http://qeconomics.org/ojs/index.php/qe/issue/feedQuantitative Economics2016-07-28T03:49:07+00:00Claire Sashiclaire.sashi@gmail.comOpen Journal Systems<p>Ragnar Frisch, the first president of the <a class="red style1" href="http://www.econometricsociety.org/"><strong>Econometric Society</strong></a> envisioned the society as promoting studies that aim at the unification of the theoretical-quantitative and the empirical-quantitative approach to economic problems and that are penetrated by constructive and rigorous thinking.</p> <p><em> Quantitative Economics</em>, a new journal sponsored by the Econometric Society, is designed to provide a home for papers that fulfill this vision. As such, it will complement the role currently played by <em>Econometrica</em>.</p><p><em>Quantitative Economics</em> will be oriented towards empirical research that is rigorously informed by econometrics and/or economic theory and econometric and theory work that is empirically directed. This does not imply, however, that the journal does not welcome theoretical and computational papers. Theory has a place in the new journal if it has an obvious empirical orientation (such as work on identification or estimation and computational techniques with practical interest).</p><p>The work published by QE will be united by substance rather than methodology. We aim at covering a variety of applied fields, including labour economics, industrial organization, development and growth economics, macroeconomics, international economics, public finance and social economics.</p><p>QE’s editorial board will strive to reduce the length of the editorial process, keeping at a minimum multiple revision and trying to avoid delays while maintaining the highest standards in the editorial process. At the same time, the editorial board is especially interested in providing a forum for papers that are innovative beyond established types of analysis and are willing to challenge conventional ways of conducting empirical work.</p>http://qeconomics.org/ojs/index.php/qe/article/view/371Bayesian inference in a class of partially identified models2016-07-28T03:41:16+00:00Brendan Klinebrendan.kline@austin.utexas.eduElie Tamerelietamer@fas.harvard.edu This paper develops a Bayesian approach to inference in a class of partially identified econometric models. Models in this class are characterized by a known mapping between a point identified reduced‐form parameter μ and the identified set for a partially identified parameter θ. The approach maps posterior inference about μ to various posterior inference statements concerning the identified set for θ, without the specification of a prior for θ. Many posterior inference statements are considered, including the posterior probability that a particular parameter value (or a set of parameter values) is in the identified set. The approach applies also to functions of θ. The paper develops general results on large sample approximations, which illustrate how the posterior probabilities over the identified set are revised by the data, and establishes conditions under which the Bayesian credible sets also are valid frequentist confidence sets. The approach is computationally attractive even in high‐dimensional models, in that the approach avoids an exhaustive search over the parameter space. The performance of the approach is illustrated via Monte Carlo experiments and an empirical application to a binary entry game involving airlines. Partial identification identified set criterion function Bayesian inference C10 C112016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/372Inference under stability of risk preferences2016-07-28T03:41:19+00:00Levon Barseghyanlb247@cornell.eduFrancesca Molinarifm72@cornell.eduJoshua C. Teitelbaumjct48@law.georgetown.edu We leverage the assumption that preferences are stable across contexts to partially identify and conduct inference on the parameters of a structural model of risky choice. Working with data on households' deductible choices across three lines of insurance coverage and a model that nests expected utility theory plus a range of non‐expected utility models, we perform a revealed preference analysis that yields household‐specific bounds on the model parameters. We then impose stability and other structural assumptions to tighten the bounds, and we explore what we can learn about households' risk preferences from the intervals defined by the bounds. We further utilize the intervals to (i) classify households into preference types and (ii) recover the single parameterization of the model that best fits the data. Our approach does not entail making distributional assumptions about unobserved heterogeneity in preferences. Inference insurance partial identification revealed preference risk preferences stability D01 D12 D81 G222016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/373Explaining the gender wage gap: Estimates from a dynamic model of job changes and hours changes2016-07-28T03:41:21+00:00Kai Liukai.liu@econ.cam.ac.uk I address the causes of the gender wage gap with a new dynamic model of wage, hours, and job changes that permits me to decompose the gap into a portion due to gender differences in preferences for hours of work and in constraints. The dynamic model allows the differences in constraints to reflect possible gender differences in job arrival rates, job destruction rates, the mean and variance of the wage offer distribution, and the wage cost of part‐time work. The model is estimated using the 1996 panel of the Survey of Income and Program Participation. I find that the preference for part‐time work increases with marriage and number of children among women but not among men. These demographic factors explain a sizable fraction of the gender gap in employment, but they explain no more than 6 percent of the gender wage gap. Differences in constraints, mainly in the form of the mean offered wages and rates of job arrival and destruction, explain most of the gender wage gap. Policy simulation results suggest that, relative to reducing the wage cost of part‐time work, providing additional employment protection to part‐time jobs is more effective in reducing the gender wage gap. Gender wage gap part‐time work job mobility women D91 J16 J31 J632016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/374Clearinghouses for two‐sided matching: An experimental study2016-07-28T03:41:23+00:00Federico Echeniquefede@hss.caltech.eduAlistair J. Wilsonalistair@pitt.eduLeeat Yarivlyariv@hss.caltech.edu We experimentally study the Gale and Shapley, 1962 mechanism, which is utilized in a wide set of applications, most prominently the National Resident Matching Program (NRMP). Several insights come out of our analysis. First, only 48% of our observed outcomes are stable, and among those a large majority culminate at the receiver‐optimal stable matching. Second, receivers rarely truncate their true preferences: it is the proposers who do not make offers in order of their preference, frequently skipping potential partners. Third, market characteristics affect behavior: both the cardinal representation and core size influence whether laboratory outcomes are stable. We conclude by using our controlled results and a behavioral model to shed light on a number of stylized facts we derive from new NRMP survey and outcome data, and to explain the small cores previously documented for the NRMP. Deferred acceptance stability experiments centralized matching C78 C90 D472016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/375Unobserved heterogeneity in dynamic games: Cannibalization and preemptive entry of hamburger chains in Canada2016-07-28T03:41:24+00:00Mitsuru Igamimitsuru.igami@yale.eduNathan Yangnathan.cc.yang@gmail.com We develop a dynamic entry model of multi‐store oligopoly with heterogeneous markets, and estimate it using data on hamburger chains in Canada (1970–2005). Because more lucrative markets attract more entry, firms appear to favor the presence of more rivals. Thus unobserved heterogeneity across geographical markets creates an endogeneity problem and poses a methodological challenge in the estimation of dynamic games, which we address by combining the procedures proposed by Kasahara and Shimotsu (2009), Arcidiacono and Miller (2011), and Bajari, Benkard, and Levin (2007). The results suggest that the omission of unobserved market heterogeneity attenuates the estimates of competition, and the trade‐off between cannibalization and preemption is an important factor behind the evolution of market structure. Dynamic oligopoly entry and exit entry deterrence market structure preemption unobserved heterogeneity L13 L812016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/376Pooling data across markets in dynamic Markov games2016-07-28T03:41:28+00:00Taisuke Otsut.otsu@lse.ac.ukMartin Pesendorferm.pesendorfer@lse.ac.ukYuya Takahashiytakahashi@jhu.edu This paper proposes several statistical tests for finite state Markov games to examine whether data from distinct markets can be pooled. We formulate homogeneity tests of (i) the conditional choice and state transition probabilities, (ii) the steady‐state distribution, and (iii) the conditional state distribution given an initial state. The null hypotheses of these homogeneity tests are necessary conditions (or maintained assumptions) for poolability of the data. Thus rejections of these null imply that the data cannot be pooled across markets. Acceptances of these null are considered as supporting evidences for the maintained assumptions of estimation using pooled data. In a Monte Carlo study we find that the test based on the steady‐state distribution performs well and has high power even with small numbers of markets and time periods. We apply the tests to the empirical study of Ryan (2012) that analyzes dynamics of the U.S. Portland cement industry and assess if the data across markets can be pooled. Dynamic Markov game poolability multiplicity of equilibria hypothesis testing C12 C72 D442016-06-30T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/377Identification and estimation of semiparametric two‐step models2016-07-28T03:41:29+00:00Juan Carlos Escancianojescanci@indiana.eduDavid Jacho‐Chávezdjachocha@emory.eduArthur Lewbellewbel@bc.edu Let H0(X) be a function that can be nonparametrically estimated. Suppose E [Y|X]=F0[X⊤β0, H0(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β0 and unknown function F0 are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional forms. We propose an estimator with asymptotic properties allowing for data dependent bandwidths and random trimming. A Monte Carlo experiment and an empirical application to migration decisions are also included. Identification by functional form double index models two‐step estimators semiparametric regression control function estimators sample selection models empirical process theory limited dependent variables migration C13 C14 C21 D242016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/378Drifts and volatilities under measurement error: Assessing monetary policy shocks over the last century2016-07-28T03:41:30+00:00Pooyan Amir‐Ahmadiamir@econ.uni-frankfurt.deChristian Mattheschristian.matthes@rich.frb.orgMu‐Chun WangMu-Chun.Wang@wiso.uni-hamburg.de How much have the dynamics of U.S. time series changed over the last century? Has the evolution of the Federal Reserve as an institution over the 100 years altered the transmission of monetary policy shocks? To tackle these questions, we build a multivariate time series model with time‐varying parameters and stochastic volatility that features measurement errors in observables. We find substantial changes in the structure of the economy. There is also large variation in the impact of monetary policy shocks, but the majority of this variation is driven by changes in exogenous volatility. Bayesian VAR time variation measurement error U.S. monetary policy C50 E31 N122016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/379The development and spread of financial innovations2016-07-28T03:41:32+00:00Isaiah Hullisaiah.hull@riksbank.se I study financial product innovation in a model with two classes of agents: “sophisticated” and “unsophisticated.” Unsophisticated agents are hit with frictions that lower the return to a conventional asset they hold. Sophisticated agents construct financial innovations that are perfect substitutes for the conventional asset, but are not subject to the friction. In the absence of complete information, unsophisticated agents learn about innovations through a contagion process, as they encounter competitors who have already adopted them. The model yields two equilibria: in one, the innovation persists; in the other, it disappears. Only one equilibrium is stable, and this is determined by the strength of the contagion and by early strategic interactions between sophisticated agents. The model suggests mechanisms for several empirical regularities in the financial innovation literature. Additionally, two applications demonstrate how to estimate the contagion parameter with a short time series of data, and how to use it to predict whether a financial innovation will spread. Financial innovation innovation financial markets G10 G12 G14 O31 O332016-06-29T00:00:00+00:00http://qeconomics.org/ojs/index.php/qe/article/view/380Perturbation methods for Markov‐switching dynamic stochastic general equilibrium models2016-07-28T03:41:32+00:00Andrew Foersterandrew.foerster@kc.frb.orgJuan F. Rubio‐Ramírezjuan.rubio-ramirez@emory.eduDaniel F. Waggonerdwaggoner@frbatlanta.orgTao Zhazmail@tzha.net Markov‐switching dynamic stochastic general equilibrium (MSDSGE) modeling has become a growing body of literature on economic and policy issues related to structural shifts. This paper develops a general perturbation methodology for constructing high‐order approximations to the solutions of MSDSGE models. Our new method—“the partition perturbation method”—partitions the Markov‐switching parameter space to keep a maximum number of time‐varying parameters from perturbation. For this method to work in practice, we show how to reduce the potentially intractable problem of solving MSDSGE models to the manageable problem of solving a system of quadratic polynomial equations. This approach allows us to first obtain all the solutions and then determine how many of them are stable. We illustrate the tractability of our methodology through two revealing examples. Partition principle naive perturbation quadratic polynomial system Taylor series high‐order expansion time‐varying coefficients nonlinearity Gröbner bases C6 E3 G12016-06-30T00:00:00+00:00