Applied Time Series Using Stata from Udemy

What's inside

Learning objectives

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

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

Syllabus

Unit 1 Introduction

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!

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.

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.

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.

This session introduces basic concepts in time series analysis.

We explore data on food prices and try some basic forecasting methods.

This session discusses the concept of stationarity, which is essential in understanding time series models.

We simulate autoregressive processes in Stata. This is a useful exercise to understand the ideas behind data-generating processes.

We explore the Dickey-Fuller test to detect non-stationary time series.

ARIMA models are quite useful for forecasting. These models tend to be simple and stable. If you work with an older version of Stata, please use the Stata 11 dta dataset provided.

This session demonstrates the use of the autocorrelation and partial autocorrelation function to identify ARIMA processes.

We discuss adjustments for seasonality. If you use an older version of Stata, please download the Stata 11 dta dataset.

This session demonstrates the use of intervention analysis to measure the impact of policy changes on time series.

We apply an intervention study to explore the impact of lockdowns on retail sales. The results are a surprise.

This session introduces vector autoregressions (VAR), which can be used to model short-term dynamics between time series.

We apply a VAR model to capture the dynamics of the UK property market. Can we forecast house prices?

This session demonstrates how to estimate VAR models in Stata.

This session explains the stability condition for VAR models. We implement stability testing in Stata.

To illustrate the dynamics of a system, impulse response functions are useful. They can be powerful tools to summarize your findings.

We obtain stock market data from Yahoo Finance and merge datasets.

This session outlines the main ideas without going too deep into the underlying theory.

This session explains the implementation of VECM models in Stata.

This session introduces the conditional variance equation. We explore ARCH and GARCH models.

This session explains how ARCH and GARCH models can be conducted in Stata.

Structural breaks make it much harder to forecast time series. This session introduces tools that can be used to detect structural breaks.

This session conducts structural break tests in Stata.

This session provides a brief introduction to panel VARs and cointegration. These are active areas of current research.

We explore panel VAR models in Stata.

Well done! We will discuss your next steps to enjoy the Joy of Data Analysis.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers a wide range of time series models, including univariate and multivariate models

Instructors have extensive experience in time series analysis

Demonstrates real-world applications of time series analysis, such as forecasting and policy evaluation

Uses Stata for data analysis, which is a widely used software in academia and industry

Provides a comprehensive understanding of time series analysis concepts and techniques

Suitable for students with a background in statistics and econometrics

Reviews summary

Applied time series with stata: practical forecasting

According to students, this course provides a highly practical and applied approach to time series analysis, focusing heavily on implementation using Stata. Learners consistently praise the clear explanations and hands-on Stata demonstrations, making it ideal for those seeking to forecast time series. The course covers a comprehensive range of models, including ARIMA, VAR, VECM, and even less common topics like structural breaks and panel VARs. While many find the instructor's style engaging and the content highly relevant for professional use, some suggest that absolute beginners or those seeking deep theoretical foundations might need supplementary resources due to the fast pace.

Instructor provides clear explanations and maintains an engaging tone.

"The instructor breaks down complex concepts into understandable segments, and the Stata demonstrations are precise and easy to follow."

"The instructor's passion for data analysis shines through. The lectures are clear, and the accompanying materials are well-organized."

Highly relevant for economic forecasting and real-world application.

"I gained immense confidence in forecasting... A must-have for quantitative professionals."

"The focus on forecasting is spot on. It really helped me consolidate my understanding and skills for my work in economic research."

"The material is up-to-date and relevant for mastering time series analysis in Stata for practical use cases."

Covers a wide array of univariate and multivariate models.

"The instructor breaks down complex concepts into understandable segments... I gained immense confidence in understanding models like VAR and VECM."

"A solid foundation in time series... I particularly appreciated the detailed sessions on structural breaks and ARCH/GARCH models, which aren't always covered elsewhere."

"Fantastic course! The coverage of topics from ARIMA to panel VARs is comprehensive and the focus on forecasting is spot on."

Excellent for applying time series models using Stata.

"This course is incredibly well-structured and highly practical for anyone looking to apply time series analysis using Stata. The Stata demonstrations are precise and easy to follow."

"The hands-on examples with Stata are the biggest strength... I loved how the course is purely application-driven with clear Stata commands."

"What a practical course! The Stata examples are excellent and immediately applicable. Highly recommended for practitioners who need to use Stata effectively."

Course pace can be fast, potentially challenging for beginners.

"The course covers the stated topics, but the pace can be quite fast, especially for someone new to time series."

"If you have a decent stats background, it's a good Stata guide; otherwise, be prepared for extra reading."

"Some parts were a little rushed, but overall, it delivers on its promise of practical application."

Favors application; theoretical depth may require supplements.

"While it's very applied, a bit more intuitive explanation of the underlying theory behind some models would have been beneficial for a deeper grasp."

"It's more of a 'how-to' guide for Stata, which is good, but I often found myself wishing for more conceptual depth."

"I found this course somewhat superficial. While it shows how to run models in Stata, the theoretical explanations were often lacking. Not what I expected for 'Applied Time Series'."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Applied Time Series Using Stata with these activities:

Brush up on time series basics

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This course builds on knowledge of time series basics. If you have some rust, this will help you refresh your memory.

Browse courses on Time Series

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Review notes or materials from a previous time series course or textbook
Revisit online or in-person tutorials on time series concepts

Solve practice problems on unit roots

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One of the key concepts in time series analysis is stationarity. Solving practice problems will help you solidify your understanding of unit roots and their implications.

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Solve at least 10 problems on unit root testing using the Dickey Fuller test
Interpret the results and determine the stationarity of time series

Follow tutorials on ARIMA modeling

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ARIMA modeling is a crucial technique in time series analysis. This activity will deepen your understanding of ARIMA and its application in forecasting.

Browse courses on ARIMA Models

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Find online tutorials or courses on ARIMA modeling using Stata
Follow the tutorials and practice building ARIMA models for different time series datasets
Validate the models and analyze their performance

Four other activities

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Conduct an intervention study to analyze the impact of an event

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Intervention analysis is a powerful technique to assess the impact of external events. This project will help you develop practical skills in conducting intervention studies using Stata.

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Choose an interesting time series dataset and identify a specific event or policy change that might have affected the series
Use Stata to build an intervention model and analyze the impact of the event
Prepare a report summarizing your findings and insights

Create an infographic on cointegration and VECM

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Cointegration and VECM are advanced concepts in time series analysis. Creating an infographic will help you synthesize your understanding and explain these concepts clearly.

Browse courses on Cointegration

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Gather information and create an outline for your infographic
Use design tools or software to create a visually appealing infographic
Share your infographic online or present it to your classmates

Attend a workshop on structural break detection

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Structural breaks can significantly impact time series analysis and forecasting. Attending a workshop will provide you with hands-on experience in detecting and modeling structural breaks.

Browse courses on Time Series Analysis

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Find and register for a workshop on structural break detection
Attend the workshop and actively participate in discussions and exercises
Apply the techniques learned in the workshop to your own time series analysis projects

Mentor a junior colleague or student in time series analysis

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Mentoring is a rewarding way to solidify your understanding of time series analysis while helping others. This activity will allow you to share your knowledge and contribute to the development of junior professionals.

Browse courses on Time Series Analysis

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Identify a junior colleague or student who is interested in learning about time series analysis
Establish a mentoring relationship and set regular meeting times
Share your knowledge and guide the mentee through their learning journey

Career center

Learners who complete Applied Time Series Using Stata will develop knowledge and skills that may be useful to these careers:

Econometrician

Econometricians apply statistical and mathematical techniques to economic data to build econometric models and forecasts. This course can help Econometricians build a foundation in time series analysis, which is essential for modeling economic data. The course covers a wide range of time series models, including ARIMA, vector autoregressions, and vector error correction models. These models can be used to forecast economic variables such as GDP, inflation, and unemployment.

See salaries and explore the career path for Econometrician

Financial Analyst

Financial Analysts use time series analysis to forecast financial markets and make investment recommendations. This course can help Financial Analysts develop the skills they need to build and evaluate time series models. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of financial time series and making accurate forecasts.

See salaries and explore the career path for Financial Analyst

Data Scientist

Data Scientists use statistical and machine learning techniques to extract insights from data. This course can help Data Scientists develop the skills they need to analyze time series data. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of time series data and making accurate forecasts.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. This course can help Statisticians develop the skills they need to analyze time series data. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of time series data and making accurate forecasts.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to develop and implement trading strategies. This course can help Quantitative Analysts develop the skills they need to build and evaluate time series models. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of financial time series and making accurate forecasts.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course can help Actuaries develop the skills they need to build and evaluate time series models. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of financial time series and making accurate forecasts.

See salaries and explore the career path for Actuary

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve business problems. This course can help Operations Research Analysts develop the skills they need to build and evaluate time series models. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of time series data and making accurate forecasts.

See salaries and explore the career path for Operations Research Analyst

Market Research Analyst

Market Research Analysts use statistical and qualitative techniques to collect and analyze data about consumer behavior. This course can help Market Research Analysts develop the skills they need to analyze time series data. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of time series data and making accurate forecasts.

See salaries and explore the career path for Market Research Analyst

Business Analyst

Business Analysts use data to help businesses make better decisions. This course can help Business Analysts develop the skills they need to analyze time series data. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of time series data and making accurate forecasts.

See salaries and explore the career path for Business Analyst

Economist

Economists use economic theory and data to analyze economic issues. This course may be useful for Economists who want to develop their skills in time series analysis. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of economic time series and making accurate forecasts.

See salaries and explore the career path for Economist

Risk Manager

Risk Managers use statistical and financial techniques to assess and manage risk. This course may be useful for Risk Managers who want to develop their skills in time series analysis. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics are essential for understanding the behavior of financial time series and making accurate forecasts.

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Project Manager

Project Managers use a variety of techniques to plan, execute, and control projects. This course may be useful for Project Managers who want to develop their skills in time series analysis. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics can be used to forecast project timelines and budgets.

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Consultant

Consultants provide advice and expertise to businesses and organizations. This course may be useful for Consultants who want to develop their skills in time series analysis. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics can be used to help businesses make better decisions about their future.

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Teacher

Teachers use a variety of methods to teach students. This course may be useful for Teachers who want to develop their skills in time series analysis. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics can be used to help students learn about the behavior of time series data.

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Researcher

Researchers use a variety of methods to conduct research. This course may be useful for Researchers who want to develop their skills in time series analysis. The course covers a wide range of topics, including stationarity, unit root testing, and ARIMA models. These topics can be used to help researchers analyze time series data and make accurate forecasts.

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