ECON 174 Lecture Notes - Lecture 10: Data Collection, Exponential Smoothing, Model Selection
5 Feb 2020
School
Department
Course
Professor
Document Summary
Holt ( ) implements methods (a, n), ad, n), (m, n), (md, n) Hw ( ) implements methods (a, a), (ad, a), (a, m), (ad, m), (m, m), (md, m) Generate same point forecasts but can also generate forecast intervals. A stochastic (or random) data generating process that can generate an entire forecast distribution. Each model has an observation equation and transition equations, one for each state (level, trend, seasonal) 2 models for each method: 1 with additive, 1 with multiplicative errors, i. e. , 18 total models. Both are equations to explain the state space models (two boxes above) Additive error when we assume error is added to the predicted. Multiplicative error when we assume error is multiplied (grows exponentially) to predicted (more rare, usually additive) Ets(a, n, n) = simple exponential smoothing with additive errors. Ets(a, a, n) = holt"s linear method with additive errors. Ets(a, a, a) = additive holt"s winters" method with additive errors.
