The shifting seasonal mean autoregressive model and seasonality in the Central England monthly temperature series, 1772–2016

Changli He, Jian Kang, Timo Teräsvirta and Shuhua Zhang

Econometrics and Statistics, 2019, vol. 12, issue C, 1-24

Abstract: A new autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time is introduced. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed as in ARCH models. This variance is also allowed to be smoothly and deterministically time-varying. Under regularity conditions, consistency and asymptotic normality of the maximum likelihood estimators of parameters of this model is proved. The purpose of the model is to find out how the average monthly temperatures in the well-known central England temperature series have been varying during the period of more than 240 years. The main result is that warming has occurred but that there are notable differences between months. In particular, no warming is found for February, April, May and June.

Keywords: Global warming; Nonlinear time series; Changing seasonality; Smooth transition; Testing constancy; Time-varying error variance (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 Q54 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Working Paper: The Shifting Seasonal Mean Autoregressive Model and Seasonality in the Central England Monthly Temperature Series, 1772-2016 (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:12:y:2019:i:c:p:1-24

DOI: 10.1016/j.ecosta.2019.05.005

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