Python: Building VAR Model to test correlation between different time series
Budget: $10 – $30 USD
This is a small project. I already started the work and have most of the needed code. The only thing missing is doing the VAR model and getting the results. Here is what is required from you:
I want to test for correlation between different time series using Python. I have data organized into groups (REIT sectors) and I wanna do a VAR model on three variables (1. 3-mo t-bill rates. 2. Inverted Yield rates. 3. S&P 500 rates).
The purpose of this project is to test if REIT securities grouped by sectors are statistically correlated with key economic variables such as the short term interest rates, the inverted yield, and the stock market index. Some REIT sectors will be more correlated than other with the variables and I want to quantify these correlations.
To achieve that, it is expected to:
1. test causality from Granger
2. test for cointegration
3. make the data stationary and test it using ADF
4. getting the results and test for serial correlation
I want to test for correlation between different time series using Python. I have data organized into groups (REIT sectors) and I wanna do a VAR model on three variables (1. 3-mo t-bill rates. 2. Inverted Yield rates. 3. S&P 500 rates).
The purpose of this project is to test if REIT securities grouped by sectors are statistically correlated with key economic variables such as the short term interest rates, the inverted yield, and the stock market index. Some REIT sectors will be more correlated than other with the variables and I want to quantify these correlations.
To achieve that, it is expected to:
1. test causality from Granger
2. test for cointegration
3. make the data stationary and test it using ADF
4. getting the results and test for serial correlation