ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)最新文献_第3页

Detection of Health Insurance Fraud with Discrete Choice Model: Evidence from Medical Expense Insurance in China 用离散选择模型检测医疗保险欺诈:来自中国医疗费用保险的证据

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2014-06-15 DOI: 10.2139/ssrn.2459343

Yi Yao, Qixiang Sun, Shan-Hui Lin

引用次数: 1

A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models 选择模型内生性和异质性的贝叶斯半参数方法

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2014-05-01 DOI: 10.2139/ssrn.2270993

Yang Li, Asim Ansari

{"title":"A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models","authors":"Yang Li, Asim Ansari","doi":"10.2139/ssrn.2270993","DOIUrl":"https://doi.org/10.2139/ssrn.2270993","url":null,"abstract":"Marketing variables that are included in consumer discrete choice models are often endogenous. Extant treatments using likelihood-based estimators impose parametric distributional assumptions, such as normality, on the source of endogeneity. These assumptions are restrictive because misspecified distributions have an impact on parameter estimates and associated elasticities. The normality assumption for endogeneity can be inconsistent with some marginal cost specifications given a price-setting process, although they are consistent with other specifications. In this paper, we propose a heterogeneous Bayesian semiparametric approach for modeling choice endogeneity that offers a flexible and robust alternative to parametric methods. Specifically, we construct centered Dirichlet process mixtures CDPM to allow uncertainty over the distribution of endogeneity errors. In a similar vein, we also model consumer preference heterogeneity nonparametrically via a CDPM. Results on simulated data show that incorrect distributional assumptions can lead to poor recovery of model parameters and price elasticities, whereas the proposed semiparametric model is able to robustly recover the true parameters in an efficient fashion. In addition, the CDPM offers the benefits of automatically inferring the number of mixture components that are appropriate for a given data set and is able to reconstruct the shape of the underlying distributions for endogeneity and heterogeneity errors. We apply our approach to two scanner panel data sets. Model comparison statistics indicate the superiority of the semiparametric specification and the results show that parameter and elasticity estimates are sensitive to the choice of distributional forms. Moreover, the CDPM specification yields evidence of multimodality, skewness, and outlying observations in these real data sets. \u0000 \u0000Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1811 . \u0000 \u0000This paper was accepted by J. Miguel Villas-Boas, marketing.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127195266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

What Clients Want: Choices between Lawyers' Offerings 客户想要什么:律师服务之间的选择

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2014-02-10 DOI: 10.2139/ssrn.2591304

F. Felsö, S. Onderstal, J. Seldeslachts

引用次数: 1

Compensated Discrete Choice with Particular Reference to Labor Supply 特别考虑劳动力供给的补偿离散选择

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2014-01-31 DOI: 10.2139/ssrn.2393896

J. Dagsvik, S. Strøm, Marilena Locatelli

{"title":"Compensated Discrete Choice with Particular Reference to Labor Supply","authors":"J. Dagsvik, S. Strøm, Marilena Locatelli","doi":"10.2139/ssrn.2393896","DOIUrl":"https://doi.org/10.2139/ssrn.2393896","url":null,"abstract":"Dagsvik and Karlstrom (2005) have demonstrated how one can compute Compensating Variation and Compensated Choice Probabilities by means of analytic formulas in the context of discrete choice models. In this paper we offer a new and simplified derivation of the compensated probabilities. Subsequently, we discuss the application of this methodology to compute compensated labor supply responses (elasticities) in a particular discrete choice labor supply model. Whereas the Slutsky equation holds in the case of the standard microeconomic model with deterministic preferences, this is not so in the case of random utility models. When the non-labor income elasticity is negative the Slutsky equation implies that the compensated wage elasticity is higher than the uncompensated one. In contrast, in our random utility model we show empirically that in a majority of cases the uncompensated wage elasticity is in fact the highest one. We also show that when only the deterministic part of the utility function is employed to yield optimal hours and related elasticities, these elasticities are numerically much higher and decline more sharply across deciles than the random utility ones.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134203984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Perceived Unreliability of Rank-Ordered Data: An Econometric Origin and Implications 秩序数据的感知不可靠性:计量经济学的起源和影响

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2012-11-04 DOI: 10.2139/ssrn.2172145

H. I. Yoo

引用次数: 3

A Copula-Based Direct Utility Approach with Various Correlations 基于copula的多种关联直接效用方法

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2012-11-01 DOI: 10.2139/ssrn.2174789

D. Jun, Chul Kim

引用次数: 0

A Tractable Estimator for General Mixed Multinomial Logit Models 一般混合多项Logit模型的可处理估计

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2012-09-01 DOI: 10.26509/WP-201219

J. James

引用次数: 2

Accounting Information Releases and CDS Spreads 会计信息发布和CDS价差

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2012-01-19 DOI: 10.2139/ssrn.1874127

Redouane Elkamhi, Kris Jacobs, Hugues Langlois, Chayawat Ornthanalai

引用次数: 18

Improving the Art, Craft and Science of Economic Credit Risk Scorecards Using Random Forests: Why Credit Scorers and Economists Should Use Random Forests 使用随机森林改进经济信用风险记分卡的艺术、工艺和科学:为什么信用评分者和经济学家应该使用随机森林

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2011-06-09 DOI: 10.2139/ssrn.1861535

Dhruv Sharma

引用次数: 13

Some Experiments Comparing Logistic Regression and Random Forests Using Synthetic Data and the Interaction Miner Algorithm 使用合成数据和交互挖掘算法比较逻辑回归和随机森林的一些实验

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2011-06-05 DOI: 10.2139/ssrn.1858424

Dhruv Sharma

{"title":"Some Experiments Comparing Logistic Regression and Random Forests Using Synthetic Data and the Interaction Miner Algorithm","authors":"Dhruv Sharma","doi":"10.2139/ssrn.1858424","DOIUrl":"https://doi.org/10.2139/ssrn.1858424","url":null,"abstract":"This paper uses synthetic datasets to classify the conditions in which random forest may outperform more traditional techniques such as logistic regression. We explore the theoretical implications of these experimental findings, and work towards building a theory based approach to data mining. During the course of these experiments we take the simulations where random forests dominate and add additional dimensionality to the data and run logistic regression using the additional attributes through the I* interaction miner algorithm outlined in Sharma 2011. Using the I* procedure with adequate amount of interaction terms the logistic regression can be made to match performance of random forests in the synthetic data sets where random forests dominate (Sharma, 2011). This makes it seem the interaction miner algorithm along with some minimal sufficient amount of interaction and transformations allow logistic regression to match ensemble performance. This implies that, without a certain amount of dimensionality in the data interaction, miner and logistic regression do not benefit from the interactions. Breiman and other work shows Random Forests thrive on dimensionality that said from experiences with various data sets adding additional artificial dimensionality doesn’t help forest (Breiman, 2001). There appears to be some minimum or necessary and sufficient amount of dimensionality after which more information cannot be extracted from the data. The good news is dimensionality can be created using the icreater function which add Tukey’s re-expressions automatically to the data (log, negative reciprocal, and sqrt).","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134266280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2