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Non-stationary parametric single-index predictive models: simulation and empirical studies

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

Abstract

This chapter considers the estimation of a parametric single-index predictive regression model with integrated predictors. This model can handle a wide variety of non-linear relationships between the regressand and the single-index component containing either the cointegrated predictors or the non-cointegrated predictors. The authors introduce a new estimation procedure for the model and investigate its finite sample properties via Monte Carlo simulations. This model is then used to examine stock return predictability via various combinations of integrated lagged economic and financial variables.
Original languageEnglish
Title of host publicationEssays in Honor of Joon Y. PArk
Subtitle of host publicationEconometric Theory
EditorsYoosoon Chang, Sokbae Lee, J. Isaac Miller
Place of PublicationBingley UK
PublisherEmerald Group Publishing Limited
Chapter12
Pages349-365
Number of pages17
Edition1st
ISBN (Electronic)9781837532087, 9781837532100
ISBN (Print)9781837532094
DOIs
Publication statusPublished - 2023

Publication series

NameAdvances in Econometrics
Volume45A
ISSN (Print)0731-9053

Keywords

  • non-lineartiy
  • non-stationarity
  • single-index models
  • Stock return predictability
  • cointegration
  • constrained least squares estimator

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