This Applied Time Series and Forecasting Tutorial in Stata is exactly what you need to take your macroeconomic forecasting skills to the next level. The main models are thoroughly covered, so you'll be able to forecast a variety of variables with confidence. Plus, the skills you learn will undoubtedly come in handy in your professional career. So why wait? Buy the tutorial today and start improving your forecasting skills!

Discover the power of time-series analysis and unlock its potential using Stata. This comprehensive course provides a thorough review of essential methods for analyzing time-dependent data, equipping you with the knowledge and skills to perform robust time-series analysis with confidence. Through practical examples and step-by-step instructions using Stata, you will gain hands-on experience in implementing various time-series models and interpreting the results.

Course Highlights:

Stationarity and Linear Regression:

• Understand the concept of stationarity and its importance in time-series analysis.
• Learn how to identify and address non-stationarity using appropriate transformations.
• Explore the application of linear regression techniques in time-series analysis.

Autoregressive Integrated Moving-Average (ARIMA) Models:

• Master the fundamentals of ARIMA models and their applications in forecasting.
• Learn how to identify the order of differencing and apply the appropriate ARIMA model.
• Interpret the model outputs and make accurate predictions.

Autoregressive and Generalized Autoregressive Conditionally Heteroskedastic (ARCH - GARCH) Models:

• Gain insights into modeling and forecasting volatility using ARCH and GARCH models.
• Understand the dynamics of volatility clustering and conditional heteroskedasticity.
• Implement ARCH and GARCH models in Stata to analyze financial and economic time series.

Cointegration and Error Correction Models:

• Explore the concept of cointegration and its significance in modeling long-term relationships.
• Learn about the error correction model (ECM) and its application in capturing short-term dynamics.
• Use Engle-Granger's methodology to estimate cointegrated models.

Multivariate Time-Series Analysis:

• Understand the vector autoregressive (VAR) framework for modeling interdependencies among multiple time series variables.
• Gain insights into structural VAR models for analyzing policy shocks and impulse response analysis.
• Apply Stata's capabilities to estimate and interpret multivariate time-series models.

Advanced Topics:

• Dive into the world of dynamic stochastic general equilibrium (DSGE) models for macroeconomic analysis (recommended for advanced users).

Course Materials:

You can purchase the course materials separately, or, save by buying the material as a bundle. Each file includes slides with detailed explanations, complete datasets, and comprehensive Stata DO files. These resources will enable you to replicate the models and results discussed in the course videos.