This chapter describes nominal constructions in the Central variety of Taleshi (Northwestern Iranian). In Taleshi, noun phrases and majority adpositional are head-final. Some modifiers as well complement postpositions linked to head phrase by a linking element––known Reverse Ezafe (Stilo 2004). this chapter, we show that appearing with possessors adpositions is different from attributive adject...

This paper describes a revenue management project with a major airline that operates in a fiercely competitive market involving two major hubs and having more than 30 parallel daily flights. The market has a number of unusual characteristics including (1) almost half of customers choose not to purchase the tickets after booking; (2) about half of customers purchase their tickets within 3 days o...

in recent years, iran has experienced high level depreciation of the nominal exchange rate (ner). the ultimate effects of such depreciation on iranian families’ welfare and income distribution have been a challenging issue among policymakers and researchers. accordingly, this study evaluates the economic effects of ner depreciation on the rice market, using spatial price equilibrium model. the ...

An exact formula for the expected compensating variation is derived for logit and nested-logit models with income effects. Intuition, examples, and an application are provided. The appendix contains a formal proof. The formula is applied to estimate the E[cv]s salmon anglers in Maine would associate with changes in catch rates at Maine and Canadian Rivers.

This article introduces a Markov chain Monte Carlo (MCMC) method for sampling the parameters of a multinomial logit model from their posterior distribution. Let yi ∈ {0, . . . ,M} denote the categorical response of subject i with covariates xi = (xi1, . . . , xip) T . Let X = (x1, . . . ,xn) T denote the design matrix, and let y = (y1, . . . , yn) T . Multinomial logit models relate yi to xi th...

3 Model Specification 5 3.1 Binary choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 The binary probit model . . . . . . . . . . . . . . . . . . 6 3.1.2 The binary logit model . . . . . . . . . . . . . . . . . . . 7 3.2 More than two choices . . . . . . . . . . . . . . . . . . . . . . . 8 3.2.1 The multinomial probit model . . . . . . . . . . . . . . . 8 3.2.2 The multinomi...

Understanding and accommodating heterogeneity in variance (also referred to as heteroscedasticity) and taste has become a major area of research within discrete choice analysis. Both scale and taste heterogeneity can be specified as continuous or discrete, the latter can be associated with socio economic characteristics (i.e. observed heterogeneity) or it can be derived probabilistically (i.e. ...