The analysis of nonstationary time series using regression, correlation and cointegration with an application to annual mean temperature and sea level
AbstractThere are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect 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.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-69.
Date of creation: 15 Oct 2010
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Web page: http://www.econ.au.dk/afn/
Regression correlation cointegration; model based inference; likelihood inference; annual mean temperature; sea level;
Other versions of this item:
- Søren Johansen, 2010. "The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration with an Application to Annual Mean Temperature and Sea Level," Discussion Papers 10-27, University of Copenhagen. Department of Economics.
- Søren Johansen, 2011. "The analysis of nonstationary time series using regression, correlation and cointegration - with an application to annual mean temperature and sea level," DSS Empirical Economics and Econometrics Working Papers Series 2011/4, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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