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Relevant bibliographies by topics / Conditional Multinomial Logit

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Academic literature on the topic 'Conditional Multinomial Logit'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Conditional Multinomial Logit"

1

LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (2011): 309–24. http://dx.doi.org/10.1142/s1793536911000738.

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For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables.

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2

Schaak, Henning, and Oliver Mußhoff. "Public Preferences for Pasture Landscapes and the Role of Scale Heterogeneity." German Journal of Agricultural Economics 70, no. 3 (2021): 182–91. http://dx.doi.org/10.30430/70.2021.3.182-191.

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The paper investigates the influence of different model specifications for interpreting the results of discrete choice experiments when investigating heterogeneous public landscape preferences. Comparing model specifications based on the Mixed Multinomial Logit and the Generalized Multinomial Logit Model reveals that the parameter estimates appear qualitatively comparable. Still, a more in-depth investigation of the conditional estimate distributions of the sample show that parameter interactions in the Generalized Multinomial Logit Model lead to different interpretations compared to the Mixed Multinomial Logit Model. This highlights the potential impact of common model specifications in the results in landscape preference studies.

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3

Hoffman, Saul D., and Greg J. Duncan. "Multinomial and Conditional Logit Discrete-Choice Models in Demography." Demography 25, no. 3 (1988): 415. http://dx.doi.org/10.2307/2061541.

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4

Jarvis, Benjamin F. "Estimating Multinomial Logit Models with Samples of Alternatives." Sociological Methodology 49, no. 1 (2018): 341–48. http://dx.doi.org/10.1177/0081175018793460.

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This comment reconsiders advice offered by Bruch and Mare regarding sampling choice sets in conditional logistic regression models of residential mobility. Contradicting Bruch and Mare’s advice, past econometric research shows that no statistical correction is needed when using simple random sampling of unchosen alternatives to pare down respondents’ choice sets. Using data on stated residential preferences contained in the Los Angeles portion of the Multi-City Study of Urban Inequality, it is shown that following Bruch and Mare’s advice—to implement a statistical correction for simple random choice set sampling—leads to biased coefficient estimates. This bias is all but eliminated if the sampling correction is omitted.

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5

Cook, Scott J., John Niehaus, and Samantha Zuhlke. "A warning on separation in multinomial logistic models." Research & Politics 5, no. 2 (2018): 205316801876951. http://dx.doi.org/10.1177/2053168018769510.

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Oppenheim et al. (2015) provides the first empirical analysis of insurgent defection during armed rebellion, estimating a series of multinomial logit models of continued rebel participation using a survey of ex-combatants in Colombia. Unfortunately, many of the main results from this analysis are an artifact of separation in these data – that is, one or more of the covariates perfectly predicts the outcome. We demonstrate that this can be identified using simple cross tabulations. Furthermore, we show that Oppenheim et al.’s (2015) results are not supported when separation is explicitly accounted for. Using a generalization of Firth’s (1993) penalized-likelihood estimator – a well-known solution for separation – we are unable to reproduce any of their conditional results. While our (re-)analysis focuses on Oppenheim et al. (2015), this problem appears in other research using multinomial logit models as well. We believe that this is both because the discussion on separation in political science has primarily focused on binary-outcome models, and because software (Stata and R) does not warn researchers about seperation in multinomial logit models. Therefore, we encourage researchers using multinomial logit models to be especially vigilant about separation, and discuss simple red flags to consider.

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6

Hudson, Darren, R. Karina Gallardo, and Terrill R. Hanson. "A Comparison of Choice Experiments and Actual Grocery Store Behavior: An Empirical Application to Seafood Products." Journal of Agricultural and Applied Economics 44, no. 1 (2012): 49–62. http://dx.doi.org/10.1017/s107407080000016x.

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In this paper we compare results from an in-store field experiment and a mail survey choice experiment (CE) to investigate CE's capacity in predicting grocery store market share. For the comparison, we used three seafood products: freshwater prawns, marine shrimp, and lobster. CE estimates were obtained via four econometric models: the conditional logit, the random parameter logit, the heteroskedastic extreme value, and the multinomial probit. We found that the level of control in the grocery store experiment and the choice of econometric model influenced the capacity of CE to predict grocery store market shares.

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7

Cong, Li, and Jeffrey S. Racine. "A SMOOTH NONPARAMETRIC CONDITIONAL DENSITY TEST FOR CATEGORICAL RESPONSES." Econometric Theory 29, no. 3 (2012): 629–41. http://dx.doi.org/10.1017/s0266466612000382.

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We propose a consistent kernel-based specification test for conditional density models when the dependent variable is categorical/discrete. The method is applicable to popular parametric binary choice models such as the logit and probit specification and their multinomial and ordered counterparts, along with parametric count models, among others. The test is valid when the conditional density function contains both categorical and real-valued covariates. Theoretical support for the test and for a bootstrap-based version of the test is provided. Monte Carlo simulations are conducted to assess the finite-sample performance of the proposed method.

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8

Bradley, Jonathan R., Christopher K. Wikle, and Scott H. Holan. "Spatio‐temporal models for big multinomial data using the conditional multivariate logit‐beta distribution." Journal of Time Series Analysis 40, no. 3 (2019): 363–82. http://dx.doi.org/10.1111/jtsa.12468.

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9

Norets, Andriy, and Debdeep Pati. "ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES." Econometric Theory 33, no. 4 (2016): 980–1012. http://dx.doi.org/10.1017/s0266466616000220.

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We consider a nonparametric Bayesian model for conditional densities. The model is a finite mixture of normal distributions with covariate dependent multinomial logit mixing probabilities. A prior for the number of mixture components is specified on positive integers. The marginal distribution of covariates is not modeled. We study asymptotic frequentist behavior of the posterior in this model. Specifically, we show that when the true conditional density has a certain smoothness level, then the posterior contraction rate around the truth is equal up to a log factor to the frequentist minimax rate of estimation. An extension to the case when the covariate space is unbounded is also established. As our result holds without a priori knowledge of the smoothness level of the true density, the established posterior contraction rates are adaptive. Moreover, we show that the rate is not affected by inclusion of irrelevant covariates in the model. In Monte Carlo simulations, a version of the model compares favorably to a cross-validated kernel conditional density estimator.

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10

Guimarães, Paulo, and Richard C. Lindrooth. "Controlling for overdispersion in grouped conditional logit models: A computationally simple application of Dirichlet‐multinomial regression." Econometrics Journal 10, no. 2 (2007): 439–52. http://dx.doi.org/10.1111/j.1368-423x.2007.00215.x.

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