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on Discrete Choice Models |
| By: | Cai, Liang; Browning, Christopher; Anselin, Luc |
| Abstract: | Despite considerable focus on clustering as a dimension of segregation and the explosion of big location data, extant literature has not explicitly examined residential segregation and the clustering of segregated space as an influence on mobility. Integrating urban sociological theories and decision science, we test criteria contributing to individuals’ selection of activity neighborhoods. Using a range of spatial data sources, we compare Whites and Blacks’ choice of frequently visited neighborhoods in Chicago, stratified by whether residing in a contiguous segregated space (CSC). Discrete choice models show strong evidence for the impact of clustered residential segregation in individual decision making. All groups are drawn/compelled to White CSC neighborhoods, largely due to the relative institutional, amenity, and safety advantages of these areas. The Black CSC boundary functions as an “invisible wall” to CSC-residing Blacks, limiting their exposure to advantaged White CSC neighborhoods. Whites exhibit a net avoidance to Black-majority spaces, CSC and non-CSC alike. Blacks are drawn to racially homophilous Black neighborhoods, potentially due to social interaction opportunities, spatial knowledge, and prior habits. Results are robust to alternative specifications of choice sets and organizational deficits. Implications for understanding spatial choice in social context and designing de-segregation policies through behavioral “nudges” are discussed. |
| Date: | 2024–11–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:xh7aj |
| By: | Fang Ying (Xiamen University) |
| Abstract: | To make the conventional synthetic control methods more |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:01 |
| By: | Zhou Xianbo (Sun Yat-sen University) |
| Abstract: | The interaction effect in endogenous probit models with an interaction term is consistently estimated in Zhou and Li (2021). However, the estimation and test are time-consuming when the sample size is large. In this presentation, a new Stata command, eivprobit, is developed to implement Zhou–Li's method in much less time. Besides, the marginal effects of the two interacted regressors and the quadratic effect of a regressor with a squared term can also be estimated by the command. The eivprobit estimation is based on the control function approach and the standard errors of the estimated effects are obtained by nonparametric bootstrapping. Moreover, the |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:07 |
| By: | Teona Tavdishvili (The University of Georgia); Ekaterine Maglakelidze (The University of Georgia) |
| Abstract: | This article examines the differences in preferences between generations regarding influencer marketing.The aim of the research was to study the preferences of these generations in relation to content offered by influencers. The role of influencers in eliciting desired responses from "Generation Z" and their rating were assessed. The research method is quantitative research. The study also highlights the role of influencer marketing in shaping consumer behavior and perception among Generations Y and Z, emphasizing the importance of influencers' knowledge, experience, sincerity, and platform relevance in ensuring the effectiveness of influencer marketing.The results of the study are valuable for companies aiming to reach Generation Y and Z consumers more effectively and efficiently through digital platforms. |
| Keywords: | Influencer marketing, Generation Y, Generation Z, Consumer preferences, Marketing strategies |
| JEL: | M31 M37 |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14716442 |
| By: | Pina-Sánchez, Jose (University of Leeds); Hamilton, Melissa; Tennant, Peter WG |
| Abstract: | To minimise confounding bias and facilitate the identification of unwarranted disparities, sentencing researchers have traditionally sought to control for as many legal factors as possible. In this article we challenge such approach. Using causal graphs we show how controlling for commonly used variables in the sentencing literature can introduce bias. Instead, we propose a new modelling framework that clarifies which types of controls are necessary to identify different definitions of sentencing disparities. We apply this framework to the estimation of race disparities in the US federal courts and gender disparities in the England and Wales magistrates’ court. We find that the model uncertainty associated to the choice of controls is substantial for gender disparities and for race disparities affecting Hispanic offenders, rendering estimates of the latter inconclusive. Disparities against black offenders are more consistent, although, they are not strong enough to be seen as definitive evidence of racial discrimination. |
| Date: | 2024–11–17 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ymzsv |
| By: | Gabriel Rodriguez-Rondon; Jean-Marie Dufour |
| Abstract: | We present the R package MSTest, which implements hypothesis testing procedures to identify the number of regimes in Markov switching models. These models have wide-ranging applications in economics, finance, and numerous other fields. The MSTest package includes the Monte Carlo likelihood ratio test procedures proposed by Rodriguez-Rondon and Dufour (2024), the moment-based tests of Dufour and Luger (2017), the parameter stability tests of Carrasco, Hu, and Ploberger (2014), and the likelihood ratio test of Hansen (1992). Additionally, the package enables users to simulate and estimate univariate and multivariate Markov switching and hidden Markov processes, using the expectation-maximization (EM) algorithm or maximum likelihood estimation (MLE). We demonstrate the functionality of the MSTest package through both simulation experiments and an application to U.S. GNP growth data. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08188 |
| By: | Di Liu (StataCorp LLC) |
| Abstract: | You can use treatment-effects estimators to draw causal inferences from observational data. You can use lasso when you want to control for many potential covariates. With standard treatment-effects models, there is an intrinsic con |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:09 |
| By: | Li Zhanfeng (Zhongnan University of Economics and Law) |
| Abstract: | Mediation models with censored outcomes play a crucial role in social and medical sciences. However, the inherent censoring characteristics of the data often lead existing models to rely on assumptions of linearity, homogeneity, and normality for estimation. Unfortunately, these assumptions may not align with the complexities of real-world problems, limiting the persuasiveness of causal analyses. In this study, I investigate causal mediation analysis within a counterfactual framework by framing it as a neural style transfer problem commonly encountered in image processing. Acknowledging the impressive capabilities of generative adversarial networks (GANs) in handling neural style transfer, I propose a novel GAN-based model named generative adversarial censored mediation network to address mediation issues under my concern. My model employs recti |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:08 |
| By: | Bram De Rock (Institute for Fiscal Studies); Florine Le Henaff (European Center for Advanced Research in Economics and Statistics) |
| Date: | 2023–11–06 |
| URL: | https://d.repec.org/n?u=RePEc:ifs:ifsewp:23/34 |