Augmented Dickey Fuller Test

Abstract

Testing data for stationarity is very important in research where the underlying variables based on time. Moreover time series data analysis has many applications in many areas including studying the relationship between wages and house prices, profits and dividends, and consumption and GDP. An important econometric task is determining the most appropriate form of the trend in the data. Many economic and financial time series exhibit trending behavior or non-stationarity in the mean. Leading examples are asset prices, exchange rates and the levels of macroeconomic aggregates like real GDP. In the beginning of the decade 1970s there was a great debate about this topic. Granger and Newbold (1974) were the researchers, who give the idea that the macroeconomic data as a rule contained stochastic trends, and this data is characterized by unit root, they also suggest that using these variables in econometric models may lead towards spurious regressions. So testing for stationarity is very important because the whole results of the regression might be fabricated. In simple words we can say that trended series is called non-stationary and with unit root and on the other hand non-trended series is a stationary series characterized by without unit root.