NDLI: Parameter estimation and linear time-series analysis

Content Provider	Semantic Scholar
Copyright Year	2008
Abstract	This computer exercise gives an introduction to time-series analysis and estimation and model choice using Maximum Likelihood. It is preferred if you use Matlab, but you are allowed to use the programming language or package of your choice. If you choose not to use Matlab, please note that you are required to document your code extra carefully. 1 Preparations for the exercise Read chapter 4 in [1] and this instruction for the computer exercise. Also review the exercises in chapter 4. Before the computer exercise some of the questions below will be posed. All of the posed question must be answered correctly in order to pass the computer exercise. You should be able to answer the following questions before the computer exercise. 1. Calculate the auto-correlation function for an AR(1) process and an MA(1) process respectively. 2. Describe how one can estimate the parameters in an AR(1) process using Maximum Likelihood. How are the parameters estimated in an MA(1) process? 3. Write down the likelihood function for the following model: y i = β 0 + β 1 x i + σε i , (1) where ε i is iid N (0, 1) or t(ν). Maximum Likelihood (ML) estimation is the preferred technique if the distribution of the innovations in the model is known and the risk for misspecification is small.
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Alternate Webpage(s)	http://www.maths.lth.se/matstat/kurser/fms161mas229/ht12/lab1/FSlab1_eng_mod.pdf
Alternate Webpage(s)	http://www.maths.lth.se/matstat/kurser/fms161mas229//ht14/lab1/FSlab1_eng.pdf
Alternate Webpage(s)	http://www.maths.lth.se/matstat/kurser/fms161mas229/ht10/lab1/FSlab1_eng_mod.pdf
Alternate Webpage(s)	http://www.maths.lth.se/matstat/kurser/fms161mas229//ht12/lab1/FSlab1_eng_mod.pdf
Alternate Webpage(s)	http://www.maths.lth.se/matstat/kurser/fms161mas229/vt08/FSlab1_eng.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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