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# notes1 - Model-Building Strategy 1 Model speci²cation(or...

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NOTES 1 Introduction : What is a time series? Previous Statistics courses focus on independent data. For ex- ample, 5 crops from di±erent farms are studied to determine the mean yield μ ; here the crop yields X 1 , X 2 , X 3 , X 4 , X 5 can be viewed as independent (Why?). Summary statis- tics ¯ X, S 2 can be used to make inferences about μ . Now we may have the following situation: A plant is weighed each week, for T weeks. Put X t = weight at the end of t th week. Then { X 1 , X 2 , ..., X T } are dependent (correlated). Problems of interest: 1. Estimating μ t = mean weight at time t . 2. Forecast future size X T + t , given data { X 1 , X 2 , ..., X T } . Examples of Time Series : 1. Sunspot data 2. Annual Sales 3. Unemployment number 4. Temperature 5. Hang Seng index
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Unformatted text preview: Model-Building Strategy : 1. Model speci²cation (or identi²cation) 2. Model ²tting (or parameter estimation) 3. Model diagnostics (or residual analysis) Remark: Principle of parsimony Statistical Package to be used : SPlus. Some good features of SPlus: (1) Easy to use; (2) Interpreted, not compiled, programming language (3) High quality graphical outputs (4) Built in statistical analysis. In the beginning, we need to know how to do: 1. Import data into SPlus 2. Make simple plots 3. Do linear (simple and multiple) regression 4. Handle output (obtain relevant material only) 5. How to get help 1...
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