nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2023‒03‒20
nine papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. An investment decision tool for adaptive building re-use By Brano Glumac
  2. Identification and Estimation of Categorical Random Coefficient Models By Zhan Gao; M. Hashem Pesaran
  3. Factors Shaping Innovative Behavior: A Meta-Analysis of Technology Adoption Studies in Agriculture By Konstantinos Chatzimichael; Charoula Daskalaki; Gregory Emvalomatis; Michail Tsagris; Vangelis Tzouvelekas
  4. Energy efficiency in institutional investment strategies – Large sample evidence from Germany and the UK By Marcelo Cajias; Anett Wins
  5. From bounded rationality to limited consideration: representation and behavioral analysis By Davide Carpentiere; Angelo Petralia
  6. ddml: Double/Debiased Machine Learning in Stata By Ahrens, Achim; Hansen, Christian B.; Schaffer, Mark E; Wiemann, Thomas
  7. Effectiveness and Heterogeneous Effects of Purchase Grants for Electric Vehicles By Peter Haan; Adrián Santonja; Aleksandar Zaklan
  8. Stochastic choice and imperfect judgments of line lengths: What is hiding in the noise? By Sean, Duffy; John, Smith
  9. Copula-based estimation of health inequality measures with an application to COVID-19 By Taoufik Bouezmarni; Mohamed Doukali; Abderrahim Taamouti

  1. By: Brano Glumac
    Abstract: The purpose of this paper is to support a better decision in choosing the most suitable vacant office(building) to house new tenants. In order to achieve that a decision support tool (DSS) based on three methods is proposed. First, a discrete choice model (DCM) can estimate the future tenants' rent willingness to pay (WTP) based on the data generated with an online experiment. Second, a multiple-criteria decision analysis (MCDA) used a pair-wise comparison of building experts to establish the weight of criteria for a building transformation potential. Afterwards, an MCDA “multiplied” with the officially published cost approximation for five different levels of transformation. Lastly, rent WTP from DCM and transformation costs from MCDA are included in a discounted cash flow (DCF). With this DCF we can rank many buildings that are available on the market and make their preselection. The possibilities of this tool have been tested in a case study. Although applying decision tools for the building transformation projects has been studied, this paper suggests a specific tool that supports the transition from office space to housing for young people.
    Keywords: DCF; discrete choice model; pairwise comparison; Real Options
    JEL: R3
    Date: 2022–01–01
  2. By: Zhan Gao; M. Hashem Pesaran
    Abstract: This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of Moments estimation procedure is proposed also employed by Peter Schmidt and his coauthors to address heterogeneity in time effects in panel data models. Using Monte Carlo simulations, we find that moments of the random coefficients can be estimated reasonably accurately, but large samples are required for estimation of the parameters of the underlying categorical distribution. The utility of the proposed estimator is illustrated by estimating the distribution of returns to education in the U.S. by gender and educational levels. We find that rising heterogeneity between educational groups is mainly due to the increasing returns to education for those with postsecondary education, whereas within group heterogeneity has been rising mostly in the case of individuals with high school or less education.
    Date: 2023–02
  3. By: Konstantinos Chatzimichael; Charoula Daskalaki; Gregory Emvalomatis; Michail Tsagris; Vangelis Tzouvelekas (Department of Economics, University of Crete, Greece)
    Abstract: In this paper, we employ a meta-regression analysis approach to synthesize empirical evidence on the average partial effects of eleven adoption determinants that regularly appear in empirical studies examining farmer's adoption behavior worldwide. Our analysis considers a total of 122 studies from the adoption literature using discrete choice models that are published in 24 peer-reviewed journals since 1985, covering farmer's adoption behavior around the world and for a wide variety of agricultural technologies.
    Keywords: Agricultural technology; Technology adoption; Average partial effect; Meta-regression analysis; Publication bias
    JEL: C21 D22 Q16 Q18
    Date: 2023–01–20
  4. By: Marcelo Cajias; Anett Wins
    Abstract: Whilst there is a broad consensus that energy efficiency as measured by the environmental performance certificates leads to higher asking rents, there is little evidence about investment strategies that consider energy efficiency as an optimisation factor. This paper focusses on identifying the conditions that lead to the highest increase in the willingness to pay for energy-conscious refurbishment. By making use of more than 1.5 m observations in Germany and the UK we disaggregate the expected willingness to pay to spatial, socioeconomic, and hedonic characteristics via Generalized Additive Models (GAMs). In a simulation study, we show that an investment strategy in residential real estate can be optimised via intelligent asset selection considering energy efficiency as an optimisation factor.
    Keywords: Energy Performance Certificate; housing; Machine Learning; Non linear effects
    JEL: R3
    Date: 2022–01–01
  5. By: Davide Carpentiere; Angelo Petralia
    Abstract: Many bounded rationality approaches discussed in the literature are models of limited consideration. We provide a novel representation and data interpretation for some of the analyzed behavioral patterns. Moreover, we characterize a testable choice procedure that allows the experimenter to uniquely infer limited consideration from irrational features of the observed behavior.
    Date: 2023–02
  6. By: Ahrens, Achim (Economic and Social Research Institute, Dublin); Hansen, Christian B. (University of Chicago); Schaffer, Mark E (Heriot-Watt University, Edinburgh); Wiemann, Thomas (University of Chicago)
    Abstract: We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.
    Keywords: st0001, causal inference, machine learning, doubly-robust estimation
    JEL: C14 C21 C87
    Date: 2023–02
  7. By: Peter Haan; Adrián Santonja; Aleksandar Zaklan
    Abstract: We evaluate German purchase subsidies for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) using data on new vehicle registrations in Germany during 2015-2022. We account for confounding time trends and interacting EU-level CO2 standards using neighboring countries as a control group. The program was cost-ineffective, as only 40% of BEV and 25% of PHEV registrations were subsidy-induced, and had strong distributional effects, with greater uptake in wealthier and greener counties. The implied abatement cost of 870 euro per ton of CO2 for BEVs and 2, 470 euro for PHEVs suggests that subsidies to PHEVs were especially cost-ineffective.
    Keywords: Decarbonizing road transport, electric mobility, purchase subsidies, policy effectiveness, distributional effects of climate policy
    JEL: Q54 Q58 H23 R48
    Date: 2023–02–13
  8. By: Sean, Duffy; John, Smith
    Abstract: Noise is a pervasive feature of economic choice. However, standard economics experiments are not well equipped to study the noise because experiments are constrained: preferences are either unknown or only imperfectly measured by experimenters. As a result of these designs--where the optimal choice is not observable to the analyst--many important questions about the noise in apparently random choice cannot be addressed. We design an experiment to better understand stochastic choice by directing subjects to make incentivized binary choices between lines. Subjects are paid a function of the length of the selected line, so subjects will attempt to select the longer of the lines. We find a gradual (not sudden) relationship between the difference in the lengths of the lines and the optimal choice. Our analysis suggests that the errors are better described as having a Gumbel distribution rather than a normal distribution, and our simulated data increase our confidence in this inference. We find evidence that suboptimal choices are associated with longer response times than optimal choices, which appears to be consistent with the predictions of Fudenberg, Strack, and Strzalecki (2018). Although we note that the relationship between response time and the optimality of choice becomes weaker across trials. In our experiment, 54 of 56 triples are consistent with Strong Stochastic Transitivity and this is the median outcome in our simulated data. Finally, we find a relationship between choice and attention, although we find strong evidence that the relationship is endogenous.
    Keywords: Stochastic transitivity, choice theory, judgment, memory, search
    JEL: C91 D12
    Date: 2023–02–17
  9. By: Taoufik Bouezmarni (Universite de Sherbrooke); Mohamed Doukali (School of Economics, University of East Anglia); Abderrahim Taamouti (University of Liverpool)
    Abstract: This paper aims to use copulas to derive alternative estimators of Health Concentration Curve, hereafter CH, and Gini coefficient for health distribution. We motivate the importance of expressing health inequality measures in terms of copula, which we in turn use to build copula-based semi and non-parametric estimators of the above measures. Thereafter, we study the asymptotic properties of these estimators. In particular, we establish their consistency and asymptotic normality. We provide expressions for their variances, which can be used to construct confidence intervals and build tests for health concentration curve and Gini health coe¢ cient. A Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Mean Squared Error. We also run an extensive empirical study where we apply our CH and Gini health coe¢ cient estimators to show that the inequalities across U.S. states socioeconomic variables like income/poverty and race/ethnicity explain the observed inequalities in the U.S. COVID-19s infections and deaths.
    Keywords: Health concentration curve, Gini health coe¢ cient, inequality, copula, semi- and non-parametric estimators, COVID-19 infections and deaths
    JEL: C13 C14 I14
    Date: 2023–01

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