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Applied Time Series Using Stata
Highest Rated
Rating: 4.7 out of 5(74 ratings)
405 students

Applied Time Series Using Stata

ARIMA, VAR, VECM, ARCH, GARCH, and structural breaks.
Highest Rated
Created byProfessor Gerhard Kling
Last updated 8/2023
English

What you'll learn

  • Understand deterministic and stochastic trends
  • Identify stationary time series
  • Determine optimal ARIMA models
  • Capture policy changes using intervention models
  • Estimate vector autoregressions and their dynamics
  • Understand vector error correction models
  • Explore panel vector autoregressions
  • Become a confident user of Stata

Course content

10 sections • 29 lectures • 6h 24m total length

  • S1 Course Introduction6:23

    Welcome to Applied Time Series Using Stata. This session provides an overview. This course covers a wide range of time series models, including univariate and multivariate models that explain the mean equation of time series. We will explore conditional variance models (ARCH and GARCH), structural breaks, and panel vector autoregressions. Come and join us!

  • S2 Introduction to Stata6:09

    I will briefly introduce Stata. First of all, you can use an older version of Stata – it does not affect the functionality. At the moment, Stata 17 is available – but I will work with Stata 13 without any issues.

  • S3 Access to Stata3:17

    This session discusses various ways to get access to Stata. You might have access to an older version of Stata, which will be perfectly fine for this introductory course. All the code has been tested on Stata 13, which I have used for many years without any problems.

  • S4 The Purpose of Time Series Analysis4:53

    What is our goal? In fact, there are different answers to this question. In time series analysis, we observe different variables, say X and Y, over time t. We can see how the values (or realisations) of X and Y change over time. These observations could lead to an understanding of causal order – put differently if X changes today – this change is followed by higher values of Y tomorrow. Establishing these sequences can help to derive causal relationships. We will explore these issues in our unit on vector autoregressions. Testing theories is another valid objective – but most practitioners are not worried about it. Hence, this course will not focus on these issues. Simulating time series is yet another area of interest – but simulations go beyond the scope of this course. Finally, forecasting is the main purpose for most practitioners. Obviously, being able to forecast time series has substantial economic benefits. Hence, this course will focus on our ability to forecast time series.

Requirements

  • Basic training in applied data analysis would be useful. I recommend my Udemy course Getting started with Stata, which provides a detailed introduction to data analysis and Stata.

Description

Mastering Time Series Analysis: From ARIMA to Advanced VAR and VECM Models

Unlock the full potential of time series data with this comprehensive course, covering essential and advanced techniques often overlooked in typical time series classes. Whether you're a beginner or an experienced analyst, this course equips you with a practical toolkit for robust time series modelling and forecasting.

What You'll Learn:

  1. Foundations of Time Series Analysis: Start with core concepts of time series, stationarity, and unit root testing, setting a strong foundation for advanced modelling.

  2. Order of Integration & ARIMA Modeling: Dive into autoregressive integrated moving average (ARIMA) models. Learn intervention analysis to capture the impact of external events like policy shifts and economic shocks.

  3. Advanced Multivariate Models (VAR & VECM): Explore vector autoregressions (VAR) and vector error correction models (VECM) to capture relationships across multiple time series, studying both short-term dynamics and the long-run equilibrium.

  4. Impulse-Response Functions & Cointegration: Understand the interplay between variables over time using impulse-response functions and cointegration techniques.

  5. Structural Breaks Detection: Identify critical points in time series data using structural break detection methods, including optimal breakpoint analysis for known and unknown points.

  6. Conditional Variance Modeling with ARCH/GARCH: Construct ARCH and GARCH models to forecast conditional variance, which is essential for financial and volatility analysis.


Software Requirements: Use Stata for analysis—even older versions are compatible!

Why This Course? Gain hands-on experience with techniques usually excluded from time series courses, like panel VARs and cointegration. Each section enhances your analytical confidence, improving your forecasting accuracy and insight into dynamic data patterns.

**Join Us on Udemy. Let’s enjoy the Joy of Data Analysis!









Who this course is for:

  • If you are interested in time series modeling and forecasting, this course is for you.

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