Question

five difference between stationery and non stationery point of inflation?​

Answer 1

Explanation:

Stationary Processes

Financial institutions and corporations, as well as individual investors and researchers, often use financial time series data (such as asset prices, exchange rates, GDP, inflation, and other macroeconomic indicators) in economic forecasts, stock market analysis, or studies of the data itself.

But refining data is key to being able to apply it to your stock analysis. In this article, we'll show you how to isolate the data points that are relevant to your stock reports.

Intro to Stationary and Non-Stationary Processes

Cooking Raw Data

Data points are often non-stationary or have means, variances, and covariances that change over time. Non-stationary behaviors can be trends, cycles, random walks, or combinations of the three.

Non-stationary data, as a rule, are unpredictable and cannot be modeled or forecasted. The results obtained by using non-stationary time series may be spurious in that they may indicate a relationship between two variables where one does not exist. In order to receive consistent, reliable results, the non-stationary data needs to be transformed into stationary data. In contrast to the non-stationary process that has a variable variance and a mean that does not remain near, or returns to a long-run mean over time, the stationary process reverts around a constant long-term mean and has a constant variance independent of time.

Differencing

A non-stationary process with a deterministic trend becomes stationary after removing the trend, or detrending. For example, Yt = α + βt + εt is transformed into a stationary process by subtracting the trend βt: Yt - βt = α + εt, as shown in the figure below. No observation is lost when detrending is used to transform a non-stationary process to a stationary one.

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