A strong assumption of time series regression, a widely used technique in econometrics, is the stationarity. It requires that the variables entered in the regression have their variances (standard deviations), covariances (auto-correlations), and means, that are independent of time. A stationary series must not wander too far from its mean. In most cases, the assumption is violated (non-stationarity, i.e., random walk) and doing such regression involves what is called a spurious regression. Possible solutions for dealing with this problem is through transformation of the variables.
Error Correction Model in Time Series Regression
Error Correction Model in Time Series…
Error Correction Model in Time Series Regression
A strong assumption of time series regression, a widely used technique in econometrics, is the stationarity. It requires that the variables entered in the regression have their variances (standard deviations), covariances (auto-correlations), and means, that are independent of time. A stationary series must not wander too far from its mean. In most cases, the assumption is violated (non-stationarity, i.e., random walk) and doing such regression involves what is called a spurious regression. Possible solutions for dealing with this problem is through transformation of the variables.