The Vector Autoregression or VAR model is an essential tool in time series analysis. It is an extension of the univariate autoregressive models and can incorporate multiple time series variables,…
The Augmented Dickey Fuller or ADF Test of stationarity is a unit root based test. It attempts to overcome the shortcomings of the original Dickey Fuller test. Researchers often observed…
Cointegration refers to the situation where the variables have a long-run or equilibrium relationship. Such variables move together over time and can be said to have a similar wavelength or…
If a non-stationary time series has to be differenced “d” times to make it stationary, that time series is integrated of order "d". In other words, the order of integration…
The Dickey Fuller Test is a unit root based test of stationarity. The unit root based tests focus on the coefficient associated with the first lag of the time series…
The ACF or Autocorrelation Function is one of the most widely used methods to check for stationarity of a time series. Moreover, it also gives valuable insights into the autoregressive…
The Random Walk Model is an example of a non-stationary time series. This model is often used to discuss as well as illustrate the concepts of stationarity, unit root process…
A time series is said to be stationary if its characteristics do not change with time. Moreover, a stationary time series can be identified as strictly stationary or weak stationary.…
Vector Autoregression or VAR can be estimated using several packages. We will use the "vars" and "tsDyn" packages to illustrate the application of VAR in R. In addition, we will…
There are several packages available for estimating the ARIMA and SARIMA in Rstudio. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models are often used for…
Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) can provide valuable insights into the behaviour of time series data. They are often used to decide the number of Autoregressive (AR)…
Seasonality, sometimes referred to as seasonal variation, is common in economic time series. The time series variables may change in a cyclical pattern with time. This cyclical pattern is termed…
Autoregressive Integrated Moving Average (ARIMA) models are often used for forecasting purposes. These models for time series data have been observed to provide accurate forecasts. Additionally, these models allow dynamic…
The Vector Error Correction Mechanism (VECM) is estimated in the presence of cointegration among the system of variables. It allows us to estimate short-run as well as long-run coefficients. Using…
Vector Error Correction Mechanism (VECM) models are a special application of VAR or Vector Autoregressive Models. The specification of VECM models involves the introduction of error correction terms into the…
