
on Discrete Choice Models 
By:  Mark Ponder; Amil Petrin; Boyoung Seo 
Abstract:  The standard Berry, Levinsohn, and Pakes (1995) (BLP) approach to estimation of demand and supply parameters assumes that the product characteristic observed by consumers and producers but not the researcher is conditionally mean independent of observed characteristics. We extend BLP to allow all product characteristics to be endogenous, so the unobserved characteristic can be correlated with the observed characteristics. We derive moment conditions based on the assumption that firms choose product characteristics to maximize expected profits given their beliefs at that time about market conditions and that the “mistake” in the amount of the characteristic that is revealed once all products are on the market is conditionally mean independent of the firm's information set. Using the original BLP dataset we find that observed and unobserved product characteristics are highly positively correlated, biasing demand elasticities upward, as average estimated price elasticities double in absolute value and average markups fall by 50%. 
JEL:  C25 L0 
Date:  2022–12 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:30778&r=dcm 
By:  Nyamapheni, Joseph; Robinson , Zurika 
Abstract:  The article investigates tax morale during different economic milieus, going hand in hand with the introduction of different currency regimes. It was guided by econometric research and data were collected using questionnaires from the 20102014 and 20172020 World Values Survey (WVS). For Zimbabwe, Wave 6 and Wave 7 had a sample size of 1500 and 1200 respectively. The article?s dependent variable, tax morale and independent variables included marital status, age, income level, employment and religion among others, and analysed them using the Ordered Logit Model. The article concludes with an understanding of how tax morale and its determinants is crucial for governments in their bid to boost voluntary compliance. Also, different economic milieus for a particular country affect the level of tax morale significantly. Tax morale was established to be high when Zimbabwe was experiencing economic growth due to the introduction of multicurrency, herein called the dollarization period, and the opposite was true for the postdollarization era. Corruption, which is a menace under study, has proven to be an important factor that influences tax morale. Results of all the models show that demographic factors have little effect on tax morale. The article introduced an important variable of hunger in its analysis of determinants of tax morale. The article showed that there is a negative relationship between hunger and tax morale for Zimbabwe in both economic situations. Based on the findings, policy makers should consider the eradication of corruption and hunger in order to boost tax morale, which in turn improves tax compliance. Also, policy makers should include improvement in the perception of democracy in the mix of enhancement strategies of tax compliance. 
Keywords:  Determinants; Tax morale; Order Logit Model; Zimbabwe 
Date:  2022–12 
URL:  http://d.repec.org/n?u=RePEc:uza:wpaper:29690&r=dcm 
By:  Vardan G. Bardakhchyan; Armen E. Allahverdyan 
Abstract:  We study a sufficiently general regret criterion for choosing between two probabilistic lotteries. For independent lotteries, the criterion is consistent with stochastic dominance and can be made transitive by a unique choice of the regret function. Together with additional (and intuitively meaningful) superadditivity property, the regret criterion resolves the Allais' paradox including the cases were the paradox disappears, and the choices agree with the expected utility. This superadditivity property is also employed for establishing consistency between regret and stochastic dominance for dependent lotteries. Furthermore, we demonstrate how the regret criterion can be used in Savage's omelet, a classical decision problem in which the lottery outcomes are not fully resolved. The expected utility cannot be used in such situations, as it discards important aspects of lotteries. 
Date:  2023–01 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2301.02447&r=dcm 
By:  Elia Lapenta 
Abstract:  This paper provides a specification test for semiparametric models with nonparametrically generated regressors. Such variables are not observed by the researcher but are nonparametrically identified and estimable. Applications of the test include models with endogenous regressors identified by control functions, semiparametric sample selection models, or binary games with incomplete information. The statistic is built from the residuals of the semiparametric model. A novel wild bootstrap procedure is shown to provide valid critical values. We consider nonparametric estimators with an automatic bias correction that makes the test implementable without undersmoothing. In simulations the test exhibits good small sample performances, and an application to women's labor force participation decisions shows its implementation in a real data context. 
Date:  2022–12 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2212.11112&r=dcm 
By:  Ben Jann; Karlson, Kristian Bernt 
Abstract:  Coefficients from logistic regression are affected by noncollapsibility, which means that the comparison of coefficients across models may be misleading. Several strategies have been proposed in the literature to respond to these difficulties, the most popular of which is to report average marginal effects (on the probability scale) rather than odds ratios. Average marginal effects (AMEs) have many desirable properties but at least in part they throw the baby out with the bathwater. The size of an AME strongly depends on the marginal distribution of the dependent variable; for events that are very likely or very unlikely the AME necessarily has to be small because the probability space is bounded. Logistic regression, in contrast, estimates odds ratios which are free from such flooring and ceiling effects. Hence, odds ratios may be more appropriate than AMEs for comparison of effect sizes in many applications. Yet, logistic regression estimates conditional odds ratios, which are not comparable across different specifications. In this paper, we aim to remedy the declining popularity of the odds ratio by introducing an estimand that we term the "marginal odds ratio"; that is, logit coefficients that have properties similar to AMEs, but which retain the odds ratio interpretation. We define the marginal odds ratio theoretically in terms of potential outcomes, both for binary and continuous treatments, we develop estimation methods using three different approaches (Gcomputation, inverse probability weighting, RIF regression), and we present an example that illustrates the usefulness and interpretation of the marginal odds ratio. 
Keywords:  marginal odds ratio, noncollapsibility, logistic regression, Gcomputation, inverse probability weighting, recentered influence functions 
JEL:  C01 C25 C87 
Date:  2023–01–06 
URL:  http://d.repec.org/n?u=RePEc:bss:wpaper:44&r=dcm 
By:  Kristle Cortés; Mandeep Singh; David H. Solomon; Philip Strahan 
Abstract:  In Australian real estate markets, about a third of properties are sold at auction. We show that properties that fail auctions sell later for a 2.6% discount. This effect increases for properties failing multiple auctions and when no bids are made. Consistent with a causal channel, the effect holds when auction failure is instrumented by the tendency of owners to anchor on nearby better properties (and thus set reserve prices too high). Prices cluster just below salient round numbers, and the discount fades over time, inconsistent with our effects reflecting unobserved property characteristics. We test for several mechanisms and conclude that most of the pricing discounts reflect stigma, which reduces potential buyers’ willingness to pay. 
JEL:  G40 R3 
Date:  2022–12 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:30760&r=dcm 
By:  Harry Pickard (Newcastle University Business School, Newcastle University, United Kingdom); Thomas Dohmen (Economics Department, University of Bonn, Germany); Bert van Landeghem (Department of Economics, University of Sheffield, United Kingdom) 
Abstract:  This paper studies the relationship between income inequality and risk taking. Increased income inequality is likely to enlarge the scope for upward comparisons and, in the presence of referencedependent preferences, to increase willingness to take risks. Using a globally representative dataset on risk preference in 76 countries, we empirically document that the distribution of income in a country has a positive and significant link with the preference for risk. This relationship is remarkably precise and holds across countries and individuals, as well as alternate measures of inequality. We find evidence that individuals who are more able to understand inequality and individuals who fall behind their inherent point of reference increase their preference for risk. Two complementary instrumental variable approaches support a causal interpretation of our results. 
Keywords:  Income inequality; risk preference; risk sensitivity 
JEL:  D91 O15 D81 D01 
Date:  2023–01 
URL:  http://d.repec.org/n?u=RePEc:ajk:ajkdps:216&r=dcm 
By:  JeanPierre Florens; Elia Lapenta 
Abstract:  We consider a semiparametric partly linear model identified by instrumental variables. We propose an estimation method that does not smooth on the instruments and we extend the LandweberFridman regularization scheme to the estimation of this semiparametric model. We then show the asymptotic normality of the parametric estimator and obtain the convergence rates for the nonparametric estimator. Our estimator that does not smooth on the instruments coincides with a typical estimator that does smooth on the instruments but keeps the respective bandwidth fixed as the sample size increases. We propose a data driven method for the selection of the regularization parameter, and in a simulation study we show the attractive performance of our estimators. 
Date:  2022–12 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2212.11012&r=dcm 
By:  Ohyun Kwon; Jangsu Yoon; Yoto V. Yotov 
Abstract:  We propose a Generalized PoissonPseudo Maximum Likelihood (GPPML) estimator that relaxes the PPML estimator’s assumption that the dependent variable’s conditional variance is proportional to its conditional mean. Instead, we employ an iterated Generalized Method of Moments (iGMM) to estimate the conditional variance of the dependent variable directly from the data, thus encompassing the standard estimators in international trade literature (i.e., PPML, GammaPML, and OLS) as special cases. With conditional variance estimates, GPPML generates coefficient estimates that are more efficient and robust to the underlying data generating process. After establishing the consistency and the asymptotic properties of the GPPML estimator, we use Monte Carlo simulations to demonstrate that GPPML shows decent finitesample performance regardless of the underlying assumption about the conditional variance. Estimations of a canonical gravity model with trade data reinforce the properties of GPPML and validate the practical importance of our methods. 
Keywords:  PoissonPseudo Maximum Likelihood, Iterated GMM, Gravity Models 
JEL:  C13 C50 F10 
Date:  2022 
URL:  http://d.repec.org/n?u=RePEc:ces:ceswps:_10145&r=dcm 
By:  Annika Camehl (Erasmus University Rotterdam); Dennis Fok (Erasmus University Rotterdam); Kathrin Gruber (Erasmus University Rotterdam) 
Abstract:  In multipleoutput quantile regression the simultaneous study of multiple response variables requires multivariate quantiles. Current definitions of such quantiles often lack a clear probability interpretation, as the defined quantiles can cover large parts of the distribution where little probability mass is located or their enclosed area does not equal the quantile level. We suggest superlevelsets of conditional multivariate density functions as an alternative multivariate quantile definition. Such a quantile set contains all points in the domain for which the density exceeds a certain level. By applying this to a conditional density, the quantile becomes a function of the conditioning variables. We show that such a quantile has favorable mathematical and intuitive features. For implementation, we, first, use an overfitted Gaussian mixture model to fit the multivariate density and, next, calculate the multivariate quantile for a conditional or marginal density of interest. Operating on the same estimated multivariate density guarantees logically consistent quantiles. In particular, the quantiles at multiple percentiles are noncrossing. We use simulation to demonstrate that we recover the true quantiles for distributions with correlation, heteroskedasticity, or asymmetry in the disturbances and we apply our method to study heterogeneity in household expenditures. 
Keywords:  Multiple Response, Bayesian Quantile Regression, Gaussian Mixture Model 
Date:  2022–12–22 
URL:  http://d.repec.org/n?u=RePEc:tin:wpaper:20220094&r=dcm 