SEMIPARAMETRIC EFFICIENCY BOUND IN TIME-SERIES MODELS FOR CONDITIONAL QUANTILES

DOI10.1017/S0266466609100038zbMath1185.62156OpenAlexW1978346741MaRDI QIDQ3557546

Ivana Komunjer, Quang H. Vuong

Publication date: 23 April 2010

Published in: Econometric Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0266466609100038

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05) Point estimation (62F10)

Related Items (12)

Conditional empirical likelihood estimation and inference for quantile regression models ⋮ Sequential testing for elicitable functionals via supermartingales ⋮ Statistically efficient construction of \(a\)-risk-minimizing portfolio ⋮ VAR for VaR: measuring tail dependence using multivariate regression quantiles ⋮ Semiparametric efficiency for partially linear single-index regression models ⋮ A joint quantile and expected shortfall regression framework ⋮ Estimating value-at-risk and expected shortfall using the intraday low and range data ⋮ Efficient estimation in dynamic conditional quantile models ⋮ Testing linearity against threshold effects: uniform inference in quantile regression ⋮ Quantile-Regression Inference With Adaptive Control of Size ⋮ ON THE RECOVERABILITY OF FORECASTERS’ PREFERENCES ⋮ ON THE ASYMPTOTIC EFFICIENCY OF GMM

Cites Work

• Quasi-maximum likelihood estimation for conditional quantiles
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• On adaptive estimation
• Profile likelihood and conditionally parametric models
• Adaptive estimation in time-series models
• Efficient estimation in the bivariate normal copula model: Normal margins are least favourable
• On adaptive estimation in stationary ARMA processes
• Exogeneity
• Pseudo Maximum Likelihood Methods: Theory
• On estimation and adaptive estimation for locally asymptotically normal families
• Efficiency Bounds Implied by Multiperiod Conditional Moment Restrictions
• On Profile Likelihood
• Sample heterogeneity and M-estimation

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