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.
{"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}
The problem of unstable coecients in the rank-ordered logit model has been traditionally interpreted as a sign that survey respondents fail to provide reliable ranking responses. This paper shows that the problem may embody the inherent sensitivity of the model to stochastic misspecification instead. Even a minor departure from the postulated random utility function can induce the problem, for instance when rank-ordered logit is estimated whereas the true additive disturbance is iid normal over alternatives. Related implications for substantive analyses and further modelling are explored. In general, a well-speci ed random coecient rank-ordered logit model can mitigate, though not eliminate, the problem and produce analytically useful results. The model can also be generalised to be more suitable for forecasting purposes, by accommodating that stochastic misspecification matters less for individuals with more deterministic preferences. An empirical analysis using an Australian nursing job preferences survey shows that the estimates behave in accordance with these implications.
{"title":"The Perceived Unreliability of Rank-Ordered Data: An Econometric Origin and Implications","authors":"H. I. Yoo","doi":"10.2139/ssrn.2172145","DOIUrl":"https://doi.org/10.2139/ssrn.2172145","url":null,"abstract":"The problem of unstable coecients in the rank-ordered logit model has been traditionally interpreted as a sign that survey respondents fail to provide reliable ranking responses. This paper shows that the problem may embody the inherent sensitivity of the model to stochastic misspecification instead. Even a minor departure from the postulated random utility function can induce the problem, for instance when rank-ordered logit is estimated whereas the true additive disturbance is iid normal over alternatives. Related implications for substantive analyses and further modelling are explored. In general, a well-speci ed random coecient rank-ordered logit model can mitigate, though not eliminate, the problem and produce analytically useful results. The model can also be generalised to be more suitable for forecasting purposes, by accommodating that stochastic misspecification matters less for individuals with more deterministic preferences. An empirical analysis using an Australian nursing job preferences survey shows that the estimates behave in accordance with these implications.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128853335","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}
A direct utility approach can handle multiple discrete/continuous choice outcomes. However, there is a trade-off between allowing correlations between unobserved perceived qualities of two alternatives and computational burden. If we allow the correlations, then we have to do numerical integrations for getting likelihoods, whereas if we give up the correlations, then we can get closed form likelihood. Thus, we suggest a new copula-based direct utility approach that not only allows the correlations but also provides closed form of likelihood. Empirically, we find the existence of the correlations between unobserved qualities of two alternatives. Ignoring the correlations may cause the misunderstanding of the joint-purchases.
{"title":"A Copula-Based Direct Utility Approach with Various Correlations","authors":"D. Jun, Chul Kim","doi":"10.2139/ssrn.2174789","DOIUrl":"https://doi.org/10.2139/ssrn.2174789","url":null,"abstract":"A direct utility approach can handle multiple discrete/continuous choice outcomes. However, there is a trade-off between allowing correlations between unobserved perceived qualities of two alternatives and computational burden. If we allow the correlations, then we have to do numerical integrations for getting likelihoods, whereas if we give up the correlations, then we can get closed form likelihood. Thus, we suggest a new copula-based direct utility approach that not only allows the correlations but also provides closed form of likelihood. Empirically, we find the existence of the correlations between unobserved qualities of two alternatives. Ignoring the correlations may cause the misunderstanding of the joint-purchases.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131295587","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}
The mixed logit is a framework for incorporating unobserved heterogeneity in discrete choice models in a general way. These models are difficult to estimate because they result in a complicated incomplete data likelihood. This paper proposes a new approach for estimating mixed logit models. The estimator is easily implemented as iteratively re-weighted least squares: the well known solution for complete data likelihood logits. The main benefit of this approach is that it requires drastically fewer evaluations of the simulated likelihood function, making it significantly faster than conventional methods that rely on numerically approximating the gradient. The method is rooted in a generalized expectation and maximization (GEM) algorithm, so it is asymptotically consistent, efficient, and globally convergent.
{"title":"A Tractable Estimator for General Mixed Multinomial Logit Models","authors":"J. James","doi":"10.26509/WP-201219","DOIUrl":"https://doi.org/10.26509/WP-201219","url":null,"abstract":"The mixed logit is a framework for incorporating unobserved heterogeneity in discrete choice models in a general way. These models are difficult to estimate because they result in a complicated incomplete data likelihood. This paper proposes a new approach for estimating mixed logit models. The estimator is easily implemented as iteratively re-weighted least squares: the well known solution for complete data likelihood logits. The main benefit of this approach is that it requires drastically fewer evaluations of the simulated likelihood function, making it significantly faster than conventional methods that rely on numerically approximating the gradient. The method is rooted in a generalized expectation and maximization (GEM) algorithm, so it is asymptotically consistent, efficient, and globally convergent.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132366957","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}
We show that accounting information releases generate large and immediate price impacts, i.e. jumps, in credit default swap (CDS) spreads. Our approach is multivariate, which allows for identification of information events under the presence of confounding news, such as credit events and other simultaneous news arrivals. The economic impact of accounting news releases is twice as large as the impact of credit-related news. Good and bad news impact jumps in CDS spreads asymmetrically, and unscheduled announcements are more likely to cause jumps than scheduled ones. The arrival of accounting information is quickly absorbed in CDS spreads, suggesting efficient price discovery in the CDS market.
{"title":"Accounting Information Releases and CDS Spreads","authors":"Redouane Elkamhi, Kris Jacobs, Hugues Langlois, Chayawat Ornthanalai","doi":"10.2139/ssrn.1874127","DOIUrl":"https://doi.org/10.2139/ssrn.1874127","url":null,"abstract":"We show that accounting information releases generate large and immediate price impacts, i.e. jumps, in credit default swap (CDS) spreads. Our approach is multivariate, which allows for identification of information events under the presence of confounding news, such as credit events and other simultaneous news arrivals. The economic impact of accounting news releases is twice as large as the impact of credit-related news. Good and bad news impact jumps in CDS spreads asymmetrically, and unscheduled announcements are more likely to cause jumps than scheduled ones. The arrival of accounting information is quickly absorbed in CDS spreads, suggesting efficient price discovery in the CDS market.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117154909","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}
This paper outlines an approach to improving credit score modeling using random forests and compares random forests with logistic regression. It is shown that on data sets where variables have multicollinearity and complex interrelationships random forests provide a more scientific approach to analyzing variable importance and achieving optimal predictive accuracy. In addition it is shown that random forests should be used in econometric and credit risk models as they provide a powerful too to assess meaning of variables not available in standard regression models and thus allow for more robust findings.
{"title":"Improving the Art, Craft and Science of Economic Credit Risk Scorecards Using Random Forests: Why Credit Scorers and Economists Should Use Random Forests","authors":"Dhruv Sharma","doi":"10.2139/ssrn.1861535","DOIUrl":"https://doi.org/10.2139/ssrn.1861535","url":null,"abstract":"This paper outlines an approach to improving credit score modeling using random forests and compares random forests with logistic regression. It is shown that on data sets where variables have multicollinearity and complex interrelationships random forests provide a more scientific approach to analyzing variable importance and achieving optimal predictive accuracy. In addition it is shown that random forests should be used in econometric and credit risk models as they provide a powerful too to assess meaning of variables not available in standard regression models and thus allow for more robust findings.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125569509","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}
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).
{"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}
How does neighborhood context affect the relationship between individual racial identity, racial attitudes, and vote choice in an urban mayoral election? Although there have been many studies about racial context, racial attitudes and political behavior, for a variety of reasons, including methodological complexities and lack of quality contextual exit poll data, few studies have focused on vote choice in an urban election. Using exit poll data from the 2005 Los Angeles election, multilevel logistic random slope models are developed to explore the relationship between vote choice, racial attitudes, and neighborhood context. Results indicate significant variation across neighborhoods in the effects of race and racial attitudes, as well as significant contextual and cross-level effects on vote choice.
{"title":"Urban Voters, Racial Attitudes, and Neighborhood Context: A Multilevel Analysis of the 2005 Los Angeles Mayoral Election","authors":"Jason A. McDaniel","doi":"10.2139/ssrn.1810466","DOIUrl":"https://doi.org/10.2139/ssrn.1810466","url":null,"abstract":"How does neighborhood context affect the relationship between individual racial identity, racial attitudes, and vote choice in an urban mayoral election? Although there have been many studies about racial context, racial attitudes and political behavior, for a variety of reasons, including methodological complexities and lack of quality contextual exit poll data, few studies have focused on vote choice in an urban election. Using exit poll data from the 2005 Los Angeles election, multilevel logistic random slope models are developed to explore the relationship between vote choice, racial attitudes, and neighborhood context. Results indicate significant variation across neighborhoods in the effects of race and racial attitudes, as well as significant contextual and cross-level effects on vote choice.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125638122","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}
Due to the increasing interest in market segmentation in modern marketing research, several methods for dealing with consumer heterogeneity and for revealing market segments have been described in the literature.In this study, the authors compare eight two-stage segmentation methods that aim to uncover consumer segments by classifying subject-specific indicator values. Four different indicators are used as a segmentation basis.The forces, which are subject-aggregated gradient values of the likelihood function, and the dfbetas, an outlier detection measure, are two indicators that express a subject’s effect on the estimation of the aggregate partworths in the conditional logit model. Although the conditional logit model is generally estimated at the aggregate level, this research obtains individual-level partworth estimates for segmentation purposes. The respondents’ raw choices are the final indicator values. The authors classify the indicators by means of cluster analysis and latent class models. The goal of the study is to compare the segmentation performance of the methods with respect to their success rate, membership recovery and segment mean parameter recovery. With regard to the individual-level estimates, the authors obtain poor segmentation results both with cluster and latent class analysis. The cluster methods based on the forces, the dfbetas and the choices yield good and similar results. Classification of the forces and the dfbetas deteriorates with the use of latent class analysis, whereas latent class modeling of the choices outperforms its cluster counterpart.
{"title":"A Comparison of Two-Stage Segmentation Methods for Choice-Based Conjoint Data: A Simulation Study","authors":"M. Crabbe, B. Jones, M. Vandebroek","doi":"10.2139/ssrn.1846504","DOIUrl":"https://doi.org/10.2139/ssrn.1846504","url":null,"abstract":"Due to the increasing interest in market segmentation in modern marketing research, several methods for dealing with consumer heterogeneity and for revealing market segments have been described in the literature.In this study, the authors compare eight two-stage segmentation methods that aim to uncover consumer segments by classifying subject-specific indicator values. Four different indicators are used as a segmentation basis.The forces, which are subject-aggregated gradient values of the likelihood function, and the dfbetas, an outlier detection measure, are two indicators that express a subject’s effect on the estimation of the aggregate partworths in the conditional logit model. Although the conditional logit model is generally estimated at the aggregate level, this research obtains individual-level partworth estimates for segmentation purposes. The respondents’ raw choices are the final indicator values. The authors classify the indicators by means of cluster analysis and latent class models. The goal of the study is to compare the segmentation performance of the methods with respect to their success rate, membership recovery and segment mean parameter recovery. With regard to the individual-level estimates, the authors obtain poor segmentation results both with cluster and latent class analysis. The cluster methods based on the forces, the dfbetas and the choices yield good and similar results. Classification of the forces and the dfbetas deteriorates with the use of latent class analysis, whereas latent class modeling of the choices outperforms its cluster counterpart.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125102717","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}
We investigate those features of Australian firms that make them likely takeover targets. To this end, we apply a logit probability model similar to the one developed by Palepu (1986). Our findings reveal that takeovers are most likely to be motivated by market under-valuation combined with high levels of tangible assets. Takeover targets may also be financially distressed with high levels of leverage and low liquidity, and may exhibit declining sales growth with decreasing profitability. Notwithstanding these insights, we find that the prediction models are unable to provide abnormal returns with a high statistical significance, thereby lending support to market efficiency.
{"title":"The Financial Profiles of Takeover Target Firms and Their Takeover Predictability: Australian Evidence","authors":"Shuyi Cai, B. Balachandran, M. Dempsey","doi":"10.22495/COCV8I3C6P1","DOIUrl":"https://doi.org/10.22495/COCV8I3C6P1","url":null,"abstract":"We investigate those features of Australian firms that make them likely takeover targets. To this end, we apply a logit probability model similar to the one developed by Palepu (1986). Our findings reveal that takeovers are most likely to be motivated by market under-valuation combined with high levels of tangible assets. Takeover targets may also be financially distressed with high levels of leverage and low liquidity, and may exhibit declining sales growth with decreasing profitability. Notwithstanding these insights, we find that the prediction models are unable to provide abnormal returns with a high statistical significance, thereby lending support to market efficiency.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122583328","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}
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