Non-parametric inference on trends in temperature data
DOI:
https://doi.org/10.26481/marble.2013.v1.133Abstract
This paper contributes to the existing literature on the topic of trend estimation in temperature series by applying a non-standard estimation procedure to data from cities all over Europe. It seems that statements about the important topic of global warming are oftentimes based on linear trend estimation, where a straight line is fitted to the temperature data. It is argued in this paper that this approach of linear trend estimation is too restrictive in the case of temperature data and that a different approach, allowing for a more flexible form of the trend, yields more accurate results. For instance, regressing the European temperature data on a linear trend provides evidence of no global warming for all series under consideration, whereas the non-parametric approach advocated in this paper shows a recent upward trend for all series underlining the importance of taking the complexity of temperature data into account.
References
Bühlmann, P. (1998). Sieve bootstrap for smoothing in nonstationary time series. Annals of Statistics, 26, 48-83.
Fomby, T., & Vogelsang, T. (2002). The application of size-robust trend statistics to global warming tempera- ture series. Journal of Climate, 15, 117-123.
Franses P., & Vogelsang, T. (2005). Are winters getting warmer? Environmental Modelling & Software, 20, 1449-1455.
Greene, W.H. (2008). Econometric Analysis (6th Edition). Pearson Prentice Hall.
Hansen, B.E. (2009). Econometrics. Univeristy of Wisconsin.
Harvey, D., & Mills, T. (2003). Modelling trends in Central England temperatures. Journal of Forecasting, 22, 35-47.
Horowitz, J.L (2001). The Bootstrap. Handbook of Econometrics, Vol. 5, Ch. 52, 3159-3228.
Intergovernmental Panel on Climate Change (2007). Ob- servations: Surface and Atmospheric Climate Change. In: Climate Change 2007: The Physical Science Basis. Trenberth, K.E., P.D. Jones, P. Ambenje, R. Bojariu, D. Easterling, A. Klein Tank, D. Parker, F. Rahimzadeh, J.A. Renwick, M. Rusticucci, B. Soden and P. Zha. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Intergovernmental Panel on Climate Change (2012). Sum- mary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.). A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 1-19.
McKinnon, J. (2006). Bootstrap Methods in Econo- metrics. Queen's University, Department of Economics, Working Paper 1028.
McKitrick, R., & Vogelsang, T. (2011). Multivariate trend comparisons between autocorrelated climate series with general trend regressors. University of Guelph, Department of Economics, Working Paper 1109.
Smeekes, S. (2009). Bootstrapping Nonstationary Time Series. Universitaire Pers Maastricht.
Vogelsang, T. (1998). Trend function hypothesis testing in the presence of serial correlation. Econometrica, 66, 123-148.
World Meterological Organization (2012). Causes of Climate Change. Retrieved from http://www.wmo.int/pages/themes/climate/causes of climate change.php on 23 June, 2012.
