Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices

By Hiroyuki Kasahara, Katsumi Shimotsu. 2009

276 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/3b6b2117017fc9d0bce2aa47edd8d6240d1ac5eb

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Citations in the corpus

Dynamic Discrete Choice Structural Models: A Survey (2010, unknown): “, vt(J, x)]}, where: vt(a, x) ≡ Eεt h Ut(a, x, εt, ω , θ) i + β X x0∈X̄t+1 Ṽ ,t+1(x 0) fx(x 0|a, x) (53)”
Using mixtures in econometric models: a brief review and some new results (2016, background): “Another relevant extension encompassing a variety of economic applications can be found in Kasahara and Shimotsu (2009).”
Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models (2015, background): “Kasahara and Shimotsu [26] considered a number of finite mixture models used in applied work in economics and, under different assumptions on Markov property, stationarity and type-invariance in transition process, obtained sufficient conditions for their identifiability.”
Non‐parametric estimation of finite mixtures from repeated measurements (2016, background): “Hu (2008) and Kasahara and Shimotsu (2009) established related results in econometrics.”
Productivity and the Decision to Import and Export: Theory and Evidence (2008, methodology): “We then use these to evaluate the log-likelihood function given by (30).”
Identifying Latent Structures in Panel Data (2016, background): “In a related thematic, Kasahara and Shimotsu (2009) and Browning and Carro (2011) study identification in discrete choice panel data models for a fixed number of groups using nonparametric discrete mixture distributions.”
Sequential Estimation of Structural Models with a Fixed Point Constraint (2008, unknown): “Kasahara and Shimotsu (2009) derived sufficient conditions for nonparametric identification of a finite mixture model of dynamic discrete choices.”
Nonparametric Identification of Multivariate Mixtures (2010, background): “Kasahara and Shimotsu (2009) study nonparametric identification of finite mixture dy- namic discrete choice models widely used in econometrics using a similar approach to this article.”
Usage-Based Pricing and Demand for Residential Broadband (2016, unknown): “For example, while different combinations of the curvature parameter βh and the disutility (12)The logic behind the identification follows closely the formal argument in Kasahara and Shimotsu (2009).”
Maximum smoothed likelihood for multivariate mixtures (2011, methodology): “(2009) proved an elegant and powerful result using a theorem of Kruskal (1977), establishing the identifiability of the parameters in (2) whenever r 3, regardless of m, as long as the density functions f1k, . . . , fmk are linearly independent except possibly on a set of Lebesgue measure zero. An EM”
Homogeneity pursuit in panel data models: Theory and application (2018, methodology): “One approach is to use finite mixture models; see Sun (2005), Kasahara and Shimotsu (2009), and Browning and Carro (2010).”
Non‐parametric identification and estimation of the number of components in multivariate mixtures (2014, unknown): “…and Allman et al. (2009) analysed non-parametric identifiability of component distributions and mixing proportions in model (1) under known M. (Kasahara and Shimotsu (2009) studied non-parametric identification of finite mixture dynamic discrete choice models that are widely used in…”
Grouped Patterns of Heterogeneity in Panel Data (2015, background): “b, and equation (44), we obtain: 1 T G ∑”
Semiparametric Inference in Dynamic Binary Choice Models, Second Version (2014, background): “Kasahara and Shimotsu (2009) studied a model in which a choice probability is a finite mixture of unobservable component CCPs, which are also conditional on unobserved heterogeneity.”