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Forecasting industrial production using non-linear methods

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Forecasting industrial production using non-linear methods. / Byers, D.; Peel, David.
In: Journal of Forecasting, Vol. 14, No. 4, 07.1995, p. 325-336.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Byers, D & Peel, D 1995, 'Forecasting industrial production using non-linear methods', Journal of Forecasting, vol. 14, no. 4, pp. 325-336. https://doi.org/10.1002/for.3980140402

APA

Byers, D., & Peel, D. (1995). Forecasting industrial production using non-linear methods. Journal of Forecasting, 14(4), 325-336. https://doi.org/10.1002/for.3980140402

Vancouver

Byers D, Peel D. Forecasting industrial production using non-linear methods. Journal of Forecasting. 1995 Jul;14(4):325-336. doi: 10.1002/for.3980140402

Author

Byers, D. ; Peel, David. / Forecasting industrial production using non-linear methods. In: Journal of Forecasting. 1995 ; Vol. 14, No. 4. pp. 325-336.

Bibtex

@article{313a0313e48f43e4a9e61b0df49fa505,
title = "Forecasting industrial production using non-linear methods",
abstract = "Numerous theoretical models suggests that business cycles involve nonlinear processes. In this paper we examine whether two parametric, nonlinear time-series models—the bilinear and threshold models—can exploit apparent non-linearity in industrial production to provide forecasts superior to those derived from the standard autoregressive models.",
keywords = "non-linear modeling, bilinear model , threshold model , industrial production",
author = "D. Byers and David Peel",
year = "1995",
month = jul,
doi = "10.1002/for.3980140402",
language = "English",
volume = "14",
pages = "325--336",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley and Sons Ltd",
number = "4",

}

RIS

TY - JOUR

T1 - Forecasting industrial production using non-linear methods

AU - Byers, D.

AU - Peel, David

PY - 1995/7

Y1 - 1995/7

N2 - Numerous theoretical models suggests that business cycles involve nonlinear processes. In this paper we examine whether two parametric, nonlinear time-series models—the bilinear and threshold models—can exploit apparent non-linearity in industrial production to provide forecasts superior to those derived from the standard autoregressive models.

AB - Numerous theoretical models suggests that business cycles involve nonlinear processes. In this paper we examine whether two parametric, nonlinear time-series models—the bilinear and threshold models—can exploit apparent non-linearity in industrial production to provide forecasts superior to those derived from the standard autoregressive models.

KW - non-linear modeling

KW - bilinear model

KW - threshold model

KW - industrial production

U2 - 10.1002/for.3980140402

DO - 10.1002/for.3980140402

M3 - Journal article

VL - 14

SP - 325

EP - 336

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 4

ER -