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Practical 7

# Practical 7 - • Use i nf or mat i on cr i t er i a t he...

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Pr act i cal 7 Mar ch 10, 2010

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2 Ti me Ser i es Use GRETL [ sw_ch12. gdt ] Gener at e a r andom var i abl e Pl ot mul t i pl e gr aphs Take l og- di f f er ence ( of GDP_JP) f or gr owt h r at es Obser ve cor r el ogr ams Per f or m Augment ed Di ckey- Ful l er t est s The nul l hypot hesi s of t he ADF t est i s t hat t he t i me ser i es has a uni t r oot and i s not st at i onar y. Hence, i f you r ej ect t hi s hypot hesi s t hen you concl ude t hat t he ser i es i s
3 AR( 1) Let ’ s gener at e an AR( 1) model t t t e y y + = - 1 25 . 0 I n consol e t ype: bet a = 0. 25 ser i es e = nor mal ( ) ser i es y1 = 0 ser i es y1 = bet a * y1( - 1) + e Gener at e y2, y3, y4 wi t h bet a 0. 5, 1 and 1. 5

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4 Est i mat i ng and For ecast i ng AR( p) pr ocesses AR( p) ? How do we know what p t o

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Unformatted text preview: • Use i nf or mat i on cr i t er i a ( t he smal l er t he bet t er ) • Def i ne i n- sampl e and out - sampl e • Eval uat e f or ecast i ng per f or mance wi t h Root Mean Squar e For ecast i ng Er r or 5 Non- st at i onar y pr ocesses • Random wal k ( st ochast i c t r end) • Det er m i ni st i c t r end • St r uct ur al br eaks 6 At home … • Tr y gener at i ng t he non-st at i onar y t i me ser i es i n pr evi ous sl i de • Check f or uni t r oot usi ng ADF t est • Tr y f or ecast i ng usi ng di f f er ent AR( p) model s of t he above ser i es you gener at ed and det er m i ne whi ch model s ar e best • Tr ansf or m t he non- st at i onar y ser i es t o st at i onar y ser i es...
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Practical 7 - • Use i nf or mat i on cr i t er i a t he...

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