Autoregressive Integrated Moving Average Models: An Introduction скачать в хорошем качестве

Autoregressive Integrated Moving Average Models: An Introduction

【Ｏｎｌｉｎｅ　Ｃｏｕｒｓｅｓ】 ⚡Getting Started with Stata: (24 lectures + 4 assignments = 5.5 hours content): available on Udemy: https://www.udemy.com/course/getting-... ⚡Applied Time Series using Stata (29 lectures + 4 assignments = 6.5 hours content): available on Udemy: https://www.udemy.com/course/applied-... This video introduces autoregressive integrated moving average models – or ARIMA in short. By the end of this video, you will understand how to use the autocorrelation and partial autocorrelation functions to identify ARIMA models. Using data generating processes, we simulate AR(1) and MA(1) processes and use the autocorrelation and partial autocorrelation functions to detect the lag structure. GitHub All material (code, slides, data) is available on GitHub (https://github.com/GerhardKling/Appli.... The course This video belongs to the course "Applied Time Series", which covers univariate and multivariate time series models. The course covers the following topics: Unit 0: Mathematical toolkit Unit 1: Time series analysis and forecasting Unit 2: ARIMA and seasonality Unit 3: Intervention analysis Unit 4: Vector autoregression (VAR) Unit 5: Cointegration and VECM Unit 6: Modelling conditional volatility Unit 7: Structural breaks Unit 8: Panel VAR and cointegration The channel YUNIKARN focuses on publishing educational content in applied statistics, mathematics, and data science. In these fields, programming skills have become essential. Hence, we cover various programming languages including Python, Stata, and C++ to tackle problems and for fun. Stay in touch Please leave comments or follow us on Twitter (  / gerhardklings  . DMs are open. Hashtags #datascience #stata #timeseries