The analysis of nonstationary time series using regression, correlation and cointegration - with an application to annual mean temperature and sea level

Soren Johansen

No 2011/4, DSS Empirical Economics and Econometrics Working Papers Series from Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome

Abstract: There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the e¤ect of nonstationarity on inference using these methods and compare them to model based inference. Finally we analyse some data on annual mean temperature and sea level, by applying the cointegrated vector autoregressive model, which explicitly takes into account the nonstationarity of the variables.

Keywords: Regression correlation cointegration; model based inference; likelihood inference; annual mean temperature; sea level (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2011-11
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (1)

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http://www.dss.uniroma1.it/RePec/sas/wpaper/20114_johansen_2.pdf First version, 2011 (application/pdf)

Related works:
Working Paper: The analysis of nonstationary time series using regression, correlation and cointegration with an application to annual mean temperature and sea level (2010)
Working Paper: The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration with an Application to Annual Mean Temperature and Sea Level (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:sas:wpaper:20114

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