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Journal articles on the topic 'Multivariate probit analysis'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Bock, R. Darrell, and Robert D. Gibbons. "High-Dimensional Multivariate Probit Analysis." Biometrics 52, no. 4 (1996): 1183. http://dx.doi.org/10.2307/2532834.

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2

Chib, S. "Analysis of multivariate probit models." Biometrika 85, no. 2 (1998): 347–61. http://dx.doi.org/10.1093/biomet/85.2.347.

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3

Edwards, Yancy D., and Greg M. Allenby. "Multivariate Analysis of Multiple Response Data." Journal of Marketing Research 40, no. 3 (2003): 321–34. http://dx.doi.org/10.1509/jmkr.40.3.321.19233.

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Multiple response questions, also known as a pick any/J format, are frequently encountered in the analysis of survey data. The relationship among the responses is difficult to explore when the number of response options, J, is large. The authors propose a multivariate binomial probit model for analyzing multiple response data and use standard multivariate analysis techniques to conduct exploratory analysis on the latent multivariate normal distribution. A challenge of estimating the probit model is addressing identifying restrictions that lead to the covariance matrix specified with unit-diagonal elements (i.e., a correlation matrix). The authors propose a general approach to handling identifying restrictions and develop specific algorithms for the multivariate binomial probit model. The estimation algorithm is efficient and can easily accommodate many response options that are frequently encountered in the analysis of marketing data. The authors illustrate multivariate analysis of multiple response data in three applications.

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Zeng, Fan, Xiao Cao, and Huan-Ming Zhang. "Analysis of Digital Life Effect of Residents’ Trust Based on Multivariate Discrete Choice Model." Journal of Function Spaces 2022 (July 18, 2022): 1–14. http://dx.doi.org/10.1155/2022/1801399.

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In view of the impact of digital life on residents’ trust, firstly, based on the data of China Household Finance Survey (CHFS), this paper analyzes the change of residents’ trust in the era of data flooding. Through basic research and marginal effect analysis, it is found that digital life enhances residents’ trust. Secondly, the robustness of the conclusion is confirmed by constructing a series of models such as ordered probit of instrumental variable (IV-O probit), ordered probit of propensity score matching method (PSM-O probit), and ordered probit Heckman (O-probit-Heckman). Finally, through the mechanism test, it is concluded that digital life can enhance residents’ trust by increasing social activities and forming an inclusive social environment. It will also increase trust by obtaining information and increasing their cognitive level. The conclusion that digital life can enhance residents’ trust is more prominent among urban residents, nonpoor Hukou residents, and Han residents. Further research shows that the breadth and depth of digital life have a positive impact on residents’ trust, and digital life can improve residents’ risk tolerance.

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5

Zhang, Xiao, W. John Boscardin, and Thomas R. Belin. "Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models." Computational Statistics & Data Analysis 52, no. 7 (2008): 3697–708. http://dx.doi.org/10.1016/j.csda.2007.12.012.

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6

Lu, Bin, and Xin-Yuan Song. "Local influence analysis of multivariate probit latent variable models." Journal of Multivariate Analysis 97, no. 8 (2006): 1783–98. http://dx.doi.org/10.1016/j.jmva.2005.10.004.

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7

Lesaffre, E., and G. Molenberghs. "Multivariate probit analysis: A neglected procedure in medical statistics." Statistics in Medicine 10, no. 9 (1991): 1391–403. http://dx.doi.org/10.1002/sim.4780100907.

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Gibbons, Robert D., and John V. Lavigne. "Emergence of childhood psychiatric disorders: a multivariate probit analysis." Statistics in Medicine 17, no. 21 (1998): 2487–99. http://dx.doi.org/10.1002/(sici)1097-0258(19981115)17:21<2487::aid-sim937>3.0.co;2-2.

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9

Thompson, Stanley R., and Doyle A. Eiler. "A MULTIVARIATE PROBIT ANALYSIS OF ADVERTISING AWARENESS ON MILK USE." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 23, no. 1 (2008): 65–73. http://dx.doi.org/10.1111/j.1744-7976.1975.tb00944.x.

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Shi, Lei. "Bayesian analysis of multivariate ordered probit model with individual heterogeneity." AStA Advances in Statistical Analysis 104, no. 4 (2020): 649–65. http://dx.doi.org/10.1007/s10182-020-00369-2.

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11

Gibbons, Robert D., and Virginia Wilcox-Gök. "Health Service Utilization and Insurance Coverage: A Multivariate Probit Analysis." Journal of the American Statistical Association 93, no. 441 (1998): 63–72. http://dx.doi.org/10.1080/01621459.1998.10474088.

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12

Poon, Wai-Yin, and Hai-Bin Wang. "Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data." Psychometrika 75, no. 3 (2010): 498–520. http://dx.doi.org/10.1007/s11336-010-9164-6.

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13

Deng, Yihao, Roy T. Sabo, and N. Rao Chaganty. "Multivariate probit analysis of binary familial data using stochastic representations." Computational Statistics & Data Analysis 56, no. 3 (2012): 656–63. http://dx.doi.org/10.1016/j.csda.2011.09.014.

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14

Konigsberg, Lyle W., and Susan R. Frankenberg. "Multivariate ordinal probit analysis in the skeletal assessment of sex." American Journal of Physical Anthropology 169, no. 2 (2019): 385–87. http://dx.doi.org/10.1002/ajpa.23832.

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15

Young Choi, Jae, Yeonbae Kim, Yungman Jun, and Yunhee Kim. "A Bayesian multivariate probit analysis of Korean firms' information system adoption." Industrial Management & Data Systems 111, no. 9 (2011): 1465–80. http://dx.doi.org/10.1108/02635571111182791.

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16

Cheng, Tsung-Chi, and Wei-Jen Wen. "Determinants of performing arts attendance in Taiwan: a multivariate probit analysis." Applied Economics Letters 18, no. 15 (2011): 1437–42. http://dx.doi.org/10.1080/13504851.2010.543060.

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Xu, Huiping, and Bruce A. Craig. "Likelihood Analysis of Multivariate Probit Models Using a Parameter Expanded MCEM Algorithm." Technometrics 52, no. 3 (2010): 340–48. http://dx.doi.org/10.1198/tech.2010.09055.

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18

Zhang, Xiao. "Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models." Journal of Mathematics and Statistics 18, no. 1 (2022): 163–75. http://dx.doi.org/10.3844/jmssp.2022.163.175.

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19

Velandia, Margarita, Roderick M. Rejesus, Thomas O. Knight, and Bruce J. Sherrick. "Factors Affecting Farmers' Utilization of Agricultural Risk Management Tools: The Case of Crop Insurance, Forward Contracting, and Spreading Sales." Journal of Agricultural and Applied Economics 41, no. 1 (2009): 107–23. http://dx.doi.org/10.1017/s1074070800002583.

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Factors affecting the adoption of crop insurance, forward contracting, and spreading sales are analyzed using multivariate and multinomial probit approaches that account for simultaneous adoption and/or correlation among the three risk management adoption decisions. Our empirical results suggest that the decision to adopt crop insurance, forward contracting, and/or spreading sales are correlated. Richer insights can be drawn from our multivariate and multinomial probit analysis than from separate, single-equation probit estimation that assumes independence of adoption decisions. Some factors significantly affecting the adoption of the risk management tools analyzed are proportion of owned acres, off-farm income, education, age, and level of business risks.

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Oude Lansink, Alfons, Mirella van den Berg, and Ruud Huirne. "Analysis of strategic planning of Dutch pig farmers using a multivariate probit model." Agricultural Systems 78, no. 1 (2003): 73–84. http://dx.doi.org/10.1016/s0308-521x(03)00034-9.

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Li, Yonghai, and Daniel W. Schafer. "Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses." Computational Statistics & Data Analysis 52, no. 7 (2008): 3474–92. http://dx.doi.org/10.1016/j.csda.2007.10.025.

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Jiang, Jie, Xinsheng Liu, and Keming Yu. "Maximum likelihood estimation of multinomial probit factor analysis models for multivariate t-distribution." Computational Statistics 28, no. 4 (2012): 1485–500. http://dx.doi.org/10.1007/s00180-012-0363-8.

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23

Denton, Frank T., Peter C. Pineo, and Byron G. Spencer. "Participation in Adult Education by the Elderly: A Multivariate Analysis and Some Implications for the Future." Canadian Journal on Aging / La Revue canadienne du vieillissement 7, no. 1 (1988): 4–16. http://dx.doi.org/10.1017/s0714980800007078.

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ABSTRACTMicro-data from a 1984 survey of adult education in Canada are used in the study. The data for persons 65 years of age and over are analysed using probit techniques. Prior level of education is found to be of considerable importance in determining the probabilities that elderly people will make use of adult education facilities. It is argued that future elderly population cohorts will have higher average education levels than present ones and that their members will therefore be more likely to take courses. Projections of increases in course enrolment by persons 65 and over are made for the next quarter century, based on the results of the probit analysis and projections of the population.

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24

Odozi, John Chiwuzulum, and Timothy Taiwo Awoyemi. "Small Farmer Adaptation to Climate Change in Nigeria: Multivariate Probit Analysis of Crop Choice." International Journal of Climate Change: Impacts and Responses 7, no. 4 (2015): 1–12. http://dx.doi.org/10.18848/1835-7156/cgp/v07i04/37251.

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25

GC, Arun, and Jun-Ho Yeo. "Perception to Adaptation of Climate Change in Nepal: An Empirical Analysis Using Multivariate Probit Model." Sci 1, no. 1 (2019): 12. http://dx.doi.org/10.3390/sci1010012.v1.

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This study assessed farmers’ perception of climate change, estimated the determinants of, and evaluated the relationship among adaptation practices using the multivariate probit model. A survey in 300 agricultural households was carried out covering 10 sample districts considering five agro-ecological zones and a vulnerability index. Four adaptation choices (change in planting date, crop variety, crop type and investment in irrigation) were deemed as outcome variables and socioeconomic, demographic, institutional, farm-level and perceptions variables were deployed as explanatory variables. Their marginal effects were determined for three climatic variables—temperature, precipitation and drought. Age, gender and education of head of household, credit access, farm area, rain-fed farming and tenure, are found to be more influential compared to other factors. All four adaptation-options are found to be complimentary to each other. Importantly, the intensity of impact of dependent variables in different models, and for available adaptation-options, are found to be unequal. Therefore, policy options and support facilities should be devised according to climatic variables and adaptation options to achieve superior results.

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GC and Yeo. "Perception to Adaptation of Climate Change in Nepal: An Empirical Analysis Using Multivariate Probit Model." Sci 1, no. 3 (2019): 57. http://dx.doi.org/10.3390/sci1030057.

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This study assessed farmers’ perception of climate change, estimated the determinants of, and evaluated the relationship among adaptation practices using the multivariate probit model. A survey in 300 agricultural households was carried out covering 10 sample districts considering five agro-ecological zones and a vulnerability index. Four adaptation choices (change in planting date, crop variety, crop type and investment in irrigation) were deemed as outcome variables and socioeconomic, demographic, institutional, farm-level and perceptions variables were deployed as explanatory variables. Their marginal effects were determined for three climatic variables—temperature, precipitation and drought. Age, gender and education of head of household, credit access, farm area, rain-fed farming and tenure, are found to be more influential compared to other factors. All four adaptation-options are found to be complimentary to each other. Importantly, the intensity of impact of dependent variables in different models, and for available adaptation-options, are found to be unequal. Therefore, policy options and support facilities should be devised according to climatic variables and adaptation options to achieve superior results.

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27

GC, Arun, and Jun-Ho Yeo. "Perception to Adaptation of Climate Change in Nepal: An Empirical Analysis Using Multivariate Probit Model." Sci 2, no. 4 (2020): 87. http://dx.doi.org/10.3390/sci2040087.

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This study assessed farmers’ perception of climate change, and estimated the determinants of, and evaluated the relationship among, adaptation practices using the multivariate probit model. A survey in 300 agricultural households was carried out covering 10 sample districts considering five agro-ecological zones and a vulnerability index. Four adaptation choices (change in planting date, crop variety, crop type and investment in irrigation) were deemed as outcome variables and socioeconomic, demographic, institutional, farm-level and perceptions variables were deployed as explanatory variables. Their marginal effects were determined for three climatic variables—temperature, precipitation and drought. Age, gender and education of head of household, credit access, farm area, rain-fed farming and tenure, were found to be more influential compared to other factors. All four adaptation options were found to be complimentary to each other. Importantly, the intensity of the impact of dependent variables in different models, and for the available adaptation options, were found to be unequal. Therefore, policy options and support facilities should be devised according to climatic variables and adaptation options to achieve superior results.

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28

Klales, Alexandra, Stephen Ousley, and Jennifer Vollner. "Response to multivariate ordinal probit analysis in the skeletal assessment of sex (Konigsberg and Frankenberg)." American Journal of Physical Anthropology 169, no. 2 (2019): 388–89. http://dx.doi.org/10.1002/ajpa.23830.

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29

Martínez, José María, and Eliana Martínez Pachón. "Multivariate analysis of the adoption of cacao productive technologies: Evidence from a case study in Colombia." Economía Agraria y Recursos Naturales 21, no. 1 (2021): 79. http://dx.doi.org/10.7201/earn.2021.01.04.

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<p>Multivariate and ordered Probit models were used to study the determinants of the adoption and extent of adoption of agronomic practices among cacao farmers in Nariño, Colombia, with data from 353 farmers in the mountain region. Results suggest that farmers’ adoption decisions are made on a joint basis, further validating the use of a multivariate approach. The presence of illegal crops creates strong disincentives that affect the possibility of more significant technological improvements. Adequate access to agricultural technical assistance strongly increases rates of adoption. Evidence suggests that efforts are necessary to better target resource-poor farmers.</p>

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Koo, Yoonmo, Sesil Lim, Kayoung Kim, and Youngsang Cho. "Analysis of user characteristics regarding social network services in South Korea using the multivariate probit model." Technological Forecasting and Social Change 88 (October 2014): 232–40. http://dx.doi.org/10.1016/j.techfore.2014.07.001.

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31

Viswanathan, Krishnan, Konstadinos G. Goulias, and Paul P. Jovanis. "Use of Traveler Information in the Puget Sound Region: Preliminary Multivariate Analysis." Transportation Research Record: Journal of the Transportation Research Board 1719, no. 1 (2000): 94–102. http://dx.doi.org/10.3141/1719-12.

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Recent developments in information technologies, providing new ways to disseminate and use information, may help alleviate congestion, reduce user cost and time, and enhance safety. This influence of technology use, however, is mediated by telecommunication and information technology ownership and use. A multivariate probit model specification is used to determine how these parameters influence traveler decision making when one is confronted with information about traffic problems before making a trip to work or school, en route to or from work or school, and before making a trip from work or school. Addressed is the key relationship between telecommunication and information technology ownership and use with travel decisions when information about traffic problems is available. Data from the Puget Sound region for 1997 are used in the analysis. The results suggest technology ownership and use influence travel decision making in different ways for each stage of travel—before leaving home, en route, and returning home.

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32

Johnson, Steve, Don J. Webber, and Wayne Thomas. "Which SMEs Use External Business Advice? A Multivariate Subregional Study." Environment and Planning A: Economy and Space 39, no. 8 (2007): 1981–97. http://dx.doi.org/10.1068/a38327.

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The authors examine factors that influence the propensity of a firm to take up external business support across four large English towns, using random effects nominal probit regression analysis to capture sector heterogeneity. The results suggest a strong positive association between the orientation of the firm towards growth and its propensity to use external business advice. ‘Push’ factors, including the existence of recruitment difficulties, are identified as key triggers to use business advice. These results provide valuable guidance to public policy organisations concerned with business development and competitiveness, and suggest a number of avenues for future research.

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Kariuki, Isaac Maina, and Jens-Peter Loy. "Contractual Farming Arrangements, Quality Control, Incentives, and Distribution Failure in Kenya's Smallholder Horticulture: A Multivariate Probit Analysis." Agribusiness 32, no. 4 (2016): 547–62. http://dx.doi.org/10.1002/agr.21462.

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Oparinde, Lawrence Olusola. "Land Rights and Improvement Investments Among Crop Farmers in Southwest, Nigeria." Nigerian Journal of Technological Research 16, no. 1 (2021): 1–12. http://dx.doi.org/10.4314/njtr.v16i1.1.

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This study examined the effects of land rights on investment decisions of farmers and determinants of land rights as well as the impact of land rights on farm productivity in Southwest Nigeria. Multistage sampling process was used in the selection of 320 respondents for the study. Two-stage conditional maximum likelihood (2SCML) approach of multivariate probit regression model and Endogenous switching regression (ESR) model were used for the analysis of the collected data. The results of Average Treatment Effect on the Treated (ATT) estimates from ESR revealed that land rights increase farm productivity. This study further confirmed the importance of land rights in facilitating investment in soil-improving measures which subsequently leads to higher productivity. One of the policy implications emanating from this study suggests that having use and transfer rights as against use only rights goes a long way in enhancing investment in soil-improving or conservation measures. Keywords: Crop, Investment, Land, Multivariate Probit Regression Model, Rights, Two-stage Conditional Maximum Likelihood.

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Aminu, F. O., H. A. Mohammed, C. E. Akhigbe-Ahonkhai, and O. B. Samuel. "Analysis of Risk Coping Strategies Among Catfish Farmers in Ikorodu Division, Lagos State, Nigeria." European Journal of Agriculture and Food Sciences 3, no. 6 (2021): 89–94. http://dx.doi.org/10.24018/ejfood.2021.3.6.427.

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The aim of this study was to analyse the risk coping strategies among catfish farmers in Ikorodu division of Lagos State, Nigeria. Primary data were obtained from 100 catfish farmers with the aid of a pre-tested questionnaire and focus group discussion using multistage sampling technique. Descriptive statistics and multivariate probit regression model were employed for data analysis. Results revealed that majority (68.1%) of the respondent were male with a mean age of 41 years and a mean stock size of 1130. The major sources of risks in the study area were fluctuation in price (89.4%), cost and quality of feed (79.8%), high cost of labour (76.6%), quality of fingerling (75.6%), bird invasion (75.6%) among others. Reduced volume of stock (87.2%), treated pond before stocking (77.7%), purchased good feed quality (74.5%), and used personal savings (72.3%) were the major coping strategies adopted by the farmers in the study area. The estimate of the multivariate probit regression model revealed that sex, age, education, household size, catfish experience, start-up capital, farming status, membership of cooperative association and access to loan were the factors determining the adoption of risk coping strategies in the study area. Therefore, government, both at the federal and state level should intensify efforts at implementing the various agricultural support programs such as anchor borrowers, poverty alleviation fund and other subsidy programs that can help the farmers expand their capital base and better cope with risk in the study area.

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Adicha Adala, Asmera Adicha, Yidnekachew Alemayehu, Gedion Ermias, and Dawit Darcho. "Value Chain Analysis of Korarima (Aframomum Corrorima) in South Omo Zone, SNNPR Ethiopia." Research on World Agricultural Economy 3, no. 3 (2022): 22. http://dx.doi.org/10.36956/rwae.v3i3.568.

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Korarima is a known cash crop in the South Omo zone and provides a wide range of economic and sociocultural benefits. Even though its economic and socio-cultural importance the development of the Korarima sector along with the value chain is hampered by several constraints. Hence, the study aimed to analyze the Korarima value chain in the South Omo zone. Using a two-stage sampling technique, 120 Kororima producers were selected to collect primary data through structured questionnaires. Descriptive statistics and econometrics model (multivariate probit model) were used for data analysis. The study identified three major Korarima market outlet choices such as collectors, retailers, and wholesalers as alternatives to Korarima producers to sell the majority of their products. Thus, collectors accounted for 82.2%, wholesalers (73.6%), and retailers (35.5%) of the total sold. The results of a multivariate probit model indicated that sex of household, credit access, family size, price information, market distance, and extension contact of farmers significantly affected the market outlet choice decisions in one or another way. Furthermore, no brand indicating this crop, inadequate infrastructural development, and market accessibility, weak extension services regarding improved varieties were major problems identified. Therefore, it is better to work on the brand name of this particular crop to trace up to the end market, infrastructural development and market accessibility, extension services provided regarding the improved Korarima variety, and accessing formal market information from the concerned body are essential to enhance Korarima producers’ benefit and bargaining power through avoiding information asymmetry.

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HARRIS, ANTHONY. "Diabetes, Cardiovascular Disease and Labour Force Participation in Australia: An Endogenous Multivariate Probit Analysis of Clinical Prevalence Data." Economic Record 85, no. 271 (2009): 472–84. http://dx.doi.org/10.1111/j.1475-4932.2009.00572.x.

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38

Roy, Surupa, and Tathagata Banerjee. "Analysis of Misclassified Correlated Binary Data Using a Multivariate Probit Model when Covariates are Subject to Measurement Error." Biometrical Journal 51, no. 3 (2009): 420–32. http://dx.doi.org/10.1002/bimj.200800127.

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Lu, Kaifeng. "Reference-based pattern-mixture models for analysis of longitudinal binary data." Statistical Methods in Medical Research 29, no. 12 (2020): 3770–82. http://dx.doi.org/10.1177/0962280220941880.

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Pattern-mixture model (PMM)-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions or to estimate treatment effectiveness. The methodology is well established for continuous responses but less well established for binary responses. In this study, we formulate the copy-reference and jump-to-reference PMMs for longitudinal binary data using a multivariate probit model with latent variables. We discuss the maximum likelihood, Bayesian, and multiple imputation methods for estimating the treatment effect under the specified PMM. Simulation studies are conducted to evaluate the performance of these methods. These methods are also illustrated using data from a bipolar mania study.

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Maguza-Tembo, Francis, Abdi-Khalil Edriss, and Julius Mangisoni. "Determinants of Climate Smart Agriculture Technology Adoption in the Drought Prone Districts of Malawi using a Multivariate Probit Analysis." Asian Journal of Agricultural Extension, Economics & Sociology 16, no. 3 (2017): 1–12. http://dx.doi.org/10.9734/ajaees/2017/32489.

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Chekol, Fasika, and Teshager Mazengia. "Determinants of Garlic Producers Market Outlet Choices in Goncha Siso Enese District, Northwest Ethiopia: A Multivariate Probit Regression Analysis." Advances in Agriculture 2022 (November 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/6719106.

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This study investigates the factors influencing garlic producers’ market outlet selection decisions in Goncha Siso Enese District, Ethiopia. A total of 359 garlic producer households were polled, and the results were analyzed using a multivariate probit (MVP) model. According to the MVP model results, extension contact, access to market information, quantity of garlic sold, and farm experience in farming were negatively and significantly associated with the choice of consumer outlet. Besides this, the estimated MVP for retailer outlet choice is positively influenced by the amount of quantity sold, and farm experiences in garlic have a significant and positive effect on the choice of retailer outlets. In contrast, education level, access to credit, extension contact, and land area allocated for garlic have a negative influence on the choice of retail outlet. Moreover, wholesaler outlet choice is significantly and positively influenced by education level, access to credit, amount of quantity sold, extension contact, and distance to market, whereas it is negatively influenced by lagged price. Assembler outlet is also positively influenced by sex, distance to market, access to market information, and quantity sold, and negatively affected by extension contact and lag price. The implication is that, if smallholder garlic producers have access to alternative market outlets, they must select an appropriate combination of market outlets to maximize their income in the long run. It is suggested that equal access to marketing infrastructure in potential production areas, the garlic production system, and rural-urban development infrastructure should be improved and made more accessible.

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Hindarti, Sri. "Ex Ante Moral Hazard in the Health Insurance Market in Indonesia." Jurnal Indonesia Sosial Sains 3, no. 4 (2022): 649–72. http://dx.doi.org/10.36418/jiss.v3i4.580.

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This paper will empirically examine the existence of ex ante moral hazard in health insurance market in Indonesia with respect to lifestyle choices such as smoking and obesity. A key challenge in understanding the moral hazard problem is endogeneity. Decisions regarding having insurance and lifestyle choices are arguably sequential, not simultaneous. Then, it is difficult to decide which one precedes the other. To address this problem, this paper uses a multivariate probit analysis. This paper uses three waves from the IFLS (Indonesian Family Life Survey) over 2000, 2007, and 2014, as these have information about insurance status, smoking behavior, and obesity condition. This paper finds evidence of the existence of ex ante moral hazard in health insurance for obesity of the head of household, but not for smoking. The multivariate probit regression shows that having health insurance increases the propensity of being obese. Using a wider sample, including all members of households, there is not sufficient evidence of the existence of ex ante moral hazard for either smoking or obesity. Surprisingly, the decisions between smoking and obesity are substitutes. This means that there are non-observable elements that increase the tendency to smoke, but reduce the propensity of being obese, and vice versa.

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43

Kim, Jong-Min, Ning Wang, Yumin Liu, and Kayoung Park. "Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model." Symmetry 12, no. 3 (2020): 381. http://dx.doi.org/10.3390/sym12030381.

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Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

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Tapsuwan, Sorada, Michael Burton, and James Perriam. "A Multivariate Probit Analysis of Willingness to Pay for Cave Conservation: A Case Study of Yanchep National Park, Western Australia." Tourism Economics 16, no. 4 (2010): 1019–35. http://dx.doi.org/10.5367/te.2010.0003.

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Chang, Y.-M., D. Gianola, B. Heringstad, and G. Klemetsdal. "A comparison between multivariate Slash, Student's t and probit threshold models for analysis of clinical mastitis in first lactation cows." Journal of Animal Breeding and Genetics 123, no. 5 (2006): 290–300. http://dx.doi.org/10.1111/j.1439-0388.2006.00605.x.

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Nakamura, Eri. "Electricity saving behavior of households by making efforts, replacing appliances, and renovations: empirical analysis using a multivariate ordered probit model." International Journal of Consumer Studies 40, no. 6 (2016): 675–84. http://dx.doi.org/10.1111/ijcs.12292.

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Mittal, Surabhi, and Mamta Mehar. "Socio-economic Factors Affecting Adoption of Modern Information and Communication Technology by Farmers in India: Analysis Using Multivariate Probit Model." Journal of Agricultural Education and Extension 22, no. 2 (2015): 199–212. http://dx.doi.org/10.1080/1389224x.2014.997255.

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Neifar, Malika, Sameh Charfeddine, and Aida Kammoun. "Financial Performance of Islamic Versus Conventional Banks a Comparative Analysis for Jordan." International Journal of Economics and Financial Issues 12, no. 6 (2022): 65–74. http://dx.doi.org/10.32479/ijefi.13539.

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This paper contributes to the empirical literature on interest-free finance by investigating the financial performance of interest-free and conventional banks in Jordan over the period 2005–2014 covering GFC period. Three models, two sub-periods, and 11 ratios are considered to compare bank performance evolutions. We give first a univariate based t-test analysis, and then a discriminant analysis is presented in order to determine which variables differentiate between conventional and Islamic banks. Finally, a multivariate nonlinear analysis from Binary outcome panel data models such as Probit and Logit model is conducted. Based on t-test univariate analysis, there is significant evidence that Islamic Banks (IBs) are in average less stable and more risky than conventional banks (CBs) for the three considered periods: full period, pre Global Financial Crisis(GFC) and post GFC. Pre GFC, IBs are more capitalized, more liquid, and more profitable in average. However, post GFC, IBs are in average only more liquid in addition to excess of instability and credit risk. From the results of Pooled Probit model, interest free banks seem again to be less stable, but less liquid, and riskier for the total period. The failure to find more stability for IBs is due to assumption of a stable relationships. Once we introduce interaction effect variables to take into account of behavior instability (due to Subprime crisis (GFC)), we show that IBs are rather more stable, more liquid but less profitable post GFC.

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Safi, Naila, Zahoor Ul Haq, and Javed Iqbal. "Understanding internal migration in Pakistan." Journl of Applied Economics and Business Studies 1, no. 1 (2017): 65–76. http://dx.doi.org/10.34260/jaebs.117.

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This study estimated the effect of socio-economic factors on internal migration for Pakistan using the Labor Force Survey data, 2013. This study used a sample of 26013 observations. Both bi-variate and multivariate Logistic and Probit analysis were performed. Estimates of Logistic and Probit regressions show that gender has negative and statistically significant effect on migration. Similarly, marriage also has a negative effect on migration indicating as individual gets married, their log-likelihood of migration decreases. Income is statistically significant determinant of migration as analysis exhibited that as income increased, the log-likelihood of internal migration increased too. Education and employment do not determine migration. This study can be used in planning internal migration as for developing countries like Pakistan socioeconomic factors are important determinants of migration. Providing employment and income opportunities can significantly help in reducing the migration. Besides socioeconomic factors, further investigation is required to better understand the perception of individuals with respect to internal migration which this study could not tackle because of nonexistence of such data in the labour force survey.

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Gillespie, Jeffrey M., Christopher G. Davis, and Noro C. Rahelizatovo. "Factors Influencing the Adoption of Breeding Technologies in U.S. Hog Production." Journal of Agricultural and Applied Economics 36, no. 1 (2004): 35–47. http://dx.doi.org/10.1017/s1074070800021842.

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An important aspect of structural change in the U.S. hog industry has been the adoption of breeding technology. The adoption of each of four breeding technologies, weekly farrowing, intensive breeding, terminal crossbreeding, and artificial insemination, in U.S. hog production is analyzed using multivariate probit analysis. Results suggest that diversification, whether the producer raised breeding stock, debt and asset levels, and producer's education influence adoption rate. Larger, more risk-averse producers were more likely to adopt technology. Producers who rated the quality of labor available as higher were more likely to adopt management-intensive technologies.

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