EconBiz - Find Economic Literature
    • Logout
    • Change account settings
  • A-Z
  • Beta
  • About EconBiz
  • News
  • Thesaurus (STW)
  • Academic Skills
  • Help
  •  My account 
    • Logout
    • Change account settings
  • Login
EconBiz - Find Economic Literature
Publications Events
Search options
Advanced Search history
My EconBiz
Favorites Loans Reservations Fines
    You are here:
  • Home
  • Search: subject:"Secondary: 62M10"
Narrow search

Narrow search

Year of publication
Subject
All
Secondary 62M10 2 Asymptotic normality 1 Bayesian estimator 1 Characteristic function 1 Compound Poisson process 1 Continuous-time diffusion 1 Edgeworth expansion 1 Fractional Ornstein–Uhlenbeck process 1 Limit distribution 1 Limit likelihood ratio 1 Maximum likelihood estimator 1 Minimum contrast estimator 1 Nonlinear threshold models 1 Periodic ARCH process 1 Primary 60H05 1 Primary 62G30 1 Primary: 62F12 1 Quasi maximum likelihood estimate 1 Secondary: 62M10 1 Strict periodic Stationarity 1 Unit root test 1 Weighted least squares estimate 1 long memory 1 secondary 62M10 1 semiparametric estimation. AMS subject classification : primary 62G20 1
more ... less ...
Online availability
All
Undetermined 3 Free 1
Type of publication
All
Article 3 Book / Working Paper 1
Language
All
Undetermined 4
Author
All
Aknouche, Abdelhakim 1 Al-Eid, Eid 1 Chan, Ngai 1 Giraitis, L. 1 Kutoyants, Yury 1 Robinson, P.M. 1 Tanaka, Katsuto 1
more ... less ...
Institution
All
London School of Economics (LSE) 1
Published in...
All
Statistical Inference for Stochastic Processes 3 LSE Research Online Documents on Economics 1
Source
All
RePEc 4
Showing 1 - 4 of 4
Cover Image
Distributions of the maximum likelihood and minimum contrast estimators associated with the fractional Ornstein–Uhlenbeck process
Tanaka, Katsuto - In: Statistical Inference for Stochastic Processes 16 (2013) 3, pp. 173-192
We discuss some inference problems associated with the fractional Ornstein–Uhlenbeck (fO–U) process driven by the fractional Brownian motion (fBm). In particular, we are concerned with the estimation of the drift parameter, assuming that the Hurst parameter <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$H$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>H</mi> </math> </EquationSource> </InlineEquation> is known and is in <InlineEquation ID="IEq2"> <EquationSource...</equationsource></inlineequation></equationsource></equationsource></inlineequation>
Persistent link: https://www.econbiz.de/10010992898
Saved in:
Cover Image
On parameter estimation of threshold autoregressive models
Chan, Ngai; Kutoyants, Yury - In: Statistical Inference for Stochastic Processes 15 (2012) 1, pp. 81-104
Persistent link: https://www.econbiz.de/10010539196
Saved in:
Cover Image
Asymptotic inference of unstable periodic ARCH processes
Aknouche, Abdelhakim; Al-Eid, Eid - In: Statistical Inference for Stochastic Processes 15 (2012) 1, pp. 61-79
Persistent link: https://www.econbiz.de/10010539199
Saved in:
Cover Image
Edgeworth expansions for semiparametric Whittle estimation of long memory
Giraitis, L.; Robinson, P.M. - London School of Economics (LSE) - 2003
The semiparametric local Whittle or Gaussian estimate of the long memory parameter is known to have especially nice limiting distributional properties, being asymptotically normal with a limiting variance that is completely known. However in moderate samples the normal approximation may not be...
Persistent link: https://www.econbiz.de/10010745104
Saved in:
A service of the
zbw
  • Sitemap
  • Plain language
  • Accessibility
  • Contact us
  • Imprint
  • Privacy

Loading...