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This paper reviews the use of logit and probit models in marketing and focuses on demonstrating the use of ordered probability models. This type of model is appropriate…
This paper reviews the use of logit and probit models in marketing and focuses on demonstrating the use of ordered probability models. This type of model is appropriate for many applications in marketing and business where the dependent variable of interest is ordinal (e.g., likert scales). A comparison between the properties of the ordinary least squares (OLS) model and ordered logit and probit models is made using consumer satisfaction data on automobiles. This comparison between the two models shows that the use of OLS for ordered categorical data gives misleading results and produces biased estimates, leading to inaccurate hypothesis testing. The paper concludes that ordered probability models, such as the ones illustrated, should be employed in marketing and business research where the dependent variable is ordinal.
Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable…
Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.
Purpose – This chapter gives an overview of methods for defining and analysing crash severity.
Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.
Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.
Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.
Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.
The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For…
The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For macroprudential supervision, it is, therefore, necessary to identify house price bubbles at an early stage to counteract speculative price developments and to ensure financial market stability. This paper aims to develop an early warning system to signal speculative price bubbles.
The results of explosivity tests are used to identify periods of excessive price increases in 18 industrialized countries. The early warning system is then based on a logit and an ordered logit regression, in which monetary, macroeconomic, regulatory, demographic and private factors are used as explanatory variables.
The empirical results show that monetary developments have the highest explanatory power for the existence of house price bubbles. Further, the study reveals currently emerging house price bubbles in Norway, Sweden and Switzerland.
The results implicate a new global housing boom, particularly in those countries that did not experience a major price correction during the global financial crisis.
The ordered logit model is an advanced approach that offers the advantage of being able to differentiate between different phases of a house price bubble, thereby allowing a multi-level assessment of the risk of speculative excesses in the housing market.
This chapter focuses on the impact of national economic conditions and voters’ attitudes on the positioning of European national political parties with regard to the…
This chapter focuses on the impact of national economic conditions and voters’ attitudes on the positioning of European national political parties with regard to the European Union (EU). We provide an empirical analysis based on data gathered through the Chapel Hill Expert Survey (CHES) covering parties from 14 European countries observed over the 1999–2010 time span. We perform a regression analysis where the dependent variable measures the position of political parties vis-à-vis EU integration and explanatory variables include a number of measures of national economic conditions, features of the national political and institutional framework and voters’ Euroscepticism. Fixed effect, ordered logit and fractional logit estimates provide the following main results. Compared with other parties, non-mainstream political parties and those acting in established economies are more prone to mirror citizens’ Eurosceptic sentiments. National economic conditions such as inflation as well as gross domestic product (GDP) growth affect mainstream party support for the EU. Smaller and ideologically extreme parties are, on average, less supportive of European integration.
The purpose of this paper is to contribute to this literature on developing countries by investigating the determinants of job satisfaction in Vietnam where the economics…
The purpose of this paper is to contribute to this literature on developing countries by investigating the determinants of job satisfaction in Vietnam where the economics literature on this issue is virtually non-existent. The authors also contribute to the literature on income comparison by extending beyond the within-firm co-worker income comparison.
The authors estimate a generalized order logit model for job satisfaction as statistical tests suggest that the parallel-lines assumption, which is often invoked in previous studies using the standard logit model, does not hold.
For Vietnam, the authors find that absolute and relative incomes as well as human resource practices such as efficiency wage and training policy have an impact on workers’ satisfaction. Workers in the foreign direct investment (FDI) sectors behave a bit differently from their peers in the domestic sector.
Taking advantage of a unique matched employer–employee data set collected in 2008 by the North-South Institute (Canada) and the Vietnam Academy of Social Sciences, the authors are able to investigate the impact of a number of important job characteristics on job satisfaction such as absolute and reference incomes, wage policy, training plan for workers, union membership and job position, and, at the same time, to disentangle the possible differences in job satisfaction of workers in domestic vs FDI firms.
The purpose of this paper is to explore the determinants of life satisfaction in Turkey. Moreover, this study explores the effects of air pollution and crime problems on…
The purpose of this paper is to explore the determinants of life satisfaction in Turkey. Moreover, this study explores the effects of air pollution and crime problems on well-being.
The estimates are based on cross-sectional data from the health survey in Turkey during the years 2010 and 2012. Various econometric models are applied such as the ordered logit and the random-effects generalized latent class ordered logit. Moreover, using pseudo panel data created based on age and region cohorts adapted probit fixed effects and the “blow-up and cluster” estimators are applied. In addition, various estimates by sex, age group, urban and rural areas as well as between individuals with good and poor health status are followed.
The results show that the individuals who self-reported who are exposed to air pollution and crimes present on average 0.2-0.5 less satisfaction scores than those who are not exposed to air pollution and crimes. In terms of monetary values, they are willing to pay more than those who are not exposed to air pollution and crimes by 13-19 Turkish Liras per month. Moreover, the generalized latent class ordered logit shows that there is considerable heterogeneity among the most satisfied and least satisfied individuals.
The originality of the paper lies in the fact that this is the first study to provide an analysis of life satisfaction using micro-level data from Turkey. Moreover, various econometric approaches are applied to compare the results. In addition, examining the heterogeneous effects among individuals with different life satisfaction rankings, it is possible to examine the effects of various factors on well-being and how they differ among individuals. Finally, by examining exposure to air pollution and crimes in the neighbourhood and their effects on well-being, it is possible to control for characteristics of the deprived areas.
This study aims to identify the determinants of transport mode choice and the constraints on shifting freight in New Zealand (NZ) from road to rail and/or coastal…
This study aims to identify the determinants of transport mode choice and the constraints on shifting freight in New Zealand (NZ) from road to rail and/or coastal shipping, and to quantify the trade-off between factors affecting shippers’ perceptions, to assist in increasing the share of freight moved by non-road transport modes.
A revealed preference survey of 183 freight shippers, including small and medium enterprises and freight agents in NZ, is used to investigate whether freight shippers’ characteristics affect their ranked preference for attributes related to mode choice and modal shift. Additionally, a rank-ordered logistic (ROL) model is estimated using the ranking data.
The results reveal several distinct types of transport mode choice behaviour within the sample and show how the preferences for timeliness, cost, accessibility, damage and loss, customer service, and suitability vary between industry groups and business types. Also, the ROL method allows us to identify heterogeneity in preferences for mode choice and mode shift factors for freight within NZ.
The results imply that NZ shippers ranked transport time as the most significant constraint upon distributing goods by rail, while accessibility and load size were the most significant constraints upon using coastal shipping. The study also identifies how NZ shippers’ modal shift constraints vary according to the firm’s individual or logistical characteristics.
This study informs freight transport policy makers about the needs of NZ shippers by providing quantitative measures of the intensity of preference for the various mode choice factors.
Those involved in freight transport have a better basis for formulating transport policy.