Correlation, Regression, and Cointegration of Nonstationary Economic Time Series

Publikation: Working paperForskning

Standard

Correlation, Regression, and Cointegration of Nonstationary Economic Time Series. / Johansen, Søren.

Department of Economics, University of Copenhagen, 2007.

Publikation: Working paperForskning

Harvard

Johansen, S 2007 'Correlation, Regression, and Cointegration of Nonstationary Economic Time Series' Department of Economics, University of Copenhagen.

APA

Johansen, S. (2007). Correlation, Regression, and Cointegration of Nonstationary Economic Time Series. Department of Economics, University of Copenhagen.

Vancouver

Johansen S. Correlation, Regression, and Cointegration of Nonstationary Economic Time Series. Department of Economics, University of Copenhagen. 2007.

Author

Johansen, Søren. / Correlation, Regression, and Cointegration of Nonstationary Economic Time Series. Department of Economics, University of Copenhagen, 2007.

Bibtex

@techreport{eff05df0912f11dcbee902004c4f4f50,
title = "Correlation, Regression, and Cointegration of Nonstationary Economic Time Series",
abstract = "Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some nonstationary processes, that the empirical correlations seem not to converge in probability even if the processes were independent. This was later discussed by Granger and Newbold (1974), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient",
keywords = "Faculty of Social Sciences",
author = "S{\o}ren Johansen",
note = "JEL Classification: C22",
year = "2007",
language = "English",
publisher = "Department of Economics, University of Copenhagen",
address = "Denmark",
type = "WorkingPaper",
institution = "Department of Economics, University of Copenhagen",

}

RIS

TY - UNPB

T1 - Correlation, Regression, and Cointegration of Nonstationary Economic Time Series

AU - Johansen, Søren

N1 - JEL Classification: C22

PY - 2007

Y1 - 2007

N2 - Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some nonstationary processes, that the empirical correlations seem not to converge in probability even if the processes were independent. This was later discussed by Granger and Newbold (1974), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient

AB - Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some nonstationary processes, that the empirical correlations seem not to converge in probability even if the processes were independent. This was later discussed by Granger and Newbold (1974), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient

KW - Faculty of Social Sciences

M3 - Working paper

BT - Correlation, Regression, and Cointegration of Nonstationary Economic Time Series

PB - Department of Economics, University of Copenhagen

ER -

ID: 1523792