Tag: time series analysis
This article explains how to test ARIMA models and identifies the appropriate one for the process of forecasting time series GDP.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometrics, trend analysisAutoregressive Integrated Moving Average (ARIMA) is popularly known as Box-Jenkins method. The emphasis of this method is on analyzing the probabilistic or stochastic properties of a single time series. Unlike regression models where Y is explained by X1 X2….XN regressor (like the introductory case where GDP is explained by GFC and PFC), ARIMA allows Y (GDP) to be explained by its own past or lagged values.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsThe previous article based on the Dickey-Fuller test established that GDP time series data is non-stationary.
assumption tests in STATA, empirical analysis with econometrics, STATA for data analysis, stationarity test, time series analysisThe purpose of this article is to explain the process of determining and creating stationarity in time series analysis. Creating a visual plot of data is the first step in time series analysis. Graphical representation of data helps understand it better.
assumption tests in STATA, empirical analysis with econometrics, STATA for data analysis, stationarity test, time series analysis, trend analysisTime series analysis works on all structures of data. It comprises of methods to extract meaningful statistics and characteristics of data. Time series test is applicable on datasets arranged periodically (yearly, quarterly, weekly or daily).
STATA for data analysis, time series analysis