# name
Nearly unbiased estimation of autoregressive models for bounded near‐integrated stochastic processes

# metadata.dc.description.abstract[].value
The paper investigates the estimation bias of autoregressive models for bounded near‐integrated stochastic processes and the performance of the standard procedures in the literature that aim to correct the estimation bias. In some cases, the bounded nature of the stochastic processes worsens the estimation bias effect. The paper extends two popular autoregressive estimation bias correction procedures to cover bounded stochastic processes. Monte Carlo simulations reveal that accounting for the bounded nature of the stochastic processes leads to improvements in the estimation of autoregressive models. Finally, an illustration is given using the unemployment rate of the G7 countries.

# metadata.dc.relation.ispartof[].value
Oxford Bulletin of Economics and Statistics, 2021, vol. 83, num. 1, p. 273-297

# metadata.dc.rights[].value
(c) The Department of Economics, University of Oxford and John Wiley & Sons , 2021

# metadata.dc.source[].value
Articles publicats en revistes  (Econometria, Estadística i Economia Aplicada)

# metadata.dc.title[].value
Nearly unbiased estimation of autoregressive models for bounded near‐integrated stochastic processes