Predictable non-linearities in U.S. inflation

Jane M. Binner; C. Thomas Elger; Birger Nilsson; Jonathan A. Tepper

doi:10.1016/j.econlet.2006.06.001

Predictable non-linearities in U.S. inflation

Jane M. Binner^*, C. Thomas Elger, Birger Nilsson, Jonathan A. Tepper

^*Corresponding author for this work

Finance

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

Abstract

This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a neural network and a Markov switching autoregressive (MS-AR) model. We find that predictable non-linearities in inflation are best accounted for by the MS-AR model.

Original language	English
Pages (from-to)	323-328
Number of pages	6
Journal	Economics Letters
Volume	93
Issue number	3
DOIs	https://doi.org/10.1016/j.econlet.2006.06.001
Publication status	Published - Dec 2006

Keywords

Inflation forecasting
Markov switching models
Recurrent neural networks

ASJC Scopus subject areas

Finance
Economics and Econometrics

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10.1016/j.econlet.2006.06.001

Cite this

@article{045e3cae061748bf9efaf6f63cd844bb,

title = "Predictable non-linearities in U.S. inflation",

abstract = "This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a neural network and a Markov switching autoregressive (MS-AR) model. We find that predictable non-linearities in inflation are best accounted for by the MS-AR model.",

keywords = "Inflation forecasting, Markov switching models, Recurrent neural networks",

author = "Binner, {Jane M.} and Elger, {C. Thomas} and Birger Nilsson and Tepper, {Jonathan A.}",

year = "2006",

month = dec,

doi = "10.1016/j.econlet.2006.06.001",

language = "English",

volume = "93",

pages = "323--328",

journal = "Economics Letters",

issn = "0165-1765",

publisher = "Elsevier",

number = "3",

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T1 - Predictable non-linearities in U.S. inflation

AU - Binner, Jane M.

AU - Elger, C. Thomas

AU - Nilsson, Birger

AU - Tepper, Jonathan A.

PY - 2006/12

Y1 - 2006/12

N2 - This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a neural network and a Markov switching autoregressive (MS-AR) model. We find that predictable non-linearities in inflation are best accounted for by the MS-AR model.

AB - This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a neural network and a Markov switching autoregressive (MS-AR) model. We find that predictable non-linearities in inflation are best accounted for by the MS-AR model.

KW - Inflation forecasting

KW - Markov switching models

KW - Recurrent neural networks

UR - http://www.scopus.com/inward/record.url?scp=33751189393&partnerID=8YFLogxK

U2 - 10.1016/j.econlet.2006.06.001

DO - 10.1016/j.econlet.2006.06.001

M3 - Article

AN - SCOPUS:33751189393

SN - 0165-1765

VL - 93

SP - 323

EP - 328

JO - Economics Letters

JF - Economics Letters

IS - 3

ER -

Predictable non-linearities in U.S. inflation

Abstract

Keywords

ASJC Scopus subject areas

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