Contents
What is second order stationary process?
Second-order stationarity (also called weak stationarity) time series have a constant mean, variance and an autocovariance that doesn’t change with time. Other statistics in the system are free to change over time. Trend-stationary models fluctuate around a deterministic trend (the series mean).
What does covariance stationary mean?
A covariance stationary (sometimes just called stationary) process is unchanged through time shifts. Specifically, the first two moments (mean and variance) don’t change with respect to time. When a series isn’t covariance stationary, any estimations from the model will have no economic meaning (Defusco, 2015).
What does the stationary process mean in statistics?
In mathematics and statistics, a stationary process (or a strict/strictly stationary process or strong/strongly stationary process) is a stochastic process whose unconditional joint probability distribution does not change when shifted in time. Consequently, parameters such as mean and variance also do not change over time.
What does stationarity mean in a stochastic process?
Having a basic definition of stochastic processes to build on, we can now introduce the concept of stationarity. Intuitively, stationarity means that the statistical properties of the process do not change over time. However, several different notions of stationarity have been suggested in econometric literature over the years.
What does stationarity mean in a time series?
In t he most intuitive sense, stationarity means that the statistical properties of a process generating a time series do not change over time. It does not mean that the series does not change over time, just that the way it changes does not itself change over time.
Can a trend be transformed into a stationary process?
A trend stationary process is not strictly stationary, but can easily be transformed into a stationary process by removing the underlying trend, which is solely a function of time.