Module 9.1 LOS 9.a: Forecasting with an AR model
The simplest form of a linear trend is: yt = b0 + b1(t) + εt where: yt = the value of the time …
Module 9.2 LOS 9.e: Testing AR models for improper specifications using t-tests
We cannot use DW tests to test for autocorrelation (serial correlation in an AR model). To identify autocorrelation we use …
Module 9.2 LOS 9.f: Mean Reversion in Time Series
Some time series can exhibit a tendency to move towards its mean. The mean reverting level can be expressed as: …
Module 7.3 LOS 7.f: Regression coefficient confidence interval
Confidence intervals for regression coefficients can be used for hypothesis testing. Instead of calculating a test statistic and a critical …
Modules 8.6 – 8.9: Heteroskedasticity, Serial Correlation, Multicollinearity and Model Misspecification
Heteroskedasticity Heteroskedasticity occurs when the variance of the residuals is not constant across all observations. We are concerned with conditional …
Module 7.5 LOS 7.j: ANOVA Tables, R-squared (adjusted and unadjusted), SEE and F-tests
The R2 (adjusted vs unadjusted) The R2 value is the also called the coefficient of determination. It is a numerical …
Module 8.2 LOS 8.c/8.d: Hypothesis testing of regression coefficients with t-statistic and p-statistic
Coefficient values in multiple regressions are not informative on their own. To determine the significance of slope coefficients in multiple …
Module 8.6 LOS 8.f: Assumptions of a multiple or simple regression model
Multiple regression models operate under the following assumptions: A linear relationship exists between dependent and independent variables The independent variables …
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