Code and Results for Chapter 4 (Regression and Exponential Smoothing Methods To Forecast Nonseasonal Time Series):

Introduction:
These are results and code for the problems and examples found in Chapter 4 of this famous book.

1. Functions for Forecasting Implemented in this Chapter:
   • gen_global_linear_seasonal_indicator_X.m (constructs the design matrix X for a global linear seasonal indicator model)
   • gen_global_linear_seasonal_trigonometric_X.m (constructs the design matrix X for a global linear seasonal trigonometric model)
   • gen_seasonal_indicator_F.m (generates the F matrix for a local linear seasonal indicator model)
   • gen_seasonal_indicator_L.m (generates the L matrix for a local linear seasonal indicator model)
   • gen_seasonal_trigonometric_F.m (generates the F matrix for a local linear seasonal trigonometric model)
   • gen_seasonal_trigonometric_L.m (generates the L matrix for a local linear seasonal trigonometric model)
   • locally_constant_indicator_model.m (the recursive update of the locally linear model coefficients)
   • locally_constant_indicator_model_optimum.m (finds the optimum relaxation coefficient to use with a locally constant indicator model)
   • locally_constant_trigonometric_model.m (the recursive update of the locally trigonometric model coefficients)
   • locally_constant_trigonometric_model_optimum.m (finds the optimum relaxation coefficient to use with a locally constant trigonometric model)
   • plot_SACF.m (plots the sample autocorrelation function with more accurate error bounds)
   • plot_SPACF.m (computes and plots the partial sample autocorrelation function)
2. Examples from the book:
   • example_4_1.m (Duplicates Example 4.1 from the book)
   • example_4_2.m (Duplicates Example 4.2 from the book)
   • example_4_3.m (Duplicates Example 4.3 from the book)
   • section_4_3_1.m (Duplicates the example in this section of the book)
   • section_4_3_2.m (Duplicates the example in this section of the book)
3. Data sets considered in this chapter:
   • load_housing_starts.m (loads the housing starts data set)
   • load_monthly_car_sales.m (loads the monthly US car sales Jan 1960 - December 1968)
   • load_monthly_us_retail_sales.m (loads the retail sales data set)
   • load_quarterly_new_plant_equipment.m (loads the quarterly new plant equipment data set)
4. Problems:
   • prob_4_1.m (performs the needed matrix inverses)
   • prob_4_2.m (performs the needed matrix inverse)
   • prob_4_3.nb (performs the some algebra for this problem)
   • prob_4_5.m (performs seasonal regression on the US retail sales data)
   • prob_4_6.m (performs seasonal regression on the housing starts data set)