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How to Select the Most Appropriate Time Series Model?

Chad M. James — Wed, 22 Feb 2012 13:47:35 +0000

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 Related posts:

ARMA Models and ARCH Testing

Chad M. James — Wed, 22 Feb 2012 13:42:40 +0000

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 Related posts:

Auto-Regressive Models – Random Walks and Unit Roots

admin — Tue, 21 Feb 2012 13:00:59 +0000

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 Related posts:

Auto-Regressive (AR) Time Series Models

Chad M. James — Tue, 21 Feb 2012 12:49:20 +0000

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 Related posts:

Time Series Analysis: Simple and Log-linear Trend Models

Chad M. James — Tue, 21 Feb 2012 12:30:48 +0000

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 Related posts:

Qualitative and Dummy Variables in Regression Modeling

Chad M. James — Mon, 20 Feb 2012 12:20:59 +0000

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 Related posts:

Regression Analysis and Assumption Violations

Chad M. James — Mon, 20 Feb 2012 12:11:04 +0000

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 Related posts:

Fcalc – the Global Test for Regression Significance

Chad M. James — Mon, 20 Feb 2012 12:03:32 +0000

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 Related posts:

Multiple Regression and Coefficient of Determination (R-Squared)

admin — Sat, 18 Feb 2012 15:27:04 +0000

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 Related posts:

Multiple Regression Analysis

admin — Sat, 18 Feb 2012 12:35:32 +0000

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 Related posts: