Code and Results for Chapter 6 (Seasonal Autoregressive Integrated Moving Average Models):

Introduction:
These are results and code for the problems and examples found in Chapter 6 of this famous book.

1. Functions for Forecasting Implemented in this Chapter:
   • sdiff.m (implements seasonal differencing)
2. Data sets considered in this chapter:
   • load_canadian_wages.m (the monthly percentage change in Canadian wages)
   • load_gas_usage.m (monthly average residential gas usage)
   • load_GM_EPS.m (uarterly earnings per share for General Moters)
3. Sections duplicated in code:
   • model_gas_section_6_7_2.m (Generates seasonal ARIMA models for the gas usage data set)
   • model_gas_section_6_7_2.R (Computes the maximum likelihood ARIMA coefficients for the gas usage data set)
   • model_housing_starts_section_6_7_3.R (Computes the maximum likelihood ARIMA coefficients for the housing starts data set.)
   • model_car_sales_6_7_4.R (Computes the maximum likelihood ARIMA coefficients for the car sales data set.)
   • model_demand_6_7_5.R (Computes the maximum likelihood ARIMA coefficients for the demand for parts data set)
   • appendix_6.nb (some algebra for this appendix)
4. Exercises for this chapter
   • prob_6_1.nb (some algebra for this problem)
   • prob_6_2.nb (some algebra for this problem)
   • prob_6_5.nb (some algebra for this problem)
   • prob_6_9.R (SARIMA model for the quarterly GM stock earnings)
   • prob_6_10.R (SARIMA model for the montly arrival of U.S. citizens)
   • prob_6_11.R (SARIMA model unemployed U.S. civilian labor force data set)
   • prob_6_12.R (SARIMA model monthly percentage changes in Canadian data set)
   • prob_6_13.R (SARIMA model for Series 8. Beer Shipments-Four-Week Totals)
   • prob_6_14.R (SARIMA model for Series 5. Monthly Traffic Fatalities in Ontario data set)
   • prob_6_15.m (SARIMA model for the Monthly Gasoline Demand in Ontario data set)