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2021-02-01

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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/173440

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

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https://doi.org/10.1111/obes.12399

Abstract

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.

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Econometria, Anàlisi de regressió, Anàlisi estocàstica, Mètode de Montecarlo

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Econometrics, Regression analysis, Analyse stochastique, Monte Carlo method

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Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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CARRIÓN I SILVESTRE, Josep Lluís, GADEA RIVAS, María Dolores and MONTAÑÉS, Antonio. Nearly unbiased estimation of autoregressive models for bounded near‐integrated stochastic processes. Oxford Bulletin of Economics and Statistics. 2021. Vol. 83, num. 1, pags. 273-297. ISSN 0305-9049. [consulted: 16 of June of 2026]. Available at: https://hdl.handle.net/2445/173440

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