نتایج جستجو برای: random coefficient choice models

تعداد نتایج: 1452438  

2016
Bindu Krishnan

In this paper, we study a first order random coefficient autoregressive model with Laplace distribution as marginal. A random coefficient moving average model of order one with Laplace as marginal distribution is introduced and its properties are studied. By combining the two models, we develop a first order random coefficient autoregressive moving average model with Laplace marginal and discus...

2005
Alexander Aue Lajos Horváth Josef Steinebach

We propose the quasi-maximum likelihood method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions.

2004
Matias Eklöf Melvyn Weeks

DCM (Discrete Choice Models) is a package, written in Ox, for estimating a class of discrete choice models. DCM represents an important development for both the OxMetric and, more generally, microeconometric computing environment in making available a broad range of discrete choice models, including standard binary response models, with notable extensions including conditional mixed logit, mixe...

2017
Mingxian Wang Yun Huang

Customers often compare and evaluate alternative products before making purchase decisions. Understanding customer preference is an important step for choice modeling in engineering design. This study presents a network approach to model co-consideration relations between products in supporting engineering design decisions. The network approach of co-consideration represents each product as a n...

2011
Jeremy T. Fox Kyoo il Kim Stephen P. Ryan Patrick Bajari Xiaohong Chen Andrew Chesher Peter Reiss Jean-Marc Robin Andrés Santos Azeem Shaikh

We propose a simple mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program, and computationally attractive compared to alternative estimators for random coefficient models. For complex structu...

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