Time Series and Forecasting in R Programming [Video-2] | Statistics Explained|

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In this video, we delve into the concept of Autoregressive models in R, a fundamental tool for understanding and forecasting time series data.
Key Points Covered:
👉 How to explore autoregressive models?
👉 How autoregressive models relate to time series variables and how they utilize past values to predict future outcomes?
👉 What is the structure of autoregressive models?
👉 How to differentiate between first-order (AR(1)) and higher-order autoregressive models (e.g., AR(2))?
👉 How to estimate autoregressive models in R using econometric analysis libraries like AER?
👉 How to import and prepare macroeconomic quarterly data for analysis in R, including data manipulation and transformation?
👉 How to forecast GDP growth rates using autoregressive models, including interpreting forecasted values and confidence intervals?
👉 How to access forecast accuracy through forecast error analysis, R-squared, and standard error of regression?
👉 How to summarize the insights gained and the importance of autoregressive models in time series analysis and forecasting?
Join us as we explore the intricacies of autoregressive modeling and its applications in understanding and predicting time series data.
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