The spatial model has been in use in political science for close to 30 years, and in that period it has achieved a place of prime importance as our paradigm of the process of candidatechoice used by voters. For much of this time political scientists have estimated models of candidatechoice using binary logit or probit, even in cases where there were more than two choices facing voters. Recently...

We compare multinomial logit and mixed logit models for data on California households’ revealed and stated preferences for automobiles. The stated preference data elicited households’ preferences among gas, electric, methanol, and CNG vehicles with various attributes. The mixed logit models provide a much better fit to these data, and forecasting exercises demonstrate substantial differences be...

This chapter examines different models commonly used to model probabilistic choice, such as eg the choice of one type of transportation from among many choices available to the consumer. Section 1 discusses derivation and limitations of conditional logit models. Section 2 discusses probit models and Section 3 discusses the nested logit (generalized extreme value models), which address some of t...

Abstract The political economy of fiscal adjustments is revisited within the framework Alesina et al. (1998). A panel that spans from 1970 to 2016 for three datasets (European Union, Eurozone and OECD-19) constructed. Both descriptive statistics regression analysis employed. We assess how successful are policies budget consolidation. Panel logit heteroskedasticity probit evaluate probability go...

This study explores the nexus between livelihood goals (LGs) and strategy (LS) selection preferences. We construct a measurement of three (survival, security, self-respect goals) by combining qualitative quantitative analysis classify strategies into groups (agricultural, non-agricultural, diversification) based on household income sources. The multinomial logit model with fixed effects empiric...

While all kinds of mixed data—from personal data, over panel and scientific data, to public and commercial data—are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the mixed models...

This paper investigates whether and how regional social contexts influence fertility decisions of women living in western Germany during the 1980s and 1990s. It is argued that regional opportunity structures as well as local patterns of social interaction and culture may translate into parameters that directly affect individual behaviour. Data from the German Socio-Economic Panel (GSOEP) are li...

In this paper, we study data on discrete labor market transitions from Austria. In particular, we follow the careers of workers who experience a job displacement due to plant closure and observe – over a period of forty quarters – whether these workers manage to return to a steady career path. To analyse these discrete-valued panel data, we apply a new method of Bayesian Markov chain clustering...

This paper presents a general approach to the analysis of categorical panel data which is based on using causal log-linear models with latent variables. Like the well-known LISREL model, these models consist of a structural and a measurement part. In the structural part, a system of logit equations is used to explain changes which occur in the dependent variable of interest. An unrestricted or ...

We examine the Lifeline Assistance Program to consider factors that cause people to not participate in a program that provides financial benefits. Using panel data we employ a feasible generalized least squares estimation in which the dependent variable is the logit of the Lifeline participation rate. Our unique database incorporates characteristics of the eligible population (rather than chara...