## Module 9.5 LOS 9.m, 9.n: Testing an AR model for ARCH, nonstationarity and cointegration

ARCH is similar to autocorrelation, in fact it could be described as autocorrelation of the residuals of an AR model. …

## 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 …

## 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 …

## Module 8.6 -8.9 Quantitative Methods – Common Issues with Regression Testing

Regression Model Assumptions Linear Regression Models operate under the following assumptions: A linear relationship exists between dependent and independent variables …

## CFA Level II: Quantitative Methods – ANOVA Part II, Significance Testing

The next concept we will look at is significance testing. Significance testing is a form of hypothesis testing focused on …