Simple Linear and Exponential Growth Models – If an analyst looks at a time series plot graph he/she may see patterns exhibiting possible linear or exponential growth relationship to the dependent variable. Serial correlation of the error terms must not … Continued
CFA Exam
ARMA Models and ARCH Testing
Autoregressive Moving Average Model (ARMA) = calculates an average value over a period of time to smooth fluctuations in a time series. ARMA models are very sensitive to minor changes and may rarely forecast well. Auto Regressive Conditional Heteroskedasticity (ARCH) … Continued
Auto-Regressive Models – Random Walks and Unit Roots
This is the case of an AR time series model where the predicted value is expected to equal the previous period plus a random error: xt = b0 + xt-1 + εt When b0 is not equal to zero, the … Continued
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 … Continued
Time Series Analysis: Simple and Log-linear Trend Models
Simple Time Series Models This is basic trend modeling. A simple trend model can be expressed as follows: yt = b0 + b1t+ εt b0 = the y-intercept; where t = 0. b1 = the slope coefficient of the time … Continued
Qualitative and Dummy Variables in Regression Modeling
Handle qualitative independent variables with a quantitative proxy or use a dummy variable. When using a dummy independent variables (such as assigning a number to the degree of consumer confidence), define a collectively exhaustive set of “j” categories, then j-1 … Continued
Regression Analysis and Assumption Violations
Heteroskedasticity There are two types, Conditional and Unconditional. The type focused on in evaluating model validity is Conditional Heteroskedasticity. Conditional = the error terms change in a systematic manner that is correlated with the values of the independent variables. Look … Continued
Fcalc – the Global Test for Regression Significance
A statistically significant Fcalc (i.e. one that passes the Fcritical threshold, based on your degrees of freedom) can indicate that your model as a whole is meaningful. This test is really applicable for multiple regressions, where there is more than … Continued
Multiple Regression and Coefficient of Determination (R-Squared)
For a multiple regression model, this value represents the percentage of total variation in Y that is explained by the regression equation. The value is between 0 and 1. R-squared has a mathematical relationship with TSS, SSE, and RSS. R2 … Continued
Multiple Regression Analysis
Much of the concepts in simple regression are applicable, but watch out when determining your degrees of freedom for different analyses, as the values will be slightly different for models similar in observation count, but different in slope coefficient count. … Continued
