Hypothesis testing in mixtures-of-experts of generalized linear time series

A. X. Carvalho, M. A. Tanner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We consider a novel class of non-linear models based on mixtures of local generalized linear time series. In our models, at any given time point, we have a certain number of generalized linear models (GLM), denoted as experts, where the vector of covariates may include functions of lags of the dependent variable. Additionally, we have a latent variable, whose distribution depends on the same covariates as the experts, that determines which GLM is observed. This structure is considerably flexible, as was shown by Jiang and Tanner in a series of papers for mixtures of GLM with independent observations. Carvalho and Tanner (2002) show that the maximum likelihood estimator is consistent and asymptotically normal for correctly specified as well as misspecified models, under the appropriate regularity conditions. In this paper, we discuss the use of the Wald test for hypothesis testing and illustrate the theory with an example using financial time series.

Original languageEnglish (US)
Title of host publication2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-292
Number of pages8
ISBN (Electronic)0780376544
DOIs
StatePublished - Jan 1 2003
Event2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Hong Kong, China
Duration: Mar 20 2003Mar 23 2003

Publication series

NameIEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Volume2003-January

Other

Other2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003
CountryChina
CityHong Kong
Period3/20/033/23/03

Keywords

  • Finance
  • Gaussian processes
  • Hidden Markov models
  • Jacobian matrices
  • Maximum likelihood estimation
  • Shape
  • State estimation
  • Statistics
  • Testing
  • Vectors

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence
  • Software
  • Applied Mathematics
  • Finance

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