Applied Time Series Using Stata

Description

This course covers univariate and multivariate time series models, including ARIMA, vector autoregressions, and vector error correction models. In addition, we explore cointegration and panel VARs, which are usually not covered in time series courses. The course starts with an introduction to time series, stationarity, and unit root testing. Then we establish the order of integration of time series before moving into autoregressive integrated moving average models (ARIMA). Intervention analysis is a useful extension of ARIMA models. This method can detect the anticipation of events such as policy changes. Multivariate models such as VARs and VECMs will be covered extensively in this course. Short-term dynamics and long-run equilibrium conditions between time series can be studied using impulse-response functions and cointegration. Most importantly, we will discuss structural break detection, which is crucial in enhancing our ability to forecast time series. Structural breaks can occur at known and unknown points in time. We will learn about methods that can find optimal breakpoints. Furthermore, we will construct ARCH and GARCH models to predict the conditional variance of time series. All material is available on Udemy. You can use older versions of Stata to conduct the analyses. Come join us. Let’s enjoy the Joy of Data Analysis!

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