Lessons
- CFA L2: Quantitative Methods - Introduction
- Quants: Correlation Analysis
- Quants: Single Variable Linear Regression Analysis
- Standard Error of the Estimate or SEE
- Confidence Intervals (CI) for Dependent Variable Prediction
- Coefficient of Determination (R-Squared)
- Analysis of Variance or ANOVA
- Multiple Regression Analysis
- Multiple Regression and Coefficient of Determination (R-Squared)
- Fcalc – the Global Test for Regression Significance
- Regression Analysis and Assumption Violations
- Qualitative and Dummy Variables in Regression Modeling
- Time Series Analysis: Simple and Log-linear Trend Models
- Auto-Regressive (AR) Time Series Models
- Auto-Regressive Models - Random Walks and Unit Roots
- ARMA Models and ARCH Testing
- How to Select the Most Appropriate Time Series Model?
Auto-Regressive (AR) Time Series Models
- Auto-Regressive (AR) Time Series Models
- This type of time series model utilizes a time period lagged observation as the independent variable to predict the dependent variable, which is the value in the next time period.
xt = b0 + b1xt-1 + εt
- There can be more than one time period lag independent variable.
- Valid statistical inferences from AR time series models only if the time series is covariance stationary; a time series with growth over time or seasonality is not covariance stationary.
- It is critical to test your AR time series model for serial correlation and the Durbin-Watson test cannot be used for this model.
- An AR time series model that is covariance stationary will exhibit mean reversion – it will tend to fall after going above the mean and rise after going below the mean.
- Root Mean Square Error (RMSE) = a method of assessing the out of sample accuracy of a time series model’s forecast. If comparing multiple models, the model will the lowest RMSE is considered to have the best forecasting capabilities.
Membership
Learn the skills required to excel in data science and data analytics covering R, Python, machine learning, and AI.
I WANT TO JOINJOIN 30,000 DATA PROFESSIONALS
Free Guides - Getting Started with R and Python
Enter your name and email address below and we will email you the guides for R programming and Python.