Parameter estimation in conditional heteroscedastic models

Snigdhansu Chatterjee, Samarjit Das

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We study asymptotics of parameter estimates in conditional heteroscedastic models. The estimators considered are those obtained by minimizing certain functionals and those obtained by solving estimation equations. We establish consistency and derive asymptotic limit laws of the estimators. Condition under which the limit law is normal is studied. Further, bootstrap for these estimators is discussed. The limiting distribution of the estimators is not necessary always normal, and we present a real data example to illustrate this.

Original languageEnglish (US)
Pages (from-to)1135-1153
Number of pages19
JournalCommunications in Statistics - Theory and Methods
Volume32
Issue number6
DOIs
StatePublished - Jun 1 2003

Fingerprint

Heteroscedastic Model
Conditional Model
Parameter Estimation
Estimator
Limit Laws
Asymptotic Limit
Limiting Distribution
Bootstrap
Necessary
Estimate

Keywords

  • Bootstrap
  • Conditional heteroscedastic
  • Estimating equation
  • Minimum contrast estimation

Cite this

Parameter estimation in conditional heteroscedastic models. / Chatterjee, Snigdhansu; Das, Samarjit.

In: Communications in Statistics - Theory and Methods, Vol. 32, No. 6, 01.06.2003, p. 1135-1153.

Research output: Contribution to journalArticle

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