Multivariate Wavelet-based Shape Preserving Estimation for Dependent Observations
Université du Luxembourg
University of Geneva - HEC; Swiss Finance Institute
Rainer Von Sachs
Catholic University of Louvain - Department of Statistics
FAME Research Paper No. 144
We present a new approach on shape preserving estimation of probability distribution and density functions using wavelet methodology for multivariate dependent data. Our estimators preserve shape constraints such as monotonicity, positivity and integration to one, and allow for low spatial regularity of the underlying functions. As important application, we discuss conditional quantile estimation for financial time series data. We show that our methodology can be easily implemented with B-splines, and performs well in a finite sample situation, through Monte Carlo simulations.
Number of Pages in PDF File: 39
Keywords: Conditional quantile, time series, shape preserving wavelet estimation, B-splines, multivariate process.
JEL Classification: C14, C15, C32working papers series
Date posted: June 1, 2005
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo1 in 0.578 seconds