Structural Time Series Analyser, Modeller and Predictor: Tutorial Guide

Prologue: How to proceed, The special features of STAMP; Introduction: General information, Basics of the program, Data storage and input, Menus and dialogs, Help system, Results storage, Using STAMP documentation, Tutorial data sets, ENERGY: energy consumption in the UK, EXCH: daily exchange rates for the US Dollar, INTEREST: short and long term interest rates, MINKMUSK: minks and muskrats in Canada, NILE: level of the Nile, RAINBRAZ: rainfall in north-east Brazil, SEATBELT and SEATBQ: road casualties in Great Britain, and the 1983 seat belt law, SPIRIT: consumption of spirits in the UK, TELEPHONE: telephone calls to three different countries, UKCYP: consumption, income and prices in the UK, USYCIMP: US macroeconomic time series, Data sets used in exercises, STAMP and PcGive/pcFilm Citation; Getting started on simple univariate modelling: Loading and graphing, Model formulation, Evaluating, and testing the model, Output, Exercises; Tutorial on components: Selection of components, Trend, Local level model, Statistical analysis of the local level model, Local linear trend and smooth trend, Statistical specification of the local linear trend model, Specification of the trend, Seasonal: Specifying and testing the seasonal component, Seasonal adjustment, Cycle: A simple cycle plus noise model, Statistical specification, Trend plus cycle, Multiple cycles, Autoregression, Exercises; Tutorial on interventions and explanatory variables : Interventions; Modelling, Detection, using auxiliary residuals, Specification of more complex interventions, Explanatory variables, Stochastic trend component, Outliers and structural breaks, Lags and differences; Forecasts, Forecasts, Incremental change, Manual input, Using models to forecast the explanatory variables, Forecasts in the database, Interventions, Error correction and unobserved components, Statistical features of the models, Exercises; Tutorial on multivariate models : SUTSE models; Covariance and correlation matrices, Homogeneity; . Cycles, Autoregression, Common factors and cointegration, Statistical specification of common levels, An example of common levels: road casualties in Britain, Further analysis of the seat-belt data: restrictions via parameter control, Common trends, Common seasonals, Common cycles, Factor rotations, Explanatory variables and interventions, Assessing the effect of the seat belt Iaw using a control group, Exercises; Applications in macroeconomics and finance: GNP and investment, Income and consumption, Expected inflation, Long and short term interest rates, Stochastic volatility, Multivariate stochastic volatility, Seasonal adjustment and detrending, Seasonal adjustment, Detrending, Exercises.