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DATA STATIONER AND DATA NON STATIONER. Example- Real GDP (2000 Prices) Seasonally Adjusted. (1) Plot Time Series - Non-Stationary (i.e. time varying mean and correlogram non-zero). GDP. Time. r. k. These are Examples of Non-Stationary Time Series. Unit Root Testing.
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Example- Real GDP (2000 Prices) Seasonally Adjusted • (1) Plot Time Series - Non-Stationary • (i.e. time varying mean and correlogram non-zero) GDP Time r k
Unit Root Testing • (1) Plot First Difference of Time Series - Stationary • (i.e. constant mean and correlogram zero) Time r k
Informal Procedures to identify non-stationary processes • (2) Diagnostic test - Correlogram • Correlation between 1980 and 1980 + k. • For stationary process correlogram dies out rapidly. • Series has no memory. 1980 is not related to 1985.
What is a Spurious Regression? A Spurious or Nonsensical relationship may result when one Non-stationary time series is regressed against one or more Non-stationary time series The best way to guard against Spurious Regressions is to check for “Cointegration” of the variables used in time series modeling
Symptoms of Likely Presence of Spurious Regression • If the R2 of the regression is greater than the Durbin-Watson Statistic • If the residual series of the regression has a Unit Root
What is a “Unit Root”? If a Non-Stationary Time Series Yt has to be “differenced” d times to make it stationary, then Yt is said to contain d “Unit Roots”. It is customary to denote Yt ~ I(d) which reads “Yt is integrated of order d”
Unit Root Testing: Formal Tests to Establish Stationarity of Time Series • Dickey-Fuller (DF) Test • Augmented Dickey-Fuller (ADF) Test • Phillips-Perron (PP) Unit Root Test • Dickey-Pantula Unit Root Test • GLS Transformed Dickey-Fuller Test • ERS Point Optimal Test • KPSS Unit Root Test • Ng and Perron Test