A SURVEY OF THE RELATIONSHIP BETWEEN INTEREST RATE AND INFLATION IN IRAN ( )

Transcription

1 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle A SURVEY OF THE RELATIONSHIP BETWEEN INTEREST RATE AND INFLATION IN IRAN ( ) *Ahmad Ali Asadpour ad Abdol-Khalegh Alidadi Deparme of Ecoomics, Badar Abbas Brach, Islamic Azad Uiversiy, Badar Abbas, Ira *Auhor for Correspodece ABSTRACT Decreasig ba ieres raes is oe of he policies o reduce he rae of iflaio ha have bee paid close aeio i rece years. I order o ivesigae he efficiecy of above policy, his research has bee coduced o ivesigae he relaioship bewee iflaio rae ad ieres rae (oe-year deposis ad loa ieres raes ad mare ieres rae) durig usig auoregressive vecor model wih disribued lags ad vecor error correcio model via Grager causaliy es. The resuls showed ha here is a log-erm relaioship bewee ba ieres raes ad iflaio rae. I order o reduce iflaio i a log erm, i is recommeded o maage he decreasig red of loa ieres rae so ha i could be possible o corol iflaio idex ad reduce i wihi he coury. Keywords: The Nomial Ieres Rae; Iflaio; Auoregressive Disribued Lags (ARDL); Error Correcio Model (ECM); Grager Causaliy INTRODUCTION Reducig ieres raes ecourages ivesme, ehaces edecy o ives, reduces producio cos, icreases aioal producio, ad creaes oversupply i he coury. Oversupply he mai facor i corollig he prices, preveig he icrease of prices, ad causig expors op boom. By ifluecig he producio cos, ieres rae reducio resuls i reducio i he price of differe goods ad iflaio. I his regard, he prese sudy was aimed a ivesigaig he relaioship bewee ba ieres rae ad iflaio rae i Ira durig The sudy objecives are: 1. Specifyig he relaioship bewee oe-year ieres rae ad iflaio rae, 2. Specifyig he relaioship bewee loa ieres rae ad iflaio rae, ad 3. Specifyig he relaioship mare ieres rae ad iflaio rae. 2. The sudy hypoheses are: 1. There is a posiive relaioship bewee oe-year ieres rae ad iflaio rae. 2. There is a posiive relaioship bewee loa ieres rae ad iflaio rae. 3. There is a posiive relaioship bewee mare ieres rae ad iflaio rae. 3. Theoreical Foudaios of he Relaioship bewee Ieres rae ad Iflaio Macroecoomic heories ca be uilized i order o ivesigae he relaioship bewee iflaio ad ieres rae. I so doig, he ifluece mechaisms of ieres rae o iflaio ad of iflaio o ieres rae will be discussed. If prices rise, he real moey balace is he firs variable o icrease; i oher words, as he level of prices icreases, real supply of moey drops. Accordig o Keyesia aalyses, reducio i real moey supply (excess demad for moey) resuls i some disorders i ecoomy. Accordig o Walrasia Equilibrium ad i geeral o creae balace i ecoomy, he icidece of excess demad for moey i moey mare resuls i oversupply i securiies mare. Therefore, i is expeced ha ieres rae rises wih a icrease i prices. So, heoreically, here is a posiive causal relaioship from iflaio rae o omial ieres rae. I oher words, icrease i he rae of ieres causes ieres rae o rise. How ieres rae affecs iflaio ca be explaied i differe ways. Oe of he ifluece mechaisms of ieres rae o iflaio is cos of capial, such ha icrease i ieres rae causes cos of capial o rise, which ulimaely icreases producio coss (Braso, 1993). A icrease i producio prices or rasmissio o he lef of he aggregae supply curve fially leads o a icrease i iflaio. I addiio, chages i ieres rae affec iflaio by ifluecig he volume of Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2295

2 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle moey. Accordig o quaiy heory of moey, moey supply icreases he level of prices i log ad shor erms. However, moey supply i exesive recess is less liely o have a sigifica effec o iflaio. I ormal saus ad a leas i medium ad log erm; however, he volume of moey has a posiive sigifica effec i iflaio (Asgharpour, 2005). Therefore, i is heoreically expeced ha icrease i ieres rae cause he level of prices o rise; herefore, i is argued ha here is a causal relaioship from ieres rae o iflaio. Aoher mechaism o explai he relaioship bewee ieres rae ad iflaio is he well-ow relaioship bewee omial ad real ieres raes, ad large body of ecoomic lieraure has log bee alloed o his issue (Mehrga e al., 2006). Accordig o Mudell heory, icrease i he expeced iflaio rae reduces real moey balace, as a resul, wealh drops. Reducio of wealh decreases cosumpio ad icreases savigs, which resuls i reducio i real ieres rae. Mudell heory saes ha a icrease of oe ui i expeced iflaio reduces real ieres rae ad he effec of expeced iflaio o omial ieres rae will be less ha ha ui. This relaioship is ow as "Mudell effec". i r, 1 (1-3) Mudell effec implies ha iflaio chages caused by moeary policy are o-eural (Mudell, 1963). Marshall (1980) ivesigaed he relaioship bewee omial ieres rae ad iflaio rae as follow: IR IR I p (2-3) r Where, IR r is real ieres rae, IR is omial ieres rae, i is iflaio rae, ad p cross effec of omial ieres rae ad iflaio rae. Therefore, Marshall believed ha omial ieres rae ad iflaio rae have a direc relaioship (Marshall, 1886). Ulie Marshall, Clar (1895) believes ha real ieres rae is fixed. He sudied he effec of iflaio rae o omial ieres rae. He believes ha omial ieres rae should chage proporioal o iflaio rae. I oher words, ieres rae has a direc relaioship wih iflaio rae ad if iflaio rae icreases/decreases 2%, he omial ieres rae should icrease/decrease 2 %, oo (Clar, 1895). A review of he relaed lieraure, i ca be argued ha iflaio rae has a posiive effec o omial ieres rae. However, he relaioship bewee real ad omial ieres raes did o have a precise aalysis framewor uil before Irvig Fisher. By aig advaage of he sudies coduced by ohers, Fisher (1896) expaded he heory of iflaio ad ieres i a sysemaic way. Fisher effec saes ha a icrease of oe ui i expeced iflaio rae causes omial ieres rae o have a oe-ui icrease ad ha real ieres rae which plays he mai role i ivesme behavior ad savig remais cosa. A highly sigifica coclusio ha is draw from Fisher effec is ha moeary policies are eural ad alhough hey cause iflaio expecaios, hey cao affec real variables of ecoomy. Therefore, Fisher effec ca be cosidered as oe impora resul of classical school ad idicaed i a classical model. I shor, Fisher model sipulaes ha a icrease of oe ui i expeced iflaio rae ehaces omial ieres rae oe ui ad expeced real ieres rae remais cosa as idicaed i he followig equaio: IR IR I r (3-3) Where, IR r is real ieres rae, IR is omial ieres rae, ad I is expeced iflaio rae. Therefore, i ca be saed ha heoreically he relaioship bewee omial ieres rae ad iflaio rae is posiive ad here is a causal relaioship bewee he wo variables (Fisher, 1930). MATERIALS AND METHODS Mehod To ivesigae he relaio bewee ieres rae ad iflaio rae, auoregressive vecor model wih disribued lags was uilized. The advaages of his mehod iclude cosiderig he problem of saic variables, avoidig he ris of spurious regressio problem (meaigless correlaios), ad differeiaig Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2296

3 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle bewee he log- ad shor-erm relaioships of variables. For shor-erm relaioship, Toda-Yamamoo causaliy procedure ad error correcio model were applied ad for log-erm relaioship, Auo Regressive Disribued Lag (ARDL) mehod was uilized. Firs, Augmeed Dicey-Fuller (ADF) is uilized o figure ou he ui roo of he variables. If here is ui roo, crieria of Aaie, Schwarz- Bayesia, ad Haa Qui are applied o calculae he amou of lag. Ay lag ha maximizes he abovemeioed crieria is cosidered as he opimal lag. To ivesigae he causaliy relaioship i log erm, error correcio model ad Shae ad Sos' ARDL model were applied. The sudy daa for saisical populaio, samplig mehod, sample size, idex of iflaio rae, ba deposi ieres rae, loa ieres rae, gross domesic producio, volume of moey, ad exchage rae were rerieved from periodic daabases of Ceral Ba of Ira durig The mehod used i he prese sudy was aalyical-documeary. Differe Persia ad Eglish resources have bee uilized i he sudy. Ecoomeric ess ad Microfi sofware were applied o ivesigae he relaio bewee ieres rae ad iflaio. Firs, a summary of he coduced domesic ad foreig sudies is preseed i Table (4-1) ad Table (4-2), below. Table 4-1: Domesic sudies Researcher(s) Tile Techique Resuls Jafreh e al., ARDL (2012) mehod Saeedi e al., (2012) Ahmadi Khosravi (2011) Tajjali Mirshamsi (2010) ad ad The effec of reducio i ba ieres rae o iflaio idex i Ira durig ad preseaio of a suiable model o maage ad corol i The relaioship bewee iflaio rae ad ieres rae i Ira's ecoomy based o Fisher Effec Theory Hsiao Causaliy relaioship bewee ieres rae ad iflaio for MENA couries The effecs of reducio i ba ieres rae o iflaio, employme, ad ivesme The effeciveess of moeary policy o macroecoomic variables i Ira OLS mehod Hsiao Causaliy Tes ARDL mehod There is a sigifica log-erm relaioship bewee loa ieres rae ad iflaio rae. As ba loa ieres rae decreases, iflaio rae drops. There is a posiive sigifica relaioship bewee oe-year ieres rae ad iflaio rae. There is o sigifica relaio bewee iflaio rae ad hree- ad five-year ieres rae. Causaliy relaioship bewee ieres rae ad iflaio exiss oly i Qaar ad Djiboui. Bu i does o exis i oher couries icludig Ira. The policy of reducig ieres rae i Ira decreases iflaio oly i log ru. Is effecs o ivesme ad employme will be adverse i boh shor ad log rus. Taherifard ad Mousa (2008) srucural paer The policy of reducig loa ieres rae resuls i icrease i liquidiy, prices, ivesme, ad iflaio bu is effec o producio is margial. Aabai (2007) A survey of effecive Hausma es Iflaio rae, operaig coss of bas, ad Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2297

4 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle facors i he differece bewee ba receive ad pay ieres raes i Ira's ecoomy Kamijai ad Examiig he logerm Bahramirad relaioship (2008) bewee ba loa ieres rae ad iflaio rae Kahzadi ad A survey of he Nowforsai effec of ieres rae (2006) chages o iflaio Mehrga ad Ivesigaig he Asgharpour causal relaioship (2006) bewee ieres rae ad iflaio: Usig Pael Daa Bidabad (2006) The effec of reducio i ba loa ieres rae o Ira's ecoomy Johase Coiegraio ARDL ad ECM mehods Hsiao Causaliy Tes Simulaio of Ira's macroecoomeric paer legal deposis of bas a Ceral Ba have a posiive effec o iflaio chages ad a reverse effec o ba ieres rae chages. A log-erm relaioship bewee ba loa ieres rae ad iflaio is obvious. I Ira's ecoomy, chages i iflaio rae ca i log ru explai chages i omial ieres rae. Ba ieres rae i log ad shor rus affecs iflaio sigificaly. This effec; however, is margial. Rise i ieres rae leads o icrease i iflaio or he here is a oe-direcio causal relaioship from ieres rae o iflaio Reducio i ieres rae leads o icrease i iflaio rae, employme, ad ieres rae i uorgaized moey mare. Table 4-2: Foreig sudies Researcher(s) Tile Techique Resuls Imra ad Deermiaes of ba ARDL ad I log ru, variables of foreig debs, Nisha (2013) credi i Paisa: A supply side approach Error Correcio ecoomic growh, exchage rae, ad volume of moey have a sigifica relaio wih privae secor credi Qaisar e al., Relaioship bewee Ordiary leas There is a sigifica relaioship (2012) GDP, iflaio ad real squares bewee GDP ad exchage rae. There ieres rae wih is o sigifica relaioship bewee exchage rae flucuaio of Africa couries iflaio ad real ieres rae ad exchage rae i Africa couries. Uami ad Exchage raes, ieres Coiegraio Chages i iflaio rae have a posiive Iaga (2009) raes, ad iflaio raes i Idoesia: The Fisher Effec Theory effec o chages i ieres rae. Obi e al., A empirical Coiegraio Presece of a edecy o a log-erm (2010) ivesigaio of he ad Error red made Fisher Effec obvious i Fisher Effec i Nigeria: Correcio Nigeria. A co-iegraio ad Error Correcio Approach Tillma (2007) Do ieres raes drive iflaio dyamics? A Vecor Auoregressive Accordig o cos chaels, higher ieres raes ad higher fial producio aalysis of he cos Approach coss lead o higher iflaio raes. chael of moeary rasmissio Gul ad Eici The causal relaioship Grager There is a log-erm cosa Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2298

5 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle (2006) bewee omial ieres raes ad iflaio: The case of Turey Kasma ad Turgulu (2005) Clemei ad Rees (2003) Mai (2003) Laric ad Valerie (2003) Brazozoza (2001) Booh ad Cier (2001) Bulloc ad Rider (1991) Fisher hypohesis revisied: A fracioal coiegraio aalysis Srucural failure, iflaio ad ieres raes: A case sudy of he G7 couries Noparameric coiegraio aalysis of he omial ieres rae ad expeced iflaio rae Fracioal coiegraio bewee omial ieres raes ad iflaio: A reexamiaio of he Fisher relaioship i he G7 couries The relaioship bewee real ieres rae ad iflaio The relaioship bewee omial ieres raes ad iflaio: Ieraioal evidece The cross-coury relaioship bewee ieres raes ad iflaio over hree decades causaliy es Coiegraio mehod Bai ad Pero mehod Johase Coiegraio Grager coiegraio Johase coiegraio Johase coiegraio OLS mehod relaioship bewee omial ieres raes ad iflaio raes, i.e. omial ieres rae ad iflaio i log ru chage proporioal o each oher. A here is a oe-direcio causal relaioship from omial ieres rae o iflaio. There is o log-erms relaioship bewee omial ieres rae ad iflaio. I his sudy, fracioal coiegraio es proves he correcess of Fisher hypoheses for mos couries. Fisher hypoheses are accepable for America, Frace, ad Japa's ecoomy. Compared o Breiug es, Johase coiegraio provides clear evidece for he balaced relaioship bewee omial ieres rae ad iflaio rae. Adjusme of real ieres rae i Japa is oliear. Based o Fisher's hypoheses, here is a fracioal relaioship ha is a log-erm balace relaioship bewee ieres rae ad iflaio. The ormal coiegraio es does o prove he presece of his relaioship. There is a cosa log-erm relaioship bewee ieres rae ad iflaio rae. There is a oe-o-oe relaio bewee commo Europea ieres raes ad iflaio raes sice iflaio rae has a more predicable role i he mare compared o omial ieres rae. I he 1970's, he relaioship bewee ieres raes ad iflaio was egaive while i he 1980's here was a posiive relaioship bewee hem i shor ru. However, he applied es cao prove he ceraiy of he hypoheses. I domesic sudies lie sudies coduced by Jafreh e al., (2012), Saeedi e al., (2012), Ahmadi e al., (2011), Kamijai e al., (2008), ad Mehrga, e al., (2006) he effecs of ieres rae ad iflaio rae have bee aalyzed. However, firs hey did o iclude oher variables ha ca affec ieres rae ad iflaio rae ad secod hey did o simulaeously aalyze loa ieres rae ad log-, medium-, ad shor-erm deposi ieres rae. However, he prese sudy i addiio o iflaio rae ivesigaed hree oher ieres raes (loa ieres rae, oe-year deposi ieres rae, ad five-year deposi ieres rae). Iclusio of oher Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2299

6 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle corol variables lie volume of moey ad GDP ad heir effecs o ieres rae ad iflaio rae differeiaes he prese from similar oes. Bearig i mid he abovemeioed discussio, i ca be cocluded ha mos scholars ad policy maers agree over Irvig Fisher's Theory, ow as Fisher Effec (i.e. a icrease of oe ui i expeced iflaio resuls i a icrease of oe ui i omial ieres rae, ad real ieres rae ha has he mai role i ivesme behavior ad savigs remais cosa) ad ha is reliabiliy ad validiy i mos couries have bee proved. I he followig secio, sudies ha have applied differe mehods lie coiegraio o affirm he validiy of Fisher Theory are summarized. Oly Kadel e al., (1996) have o cofirmed validiy of his heory. Table 4-3: A summary of domesic ad foreig sudies Sudies ha have affirmed Fisher Theory Sudies ha have rejeced Fisher Theory Bulloc ad Rider (1991), Saeedi e al., (2012), Ahmadi ad Kadel e al., (1996) Khosravi (2011), Kamijai ad Bahramirad (2008), Mehrga ad Asgharpour (2006), Kahzadi ad Nowforsai (2006), Gul ad Eici (2006), Uami ad Iaga (2009), Obi e al., (2010), Kasma ad Turgulu (2005), Clemei ad Rees (2003), Mai (2003) Laric ad Valerie (2003), Booh ad Cier (2001), ad Jafreh e al., (2012). Followig ables ad figures idicae he red of shor-erm ad oe-year deposi ieres raes ad he weighed average loa ieres raes durig Table 4-4: Oe-year deposi ieres rae, weighed average loa ieres rae, mare ieres rae, ad iflaio rae durig (perce per year) Year oe-year ieres weighed average loa mare ieres iflaio rae rae ieres rae 1 rae Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2300

7 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle Source: Ecoomic Repors of Differe Years, The Ceral Ba's Balace Shee, Ceral Ba of he Islamic Republic of Ira 1 Calculaio of weighed average loa ieres rae: Firs he ieres rae of each secio is muliplied by he amou of he loa i he same secio, he by addig hem up ad dividig hem by he umber of ecoomic secios, he weighed average is calculaed Source: Ecoomic Repors of Differe Years, The Ceral Ba's Balace Shee, Ceral Ba of he Islamic Republic of Ira Diagram 4-1: The red of ieres rae i Ira durig Source: Ecoomic Repors of Differe Years, The Ceral Ba's Balace Shee, Ceral Ba of he Islamic Republic of Ira Diagram 4-2: The red of iflaio i Ira durig Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2301

8 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle Explaaio of he Model Followig models have bee uilized i order o ivesigae he relaioship bewee ieres rae ad iflaio. To examie he relaioship bewee oe-year deposi ieres rae ad iflaio, he followig model is applied. rio rio if Lgdp 0 1i i 2i i 3i i i 1 i 1 i 1 Lm Lex dw u 4i i 5i i i1 i1 if if rio Lgdp 0 1i i 2i i 3i i i 1 i 1 i 1 Lm Lex dw u 4i i 5i i i1 i1 (1-5) To examie he relaioship bewee loa ieres rae ad iflaio, he followig model i used. ril ril if Lgdp 0 1i i 2i i 3i i i 1 i 1 i 1 Lm Lex dw u 4i i 5i i i1 i1 if if ril Lgdp 0 1i i 2i i 3i i i 1 i 1 i 1 Lm Lex dw u 4i i 5i i i1 i1 To examie he relaioship bewee mare ieres rae ad iflaio, he followig model is applied. 0 1i i 2i i 3i i i 1 i 1 i 1 4i i 5i i i1 i1 Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2302 (2-5) rim rim if Lgdp Lm Lex u if if rim Lgdp 0 1i i 2i i 3i i i 1 i 1 i 1 Lm Lex dw u 4i i 5i i i1 i1 (3-5) Therefore, he sudy variables are defied as follows: if: iflaio rae, rio: oe-year deposi ieres rae, ril: loa ieres rae, rim: mare ieres rae, Lm: logarihm of liquidiy, Lgdp: logarihm of gross domesic producio, Lex: logarihm of exchage rae, dw: dummy variable, u : error erm, : ime, ad i: umber of lags As oe of he mos commo ess, ui roo es is ha is used o disiguish wheher a ime series process is saioary. I so doig, Augmeed Dicey-Fuller (ADF) es has bee applied for all of he variables of

9 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle , where he ull hypohesis H 0 : ρ = 1 (presece of ui roo) ad firs hypohesis H A : ρ < 1 (absece of ui roo) are esed. If he absolue value of augmeed Dicey-Fuller saisic bigger ha he criical value, he ull hypohesis is rejeced ad he variable will be saioary, oherwise i is osaioary ad he saioary es should be applied o he firs-order differece of he variable. Tables (5-1), (5-2), ad (5-3) idicae he resuls of saioary es of he variables hrough augmeed Dicey-Fuller mehod. The resuls of he es for he model variables a surface ad wih iercep ad red are showed i Tables (5-1) ad (5-2). Amog all model variables, iflaio variable is saioary. Table 5-1: The resuls of saioary es for a surface ad wih iercep Variables Tes saisic Tes resul Oe-year deposi ieres rae No-saioary Loa ieres rae No-saioary Mare ieres rae No-saioary Iflaio rae No-saioary Logarihm of GDP No-saioary Logarihm of liquidiy No-saioary Logarihm of exchage rae No-saioary Criical value a sigificace level of 5% = Dicey-Fuller addiioal regressio wih four lags ad wihou red has bee calculaed usig Microfi 4.1 ad he es saisics have bee seleced based o Schwarz Bayesia Crierio. Table 5-2: The resuls of saioary es for variables a surface ad wih iercep ad red Variables Tes saisic Tes resul Oe-year deposi ieres rae No-saioary Loa ieres rae No-saioary Mare ieres rae No-saioary Iflaio rae No-saioary Logarihm of GDP No-saioary Logarihm of liquidiy No-saioary Logarihm of exchage rae No-saioary Criical value a sigificace level of 5% = Table (5-3) idicaes ha he variables ha were o saioary a surfaces became saioary by coducig heir firs-order differece. Table 5-3: The resuls of he saioary es i firs-order differece ad wih iercep Variables Tes saisic Tes resul Oe-year deposi ieres rae saioary Loa ieres rae saioary Mare ieres rae saioary Iflaio rae saioary Logarihm of GDP saioary Logarihm of liquidiy saioary Logarihm of exchage rae saioary Criical value a sigificace level of 5% = As was observed i Tables (5-1), (5-2), ad (5-3), he calculaed absolue values of augmeed Dicey- Fuller saisic for variables of iflaio rae a surface ad red is bigger ha he absolue criical value. Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2303

10 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle Ad for variables of oe-year deposi ieres rae, five-year deposi ieres rae, loa ieres rae, ad mare ieres rae, his saisic becomes bigger ha he criical values afer coducig differece oce; herefore, i is cocluded ha he variables are saioary. Toal resuls of he saioary es for he variables are preseed i Table (5-4). Table 5-4: Fial resuls of he saioary es for he variables Variable The resul of he saioary es Oe-year deposi ieres rae I(1) Loa ieres rae I(1) Mare ieres rae I(1) Iflaio rae I(1) Logarihm of GDP I(1) Logarihm of liquidiy I(1) Logarihm of exchage rae I(1) Esimaio of he Model Based o Auoregressive Model wih Disribued Lags ad is Resuls Sice all of he variables are saioary i a equal order, Auoregressive Disribued Lags (ARDL) model is applied o esimae he relaioship bewee log-erm ad error correcio. A his sage, afer i is assured ha here is a log-erm relaioship, ARDL model wih lags ha are specified usig Schwarz Bayesia Crierio is esimaed. Accordig o Schwarz Bayesia Crierio, he opimal lag of he model is seleced o be 2. The reaso for applyig his equaio is ha Schwarz Bayesia Crierio saves deermiig he lags; herefore, i has a higher degree of freedom. The Resuls of esimaig o-year Ieres Rae Paer Esimaig he Shor-erm Dyamic Paer I esimaig he model i auoregressive disribued lags paer, firs is shor-erm dyamic model is represeed as idicaed i Table (6-1). Ad accordig o Schwarz Bayesia Crierio, he opimal lags of he variables are ARDL (1, 1, 0, 0, 1). T is red variable ad c is iercep. Table 6-1: The resuls of oe-year deposi ieres rae es ARDL (1, 1, 0, 0, 1) Variable Coefficie saisic Probabiliy rio(-1) If If(-1) LGDP LM LEX LEX(-1) DW C R 2 : 0.91 F-Sa : (0.000) D.W: 2.25 Accordig o Table (6-1), i is cocluded ha he depede variable of oe-year deposi ieres rae ad he idepede variable of iflaio rae have appeared wih oe lag. The variable of ieres rae wih oe lag has a posiive effec o he depede variable of ieres rae, which is sigifica a 1%. Tha is, he ieres rae of oe year ago has a posiive effec of he ieres rae of he ex year, such ha if i shor ru he ieres rae of oe year ago icreases 1%, he ieres rae of he ex year will have a icrease of 0.40%. Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2304

11 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle The coefficie of he idepede variable of iflaio rae wih oe lag has a posiive effec o oe-year deposi ieres rae ad saisically is sigifica a 10%. The coefficie of he idepede variable is 0.049, i.e. if i he shor ru iflaio rae (% ρ) icreases 1%, ieres rae will icrease 0.049%. Therefore, he effec of iflaio rae i shor ru does o cause a sigifica chage i oe-year deposi ieres rae. Moreover, i ca be cocluded ha a icrease of 1% i GDP causes a decrease of 0.14% i oe-year ieres rae ad ha if volume of moey icrease 1%, oe-year ieres rae icreases 0.12%. Ad if exchage rae icreases 1%, oe-year ieres rae icreases 0.26%. Idex R 2 equals 0.91, i.e. he idepede variable explais 91% of chages i he depede variable (oe-year deposi ieres rae). F saisic is a reaso for he whole regressio o be sigifica. A a sigifica level of 1%, he ull hypohesis rejecs ha all coefficies of he paer are zero. Durbi- Waso saisic is relaed o he coiegraio regressio of bigger ha criical values a 1%.; herefore, a log-erm balaced relaioship exiss amog he variables. Saisics relaed he diagosic ess of he model srucural form admi classical assumpios ad lac of srucural failure. I he shor-erm relaioship, here are o serial auocorrelaio ad variace aisoropy, he cosequeial form is appropriae, ad he disribuio is ormal. Table 6-2: The resuls of he diagosic ess F saisic Null hypohesis 1.81)0.17( Lac of serial auocorrelaio 2.16)0.14( Presece of cosequeial appropriae form 1.30)0.52( Presece of ormal disribuio 0.71)0.39( Variace aisoropy 6.2. The Tes of Log-erm Relaioship Immediaely afer he dyamic equaio is esimaed ad before he balaced log-erm relaioship bewee he variables of he model is deermied, coiegraio es should be coduced for he variables. If oe of he mehods affirms log-erm relaioship bewee he variables, he mehod ca be seleced as he efficie model. I he prese sudy, Shae ad Sos (1996) mehod is seleced o deermie he logerm relaioship. If he calculaed F saisic is more ha he criical value I(1), he ull hypohesis is rejeced ad a log-erm relaioship amog he variables will be proved. The resuls of his es are preseed i Table (6-2). I so doig, followig equaios where rio ad if are respecively oe-year deposi ieres rae ad iflaio rae are esimaed. The equaio i which rio is he depede variable is as follow: rio a b rio c if rio if 1 i 1 i i 1 i i1 i1 The equaio i which if is he depede variable is as follow: if a b rio c if rio if 2 i 2 i i 2 i i1 i1 The ull hypohesis o he absece of log-erm relaioship amog he variables ess H 0 : 1 2 0i relaio o he opposie hypohesis H1 : I his es, F saisics Frio ( rio if, Lgdp, Lm,Lex ) is uilized as. As idicaed i Table (6-3), F saisic F if (if rio, Lgdp, Lm,Lex ) exceeds he criical values a %5 whe. Therefore, i hese codiios, here will be a log-erm relaioship amog he variables a his criical level. Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2305

12 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle Table 6-3: The es of log-erm relaioship he rage of criical values a 5% F saisic he relaioship bewee he variables sigificace level* I(0)I(1) Frio ( rio if, Lgdp, Lm, Lex ) F if (if rio, Lgdp, Lm, Lex ) * Criical values wih iercep a wihou red Sice he calculaed F is more ha he criical value of I(1) a 5% sigificace level, he log-erm relaioship bewee iflaio rae ad oe-year deposi ieres rae is affirmed ad he ull hypohesis is rejeced a a level of 5%. Afer i is assured ha here is a log-erm relaioship, i ca be ierpreed. The resuls of he log-erm relaioship amog he variables of ARDL model wih lags ha are deermied by Schwarz Bayesia Crierio are idicaed i Table (6-4). Table 6-4: The resuls of esimaig he log-erm relaioship i he model Variable Coefficie T saisic Probabiliy if lgdp lm lex dw c Accordig o he resuls preseed i Table (6-4), i ca be saed ha i log erm iflaio rae is saisically sigifica a 5% sigificace level ad has a posiive effec o oe-year deposi ieres rae. I log ru, a icrease of 1% i iflaio rae resuls i a icrease of 0.41% i oe-year deposi ieres rae, assumig all oher variables o be cosa. This issue is i lie wih ecoomic heories. I log ru, he variable of GDP is saisically sigifica a 5% sigificace level ad has a posiive effec o oe-year deposi ieres rae. I log ru, a icrease of 1% i GDP causes a icrease of 0.25% i oe-year deposi ieres rae, assumig all oher variables o be cosa. This issue is i lie wih ecoomic heories. Table 6-5: The resuls of error correcio model Variable Coefficie T saisic Probabiliy Dif DLgdp DLm DLex DW DC ecm(-1) I log ru, he variable of moey volume is saisically sigifica a 5% sigificace level ad has a posiive effec o oe-year deposi ieres rae. I log ru, a icrease of 1% i moey volume causes a icrease of 0.02% i oe-year deposi ieres rae. Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2306

13 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle I log ru, a icrease of 1% i log-erm ieres rae causes a icrease of 0.5 i oe-year deposi ieres rae Error Correcio Model of oe-year Ieres Rae I followig secio, he esimaio of error correcio model ha relaes shor-erm flucuaios o heir log-erm balace values is preseed. To regulae he error correcio model, i is eough o pu he error erm relaed o he coiegraio regressio of esimaig he log-erm model wih oe lag as he explaaory variable beside he firs-order differece of oher variables of he model. Aferwards, he coefficies of he model should be calculaed. The resuls of error correcio model are preseed i Table (6-5). D ad ecm(-1) sad for firs-order differece ad he coefficie of he error correcio model, respecively. Wha is he mos impora issue is he coefficie of he correcio erm ha idicaes he adjusme speed of imbalace process oward balace i log ru. As was observed i Table (6-5), his coefficie is sigifica ad egaive. Sice ECM coefficie is locaed bewee 0 ad -1 ad is sigifica, presece of log-erm coiegraio relaioship bewee he variables is cofirmed. Sice he coefficie of error correcio erm is 0.59, i ca be cocluded ha he coefficie of ecm(-1) i shor ru is equal o This coefficie is saisically meaigful ad idicaes he adjusme speed i shor ru oward logerm balace. I fac, his coefficie idicaes ha i each period 59% of imbalace of he previous period will be adjused (correced). This period lass less ha wo years The Resuls of Esimaig Loa Ieres Rae Model Esimaig he Shor-erm Dyamic Model I esimaig he auoregressive model wih disribued lags, firs is shor-erm dyamic model is represeed i Table (6-6). Based o Schwarz Bayesia Crierio, he opimal lag of he variables is ARDL (1, 1). The resuls of he dyamic model idicae ha all of he variables are sigifica a 5% sigificace level. Table 6-6: The resuls of loa ieres rae es ARDL (1, 0, 2, 0, 1) Variable Coefficie T saisic Probabiliy ril(-1) if lgdp lgdp (-1) lgdp (-2) lm lex lex(-1) dw c R 2 : 0.94 F-Sa : (0.000) D.W: 2.17 Accordig o Table (6-6) he depede variable of loa ieres rae ad he idepede variable of iflaio rae appeared wih oe lag. The variable of ieres rae wih oe lag has a posiive effec o he depede variable of ieres rae ad saisically is sigifica a 5% sigificace level. Tha is, ieres rae of oe year ago has a posiive effec o he ieres rae of he ex year. So, if i shor ru he ieres rae of oe year ago icreases 1%, he ieres rae of he ex rae icreases 0.61%. The coefficie of he idepede variable of iflaio rae has a posiive effec o loa ieres rae ad saisically is sigifica a 5% sigificace level. The coefficie of he idepede variable is equal o 0.048, i.e. if i shor ru iflaio rae (% ρ) icreases 1%, ieres rae will experiece a icrease of 0.048%. Therefore, he effec of iflaio rae i shor ru does o cause a sigifica chage i loa Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2307

14 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle ieres rae. Moreover, i ca be cocluded ha a icrease of 1% i GDP of wo years ago resuls i a reducio of 0.43% i loa ieres rae. If volume of moey icreases 1%, a icrease of 0.042% occurs i loa ieres rae. A icrease of 1% i exchage rae leads o a reducio of 0.56% i loa ieres rae. As was observed, he sig of he esimaed coefficies is i lie wih heoreical priciples ad all of hem are saisically sigifica a 5% sigificace level. The idex of R 2 is equal o 0.94, which meas ha 94% of he chages i he depede variable (loa ieres rae) ca be explaied rough he idepede variable. F saisic is a reaso for he whole regressio o be sigifica. The saisics of he diagosic es ad he srucural form of he model idicae he provisio of classical assumpios ad lac of srucural failure. I shor ru, serial auocorrelaio ad variace aisoropy do o exis ad he cosequeial form is appropriae ad he disribuio is ormal (see Table 6-7). Table 6-7: The resuls of he diagosic ess F saisic Null hypohesis 0.10 (0.19) Lac of serial auocorrelaio 0.07 (0.32) Presece of cosequeial appropriae form 0.58 (0.45) Presece of ormal disribuio 1.87 (0.17) Variace aisoropy Log-erm Relaioship Tes Righ afer he dyamic equaio is esimaed ad before he log-erm balace relaioship bewee he variables of he model is deal wih, coiegraio es should be admiisered for he variables. I he prese sudy, Shae ad Sos (1996) mehod is used o chec he presece of log-erm relaioship. If he calculaed F saisic is bigger ha he above criical value I(1), he he ull hypohesis will be rejeced ad a log-erm relaioship exiss amog he variables. These resuls are preseed i Table (4-12). For his purpose, followig equaios where ril ad if are respecively loa ieres rae ad iflaio rae are esimaed. The equaio where ril is a depede variable: ril a b ril c if ril if 1 i 1 i i 1 i i1 i1 The equaio where if is a depede variable: if a b ril c if ril if 2 i 2 i i 2 i i1 i1 These equaios es he ull hypohesis, i.e. here is o log-erm relaioship amog he variables, H : 0) ad he opposie hypohesis ( H1 : 1 2 0). I his es, F saisic is applied ( as F if (if ril, Lgdp, Lm,Lex ) Fril ( ril if, Lgdp, Lm,Lex ). As is observed i Table (6-8), he calculaed F saisic exceeds he criical value a 5% sigificace level. Therefore, here is a log-erm relaioship bewee loa ieres rae ad iflaio rae a his criical level. Table 6-8: Log-erm relaioship es he rage of criical values a 5% F saisic he relaioship bewee he variables sigificace level* I(0)I(1) Fril ( ril if, Lgdp, Lm, Lex ) F if (if ril, Lgdp, Lm, Lex ) * Criical values wih iercep a wihou red Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2308

15 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle Sice he calculaed F is more ha he criical value of I(1) a 5% sigificace level, he log-erm relaioship bewee iflaio rae ad loa ieres rae is affirmed. Afer i is assured ha here is a log-erm relaioship, i ca be ierpreed. The resuls of he log-erm relaioship amog he variables of ARDL model wih lags ha are deermied by Schwarz Bayesia Crierio are idicaed i Table (6-9). Table 6-9: The resuls of esimaig he log-erm relaioship i he model Variable Coefficie T saisic Probabiliy if lgdp lm lex dw c Accordig o he resuls preseed i Table (6-9), i ca be saed ha i log ru he variable of loa ieres rae is saisically sigifica a 5% sigificace level ad has a posiive effec o iflaio rae. I log ru, a icrease of 1% i iflaio rae resuls i a icrease of 0.12% i loa ieres rae. This issue is i agreeme wih ecoomic heories. I log ru, he variable of GDP is saisically sigifica a 5% sigificace level ad has a egaive effec o loa ieres rae. I log ru, if GDP icreases 1%, loa ieres rae will reduce 0.45%. I log ru, volume of moey is saisically sigifica ad has a posiive effec o loa ieres rae. I log ru, if volume of moey rises 1%, loa ieres rae icreases 0.11%. I log ru, a icrease of 1% i exchage rae causes loa ieres rae o icrease 0.71% Error Correcio Model of Loa Ieres Rae I followig secio, he esimaio of error correcio model ha relaes shor-erm flucuaios o heir log-erm balace values is preseed. Aferwards, he coefficies of he model should be calculaed. The resuls of error correcio model are preseed i Table (6-10). Table 6-10: The resuls of error correcio model Variable Coefficie T saisic Probabiliy Dif DLgdp DLm DLex DW DC ecm(-1) Wha is he mos impora issue is he coefficie of he correcio erm ha idicaes he adjusme speed of imbalace process oward balace i log ru. As was observed i Table (6-5), his coefficie is sigifica ad is sig is egaive. Sice ECM coefficie is locaed bewee 0 ad -1 ad is sigifica, presece of log-erm coiegraio relaioship bewee he variables is cofirmed. Sice he coefficie of error correcio erm is 0.59, i ca be cocluded ha he coefficie of ecm(-1) i shor ru is equal o This coefficie is saisically meaigful ad idicaes he adjusme speed i shor ru oward log-erm balace. I fac, his coefficie idicaes ha i each period 50% of imbalace of he previous period will be adjused (correced). This period lass more ha wo years. Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2309

16 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle 6.5. The Resuls of Esimaig Mare Ieres Rae Model Esimaig he Shor-erm Dyamic Model I esimaig he auoregressive model wih disribued lags, firs is shor-erm dyamic model is represeed i Table (6-11). Based o Schwarz Bayesia Crierio, he opimal lag of he variables is ARDL (0, 2). The resuls of he dyamic model idicae ha all of he variables are sigifica a 5% sigificace level. Table 6-11: The resuls of mare ieres rae es ARDL (1, 0, 0, 0, 0) Variable Coefficie T saisic Probabiliy rim(-1) if lgdp lm lex dw c R 2 :0.932 F-Sa : (0.000) D.W: 2.02 The coefficie of he idepede variable of iflaio rae wih wo lags has a posiive effec o mare ieres rae ad is saisically sigifica a 10%. The coefficie of he idepede variable is equal o Tha is, if i shor ru iflaio rae (% ρ) icreases 1%, he ieres rae rises 0.44%. Moreover, i ca be cocluded ha a icrease of 1% i GDP of wo years ago, mare ieres rae will experiece a icrease of 0.26%. Ad if volume of moey icreases 1%, mare ieres rae will rise 0.90%. Wih a icrease of 1% i exchage rae, mare ieres rae will rise 0.90%. As was observed, he sig of he esimaed coefficies is i lie wih heoreical priciples ad all of hem are saisically sigifica a 5% sigificace level. The idex of R 2 is equal o 0.92, which meas ha 92% of he chages i he depede variable (loa ieres rae) ca be explaied rough he idepede variable. F saisic is a reaso for he whole regressio o be sigifica ad a a level of 1% rejecs he ull hypohesis, i.e. all coefficies are zero. The saisics of he diagosic es ad he srucural form of he model idicae he provisio of classical assumpios ad lac of srucural failure. I shor-erm relaioship, serial auocorrelaio ad variace aisoropy do o exis ad he cosequeial form is appropriae ad he disribuio is ormal (see Table 6-12). Table 6-12: The resuls of he diagosic ess F saisic Null hypohesis 0.23 (0.22) Lac of serial auocorrelaio 2.20 (0.15) Presece of cosequeial appropriae form 3.60 (0.15) Presece of ormal disribuio 0.49 (0.48) Variace aisoropy Log-erm Relaioship Tes Righ afer he dyamic equaio is esimaed ad before he log-erm balace relaioship bewee he variables of he model is deal wih, coiegraio es should be admiisered for he variables. I he prese sudy, Shae ad Sos (1996) mehod is used o chec he presece of log-erm relaioship. If he calculaed F saisic is bigger ha he above criical value I(1), he he ull hypohesis will be rejeced ad a log-erm relaioship exiss amog he variables. These resuls are preseed i Table (4- Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2310

17 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle 13). For his purpose, followig equaios where rim ad if are respecively mare ieres rae ad iflaio rae are esimaed. The equaio where rim is a depede variable: rim a b rim c if rim if 1 i 1 i i 1 i i1 i1 The equaio where if is a depede variable: if a b rim c if rim if 2 i 2 i i 2 i i1 i1 These equaios es he ull hypohesis, i.e. here is o log-erm relaioship amog he variables, ( H 0 : 1 2 0) ad he opposie hypohesis ( H1 : 1 2 0). I his es, F saisic is Frim ( rim if, Lgdp, Lm,Lex ) applied as. As is observed i Table (6-13), he calculaed F F if (if rim, Lgdp, Lm,Lex ) saisic exceeds he criical value a 5% sigificace level. Therefore, here is a log-erm relaioship from iflaio rae o ieres rae a his criical level. Table 6-13: Log-erm relaioship es he rage of criical values a 5% F saisic he relaioship bewee he variables sigificace level* I(0)I(1) Frim ( rim if, Lgdp, Lm, Lex ) F if (if rim, Lgdp, Lm, Lex ) * Criical values wih iercep a wihou red Sice he calculaed F is more ha he criical value of I(1) a 5% sigificace level, he log-erm relaioship bewee iflaio rae ad mare ieres rae is affirmed. Therefore, he ull hypohesis is rejeced. Afer i is assured ha here is a log-erm relaioship, i ca be ierpreed. The resuls of he log-erm relaioship amog he variables of ARDL model wih lags ha are deermied by Schwarz Bayesia Crierio are idicaed i Table (6-14). Table 6-14: The resuls of esimaig he log-erm relaioship i he model Variable Coefficie T saisic Probabiliy if lgdp lm lex dw c Accordig o he resuls preseed i Table (6-14), i ca be saed ha i log ru he variable of iflaio rae is saisically sigifica a 5% sigificace level ad has a posiive effec o loa ieres rae. I log ru, a icrease of 1% i iflaio rae resuls i a icrease of 0.80% i mare ieres rae. This issue is i agreeme wih ecoomic heories. I log ru, he variable of GDP is saisically sigifica a 5% sigificace level ad has a egaive effec o mare ieres rae. I log ru, if GDP icreases 1%, mare ieres rae will reduce 0.67%. Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2311

18 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle I log ru, volume of moey is saisically sigifica ad has a posiive effec o mare ieres rae. I log ru, if volume of moey rises 1%, mare ieres rae icreases 0.17%. I log ru, a icrease of 1% i exchage rae causes mare ieres rae o decrease 0.02% Error Correcio Model of Mare Ieres Rae I followig secio, he esimaio of error correcio model ha relaes shor-erm flucuaios o heir log-erm balace values is preseed. Aferwards, he coefficies of he model should be calculaed. The resuls of error correcio model are preseed i Table (6-15). Table 6-15: The resuls of error correcio model Variable Coefficie T saisic Probabiliy Dif DLgdp DLm DLex DW DC ecm(-1) Wha is he mos impora issue is he coefficie of he correcio erm ha idicaes he adjusme speed of imbalace process oward balace i log ru. As was observed i Table (6-15), his coefficie is sigifica ad is sig is egaive. Sice ECM coefficie is locaed bewee 0 ad -1 ad is sigifica, presece of log-erm coiegraio relaioship bewee he variables is cofirmed. Sice he coefficie of error correcio erm is 0.64, i ca be cocluded ha he coefficie of ecm(-1) i shor ru is equal o This coefficie is saisically meaigful ad idicaes he adjusme speed i shor ru oward log-erm balace. I fac, his coefficie idicaes ha i each period 64% of imbalace of he previous period will be adjused (correced). This period lass less ha wo years Shor- ad Log-erm Grager Causaliy Tes usig Error Correcio Model Table 6-16 represes he resuls of shor- ad log-erm Grager causaliy es usig a error correcio model as he followig equaio. The firs model shows a siuaio i which oe-year ieres rae (rio) is a depede variable ad variables of iflaio rae (if), GDP, volume of moey, ad exchage rae are idepede. Drio Drio Dif DL g dp i i i i i i i1 i1 i1 DLm DLex ECT i i i i 1 i1 i1 The followig equaio idicaes he firs model i a siuaio where iflaio rae is a depede variable ad oher variables of oe-year ieres rae, GDP, volume of moey, ad exchage rae are idepede. Dif Drio Dif DL g dp i i i i i i i1 i1 i1 DLm DLex ECT i i i i 1 i1 i1 Shor- ad log-erm Grager causaliy es usig a error correcio model is idicaed as he followig equaio. The followig equaio idicaes he secod model i a siuaio where loa ieres rae (ril) is a depede variable ad oher variables of iflaio rae (if), GDP, volume of moey, ad exchage rae are idepede. Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2312

19 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle Dril Dril Dif DL g dp i i i i i i i1 i1 i1 DLm DLex ECT i i i i 1 i1 i1 The followig equaio idicaes he secod model i a siuaio where iflaio rae is a depede variable ad oher variables of loa ieres rae, GDP, volume of moey, ad exchage rae are idepede. Dif Dril Dif DL g dp i i i i i i i1 i1 i1 DLm DLex ECT i i i i 1 i1 i1 Shor- ad log-erm Grager causaliy es usig a error correcio model is idicaed as he followig equaio. The followig equaio idicaes he hird model i a siuaio where mare ieres rae (rim) is a depede variable ad oher variables of iflaio rae (if), GDP, volume of moey, ad exchage rae are idepede. Drim Drim Dif DL g dp i i i i i i i1 i1 i1 DLm DLex ECT i i i i 1 i1 i1 The followig equaio idicaes he hird model i a siuaio where iflaio rae is a depede variable ad oher variables of mare ieres rae, GDP, volume of moey, ad exchage rae are idepede. Dif Drim Dif DL g dp i i i i i i i1 i1 i1 DLm DLex ECT i i i i 1 i1 i1 The resuls preseed i Table (6-16) idicae ha i shor ru here is a Grager causaliy relaioship bewee oe-year ieres rae ad iflaio rae. O he oher had, ha he coefficie of error correcio wih he lag of (ECT -1 ) is sigifica meas his relaioship exiss i log ru, oo. This resul is cofirmed hrough he es ad ha he coefficies are simulaeously sigifica ECT, if,,, 1 D DLgdp DLm DLex ECT ad,,,, 1 Drio DLgdp DLm DLex wih x 2 saisic. I log ru, here is a Grager causaliy relaioship from loa ieres rae o iflaio rae. I Ira, loa ieres rae is deermied by Moey ad Credi Coucil ad due o he disassociaio, here is o mechaism ha acualizes ieres rae ad leads o balace i ecoomy. Therefore, iflaio cao be due o loa ieres rae because ieres rae cao be affeced by iflaio i log ru. However, ieres rae ca ifluece iflaio. Research idicaed ha ieres rae flucuaios i log ru affec iflaio i a sigifica maer. A decrease of 1% i ieres rae leads o a reducio of 0.12% i iflaio. However, his effec is o sigifica i shor ru Sabiliy of Coefficies I order o chec he sabiliy of he model ad deermie presece or absece of srucural failure, cumulaive square es of Cumulaive Sum of Recursive Residuals (CUSUM) ad remaiig squares es (CUSUMSQ) ha have log bee paid aeio i ecoomeric lieraure are applied. I CUSUM ad CUSUMSQ ess, he ull hypohesis ad he sabiliy of he parameers are esed a 5% sigificace level. The cofidece ierval of hese wo ess icludes wo sraigh lies ha idicae a Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2313

20 Idia Joural of Fudameal ad Applied Life Scieces ISSN: (Olie) A Ope Access, Olie Ieraioal Joural Available a Research Aricle cofidece level of 95%. If he saisic of CUSUM ad CUSUMSQ ess locaes wihi hese lies, he ull hypohesis of sabiliy of he coefficies cao be rejeced The Sabiliy of he Calculaed Coefficies for oe-year Ieres Rae Model The sabiliy of he calculaed coefficies of he model is esed hrough cumulaive square es of CUSUM. The resuls of his es idicae ha he calculaed coefficies are sable ad due o locaig a cofidece ierval of 95%, srucural failure does o exis (see Diagram 6-17). Diagram 6-17: Sabiliy of he coefficies (CUSUM) Diagram 6-18: Sabiliy of he coefficies (CUSUMSQ) The Sabiliy of he Calculaed Coefficies for Loa Ieres Rae Model The sabiliy of he calculaed coefficies of he model is esed hrough cumulaive square es of CUSUM. The resuls of his es idicae ha he calculaed coefficies are sable ad due o locaig a cofidece ierval of 95%, srucural failure does o exis (see Diagram 6-19). Diagram 6-19: Sabiliy of he coefficies (CUSUM) Copyrigh 2014 Cere for Ifo Bio Techology (CIBTech) 2314