NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS

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NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS. / Ibragimov, Rustam; Kim, Jihyun; Skrobotov, Anton .

In: Econometric Theory, 03.05.2023, p. 1-27.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{7331efbc6c074dc9bee00c5fd8d6f75b,

title = "NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS",

abstract = "We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively. Our method is simple to implement and is applicable both in the case of continuous and discrete time models. According to our simulation study, the proposed method performs well compared with widely used alternative inference procedures in terms of its finite sample properties in various dependence and persistence settings observed in real-world financial and economic markets.",

author = "Rustam Ibragimov and Jihyun Kim and Anton Skrobotov",

note = "Ibragimov, R., Kim, J., & Skrobotov, A. (2023). NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS. Econometric Theory, 1-27. doi:10.1017/S0266466623000117",

year = "2023",

month = may,

day = "3",

doi = "10.1017/s0266466623000117",

language = "English",

pages = "1--27",

journal = "Econometric Theory",

issn = "0266-4666",

publisher = "Cambridge University Press",

}

RIS

TY - JOUR

T1 - NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS

AU - Ibragimov, Rustam

AU - Kim, Jihyun

AU - Skrobotov, Anton

N1 - Ibragimov, R., Kim, J., & Skrobotov, A. (2023). NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS. Econometric Theory, 1-27. doi:10.1017/S0266466623000117

PY - 2023/5/3

Y1 - 2023/5/3

N2 - We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively. Our method is simple to implement and is applicable both in the case of continuous and discrete time models. According to our simulation study, the proposed method performs well compared with widely used alternative inference procedures in terms of its finite sample properties in various dependence and persistence settings observed in real-world financial and economic markets.

AB - We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively. Our method is simple to implement and is applicable both in the case of continuous and discrete time models. According to our simulation study, the proposed method performs well compared with widely used alternative inference procedures in terms of its finite sample properties in various dependence and persistence settings observed in real-world financial and economic markets.

UR - https://www.mendeley.com/catalogue/11a65ff1-02d1-33bf-a58d-882e61e31d72/

U2 - 10.1017/s0266466623000117

DO - 10.1017/s0266466623000117

M3 - Article

SP - 1

EP - 27

JO - Econometric Theory

JF - Econometric Theory

SN - 0266-4666

ER -

ID: 107448382