This course, Policy Analysis Using Interrupted Time Series, provides a comprehensive introduction to the use of interrupted time series analysis and regression discontinuity designs to evaluate policies with routinely collected data. Students will gain the skills to become a go-to person in their company, government department, or academic department as the technical expert on this topic. Through the course, students will learn how to select and set up data sources, conduct statistical analysis, interpret and present results, and identify potential pitfalls. Examples from the social sciences will be used to illustrate the application of these techniques.
Sequences Time Series and Prediction
5.0
Coursera274 learners
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This course, Sequences Time Series and Prediction, is part of the deeplearning.ai TensorFlow Specialization. It will teach you how to use TensorFlow to build time series models and apply them to real-world data. You will learn best practices for preparing time series data, and how to use RNNs and 1D ConvNets for prediction. With this course, you will gain the skills to build AI-powered algorithms that are scalable and can be applied to real-world problems.
Practical Time Series Analysis
1.5
Coursera0 learners
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This course, Practical Time Series Analysis, is designed for people with some technical competencies who need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of their professional topics. It covers data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. It also looks at mathematical models and graphical representations that provide insights into the data, as well as how to make forecasts. The language used is R, a free implementation of the S language. With video lectures, supporting written materials, quizzes, and discussion with fellow learners, this course is a great way to learn Time Series Analysis.
The Econometrics of Time Series Data
1.5
Coursera127 learners
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This course will provide you with the knowledge and skills to understand and apply econometric models to time series data. You will learn about stationary and non-stationary models, and how to test for non-stationarity. You will also explore the applications of time series models, forecasting exercises, and models for changing volatility. With the help of real financial market data, you will be able to estimate and interpret the empirical autocorrelation function, cointegration equations, and ARCH(p) and GARCH(p,q) models. By the end of this course, you will be able to confidently manipulate and plot data, and perform in-sample and out-of-sample forecasting exercises.
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