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Estimation of long memory in integrated variance

Rossi, Eduardo; Santucci de Magistris, Paolo - In: Econometric reviews 33 (2014) 7, pp. 785-814

Persistent link: https://www.econbiz.de/10010363876

Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors

Makieła, Kamil; Mazur, Błażej - In: Journal of productivity analysis : an official journal … 58 (2022) 1, pp. 35-54

Persistent link: https://www.econbiz.de/10013453679

Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility

Dimitrakopoulos, Stefanos - In: Economics letters 150 (2017), pp. 10-14

Persistent link: https://www.econbiz.de/10011761750

Dynamic price jumps : the performance of high frequency tests and measures, and the robustness of inference

Maneesoonthorn, Worapree; Martin, Gael M.; Forbes, … - 2018

Persistent link: https://www.econbiz.de/10012583570

Volatility estimation and jump detection for drift-diffusion processes

Laurent, Sébastien; Shi, Shuping - In: Journal of econometrics 217 (2020) 2, pp. 259-290

Persistent link: https://www.econbiz.de/10012482762

A simulated semiparametric estimation of nonlinear errors-in-variables models

Wang, Liqun; Hsiao, Cheng - 1996

Persistent link: https://www.econbiz.de/10000592461

Simple estimation of semiparametric models with measurement errors

Evdokimov, Kirill S.; Zeleneev, Andrei - 2025 - This version: November 28, 2024

We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variability of the EIV is a fraction of that of the mismeasured variables, which is typical for empirical applications. For...

Persistent link: https://www.econbiz.de/10015178608

Simple estimation of semiparametric models with measurement errors

Evdokimov, Kirill S.; Zeleneev, Andrei - 2024 - This version: March 15, 2024

Persistent link: https://www.econbiz.de/10014513441

Nonparametric learning rules from bandit experiments : the eyes have it!

Hu, Yingyao; Kayaba, Yutaka; Shum, Matthew - 2010

accordance with current beliefs). Simulation results show how the estimated nonparametric learning rules fit aspects of subjects …

Persistent link: https://www.econbiz.de/10003989987

Nonparametric learning rules from bandit experiments : the eyes have it!

Hu, Yingyao; Kayaba, Yutaka; Shum, Matthew - 2010

How do people learn? We assess, in a distribution-free manner, subjects' learning and choice rules in dynamic two-armed bandit (probabilistic reversal learning) experiments. To aid in identification and estimation, we use auxiliary measures of subjects' beliefs, in the form of their...

Persistent link: https://www.econbiz.de/10008652140