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.
