Abstract
Since many economic variables are non-stationary, a heavy burden is imposed among researchers to determine the stationarity of their data. One price to pay in using non-stationary data is that the conventional student t-test and F-distribution break down and become invalid. But the most serious problem faced when using non-stationary data, is the spurious regression problem. If at least one of the explanatory variables in a regression equation is non-stationary, it is very likely that the dependent variable in the equation will display a similar trend. When both dependent variable and regressor(s) in an equation are trend-dominated, we are likely to obtain highly 'significant' regression coefficients and high values for the coefficient of determination (R²). even if the variables are completely unrelated (Thomas 1996).
Recommended Citation
Pontines, Victor
(1998)
"Purchasing Power Parity: AN APPLICATION OF COINTEGRATION ANALYSIS,"
DLSU Business & Economics Review: Vol. 10:
No.
1, Article 6.
DOI: https://doi.org/10.59588/2243-786X.1408
Available at:
https://animorepository.dlsu.edu.ph/ber/vol10/iss1/6
