable characteristics. To this end, we introduce a factor analytic model for identifying treatment effects of bullying in which latent cognitive and noncognitive skills determine victimization and multiple outcomes. We use early test scores to identify the distribution of these skills. Individual‐, classroom‐ and district‐level variables are also accounted for. Applying our method to longitudinal data from South Korea, we first show that while noncognitive skills reduce the chances of being bullied during middle school, the probability of being victimized is greater in classrooms with relatively high concentration of boys, previously self‐assessed bullies and students that come from violent families. We report bullying at age 15 has negative effects on physical and mental health outcomes at age 18. We also uncover heterogeneous effects by latent skills, from which we document positive effects on the take‐up of risky behaviors and negative effects on schooling attainment. Our findings suggest that investing in noncognitive development should guide policy efforts intended to deter this problematic behavior.

Bullying cognitive and noncognitive skills unobserved heterogeneity C34 C38 I21 J24
---
Peer effects on the United States Supreme Court
Richard Holden, Michael Keane, Matthew Lilley

Abstract

 

Using data on essentially every U.S. Supreme Court decision since 1946, we estimate a model of peer effects on the Court. We estimate the impact of justice ideology and justice votes on the votes of their peers. To identify the peer effects, we use two instruments that generate plausibly exogenous variation in the peer group itself, or in the votes of peers. The first instrument utilizes the fact that the composition of the Court varies from case to case due to recusals or absences for health reasons. The second utilizes the fact that many justices previously sat on Federal Circuit Courts, and justices are generally much less likely to overturn decisions in cases sourced from their former “home” court. We find large peer effects. For example, we can use our model to predict the impact of replacing Justice Ginsburg with Justice Barrett. Under the the assumption that Justice Barrett's ideological position aligns closely with Justice Scalia, for whom she clerked, we predict that her influence on the Court will increase the Conservative vote propensity of the other justices by 4.7 percentage points. That translates into 0.38 extra conservative votes per case on top of the impact of her own vote. In general, we find indirect effects are large relative to the direct mechanical effect of a justice's own vote.

Peer effects Supreme Court voting political economy C31 C33 D72 K40
---
Bandits in the lab
Johannes Hoelzemann, Nicolas Klein

Abstract

 

We experimentally implement a dynamic public‐good problem, where the public good in question is the dynamically evolving information about agents' common state of the world. Subjects' behavior is consistent with free‐riding because of strategic concerns. We also find that subjects adopt more complex behaviors than predicted by the welfare‐optimal equilibrium, such as noncut‐off behavior, lonely pioneers, and frequent switches of action.

Dynamic public‐good problem strategic experimentation exponential bandits learning dynamic games laboratory experiments C73 C92 D83 O32

 

 

Publication Date: 
Wednesday, July 14, 2021

Back