slide 1 DSCI 5340: Predictive Modeling and Business Forecasting Spring 2013 – Dr. Nick Evangelopoulos Lecture 8: Estimation & Diagnostic Checking in Box-Jenkins Models (Ch. 10) Material based on: Bowerman-O’Connell-Koehler, Brooks/Cole

slide 2 DSCI 5340 FORECASTING Page Ex 9.5, Ex 9.6 Ex 9.7 Homework in Textbook

slide 3 DSCI 5340 FORECASTING EX 9.5 Page 443 Assume time origin is 122. y122 = is given. Y122hat = from page 427 Y122 – Y122hat = a122hat = =

slide 4 DSCI 5340 FORECASTING EX 9.5 Page 443 Y123hat y123hat = y122hat + a123hat -  1 hat*a122hat Note that at time origin 122, we do know y122. Note that we can compute a122hat since we know y122. a123hat is zero since we do not know y123. y123hat = y122hat + a123hat -  1 hat*a122hat y123(hat) = (-.3534)(-.2615) =

slide 5 DSCI 5340 FORECASTING EX 9.5 Page 443 Y124hat y124hat = y123hat + a124hat -  1 hat*a123hat Note that at time origin 122, we do not know y123 – so we need to use y123hat. Note that we cannot compute a123hat or a124 since we are at time origin 122. y124hat = y123hat + a124hat -  1 hat*a123hat y124(hat) = (-.3534)(0) =

slide 6 DSCI 5340 FORECASTING EX 9.6 Page 443 SAC cutoffs after lag 2. SPAC dies down. Second order moving average

slide 7 DSCI 5340 FORECASTING EX 9.7 Page 445 Part a: Close call – first difference would be better.

slide 8 DSCI 5340 FORECASTING EX 9.7 Page 445 part b SAC dies down and SPAC cuts off after lag of 2. AR(2) is best fit.

slide 9 DSCI 5340 FORECASTING EX 9.7 Page 445 part c. SPAC seems to die down slower than SAC. Assuming SAC cuts off, we can say that SAC cuts off after lag 1. Hence, MA(1).

slide 10 DSCI 5340 FORECASTING Questions 1. What is the name of the function that identifies the order of an autoregressive B-J model? SPAC – Sample Partial Autocorrelation function 2. What is the name of the function that identifies the order of a moving average B-J model? SAC – Sample Autocorrelation function

slide 11 DSCI 5340 FORECASTING Questions 3. What happens to the mean and variance of an AR(1) model if the  1 coefficient is equal to 1? Greater than 1? Mean is undefined for  1 value equal to 1 and has a sign opposite to that of the constant for a  1 value > 1. Variance is undefined for value =1.

slide 12 DSCI 5340 FORECASTING Questions 4. Is the model y t = y t-1 + a t stationary or nonstationary? Why? Note that y t = y t-1 + a t implies y t = (y t-2 + a t-1 ) + a t and this implies y t = (y t-3 + a t-2 ) + a t-1 + a t So y t = a t + a t-1 + a t-2 + … The variance of y t is the sum of approximately infinite variances from each independent noise term. Each y t has higher variance then the one before it. y t is not stationary. But z t = y t –y t-1 is stationary.

slide 13 DSCI 5340 FORECASTING 13 The condition for stationarity of a general AR(p) model is that the roots of all lie outside the unit circle. A stationary AR(p) model is required for it to have an MA(  ) representation. Example 1: Is y t = y t-1 + u t stationary? The characteristic root is 1, so it is a unit root process (so non- stationary) Example 2: Is y t = 3y t y t y t-3 +u t stationary? The characteristic roots are 1, 2/3, and 2. Since only one of these lies outside the unit circle, the process is non-stationary. The Stationary Condition for an AR Model

slide 14 DSCI 5340 FORECASTING Invertibility of a MA process z t can be expressed as an infinite series of past z-observations. However, if the coefficients (weights) do not decline fast enough then the model will be unstable. Invertibility implies that recent observations count more heavily than distant past observations.

slide 15 DSCI 5340 FORECASTING Conditions for Stationarity and Invertibility

slide 16 DSCI 5340 FORECASTING Point Estimates – Preliminary & Final Computer Packages ( such as MINITAB and SAS) supply default preliminary estimates for time series models. These estimates must meet stationarity and invertibility conditions. The easiest preliminary point estimate that almost always satisfies these conditions is the value of.1. We can usually do better. For example, for an AR(1), the first order autocorrelation is equal to the autoregressive coefficient   

slide 17 DSCI 5340 FORECASTING Preliminary Estimate for   constant term)  in an AR(1)

slide 18 DSCI 5340 FORECASTING Iterative Search for Coefficient Estimates

slide 19 DSCI 5340 FORECASTING Box and Jenkins (1970) were the first to approach the task of estimating an ARMA model in a systematic manner. There are 3 steps to their approach: 1. Identification 2. Estimation 3. Model diagnostic checking Step 1: - Involves determining the order of the model. - Use of graphical procedures - A better procedure is now available Building ARMA Models – The Box Jenkins Approach

slide 20 DSCI 5340 FORECASTING Step 2: - Estimation of the parameters - Can be done using least squares or maximum likelihood depending on the model. Step 3: - Model checking Box and Jenkins suggest 2 methods: - deliberate overfitting - residual diagnostics Building ARMA Models – The Box Jenkins Approach (cont’d)

slide 21 DSCI 5340 FORECASTING Diagnostic Checking Using Autocorrelations of Residuals

slide 22 DSCI 5340 FORECASTING Chi-Square Distribution Associated with Ljung-Box Statistic

slide 23 DSCI 5340 FORECASTING Ljung-Box Values in SAS printout Large p-values under Pr > ChiSq indicate that the model is adequate.

slide 24 DSCI 5340 FORECASTING MINITAB Example of Ljung-Box Statistic Large p-values indicate no more terms need to be added to model.

slide 25 DSCI 5340 FORECASTING RSAC and RSPAC should not have significant spikes

slide 26 DSCI 5340 FORECASTING Prediction Intervals n p is the number of parameters being estimated in the model.

slide 27 DSCI 5340 FORECASTING Page 480 Ex 10.1 through 10.6 Homework in Textbook