Quantitative Economics, July 2022, Volume 13, Issue 3, now online

Quantitative Economics

TABLE OF CONTENTS, July 2022, Volume 13, Issue 3
Full Issue

Articles
Abstracts follow the listing of articles.

A discrete choice model for partially ordered alternatives
Eleni Aristodemou, Adam M. Rosen

Minimizing sensitivity to model misspecification
Stéphane Bonhomme, Martin Weidner

Unconditional quantile regression with high‐dimensional data
Yuya Sasaki, Takuya Ura, Yichong Zhang

Uncertainty measures from partially rounded probabilistic forecast surveys
Alexander Glas, Matthias Hartmann

Permanent‐income inequality
Brant Abbott, Giovanni Gallipoli

Rising skill premium and the dynamics of optimal capital and labor taxation
Yi‐Chan Tsai, C. C. Yang, Hsin‐Jung Yu

The importance of hiring frictions in business cycles
Renato Faccini, Eran Yashiv

Asymmetric conjugate priors for large Bayesian VARs
Joshua C. C. Chan

The extended perturbation method: With applications to the New Keynesian model and the zero lower bound
Martin M. Andreasen, Anders F. Kronborg

Secret reserve prices by uninformed sellers
Pasha Andreyanov, El Hadi Caoui

Consumption peer effects and utility needs in India
Arthur Lewbel, Samuel Norris, Krishna Pendakur, Xi Qu

Choice, deferral, and consistency
Miguel A. Costa‐Gomes, Carlos Cueva, Georgios Gerasimou, Matúš Tejiščák

A discrete choice model for partially ordered alternatives
Eleni Aristodemou, Adam M. Rosen

Abstract

 

In this paper, we analyze a discrete choice model for partially ordered alternatives. The alternatives are differentiated along two dimensions: the first an unordered “horizontal” dimension, and the second an ordered “vertical” dimension. The model can be used in circumstances in which individuals choose among products of different brands, wherein each brand offers an ordered choice menu, for example, by offering products of varying quality. The unordered–ordered nature of the discrete choice problem is used to characterize the identified set of model parameters. Following an initial nonparametric analysis that relies on shape restrictions inherent in the ordered dimension of the problem, we then provide a specialized analysis for parametric specifications that generalize common ordered choice models. We characterize conditional choice probabilities as a function of model primitives with particular analysis focusing on cases in which unobservable taste for quality of each brand offering is multivariate normally distributed. We provide explicit formulae used for estimation and inference via maximum likelihood, and we consider inference based on Wald and quasi‐likelihood ratio statistics, the latter of which can be robust to a possible lack of point identification. An empirical illustration is conducted using data on razor blade purchases in which each brand has product offerings vertically differentiated by quality.

Discrete choice models ordered response differentiated products C01 C31 C35
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Minimizing sensitivity to model misspecification
Stéphane Bonhomme, Martin Weidner

Abstract

 

 

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. As illustrations, we study three applications: an empirical analysis of the impact of conditional cash transfers in Mexico where misspecification stems from the presence of stigma effects of the program, a cross‐sectional binary choice model where the error distribution is misspecified, and a dynamic panel data binary choice model where the number of time periods is small and the distribution of individual effects is misspecified.

Model misspecification robustness sensitivity analysis C13 C23
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Unconditional quantile regression with high‐dimensional data
Yuya Sasaki, Takuya Ura, Yichong Zhang

Abstract

 

 

This paper considers estimation and inference for heterogeneous counterfactual effects with high‐dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux (2009)) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that a policy, which counterfactually extends the duration of exposures to the Job Corps training program, will be effective especially for the targeted subpopulations of lower potential wage earners.

Counterfactual analysis debiased machine learning doubly/locally robust score C14 C21
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Uncertainty measures from partially rounded probabilistic forecast surveys
Alexander Glas, Matthias Hartmann

Abstract

 

 

Although survey‐based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Professional forecasters who report inconspicuously low ex ante variances often produce squared forecast errors that are much larger on average. In this paper, we document the novel stylized fact that this variance misalignment is related to the rounding behavior of survey participants. Rounding may reflect the fact that some survey participants employ a rather judgmental approach to forecasting as opposed to using a formal model. We use the distinct numerical accuracies of panelists' reported probabilities as a way to propose several alternative and easily implementable corrections that (i) can be carried out in real time, that is, before outcomes are observed, and (ii) deliver a significantly improved match between ex ante and ex post forecast uncertainty. According to our estimates, uncertainty about inflation, output growth and unemployment in the U.S. and the Euro area is higher after correcting for the rounding effect. The increase in the share of nonrounded responses in recent years also helps to understand the trajectory of survey‐based average uncertainty during the years since the financial and sovereign debt crisis.

Survey data probabilistic forecasting rounding uncertainty C32 C52 C53 C83
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Permanent‐income inequality
Brant Abbott, Giovanni Gallipoli

Abstract

 

 

Through certainty equivalent consumption (CE) measures, we show that dispersion of current earnings, expenditures, and net worth overstate welfare inequality. This is largely due to the unaccounted value of future earnings, which we call human wealth. The latter mitigates permanent‐income inequality, though its influence is diminished by the growing importance of assets in lifetime wealth. Average expenditures and CE inequality roughly doubled between 1983 and 2016 and, to weigh these offsetting forces, we decompose aggregate welfare changes into contributions from the level and dispersion of consumption, as well as uncertainty and demographic composition. Rising inequality has offset about 1/4 of the welfare gains from higher consumption, with most of the losses accruing after 2000.

Wealth human capital permanent income consumption inequality D31 E2 E21 I24
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Rising skill premium and the dynamics of optimal capital and labor taxation
Yi‐Chan Tsai, C. C. Yang, Hsin‐Jung Yu

Abstract

 

 

With capital‐skill complementarity, the secular decline in the price of capital equipment due to equipment‐specific technological progress (ESTP) keeps pushing up the demand for skilled relative to unskilled labor and raising the skill premium. This paper quantitatively characterizes the dynamics of optimal taxation in response. Two main results emerge, regardless of whether the Ramsey (1927) or the Mirrlees (1971) approach is adopted. First, a tax on capital equipment corrects the “pecuniary externalities” caused by ESTP. The correction prescribes a downward or an upward adjustment of tax rates over time, depending on whether ESTP takes place at an accelerated or a decelerated pace. Second, both Ramsey and Mirrlees approaches prescribe an increasing marginal tax rate on labor income over time. Interestingly, we find that the prescribed pattern of optimal taxation resembles the empirical decline in capital taxes and the increase in labor taxes observed in the United States. In particular, despite the significant rise in the skill premium, the welfare gains of tax reform toward optimal Ramsey taxes are modest and small.

Skill premium optimal taxation capital‐skill complementarity equipment‐specific technological progress C61 E22 E62 H21
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The importance of hiring frictions in business cycles
Renato Faccini, Eran Yashiv

Abstract

 

 

Hiring is a costly activity reflecting firms' investment in their workers. Microdata show that hiring costs involve production disruption. Thus, cyclical fluctuations in the value of output, induced by price frictions, have consequences for the optimal allocation of hiring activities. We outline a mechanism based on cyclical markup fluctuations, placing emphasis on hiring frictions interacting with price frictions. This mechanism generates strong propagation and amplification of all key macroeconomic variables in response to technology shocks and mutes the traditional transmission of monetary policy shocks. A local projection analysis of aggregate U.S. data shows that the empirical results, including the cyclicality of markups, are consistent with the model's impulse response functions.

Business cycles propagation and amplification markup cyclicality hiring as investment intertemporal allocation confluence of hiring and price frictions E22 E24 E32 E52
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Asymmetric conjugate priors for large Bayesian VARs
Joshua C. C. Chan

Abstract

 

 

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as it rules out cross‐variable shrinkage, that is, shrinking coefficients on lags of other variables more aggressively than those on own lags. We develop a prior that has the best of both worlds: it can accommodate cross‐variable shrinkage, while maintaining many useful analytical results, such as a closed‐form expression of the marginal likelihood. This new prior also leads to fast posterior simulation—for a BVAR with 100 variables and 4 lags, obtaining 10,000 posterior draws takes less than half a minute on a standard desktop. We demonstrate the usefulness of the new prior via a structural analysis using a 15‐variable VAR with sign restrictions to identify 5 structural shocks.

Shrinkage prior marginal likelihood optimal hyperparameters structural VAR sign restrictions C11 C52 C55 E44
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The extended perturbation method: With applications to the New Keynesian model and the zero lower bound
Martin M. Andreasen, Anders F. Kronborg

Abstract

 

 

We introduce the extended perturbation method, which improves the accuracy of standard perturbation by reducing approximation errors under certainty equivalence. For the New Keynesian model with Calvo pricing, extended perturbation is more accurate than standard perturbation, which implies explosive dynamics because it omits the upper bound on inflation implied by this model. In contrast, extended perturbation enforces this bound and generates stable dynamics. We also show that extended perturbation can accurately solve a New Keynesian model that enforces the zero lower bound for the monetary policy rate by considering a smooth nonlinear modification of the standard Taylor rule.

Stability of perturbation approximations upper bound on inflation zero lower bound on policy rate C62 C68 E30
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Secret reserve prices by uninformed sellers
Pasha Andreyanov, El Hadi Caoui

Abstract

 

 

If bidders are better informed than the seller about a common component of auction heterogeneity, the seller can allocate more efficiently by keeping her reserve price secret and revising it using submitted bids. We build a model of a first‐price auction under unobserved auction heterogeneity—imperfectly observed by the seller—that captures this rationale and derive conditions for identification. An application to French timber auctions, where such revisions are widely used, shows that having perfect information about unobserved auction heterogeneity would increase surplus by 5.22%. Combining a secret reserve price with learning from submitted bids reduces this surplus gap by up to 84%.

First‐price auction reserve price unobserved heterogeneity timber industry C57 D44 Q23
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Consumption peer effects and utility needs in India
Arthur Lewbel, Samuel Norris, Krishna Pendakur, Xi Qu

Abstract

 

 

We construct a peer effects model where mean expenditures of consumers in one's peer group affect utility through perceived consumption needs. We provide a novel method for obtaining identification in social interactions models like ours, using ordinary survey data, where very few members of each peer group are observed. We implement the model using standard household‐level consumer expenditure survey microdata from India. We find that each additional rupee spent by one's peers increases perceived needs, and thereby reduces one's utility, by the equivalent of a 0.25 rupee decrease in one's own expenditures. These peer costs may be larger for richer households, meaning transfers from rich to poor could improve even inequality‐neutral social welfare, by reducing peer consumption externalities. We show welfare gains of billions of dollars per year might be possible by replacing government transfers of private goods to households with providing public goods or services, to reduce peer effects.

Consumer demand consumption measurement error welfare peer effects C21 C31 D12
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Choice, deferral, and consistency
Miguel A. Costa‐Gomes, Carlos Cueva, Georgios Gerasimou, Matúš Tejiščák

Abstract

 

 

We report on two novel choice experiments with real goods where subjects in one treatment are forced to choose, as is the norm in economic experiments, while in the other they are not but can instead incur a small cost to defer choice. Using a variety of measures, we find that the active choices (i.e., those that exclude the deferral outside option) of subjects in the nonforced‐choice treatment are generally more consistent. We also find that the combined deferral and active‐choice behavior of subjects in that treatment is explained better by a model of dominant choice with incomplete preferences than it is by rational choice. Our results suggest that nonforced‐choice experiments and models can be helpful in separating people's rational, hesitant/not‐yet‐rational and genuinely irrational behavior, and can potentially offer important new insights in revealed preference analysis.

Choice deferral active choices choice consistency revealed preferences decision difficulty experiments C91 D01 D03 D11 D12

 

 

Publication Date: 
Wednesday, July 13, 2022

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