## ADF Test: Augmented Dickey Fuller Equation

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…

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# Time Series

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ADF Test: Augmented Dickey Fuller Equation

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Cointegration: Meaning, Tests and Models

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Order of Integration of a time series

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Dickey Fuller Test of Stationarity

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Autocorrelation function and Stationarity

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Random Walk Model and Stationarity

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Stationarity and Stationary Time Series

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VAR in R: Estimation, Goodness and IRFs

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ARIMA and SARIMA in Rstudio

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Interpreting ACF and PACF plots

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Seasonality and Seasonal-ARIMA models

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ARIMA Model Estimation and Model Selection

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VECM Estimation and Interpretation

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Vector Error Correction (VECM) and VAR: Theory

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Impulse Response Functions after VAR and VECM

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…

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August 5, 2024

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…

January 31, 2023

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…

January 30, 2023

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…

January 26, 2023

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…

January 25, 2023

The Random Walk Model is an example of a non-stationary time series. This model is often used to discuss and illustrate the concepts of stationarity, unit root process and order…

January 24, 2023

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.…

January 23, 2023

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…

December 1, 2022

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…

November 14, 2022

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)…

June 21, 2022

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…

May 27, 2022

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…

May 25, 2022

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…

May 9, 2022

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…

May 4, 2022

Impulse Response Functions or IRFs are used to study the effects of shocks or impulses in a VAR or VECM system. It traces out one unit or one standard deviation…

April 13, 2022