Data Speaks: How to Model, Forecast, and (Almost) Predict the Future
This three-hour hands-on seminar focuses on time series forecasting using AR, MA, ARMA, and ARIMA models, with an emphasis on model selection, assumptions, interpretation, and implementation in R. Designed for undergraduate and master’s students with some background in econometrics and data analysis, the session offers both conceptual understanding and applied skills in forecasting — a key tool in economic and social science research.
We begin with a brief introduction to types of data (cross-sectional, time series, panel) and a refresher on regression analysis, setting the stage for understanding when and why forecasting is useful — for example, when dealing with missing data or modeling future trends. The main focus will be on the intuition, assumptions, and limitations behind time series models, with step-by-step R coding examples. We will conclude with a brief overview of more advanced models such as SARIMA and others, so participants are aware of the broader forecasting landscape.
Learning Objectives:
By the end of this seminar, participants will:
- Understand the differences between types of data and why time series forecasting is used
- Gain a working knowledge of AR, MA, ARMA, and ARIMA models: structure, assumptions, and
interpretation - Learn how to implement and compare basic time series models in R
- Recognize limitations and practical considerations when using forecasting techniques
- Become aware of more advanced forecasting models (e.g. SARIMA) and when they might be used
Required Background:
Participants are expected to have:
- A basic understanding of regression and econometrics
- Familiarity with working in R (basic coding and data handling)
- A general understanding of what variables and data represent
Participants should bring a laptop with R and RStudio installed (optional)
A short introductory refresher will be provided at the beginning to ensure everyone is aligned.
Which transversal skills will the session develop?
- critical thinking
- problem solving
- creativity
- decision making
Keywords:
- Economics
- Econometrics
- Forecasting
- Coding