Cyclical Time Series: An Empirical Analysis of Temperatures in Central England Over Three Centuries

Liudas Giraitis, Fulvia Marotta and Peter Phillips
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Liudas Giraitis: Queen Mary University of London
Fulvia Marotta: De Nederlandsche Bank, University of Oxford

No 2409, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper builds on methodology that corrects for irregular spacing between realizations of unevenly spaced time series and provides appropriately corrected estimates of autoregressive model parameters. Using these methods for dealing with missing data, we develop time series tools for forecasting and estimation of autoregressions with cyclically varying parameters in which periodicity is assumed. To illustrate the robustness and flexibility of the methodology, an application is conducted to model daily temperature data. The approach helps to uncover cyclical (daily as well as annual) patterns in the data without imposing restrictive assumptions. Using the Central England Temperature (CET) time series (1772 - present) we find with a high level of accuracy that temperature intra-year averages and persistence have increased in the later sample 1850-2020 compared to 1772 - 1850, especially for the winter months, whereas the estimated variance of the random shocks in the autoregression seems to have decreased over time.

Pages: 36 pages
Date: 2024-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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