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"

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LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (July 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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Schaak, Henning, and Oliver Mußhoff. "Public Preferences for Pasture Landscapes and the Role of Scale Heterogeneity." German Journal of Agricultural Economics 70, no. 3 (September 1, 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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Hoffman, Saul D., and Greg J. Duncan. "Multinomial and Conditional Logit Discrete-Choice Models in Demography." Demography 25, no. 3 (August 1988): 415. http://dx.doi.org/10.2307/2061541.

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Jarvis, Benjamin F. "Estimating Multinomial Logit Models with Samples of Alternatives." Sociological Methodology 49, no. 1 (August 30, 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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Cook, Scott J., John Niehaus, and Samantha Zuhlke. "A warning on separation in multinomial logistic models." Research & Politics 5, no. 2 (April 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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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 (February 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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Cong, Li, and Jeffrey S. Racine. "A SMOOTH NONPARAMETRIC CONDITIONAL DENSITY TEST FOR CATEGORICAL RESPONSES." Econometric Theory 29, no. 3 (July 30, 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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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 (April 12, 2019): 363–82. http://dx.doi.org/10.1111/jtsa.12468.

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Norets, Andriy, and Debdeep Pati. "ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES." Econometric Theory 33, no. 4 (July 13, 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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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 (June 20, 2007): 439–52. http://dx.doi.org/10.1111/j.1368-423x.2007.00215.x.

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Dissertations / Theses on the topic "Conditional Multinomial Logit"

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Lombardi, Gabriele. "Three Essays on Grading Policies, Student Mobility and University-to-Work Transition." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1107675.

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This Thesis uses administrative data in order to investigate some crucial issues about Italian Student Population such as internal mobility with a particular focus to South-North Migration; freshmen performance in STEM area and School-to-Work Transition. The analysis are based on two novel datasets and they benefit of three different modelling techniques in order to appropriately explore the three research questions.
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Elamin, Obbey Ahmed. "Nonparametric kernel estimation methods for discrete conditional functions in econometrics." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/nonparametric-kernel-estimation-methods-for-discrete-conditional-functions-in-econometrics(d443e56a-dfb8-4f23-bfbe-ec98ecac030b).html.

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This thesis studies the mixed data types kernel estimation framework for the models of discrete dependent variables, which are known as kernel discrete conditional functions. The conventional parametric multinomial logit MNL model is compared with the mixed data types kernel conditional density estimator in Chapter (2). A new kernel estimator for discrete time single state hazard models is developed in Chapter (3), and named as the discrete time “external kernel hazard” estimator. The discrete time (mixed) proportional hazard estimators are then compared with the discrete time external kernel hazard estimator empirically in Chapter (4). The work in Chapter (2) attempts to estimate a labour force participation decision model using a cross-section data from the UK labour force survey in 2007. The work in Chapter (4) estimates a hazard rate for job-vacancies in weeks, using data from Lancashire Careers Service (LCS) between the period from March 1988 to June 1992. The evidences from the vast literature regarding female labour force participation and the job-market random matching theory are used to examine the empirical results of the estimators. The parametric estimator are tighten by the restrictive assumption regarding the link function of the discrete dependent variable and the dummy variables of the discrete covariates. Adding interaction terms improves the performance of the parametric models but encounters other risks like generating multicollinearity problem, increasing the singularity of the data matrix and complicates the computation of the ML function. On the other hand, the mixed data types kernel estimation framework shows an outstanding performance compared with the conventional parametric estimation methods. The kernel functions that are used for the discrete variables, including the dependent variable, in the mixed data types estimation framework, have substantially improved the performance of the kernel estimators. The kernel framework uses very few assumptions about the functional form of the variables in the model, and relay on the right choice of the kernel functions in the estimator. The outcomes of the kernel conditional density shows that female education level and fertility have high impact on females propensity to work and be in the labour force. The kernel conditional density estimator captures more heterogeneity among the females in the sample than the MNL model due to the restrictive parametric assumptions in the later. The (mixed) proportional hazard framework, on the other hand, missed to capture the effect of the job-market tightness in the job-vacancies hazard rate and produce inconsistent results when the assumptions regarding the distribution of the unobserved heterogeneity are changed. The external kernel hazard estimator overcomes those problems and produce results that consistent with the job market random matching theory. The results in this thesis are useful for nonparametric estimation research in econometrics and in labour economics research.
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Sandström, F. Mikael. "Evaluating the benefits and effectiveness of public policy." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-636.

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The dissertation consists of four essays that treat different aspects or the evaluation of public policy. Two essays are applications of the travel cost method. In the first of these, recreational travel to the Swedish coast is studied to obtain estimates of the social benefits from reduced eutrophication of the sea. The second travel cost essay attempts at estimating how the probability that a woman will undergo mammographic screening for breast cancer is affected by the distance she has to travel to undergo such an examination. Using these estimated probabilities, the woman's valuation of the examination is obtained. The two other essays deal with automobile taxation. One essay analyzes how taxation and the Swedish eco-labeling system of automobiles have affected the sale of different car models. The last essay treats the effects of taxes and of scrappage premiums on the life length of cars.
Diss. Stockholm : Handelshögskolan, 1999
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"Estimating hospital choice for pneumonia patients using conditional multinomial logit analysis." Tulane University, 2004.

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(8300103), Shams R. Rahmani. "Digital Soil Mapping of the Purdue Agronomy Center for Research and Education." Thesis, 2020.

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This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.
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Book chapters on the topic "Conditional Multinomial Logit"

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Higano, Yoshiro. "Introduction: Real Estate Tax System and Real Estate Market in Japan." In New Frontiers in Regional Science: Asian Perspectives, 115–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8848-8_8.

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AbstractThis introduction summarizes chapters of Part II. In Chap. 10.1007/978-981-15-8848-8_9, Yamamoto (Jpn J Real Estate Sci 31:88–96, 2018) has compared between the street method, the asset valuation adopted for the imposition of property tax in Japan, and the computer-assisted mass appraisal (CAMA) method generally adopted in North America focusing on education and training of valuators. In Chap. 10.1007/978-981-15-8848-8_10, Yamazaki (Jpn J Real Estate Sci 31:97–101, 2018) argues that one of the major causes for relatively low density use of land in the city in Japan is the land tax system. He focuses on property tax and examines defects of the tax from view of economist. In Chap. 10.1007/978-981-15-8848-8_11, Kobayashi (Jpn J Real Estate Sci 31:129–138, 2018), taking an actual example, has examined difference between precise legal interpretation of ‘exemption from real estate acquisition tax due to purpose of use’ and taxation practices conducted by local administrative bodies. In Chap. 10.1007/978-981-15-8848-8_12, Shirakawa and Okoshi (Jpn J Real Estate Sci 31:88–96, 2017) have shown that the real estate companies were committed to transactions as dual agencies to whatsoever degree, and analyzed attributes of real estate brokerage companies which are able to be dual agencies and how such dual agency affects contract price.In Chap. 10.1007/978-981-15-8848-8_13, Ueno (Jpn J Real Estate Sci 31:97–105, 2017) has analyzed impacts of the macroeconomic conditions on the land price gradient curves which are estimated using real estate data of the Tokyo Metropolitan Area in 1970, 1976, 1985, 1988, 1994, 2008, 2010, and 2016. In Chap. 10.1007/978-981-15-8848-8_14, Komatsu (Jpn J Real Estate Sci 31:110–118, 2017) has analyzed impacts that refurbishment of existing apartment has on possible increase in rent using the multinomial probit model and the Tobit model. In Chap. 10.1007/978-981-15-8848-8_15, Hanazato (Jpn J Real Estate Sci 31:119–128, 2017) has shown that around 90% of condominium reconstruction cases are predictable using the estimated discriminant function in terms of objective real estate data only. In Chap. 10.1007/978-981-15-8848-8_16, Ota et al. (Jpn J Real Estate Sci 31:109–119, 2018) have analyzed determinants of rent for rental house, office, and shop within 10-min walking distance from Shibuya Station in Tokyo. Multiple regression analyses are conducted and have shown that space syntax (SS) measures (Hillier and Hanson, The Social Logic of Space. Cambridge University Press, Cambridge, 1984) significantly affect rent as well as conventional location attributes.
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Baltagi, Badi H., and Myoung-jae Lee. "Panel Conditional and Multinomial Logit Estimators." In The Oxford Handbook of Panel Data. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199940042.013.0007.

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"Conditional moments in the generalized extreme value family1 1In the course of the exposition, several theorems related to the independent form of the generalized extreme value family, i.e., the multinomial logit model, are derived. Specifically, Corollary B.2.2 and Theorems B.3.3, B.4.1, B.4.2 and, B.4.3, which present the conditional moments for the multinomial logit model, are stated in Dubin and McFadden (1984). It should be further noted that Theorems B.3.3, B.4.1, and B.4.2 have been independently demonstrated by Hay (1980)." In Contributions to Economic Analysis, 217–54. Elsevier, 1985. http://dx.doi.org/10.1016/b978-0-444-87766-6.70030-6.

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Conference papers on the topic "Conditional Multinomial Logit"

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Kritikos, Alexandra, Dominic Hodgkin, and Rosalie Liccardo Pacula. "Time Trajectories of Medical Cannabis Purchases by Patients with Medical Cannabis Prescriptions." In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.21.

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Background: Medical cannabis (MC) use is rising with limited clinical data to support products and dosing for specific conditions. This study relies on observational data to examine MC purchases across time, and to assess dosing trajectories for different conditions. Methods: A retrospective study of MC patients of dispensaries located in New York (NY). This study relies on secondary analysis of point-of-sale (POS) invoice data from 16,727 unique patients with 79,885 purchases between 2016-2019. Group-based-trajectory modeling (GBTM) was used to identify clusters of MC patients following similar progressions in potency utilization (e.g., THC and CBD) over time. Multinomial logit models were estimated to identify group membership based on patient level characteristics and qualifying medical conditions. Results: Six distinct trajectory groups were identified. Four of the groups compromised (75%), (39.9%), (8.2%), and (8.5%) of the population and purchased a steady dosage (ranging from low to high) of THC over time. The fifth (14.9%) and sixth group (10.1%) demonstrated MC patients who gradually increased their THC dosage across time. Patient characteristics and qualifying medical conditions for MC use were strong predictors of group membership. Men, older individuals, and those with a qualifying pain condition were more likely to be part of a group that consumed higher doses of THC across time, compared to other reference groups. Conclusion: This study identified distinctive trajectories of monthly THC and CBD potency levels purchased, and factors associated with these trajectories. Examining MC purchasing patterns over time may help understand whether MC treatment works, subgroups of MC patients, and risk factors. This study pioneer’s analysis of POS data, which could help guide policy decisions to effectively monitor MC use, aid in the design of future MC programs and target prevention efforts. This study provides a strong foundation upon which that research can build by utilizing new-technologically advanced sales data sets.
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