Time series Analysis:
Stata Course
About the Stata Course
In this series of tutorials, you will learn the topics most commonly covered in any Applied Time Series and Forecasting course. In each tutorial, I will you show step by step how to solve the models in STATA. I use real data, which you can download for free to replicate the content covered. Also, you can buy the slides and STATA DO Files if desired. Lastly, please feel free to subscribe to my YouTube Channel, where you will get notified of any new video I cover. Let's Begin!
Table of Contents
The videos follow a progressive order. At the end, you can download the datasets (for free) to replicate the content, and you can buy the Do files along with the slides (if desired).
1-Import & Generate Time Series Variables
In this first tutorial, I show you how to import data into STATA and how to tsset a time series. Tsset is the command that tells STATA we are working with time series.
2-ARIMA Models. Part 1: Identification
Learn how to estimate ARIMA models. In this tutorial, I show you how to identify the possible ARIMA candidate for our estimates.
3-ARIMA Models. Part 2: Estimation
Now that we have our possible ARIMA candidates, it is time to estimate the models and decide which one is our best option.
4-ARIMA Models. Part 3: Forecasting
We have the most appropriate candidate model estimated. Let's see how well it forecasts the near future!
5-ARCH and GARCH Models
In the previous tutorial, we have estimated the mean equation (ARIMA). Now, it is time to estimate the variance with an ARCH/GARCH model.
6-Cointegration and Error Correction Model
Two non stationary series can have a long run equilibrium. If so, we can estimate the long run model, and the short run dynamics. How? With the error correction model. Let's get into it!
7-VAR Models: Part 1
Learn how to estimate multivariate time series models. VAR models allows us to analyze how different variables interact with each other. Topics covered: Stationarity, VAR command, granger causality test, VAR stability conditions and more!
8-VAR Models: Part 2
In this tutorial, we cover Impulse Response Functions (IRF) and Variance Decomposition.
9- VAR Out of sample Forecast
Learn how to produce out of sample forecasts with confidence bands with VAR models in Stata. In this example, we estimate a model for money supply and CPI.
We analyze how shocks in money supply affect prices. Next, we forecast the values of CPI until 2025Q1.
10- DSGE Models
This is a full free course where I teach you how to estimate DSGE models in Stata. DSGE models focus on economic growth and business cycles. Learn how to estimate the models used by monetary entities.
Datasets to Replicate the Content
Below you will find the datasets to replicate the content covered in my videos. Also, a link to buy the DO files and slides if you are interested.
NOTE: The download is DIRECT. The file will download with No adds, NO new window openings and NO suspicious links.
Import & Generate Time Series Variables
ARIMA Models
ARCH/GARCH Models
Cointegration and Error Correction Model
VAR Models
VAR Model Forecast with Confidence BAnds