time series analysis

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THE IMPACT OF DEPRECIATION OF EXCHANGE RATE TOWARD

TRADE BALANCE IN INDONESIA: TIME SERIES

ANALYSIS

By

PUTRI, Kusuma Hani

THESIS

Submitted to

KDI School of Public Policy and Management In Partial Fulfillment of the Requirements

For the Degree of

MASTER OF PUBLIC POLICY

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THE IMPACT OF DEPRECIATION OF EXCHANGE RATE TOWARD

TRADE BALANCE IN INDONESIA: TIME SERIES

ANALYSIS

By

THESIS

Submitted to

For the Degree of

2019

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THE IMPACT OF DEPRECIATION OF EXCHANGE RATE TOWARD

TRADE BALANCE IN INDONESIA: TIME SERIES

ANALYSIS

By

THESIS

Submitted to

For the Degree of

Committee in charge:

Professor Kim, Dongseok, Supervisor

Professor Kim, Hyeon-Wook

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ABSTRACT

THEIMPACTOFIDEPRECIATIONIOFIEXCHANGERATETOWARDITRADE

BALANCEININDONESIA:TIMESERIESANALYSIS

By

PUTRI,KusumaHani

Currently,therecoveringUSeconomyandtheraisingofprotectionism havetriggered externalshockinseveralcountriesincludingIndonesia.Hence,thisstudyaimstoanalyzethe causalityIbetweenItheexchangeIrateandtheItradeIbalanceinIndonesia, tomeasureandforecast

theimpactofthedepreciationoftheexchangerateandtradebalanceinIndonesia,and toidentify

ithe impact ofi depreciation of iexchange irate toward Indonesia’s export performance of

manufactured,agricultural,andminingcommodities.Inaccordancetoachievetheobjectiveof thestudy, this study use time series analysis.Thefindingsshowthatthe irelationship ibetween itrade ibalance iand iexchange irate inIndonesiaisone-way;only ithe iexchange irateaffectsthe

tradebalanceinIndonesia.Furthermore,ifthereisashockfromtheexchangerate,it iwill ilead

to a decrease in itradeibalance by about 0.45 percent in 6 months. In the commodity level,

agricultural commodity gets higher deterioration compared to manufactured, mining

commoditywillincreaseifthereisdepreciationoftheexchangerate.Moreover,J-curvedoes notexisteitherattheaggregatelevelorcommoditylevelinIndonesia.

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ACKNOWLEDGMENTS

All praise be to ALLAH almighty, Who always gives me health and blessing everyday.

ThereforeIcanwriteandsubmitthisthesistimely.

IwouldliketogiveahighappreciationtothePOSCOforgivingmetheopportunityforbeing apartofthePOSCOAsianFellowship.Hence,Icouldobtaintheopportunitytopursuethemaster degreeprograminKDISchoolofPublicPolicy.

Furthermore,Iiwould liketoiexpress my igratitude to imysupervisor,Prof.Dongseok Kim,

whoprovidedgreatencouragement,insightfulcomments,andsuggestionsformythesis.Moreover, it is also a great honor to haveProf. Hyeon-Wook Kimas my second supervisor who provides thoughtfulcommentsandsuggestionsfromthemacroeconomicpointofview.Ialsowouldliketo expressgratitudetotheKDISchoolforsupportingtheadministrativeprocedureformythesis.

“”Above all, I wouldlike to expresshighest appreciation to myparents and mysister for supportandencouragementduringmystudyinKDISchool,andallmyfriendsandallpartieshave givenmuchsupportandhelpinconductingthisthesis.

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iTABLEOFiCONTENTS

“”iLISTOF iFIGURES” ... “ iv

“”iLISTOF iTABLES” ... “ v

I. iINTRODUCTION” ... 1

I.1 THEIMPORTANCEOFSTUDY ... 1

I.2 THESCOPEOFSTUDY ... 2

I.3 THEOBJECTIVESOFSTUDY ... 4

I.4 THERESEARCHQUESTIONSANDHYPOTHESIS ... 4

II. LITERATUREREVIEW... 5

II.1 DEFINITIONANDTHEORETICALLITERATURE ... 5

II.2 EMPIRICALLITERATURE ... 7

II.3 PROPOSEDMODELBETWEENEXCHANGERATEAND TRADEBALANCE ... 9

II.4 RESEARCHFRAMEWORK ... 10

III. DATAANDMETHODOLOGY ... 11

III.1 VARIABLEANDRESOURCES ... 11

III.2 TIMESERIESANALYSIS ... 12

IV. EMPIRICALRESULTANDDISCUSSION ... 18

IV.1 iANALYSISOFiTHEIMPACT iOF iEXCHANGE iRATE TOWARD iTRADE iBALANCEINNATIONALLEVEL ... 18

IV.2 iANALYSIS iOFiHEIMPACTOF iEXCHANGE iRATE TOWARDTRADEBALANCEINCOMMODITYLEVEL ... 26

V. CONCLUSION... 32

V.1 THEMAINCONCLUSION ... 32

V.2 POLICYRECOMMENDATION ... 33

V.3 RECOMMENDATIONFORFURTHERSTUDIES ... 33

REFERENCES ... 34

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LISTOFFIGURES

Figure1:DynamicofExchangeRate,TradeBalance,andMajorCommoditiesExport

Figure2:J-curve

Figure3:ResearchFramework

Figure4:VAR/VECMProcessandDifferences

Figure5:ExchangeRateandTradeBalanceinIndonesia

Figure6:Export,Import,TradeBalanceIndonesia

Figure7:TheResultofImpulseResponseFunction

Figure8:TradecompositionofIndonesiaandTradePerformanceonManufactured

Commodity

Figure9:TradeinAgricultural Commodity

Figure10:TradeinManufacturedCommodity

Figure11:TradeActivityintheMiningCommodity

Figure12:Impulseof iReal iExchange iRatetoward iTrade iBalanceinAgricultural

Commodity

Figure13:Impulseof iReal iExchange iRatetowardTradeBalanceinManufactured

Commodity

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LISTOFTABLES

Table1:Variable,Period,andDataResources

Table2:StationarityTestSignificancebyP-Value

Table3:GrangerCausality

Table4:VARShortRunEstimation

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I. INTRODUCTION

I.1 THEIMPORTANCEOFSTUDY

Globalizationandopeneconomyprovidebroaderaccesstoamarketforeverycountry.In contrast,theopeneconomyalsocreatesexternalchallengesthatcanaffectthesustainabilityof theworldeconomy.

Recently, The Fed decided to increase the interest irate iniorder to recover The US’

economy. BasedonFederalReserve(2018),theFedhasincreasedtherate3timessinceMarch 2018(1.50-1.75),June(1.75-2.00)andSeptember(2.00-2.25).Thisshocktriggersa ifall iin ithe

valueoftheicurrency inseveralcountriessuchasIndiaby (8.2%),China by(6.8%),Philippine

by (6.3%),andIndonesia by (5.1%).

Moreover,theraisingissueofprotectionismcreatesanewpatternofinternationaltrade. According to WTO (2014), world trade has focused on inter-developing country trade. Furthermore,worldproductivitytendstodecreaseduetolowerinvestment,thenitwillimpact the fall of capital accumulation and technology innovation. Hence, these challenges will contributetoeconomicperformance,especiallytradeperformance.

Manyeconomistsandresearchershaveshedlightontheseissues,especiallyissuesonthe relationbetweenthedepreciationofcurrency andtradeperformance (Hook&Boon,2000; Taylor&Sarno,1998;Thirlwall&Gibson,1986).“Inthecaseof iMalaysiaandThailand, ithe irelationship ibetween iexchange irate and itrade ibalance is positive (Onafora, 2003).

Furthermore,theeffectofexchangeratedepreciationandtradebalanceisdifferentinamong countrries.BasedonStucka(2004),every1percent idepreciation iof ithe idomestic icurrencyin

Croatiacouldimprovethetradebalancebetween0.94-1.3percentinthe long-run.InIndonesia, the idepreciation of iRupiah had ishort-run ieffects (Oskoee & Harvey, 2009). Whereas

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balance at the aggregate level, I intend ito iexamine ithe iimpact iof ithe depreciation of

Iexchange Irate Iand Itrade Ibalance Iat Icommodity Ilevel(Oskoee &Harvey, 2009;Stucka,

2004; Onafora, 2003). Thisstudy will also enhance our iunderstanding iofforecasting ithe

depreciationofthe iexchange iratetoward itrade ibalance.”

In regards to the statement above, the deprecation of exchange rate could be an opportunity for boosting the trading activity for thecountry inthe future. Meanwhile, we should iidentify ithe irelationship ibetween iexchange irateand itrade ibalancebecauseevery

countryhasdifferentcharacteristicandmonetaryandtradepolicy.Therefore,furtherstudies onforecastingtheimpactondepreciationexchangerateareneeded inordertomeasureand formulateprominentpolicyinthefuture.

I.2 THESCOPEOFTHESTUDY

Asa memberofG-20,Indonesiahasplayedanimportantroleintheworldtrade,thusthis studywillcontributetoidentifyingtheiimpactof itheidepreciation iof the iRupiah toward itrade ibalanceinIndonesia.Iwanttofocusonthesetwovariablesbecausetheseexternaleconomic

shocksmakeabigimpactontradeandexchangerateinIndonesia.Inregardstotradeactivity, BankIndonesia(2018)forecastsIndonesia’stradeactivityinIndonesiastillhasanobstacle with the limitation of capability and capacity of industry in the mid-term. In regard to macroeconomic performance, BankIndonesia (2018)states thattheexchangerate isstable during2017andhaslowervolatilityamongotherpeercountries by(8.4%).In contrast,the WorldBank(2018)statesthatthegrowthofRupiahismorevolatilecomparedtotheprevious yearandhasatendencytomakegreatermovecomparedtoothercurrenciesinAsia.

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Figure1:DynamicofExchangeRate,TradeBalance,andMajorCommoditiesExport

Moreover, exports of Indonesia still depends on primary commodity and natural

resource-based manufactured goods. According to UNCTAD (2018), 53 percent of

Indonesia’s export based on commodityproducts. In addition, the MinistryofFinance of Indonesia(2014)claimsmajorcommoditiesexportaredominatedbyIndustrialproductby (48.6%),miningandgas by(13.3%),andagriculturalcommoditiesby (2.3%),respectively. Thus,theanalysisofthecommoditylevelisnecessarytoidentify.

Inaccordancewiththissituation,furtherstudytoanalyzerelationandforecastingtrade performanceandmonetaryindicatorinIndonesiaisneeded.Theexchangerateasthemain monetary indicator determines thetrade performance. Hence, shock inthe exchange rate couldimpactnotonlytradeperformanceattheaggregatelevelbutalsoatcommoditylevel.

The iremainder iof ithis ipaper iis iorganized ias ifollows: section”onewillexplainthe

background of the impact of idepreciation iof iexchange irate toward itrade ibalance in

Indonesia, sectiontwowill identifythedefinition ofvariableandeconomictheoryamong variables,sectionthreewilldescribethemethodofthisstudy,sectionfivewilldiscussresult anddiscussion, andsectionsix istheconclusion.Inthefollowing section,Iwillpresent a

-5,000 5,000 10,000 15,000 0,00 5000,00 10000,00 15000,00 20000,00 20 00 /Q 1 20 01 /Q 3 20 03 /Q 1 20 04 /Q 3 20 06 /Q 1 20 07 /Q 3 20 09 /Q 1 20 10 /Q 3 20 12 /Q 1 20 13 /Q 3 20 15 /Q 1 20 16 /Q 3 20 18 /Q 1

Exchange Rate and Trade Balance

JISDOR (IDR/USD)

13.3% 48.6%

2.3%

Major Commodities Expor

Mining, Oil and gas Industrial Products Agricultural Product

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literaturereviewandtheoryonthetopicof itherelationship ibetween iexchange irateand itrade ibalance.”

I.3 THEOBJECTIVESOFSTUDY

“Accordingtothebackgroundandtheimportanceofthisstudyabove,the “iobjectives iof ithis istudy iareasfollows:”

1. Analyzingthecausalityrelation ibetween ithe iexchange irateand ithe itrade ibalance iin” iIndonesia.

2. Measuringandforecastingtheimpact iof ithedepreciation iof ithe iexchange irateand itrade ibalanceinIndonesia.

3. Identifyingtheimpactofdepreciation ofexchangeratetowardexportperformanceonthe manufacture,agriculture,andminingcommodityinIndonesia.”

I.4 THERESEARCHQUESTIONSANDHYPOTHESIS

“Thisstudywillundertake ito ianswer ithe ifollowing iresearch iquestions:”

1. “Howis itherelation ibetween iexchange irateand itrade ibalance”?

2. “How largeisthemagnitudeofthe iimpact iof ithe idepreciation iof itheRupiah towards itrade ibalance iin Indonesia?”

3. “Howlongwillthedepreciatingoftheexchangerateimpacttotradebalance?” 4. Whichexportcommoditywillgetahigherimpactfromthedepreciationof Rupiah?

Accordingtosomeliteraturereview,thehypothesisofthisstudyisasfollows: 1. Theexchangeratehasbi-directionaltowardtradebalance.

2. The idepreciation iof itheexchange irate iwilldeteriorate ithe itrade ibalance iin ishort-run, ibutitwill iimprove itrade ibalance iin ilong-run”.”

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II. LITERATUREREVIEW

II.1 DEFINITIONANDTHEORETICALLITERATURE

Beforethisstudyidentifiesthe iimpact iof ithe idepreciationof itheexchangeratetowards itrade ibalance, iit iis inecessarytoclearlydefinekeyterminologiesreferredto inthispaper.The

keyterminologiesforthisstudyareexchangerateandtradebalance.Thisprocessisimportant becauseit will determine the interpretation of estimation result inthe next chapter. Inthe

definition and economic theoretical section, this study will focus on two greatest

macroeconomist’s pointof viewswhich are Krugman(2012)andMankiw(2012),because

theirtheoriesarethemostinfluentialtowardthisstudy. a) DefinitionofExchangeRate

“Indefiningtheexchangerate,Krugman(2012, pp.320-321)defines ithe iexchange irate ias “ithe pricei of ione idomestic icurrency in terms of ianother foreign icurrency”. The

exchangeratealsocanbeseenastheassetpriceandcanrepresentacomparisonofthepriceof goodsandserviceswhichisproducedindifferentcountries (pp.321-324).”

“However,Mankiw(2012)definesexchangerate intheoppositeway, “the iexchange irate iis itheprice iof ione iforeign icurrencyintermof idomestic icurrency”(pp.149-150).In

accordancewiththisdifference,itcaninfluencetheinterpretationofraisingorfallinginthe value of the exchange rate. The fluctuations of the exchange rate can be described as depreciationandappreciations.”

“Inregardtothisdifference,thisstudywillutilizethe idefinitionofthe iexchange irateas

“itheprice iof ionedomesticcurrencyinterm iof ianotherforeigncurrency”.Furthermore,we

candefinethedepreciationastherisingvalueofadomesticcurrencyagainstforeigncurrency, and appreciation of the iexchange irate can be defined as the falling value of a domestic

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Krugman(2012, p.323)emphasizes“iWhen ia icountry’s icurrency idepreciates,theforeign

countries ifind ithat iits iexports iare icheaper iand idomestic iresidents ifind ithat iimports iare imore iexpensive. iAn iappreciation ihas ithe iopposite ieffects: iForeigners ipay imore ifor ithe icountry’sproducts iand idomestic iconsumers ipay iless ifor iforeign iproducts”.”

b) DefinitionofTradeBalance

Indefiningtradebalance,thisstudywillusethedefinitionoftradebalancefromKrugman (2012,pp.300-301)asthedifferencebetweentheexportandimportofcommodity.Thisalso canbeshownintheformulationasfollows:

𝐶𝐴 = 𝐸𝑋 − 𝐼𝑀

CA = Current Account EX= Export

IM= Import

Thetradebalancesurplusshowswhenexportsexceededimports.This meansthatthe countrygainedmorefromexportactivityratherthanspendingonimports.Ontheotherhand, whenimportsexceedexportsiscalledtradebalancedeficit.Itmeansthatthecountryspends moreonimportedcommodityratherthangaininginexportactivity.

c) J-CurvePhenomenon

Inthepreviousterm,“Krugman(2012, p.323)claims “iWhen ia icountry’s icurrency idepreciates,theforeigncountries ifind ithat iits iexports iare icheaper iand idomestic iresidents ifind ithat iimports iare imore iexpensive. iAn iappreciation ihas ithe iopposite ieffects: iForeigners ipay imore ifor ithe icountry’sproducts iand idomestic iconsumers ipay iless ifor iforeign iproducts”.”Inreality, ithe idepreciation of ithe iexchange irate idoes inot idirectly iraise ithe itrade ibalance,anditneedstimelagtoadjust.J-curvewillshowtheadjustmentof idepreciationofthe iexchangeratetoward itrade ibalance.”

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J-Curve shows the depreciation of exchange rate will deteriorate itrade ibalance iin ishort-run, ibut iit will iimprove itrade ibalancein the long-run, iand J-curvepresentsthe

relationbetweentimeandtradebalance(indomesticoutputunit).

Figure2:J-curve

Source:Krugman,2012

Accordingtothegraph,thetradebalancewilldeteriorateimmediatelyafterexchange rateshock,whichisshownbybullet1and2,becausetherisingvalueofimportedthatalready orderedintermofthedomesticproduct.Then,thetradebalancewillriseatbullet3dueto timeadjustmentonproductionandconsumption.AccordingtoKrugman(2012),thetime frameofJ-Curveintheindustrialcountryisabout6monthsto1year.

II.2 EMPIRICALLITERATURE

Severalstudiesonthe irelationship ibetween itheexchange irate iand itrade ibalance

havebeengrowinginterestsincethe1930sto1950s.McKinnon(1990)claimsdepreciation ina fixed regimeexchange ratewould reducetrade balance, andit caused theseparation exchangeratefrommonetarypolicyinthe1930sto1950s.Moreover,Carnieroetal.(1998)

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Furthermore,theJ-curvephenomenonisamatterofconcernamongeconomistsand researchers inseveral countriesnowadays (Sezer,2017;Sanadheera, 2015; Vural,2015; Shao,2007;Stucka,2004).InwesternAsiaandBalkancountries,theexchangerateandtrade balancesshowedpositiverelationshipsandtheJ-curvehadbeenfoundinCroatiaandTurkey (Sezer,2017;Stucka,2004).Incontrast,YangandAhmad(2004)claimtherelationofthe

iexchange irateand itrade ibalancewaspositiveinthelongrunandJ-curvewasnotfoundin

China.It alsowasfoundthattheJ-curvephenomenondidnotexistinJapan(Shao,2007).In contrast,Stucka,(2004), YangandAhmad(2004),andSezer (2017)failtoscrutinize the existence of J-curve at commodity level. Therefore, Vural (2015) adds to identify the

iexistenceofthe iJ-curve ieffectin i20outof i96 icommodity igroupsinTurkey.Onanother

side, Sanadheera (2015) also identifies J-curve did not subsist in 5 main commodities. Accordingto several studiesabove, the existence ofJ-Curve diversecross thecountries, becauseeverycountryhasdifferenteconomicstability,andbackground,andanalsodifferent patternon socialeconomic.Having hadadiscussionon ithe iexistence iof ithe iJ-curvein

severalcountries,thisstudywillnow ifocus ion itheidentificationof iJ-curveinIndonesiaas

afocuscountryinthisresearch.

TheresearchontheexistingJ-CurvethatfocusesonIndonesia’stradepartnershas been observed by several researchers (Ramadhona, 2016; Oskooe & Harvey, 2009; Onafowara, 2003). Oskoee and Harvey (2009) claim J-curve existed in Indonesia and Indonesia’stradepartnerssuchasCanada,Japan,Malaysia,andtheUnitedKingdom.This

result was emphasized by the Onafowara (2003) that the J-Curve was found between

IndonesiaandtheUnitedStatesasatradingpartner.Incontrast,Ramadhona(2016)claims theJ-curvewasnotfoundbetweenIndonesiaandseveraltradingpartners.Accordingtothe result, these papers have the same observation to draws attention to the iimpact of the

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idepreciation iof ithe iexchange iratetowardIndonesia’stradingpartners.However,itisstill

lackingtorecognizethe iexistenceof iJ-curveincommoditylevel.

II.3 PROPOSEDMODEL iBETWEEN iEXCHANGE iRATE iAND iTRADE iBALANCE””

“Accordingtothedefinition,theoryandseveralempiricalstudiesabove,itcanbe assumedthat theexchange rateandtrade balance hasapositive relation.”Therefore, the proposedmodelcanbeconstructedaccordingtothedataandmethodologyused,asfollows:

Explanation:

LN_REERt =ExchangeRate(IDR/USD)

LN_TBt =TradeBalance(MillionRupiah)

LN_GDPt =GrossDomesticProduct(MillionRupiah)

α

=Intercept

β,

𝛾

,ώ

=Coefficient

𝜇

_𝑡 =ErrorTerm 𝐿𝑁_𝑇𝐵_𝑡= 𝛼₁₀+ 𝛽₁₁𝐿𝑁_𝑇𝐵_𝑡−1+ ⋯ + 𝛽_1𝑝𝐿𝑁_𝑇𝐵_𝑡−𝑝+ 𝛾₁₁𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡−1+ ⋯ + 𝛾_1𝑝𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡−𝑝+ ώ₁₁𝐿𝑁_𝐺𝐷𝑃_𝑡−1+ ⋯ + ώ_1𝑝𝐿𝑁_𝐺𝐷𝑃_𝑡−𝑝+ 𝜇_1𝑡 𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡= 𝛼₂₀ + 𝛽₂₁𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡−1+ ⋯ + 𝛽_2𝑝𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡−𝑝 + 𝛾₂₁𝐿𝑁_𝑇𝐵_𝑡−1+ ⋯ + 𝛾_2𝑝𝐿𝑁_𝑇𝐵_𝑡−𝑝+ ώ₂₁𝐿𝑁_𝐺𝐷𝑃_𝑡−1+ ⋯ + ώ_2𝑝𝐿𝑁_𝐺𝐷𝑃_𝑡−𝑝+ 𝜇_2𝑡 𝐿𝑁_𝐺𝐷𝑃_𝑡 = 𝛼₃₀+ 𝛽₃₁𝐿𝑁_𝐺𝐷𝑃_𝑡−1+ ⋯ + 𝛽_3𝑝𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡−𝑝+ 𝛾₃₁𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡−1+ ⋯ + 𝛾_3𝑝𝐿𝑁_𝑅𝐸𝐸𝑅_𝑡−𝑝+ ώ₃₁𝐿𝑁_𝑇𝐵_𝑡−1+ ⋯ + ώ_3𝑝𝐿𝑁_𝑇𝐵_𝑡−𝑝+ 𝜇_3𝑡

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II.4 RESEARCHFRAMEWORK

Figure3:ResearchFramework

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III. DATAANDMETHODOLOGY

III.1 VARIABLE, PERIODE, AND DATA RESOURCES

Thisstudywillutilize quarterlytime series data withperiod from 2000to 2018for aggregate leveldataandquarterlydatafrom2005-2018fortradebalanceofcommodity-level. Thisstudywillusethisperiodbecausethisstudywillfocuson the period aftertheAsian financialcrisisthatmadeIndonesia’sexchangeregimeshiftintoafloatingexchangeregime. Furtherinformationregardingthedataisdescribedasfollows:

Table1:Variable,Period,andDataResources

No Variable Period Unit DataResource 1. ExchangeRate (iReal iEffective iExchange iRate) Constant2010=100 2000q1-2018q4

(Index) iFederal iReserve Economic

Data”

2. NationalExport (NominalValue)

2000q1-2018q4

USDmillion InternationalFinancial Statistic(IFS)-IMF 3. ExportonAgriculture Commodity (NominalValue) 2005q1-2018q4

USDThousand BankIndonesia

4. ExportonManufacture Commodity (NominalValue) 2005q1-2018q4 2005q1-2018q4 BankIndonesia 5. ExportonMining Commodity (NominalValue) 2005q1-2018q4 2005q1-2018q4 BankIndonesia 6. Import 2000q1-2018q4

USDmillion InternationalFinancial Statistic(IFS)-IMF 7. Import onAgriculture Commodity (NominalValue) 2005q1-2018q4

USDThousand BankIndonesia

8. ImportonManufacture Commodity (NominalValue) 2005q1-2018q4 2005q1-2018q4 BankIndonesia 9. ImportonMining Commodity (NominalValue) 2005q1-2018q4 2005q1-2018q4 BankIndonesia

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10. GrossDomesticProduct 2000q1-2018q4

Rupiah BankIndonesia

For further processes, all the variables will betransformed into a natural logarithm. Moreover, export and import will be calculated in order to generate a trade balance in Indonesia.Therefore,thisstudywillconvertthetradebalanceintonaturallogarithmform,as follows:

Ln_Export–Ln_Import=Ln(𝐸𝑥𝑝𝑜𝑟𝑡

𝐼𝑚𝑝𝑜𝑟𝑡

)

III.2 TIMESERIESANALYSIS

This study will apply time series analysis in order to identify the impact of the depreciationoftheexchangerateontradebalance.Therefore,iVector iAutoregressive(VAR)/ iVector iError iCorrection iModel i(VECM)”andEngle-Grangerwillbeutilizedinthisstudy,

becausetheycancapturetherelationbetweenvariablesandforecasttheshockinthefuture. Inregardstotheapplicationofthemodel,itisnecessarytoidentifythestationarylevel ofthevariable.Thisstudywillutilize iVector iAutoregressive(VAR)ifallthevariablesare

stationaryinlevel.Incontrast, iVector iError iCorrection iModel(VECM)”willbeutilizedin

thisstudy,ifallvariablesarestationaryinfirst differentandhaveaco-integrationlevel.In contrast,ifthedataisstationaryinfirstdifferenceandthereisnoco-integrationinthevariable, theVARfirstdifferencewillbeappliedinthisstudy.Forfurtherdescriptionregardingtwo models,itisdescribedasfollows:

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Figure4:VAR/VECMProcessandDifferences

Microsoft Excel2018 and STATA 13 are applied in this studyin order to determine and measurethemodel.

Overall,thesemethodologiesaimtoanswertheresearchquestion,provingtheproposed hypothesis, and creating the conclusion of the study according to theoretical economics, literaturereview,andempiricalstudies.

a. VectorAutoregressive(VAR)

BasedonStockandWatson(2014),avectorautoregressive isatimeseriesmodel whichconsistofasetofktimeseriesregressionsandtheindependentvariablearelagged

valueofallkseries. Forinstance,iftherearetwovariablesinVARmodelthatconsistsofYt DataExploration

DataTransformation (NaturalLogarithm)

StationarityTest (AugmentedDickyFullerTest)

StationerI(0)

VAR

FirstDifference _ECM

Rank Co-Integration Innovation

Accounting

ImpulseResponseFunction FED

NotStationerI(1)

MaxOrdo/lag VAR

Ordo MaxVAR

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andXt,andthelagineachoftheequationsisthesameandequalto(p),VAR(p),then the

equationsareasfollows:

𝑌_𝑡 = 𝛽₁₀ + 𝛽₁₁𝑌_𝑡−1 + ⋯ + 𝛽_1𝑝𝑌_𝑡−𝑝+ 𝛾₁₁𝑋_𝑡−1+ ⋯ + 𝛾_1𝑝𝑋_𝑡−𝑝+ 𝜇_1𝑡……...(1)

𝑋_𝑡 = 𝛽₂₀ + 𝛽₂₁𝑌_𝑡−1 + ⋯ + 𝛽_2𝑝𝑌_𝑡−𝑝+ 𝛾₂₁𝑋_𝑡−1+ ⋯ + 𝛾_2𝑝𝑋_𝑡−𝑝+ 𝜇_2𝑡……..(2)

Thecoefficientofthisequationisrepresentedwithβand𝛾,and𝜇1𝑡and𝜇2𝑡aretheerror

terms.

AccordingtoGujarati(2007),theVARmodelhasseveraladvantagescomparedto theothermodelthatdescribesasfollows:

1) VARisa simplemodel,andit isnotnecessaryto determinewhetherthevariableis

dependent or independent. The variable in this model can be a dependent and

independentvariable.

2) ForecastingoftheVARmodelisbettercomparedtotheotherforecastingmodel. 3) VARcanpresentbetterinterrelationshipsbetweeneconomicvariables.

4) This model canovercome spurious regressions, thus this model can overcome the wronginterpretation.

However,this modelalsohas severalweaknesses. Accordingto Enders(2004),the VARmodelisnotbasedonthetheory,isdifficulttodeterminethelagofthemodel,and is hardtointerprettheestimationquestion.

b. iVector iError iCorrection iModel(VECM)

Thismodelisappliedforvariableswhicharenon-stationaryinthelevel.According toStockandWatson(2014),thenon-stationarydatadescribeswhentheregressorvariable contains a unit root, and shows the non-normal distribution of unit root. In order to overcomethisproblem,theregressorneedsto betestedinthefirstdifferenceI(1).Data

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which is stationary in first difference could not represent the long-term relationship. Therefore,co-integratedVECMcouldovercomethisproblem.

AccordingtoStockJ.HandWatsonM.W(2014),iftwovariablesareco-integrated (XtandYt),XtandYtinfirstdifferencecanbepresentedintheVECMmodelasfollows:

∆𝑌_𝑡 = 𝛽₁₀+ 𝛽₁₁∆𝑌_𝑡−1+ ⋯ + 𝛽_1𝑝∆𝑌_𝑡−𝑝+ 𝛾₁₁∆𝑋_𝑡−1+ ⋯ + 𝛾_1𝑝∆𝑋_𝑡−𝑝+

𝛼₁(𝑌_𝑡−1− 𝜃𝑋_𝑡−1) + 𝜇_1𝑡 (3)

∆𝑋_𝑡 = 𝛽₂₀ + 𝛽₂₁∆𝑌_𝑡−1+ ⋯ + 𝛽_2𝑝∆𝑌_𝑡−𝑝+ 𝛾₂₁∆𝑋_𝑡−1+ ⋯ + 𝛾_2𝑝∆𝑋_𝑡−𝑝 +

𝛼₂(𝑌_𝑡−1− 𝜃𝑋_𝑡−1) + 𝜇_2𝑡 (4)

c. VAR/VECMModelDeterminationProcess

(i) UnitRoot/StationaryTesting

Inthetimeseriesmodel,thereisanimportantproblemoftestingregardingunit root(Wooldridge, 2018).This testingisthe mostimportant fortimeseriesanalysis becauseitcanovercomespuriousregression.AugmentedDickyFuller(ADF)isused fortestingstationaryofdata.Inthistesting, Wooldridge(2015)alsoassertsthatthe simple iapproach ito itesting iunit iroot ibegins iwith ian iAR(1)imodel:

𝑌_𝑡= 𝛼 + 𝜌𝑦_𝑡−1+ 𝑒_𝑡.𝑡 = 1,2 …

iThroughout ithis isection, iweletetdenote ithe iprocess ithat ihas izero imean, igiven ipast iobserved iy:

𝐸(𝑒_𝑡|𝑦_𝑡−1, 𝑦_𝑡−2, … , 𝑦₀) = 0

Inregardstothismodel,ρistheindicatorforestimatingthewhitenoisewhichis presentwhetherthevariablehasaunitrootornot.Therefore,thehypothesisaccording toWoolridge (2018)isasfollows:

H0:ρ=1,variabledatacontainsunitroot(non-stationary)

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InADFtesting,ifthecoefficientestimationofADFstatisticislowerthanthe criticalvalue(1%,5%,10%),werejectHo.Thismeansthatthereisno iunit iroot. iOnthe

other ihand, iifthecoefficientestimationishigherthanthecriticalvalue(1%,5%,10%),

weacceptHo.Thismeansthatthevariabledatacontainsunitrootornon-stationary.If thedataisnotstationaryinlevel,thefurtherADFtestinginfirstdifferenceisneeded, andalsoco-integrationtest.

Thisstationarytestwilldeterminethemodelthatwillbeusedinthisstudy.Ifthe model isstationary inthe levelI(0), the modelthatwill beused is VAR. Ifdata is

istationary iin ithe ifirst idifferenceI(1)orseconddifferenceI(2),co-integrationtesting

isneeded iin iorder ito icapture ithe ishort-termand ilong-term irelationship ibetween ithe

variable. If there is any co-integration between variables, thus theiVector iError iCorrection iModelcanbeusedinthisstudy.Moreover,ifthereisnoco-integrationin

firstdifferenceI(1),themodelthatwillbeusedinthisstudyisVARfirstdifference.

(ii) Co-IntegrationTest

Co-IntegrationtestwillbetestedifthedatavariableisnotstationaryinlevelI(0). According to Engle and Granger (1987), the co-integrated test is variable which consistsoftwoormorethanpresentthecommonstochastictrend. iThe ico-integration

testaimstoidentifythe ilong-termrelationshipof ivariables.Severaltestingmethods

that can be used consist of Johansen Co-integration Test, Engle-Granger

Co-integrationTest,andCo-integrationRegressionDurbin-WatsonTest. (iii) DeterminingLag-Length

Indetermininglag,itdependsonthe inumberof ivariablesthatare iincluded iin ithe imodelVARorVECM.Ifthereare5 ivariables iin itheVAR/VECM model,the

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determining VAR lag can be determined using F-test information criteria. In

determining lag length, there are several tests that can be used such as “iAkaike

iInformation iCriterion (AIC), iSchwarz iInformation iCriterion(SIC),andi

Hannan-iQuinn iCriterion(HQ)”.Thelag-lengthisdeterminedbytheestimatedlength𝑝̂that

minimizeBIC(p)thatdescribetheequationasfollows:

𝐵𝐼𝐶(𝑝) = ln ⌈𝑑𝑒𝑡 (∑ 𝑢̂)⌉ + 𝑘(𝑘𝑝 + 1)ln (𝑇)

𝑇 (iv) iGrangeriCausality iTest

Thistest isusedinordertoidentifycausalityrelationbetweenvariables.This testingwasinventedbyGranger(1969)whichassertthatthistestingaimstoprovethe contributionofvariableXtowardpredictionofanotherseriesY.

(v) StabilityTestVAR

Thetest stability VAR is important inorder to validate the impulseresponse function(IRF).Thistestwillmeasuretherootofthecharacteristicpolynomial.Ifthe rootofpolynomialvalueisbetweentheunitcircle,thusIRFresultwillbevalidandthe VARmodelisstable.

(vi) iImpulse iResponse iFunction(IRF)

iImpulse iResponse iFunction(IRF)isappliedtoovercomethedifficultiesof

interpretationofthe VAR/VECM estimation model. This functionwill explain the

ieffectofthe ishockinoneof ithe iendogenic ivariablesinthepresenttimeandfuture

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IV. EMPIRICALRESULTANDDISCUSSION

IV.1 iANALYSIS iOF iTHE iIMPACT iOF iEXCHANGE iRATE iTOWARD iTRADE iBALANCE iIN iNATIONAL iLEVEL”

a. iDynamics iof iExchange iRateand iTrade iBalanceinIndonesia

During2000-2018,severalexternalshockssuchastherecoveryofthe iAsian iFinancial iCrisisinearly2000,and iGlobal iFinancial iCrisischallenged itheeconomicperformanceof

Indonesia.Thisgraphpresentsthetrendofthe ireal iexchange irate iand itrade ibalance ifrom

2000q1to2019q4.

Figure5: iExchange iRateand iTrade iBalanceinIndonesia

Source: Bank Indonesia, 2018

In early 2000, Indonesia encountered an ieconomic irecovery period from the iAsian iFinancial iCrisis.Inthistime,therealexchangeratebetweenRupiah(IDR)andDollar(USD)

wasabout9000-9500IDR/USD.Intradeactivities,theincreaseinexportperformanceinoil andgascontributed to therise of thetrade balance.The increase ofthe trade balance was supportedbytheincreaseofexportactivityinthecommodityindustryandthemaincommodity ofelectronic(BankIndonesia,2002).

-4000,00 -2000,00 0,00 2000,00 4000,00 6000,00 8000,00 10000,00 12000,00 0,00 2000,00 4000,00 6000,00 8000,00 10000,00 12000,00 14000,00 16000,00 20 00 q1 20 00 q4 20 01 q3 20 02 q2 20 03 q1 20 03 q4 20 04 q3 20 05 q2 20 06 q1 20 06 q4 20 07 q3 20 08 q2 20 09 q1 20 09 q4 20 10 q3 20 11 q2 20 12 q1 20 12 q4 20 13 q3 20 14 q2 20 15 q1 20 15 q4 20 16 q3 20 17 q2 20 18 q1 20 18 q4 REER TB

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AccordingtoBankIndonesia(2007)theincreaseofglobalinterestrate,theincreaseofthe oilprice,andtheshiftingoftheperceptionofcapitalflowtriggeredvolatilityofmonetaryand rillsectorinIndonesiaat2005.Inthemonetarysector,rupiahdepreciatedfrom8000IDR/USD toabout9300IDR/USDandthesharpdecreasingofthetradebalancefrom2004to2005.

In 2007-2008, the slow growth of the world economy, the impact of the European

economic crisis and sub-prime mortgage in the US significantly affected the Indonesian Economy. According to Bank Indonesia (2009), the real exchange rate of Indonesia was depreciated5.4percentduetothedecreaseofglobaldemand,thereforethe tradebalancefell from2007-2008.

In2012, thespeculation ofGreece is exit from the EuropeanUnion, andquantitative easingchapter3fromTheFedsignificantlytriggeredthefluctuationofrupiah.Thisshockalso impacted the decrease of the term of trade Indonesia, therefore, the fall in the value of Indonesia’sexportandthedeficitoftradebalance.From2012to2018,eithertradebalanceto

exchange rate were worsened due to the recovery of the global economy or raising of

protectionismfromadevelopedcountry.

From 2012 to 2018, this period showed the high fluctuation from trade balance and exchangerateIndonesia.Rupiahremainedtodepreciateeveryperiodandthefallinthevalue oftradebalance.Rupiahcontinuedtodepreciatesincethefedrosethefedratefortheeconomic recoveryoftheUSAandthedecreaseoftradebalanceduetotheraisingofprotectionism.

According to the explanation above, these external shocks determine exchange rate fluctuation,andthenitwillimpacttotradebalanceinIndonesia.

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➢ DynamicofExportandImportofIndonesia

ExportandimportperformancesinIndonesiamovedalongsideeachother.Thetrendof bothtradingactivityroseeveryyearandreachedapeakin2011,thenstarttofallfrom2012.In 2012,thetradebalanceofIndonesiawasadeficitduetohighimportgoodsandservices.

Figure6:Export,Import,TradeBalanceIndonesia

Sources:BankIndonesia,2018

Theslowdownoftradeperformancefrom2012to2018wasdeterminedbythedeclining worldpricesandtheriseofdomesticdemand.Moreover,thegovernmentpolicyorientedon infrastructurealso triggeredthedemandfor importedgoodsforaninfrastructureproject in Indonesia(BankIndonesia,2019).

b. DataPre-Estimation

Accordingto the ADF test, the test showed that all variableswere stationary in first difference,althoughsomevariableswerestationaryinlevel.Itwasindicatedbythep-valueof thevariablewaslessthanthesignificantlevelin5percent,andt-statisticinADFtestingwas smallerthanthecriticalvalue.

-20000000,0 -10000000,0 -10000000,0 20000000,0 30000000,0 40000000,0 -50000000,0 100000000,0 150000000,0 200000000,0 250000000,0

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Variable “iLevel” “iFirst iDifference”

Non-iConstant Intercept and iTrend Non-iConstant Intercept iand iTrend Ln_REER 0.721 0.064 0.0000* 0.000* Ln_TB 0.040* 0.0000* 0.0000* 0.000* Ln_GDP 0.000* 0.765 0.0000* 0.000* n.b:*significantin5%

Inaccordancewith the result of stationary test,co-integrationtestingwasrequiredin order to examine the ilong-run irelationship ibetween ivariables. According to ithe i

co-integrationtest byJohansen-Test,thetestsevidencedtherewasnoco-integration.Inother words,thevariablescouldnotdescribethelong-runrelationship,hence,thisstudywouldfocus ontheshort-runanalysis.ThisresultalsowasemphasizedbyOskoeeandHarvey(2009, p.10) that “the imajorityi of the cases a ireal idepreciation of irupiahi has short-run effects in

Indonesia”.

Moreover,determiningthelag-lengthisnecessaryforfurtherprocess. iAccording ito ithe

AkaikeInformationCriterion(AIC),thecriteriashowedtheoptimumlag-lengthforthismodel islag1.

c. Engle-iGrangerCausalityTest

Inregardtodeterminingthe icausality(Engle-Granger)ofvariables,thisstudyappliedthe

Engle-GrangerWaldTest.Accordingtothetest,itindicated ithe ireal iexchangerateaffected ithe itrade ibalance, and this was proved by the p-value of the variables were below the

significant level(5 and 10 percent), therefore it rejected H0. On theother hand, the trade balancedidnotcausetherealexchangerate.Moreover,thetestalsosignifiedtherewasnot ia isignificantcausal irelationship ibetween iGDP, itrade ibalanceand iexchange irate.

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Inregardtothisresult,thisstudycannotprovehypothesisnumber1,“ithere iis ia

bi-directional irelationship ibetween iexchange irateand itrade ibalance”.Thepreviousstudy

on ithe irelationship ibetween iexchange irate iand itrade ibalancealsofoundthatthere ino icausal irelationship ibetween iexchange irate iand itrade ibalance (Mostafa & Rashid,2014; Shao

Ziwei,2008).

Table3.GrangerCausality

NullHypothesis P-value

LN_REERt idoes inot iGranger icauseLN_TBt 0.326

LN_REERt idoes inot iGranger icauseLN_GDPt 0.502

LN_TBtidoes inot iGranger icauseLN_REERt 0.032*

LN_TBtidoes inot iGranger icauseLN_GDPt 0.355

LN_GDPtidoes inot iGranger icauseLN_REERt 0.354

LN_GDPt idoes inot iGranger icauseLN_TBt 0.733 *) significant in 5%

d. VARShort-Run

Inaccordancewiththeresultoftheco-integrationtest,itshowedthatthevariablecould not capture the long-run relation. Therefore, the estimation of the short-run variance autoregression(VARfirstdifference)wasappliedinthisstudy.Accordingtotheestimation, only the independent variable from D.Ln_Tb equation could show the significant effect

ibetween iexchange irate iand itrade ibalance.Itwasshownbythep-valueofchi-squarewas

below5percent(0.0126). Inregardtotheequation,therealexchangeratefromtheprevious

period(t-1, andt-2) hada significant ieffect ion ithe itrade ibalance iin ishort-run. Thereal

exchangeratefromt-2hada ipositive ieffecton ithe itrade ibalance,, butithe ireal iexchangerate

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Table4:VARShortRunEstimation

e. iImpulse-iResponse iFunction(IRF)

Theimpulse-responsefunctionisthemainpartofthisstudy.Accordingtotheunitroottest, thisstudysimplycancaptureshort-runanalysis.Therefore,theresultofthisimpulse-response willpresenttheshort-runanalysis.

Beforethisstudy willidentifytheresultoftheimpulse-responsefunction,itisnecessaryto examinethestabilityoftheVARmodel.Accordingtothestabilitytest,itdenotedtheVARwas stable,anditwasshowedbytheeigenvalueliesinsidetheunitcircle.Thus,theresultofthe impulseresponsefunctionwasvalid.

Impulseresponsefunctionpresentsthemagnitudeandtheperiodoftimewhenoneofthe variableswillshock (impulse)byoneunitofstandarddeviation.InregardtoIRF,thisstudy willfocusontheimpulsefromtherealexchangeratevariabletowardtradebalance.

Variable D.Ln_TB D.Ln_REER D.Ln_GDP D.Ln_TB(-1) -0.125 (0.116) 0.084 (0.727) -0794 (0.106) D.Ln_TB(-2) -0.100 (0.112) 0.047 (0.070) -0.016 (0.102) D.Ln_REER(-1) -0.474 (0.192)* -0.028 (0.120) -0.039 (0.175) D.Ln_REER(-2) 0.408 (0.031)* 0.029 (0.118) 0.003 (0.172) D.Ln_GDP(-1) 0.136 (0.1300) 0.067 (0.081) 0.020 (0.118) D.Ln_GDP(-2) -0.009 (0.230) -0.061 (0.806) -0.437 (0.117) _Cons -0.009 (0.009) 0.004 (0.005) (0.029) (0.08) R-sq 0.1818 0.032 0.0153 Chi2 16.216 2.44 1.13 Prob 0.0126 0.8748 0.9799

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Figure7:TheResultofImpulseResponseFunction

Accordingtotheimpulseresponsefunction,ifthereisashock(impulse)fromexchangerate aboutoneunitofstandarddeviation,itwillimpacttothedecreaseoftradebalanceabout0.45 percentinshortrun.Itisevidencedbythesharpdecreasinginperiod1,thenitstartstoincrease untilperiod2.Moreover,theresponseofthisshockwilltake2periodor6months,anditwill stabilizeafter6months.Thisstudyindicatestheimpulseofexchangerateonlywillimpact significantlyinshort-runwhichis6month,andthisresultshowedthattherewasnoJ-curvein Indonesia.Thereforethisstudycannotfullyprovethehypothesis“Thedepreciationof exchangeratewilldeteriorate itrade ibalance iin ishort-run, ibut iitwillimprovetrade

balanceinlong-run”.Theresultalignedwiththepreviousstudythathasbeenconductedin Indonesia. Ramadhona (2016) conducted the research of J-curve between Indonesia and tradingpartner,andtheresultindicatedJ-curvedoesnotexistinbetweenIndonesiaandtrading partnerexceptwithJapan.

Furthermore, several studies also evidence there is no existence of J-curve in several countriessuch asChinaand Japan(Shao,2007; Ahmad&Yang, 2004).In China, thereal devaluationoftherealexchangeratewillimprovethetradebalance,andJ-Curvedidnotexist duetothereisnonegativeshort-runresponsefromthedevaluationofYuan(Ahmad&Yang,

-1 -.5 0 .5 1 -1 -.5 0 .5 1 -1 -.5 0 .5 1 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8

varbasic, D.ln_gdp, D.ln_gdp varbasic, D.ln_gdp, D.ln_reer varbasic, D.ln_gdp, D.ln_tb

varbasic, D.ln_reer, D.ln_gdp varbasic, D.ln_reer, D.ln_reer varbasic, D.ln_reer, D.ln_tb

varbasic, D.ln_tb, D.ln_gdp varbasic, D.ln_tb, D.ln_reer varbasic, D.ln_tb, D.ln_tb

95% CI

impulse response function (irf)

step

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2004).Inaddition,Shao(2007)claims iexchange irate iand itrade ibalancehadnotasignificant

long-runrelationship,henceJ-curvedidnotexistinJapan.

f. Discussion

Inaccordancewiththeresult,thisstudycannotcapturethelong-runrelationship.Therefore, it seems thisstudycannotprovethehypothesis1and2.Inaddition,theidentificationofthe tradecharacteristicsinIndonesiaisnecessarytoexaminetheintuitivereasonbehindthisstudy.

Figure8:TradecompositionofIndonesiaandTradePerformanceonManufactured

Commodity

According to the trade composition in Indonesia, manufactured commodity dominate export by(48.6%)comparedtoanother commodity, agriculture by(13.3%), andminingby

(2.3%), respectively. Furthermore, the characteristic of manufactured commodity in

Indonesia showed the export of commodity in Indonesia depends on imported goods.

Therefore,ifthereisashockintheiexchangeirate, ithe itradeibalance iwilllargelydeteriorate

iin ithe ishort irunratherthan iin ithe ilongrun,anditalignedwiththeresultofthisstudy.

13.3%

48.6% 2.3%

Mining, Oil and gas Industrial Products Agricultural Product

Souce:MinistryofFinance,2013

-20.000.000 40.000.000 60.000.000 80.000.000 100.000.000 120.000.000 140.000.000 160.000.000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Ekspor Impor

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IV.2 iANALYSIS iOF iTHE iIMPACT iOF iEXCHANGE iRATETOWARD iTRADE

iBALANCE iINCOMMODITYLEVEL

Indonesia’s exportandimportofarecontributedby3maincommodities consistofagriculture, manufacture, and mining (CentralStatisticsBureauofIndonesia,2018). Therefore,thedynamic

ofexportandimportIndonesiaincommoditieslevelexplainedasfollows: a. TradePerformanceofAgriculturalCommodity

Indonesia’sagricultureexport performance indicated astablepositivetrend from 2005-2018.Ontheotherside,importagriculturesectorshowedhighfluctuationandpositivestrend comparedtoIndonesia’sagricultureexportperformance.

Figure9:TradeinAgricultural Commodity

Sources: Bank Indonesia, 2018

Indonesia’s import agriculture commodity increased dramatically compared to export agricultureperformance.AccordingtotheMinistryofFinanceofIndonesia(2014)andBank Indonesia(2019),agricultureimportgoodsroseduetohighdomesticdemandforagriculture goodsfromIndonesiasuchasrice,andspices.Moreover,thedecliningofagriculturetermof trademadethevalueofagricultureexportdecreasing.

-2.000.000 4.000.000 6.000.000 8.000.000 10.000.000 12.000.000 14.000.000 16.000.000 18.000.000 Ekspor Impor

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b. TradePerformanceofManufactured Commodity

Manufactured commodityisthe fundamental and main commoditytowardIndonesia’s economy.Manufactured exportperformanceshowedafluctuationtrendfrom2005to2018.In 2009,Indonesia’smanufactureexportperformancedroppeddramatically,thenit rose until reachpeakin2012,and fell in2016.Then,tradebalancestartedtorisefrom2017.Furthermore. Indonesia’smanufactureimportperformancemovedalongsidewiththeexportofmanufacture from2005-2018.

Figure10:TradeinManufacturedCommodity

Sources:BankIndonesia,2018

Accordingtothegraphabove,theexportofmanufactureinIndonesiadependsonimported rawgoods fromanother country (MinistryofIndustry of Indonesia, 2018). This was also emphasized by Bank Indonesia (2019) that the raw material imported goods were raised becausetheysupportinputproductioninordertofulfilldomesticdemandandexport.

c. TradePerformanceofMiningCommodity

Indonesia hasabundantofmineralandoilresources,thereforetheminingcommodityin Indonesiaisoneofthebiggestcontributiontowardtheeconomy.Exportofminingcommodity increasedintheperiod2005andreachedapeakin2011,thenitdeclinedin2016andslightly

-20.000.000 40.000.000 60.000.000 80.000.000 100.000.000 120.000.000 140.000.000 160.000.000 Ekspor Impor

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increasedin2018.GovernmentofIndonesiaimposedactno4regardingtheprocessingand purifiedofmininginIndonesia,thusitdeceasedtheexportperformanceofIndonesiain2012 (CentralStatisticsBureau,2018).Inaddition,BankIndonesiaclaimsthedecreaseoftheexport miningsector,becauseofthefallinthepriceofcoalintheinternationalmarket.

Figure11:TradeActivityintheMining Commodity

Sources: Bank Indonesia, 2018

However, Indonesia imports small size of mining commodity from another country,

becauseIndonesiacanfulfillthedomesticdemandformining.

d. DataPre-Estimation

Beforethisstudywill identifytheJ-curveincommoditylevel, iit iis inecessary itoexamine

thelevelof iunit iroot itest,byADFtest.Accordingtothetest,thevariablesincommodities

werestationaryinlevel I(0).Itwasindicatedbythep-valueofthevariableisbelow5percent significantlevel.Therefore,theanalysisinthecommoditylevelcouldcapturelong-run, and analysisvarianceautoregression (VAR)willbeappliedinthisanalysis.Thestationarytable waspresentedasfollow:

-10.000.000 20.000.000 30.000.000 40.000.000 50.000.000 60.000.000 70.000.000 Ekspor Impor

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Variable Level Non-Constant Ln_REER 0.007* Ln_TBAgriculture 0.007* Ln_TBManufacture 0.046* Ln_Mining 0.014* n.b:*significantin5%

Moreover,VARstabilizationtestwillberequiredtoexaminethevalidityof IRFandFEVD thenextsection.Accordingtothetest,alltheeigenvalueslieinsidetheunitcircle,therefore, VARwassable.Inaddition,The optimum lag-lengthisat1.

e. iImpulse iResponse iFunction(IRF) iand iForecast iError iVariance iDecomposition

(FEVD)”

AlignedwiththeprevioussectiononIRFforthenationallevel,thissectionalsoisthemain important part of this study.Inthissection, thereare3figureswhichwillshowtheshock (impulse)of ithe ireal iexchange iratetoward itrade ibalancein3mainsectorsinIndonesia.

Figure12:Impulseof iReal iExchange iRatetoward iTrade iBalanceinAgriculturalCommodity

Accordingto IRFinagriculturalcommodity,if ithe ireal iexchange iratedepreciatesithe itrade ibalanceofagriculturalcommoditywillgraduallydeteriorateeveryperioduntilthe8th

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periodor2years.AccordingtoFEVD,theworstdeteriorationofagriculturaltradebalanceis 0.14 percentat 8thperiodorat2ndyears.BasedonIRF,wecanconcludetheJ-Curvedoesnot existinagriculturecommodity.

Figure13:Impulseof iReal iExchange iRatetoward iTrade iBalanceinManufacturedCommodity

Furthermore,manufacturedcommodityhashighelasticitytoward iexchange irate.Hence,

if ithe ireal iexchange irate idepreciates, ithetradebalance iofmanufacturedcommoditywill

graduallydeteriorateeveryperioduntilthe8thperiodor2years.BasedonFEVD,theworst pointis0.118 percentat 8th_period_or _in₂nd _years._In_accordance_with_the_analysis, _we_can

concludethatJ-Curvealsodoesnotexistinmanufacturedcommodity.

Figure14:Impulseof iReal iExchange iRatetoward iTrade iBalanceinMiningCommodity

Incontrast,miningcommodityresponsedifferentlywhenthereisdepreciationofthereal exchangerate. iIfithereal iexchange irate idepreciates,thetradebalancewillgraduallyincrease

until8th_{period or at 2}nd_year._Based_on_FEVD,_the_highest_point_is_{0.99 percent}_at₈th_period

or

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f. Discussion

Inaccordancewiththeanalysisabove,wecanexaminethatagriculturalcommodityget higherdeteriorationcomparedtomanufactured commodity.Incontrast,miningcommodity willincreaseifthereisdepreciationoftherealexchangerate.Inaddition,J-curvealsodoesnot existamongthethreecommodities.Therefore,thisstudyalsocannotprovethehypothesis2, “thedepreciation ofexchange rate will deteriorate itrade ibalance in ishort-irun, but it will iimprove itrade ibalancein ilong-run” in commodity level.

IfweexaminethecharacterofcommoditytradeinIndonesiaintheprevioussection,we canidentifyeverycommodityhasitsowncharacteristic.Hence,wecanidentifytheintuitive reasonbehindtheresultofthisstudy. Inagriculturalcommodity,theimportofagricultural commodityislargerthanexport,andtheexportandimportofmanufacturedcommoditymove alongsideeachother.Therefore, if ithedepreciationof ithe ireal iexchangeratehappens,the

impactofagriculturalcommodityislargerthanmanufacturedcommodity.Ontheotherhand, theexportofminingcommodityisbiggerthanimport.Therefore,thisstudyfoundthatifthere is idepreciation iof ithe ireal iexchangerate,itwillimprove ithe itrade ibalance.

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V. CONCLUSION

V.1 THEMAINCONCLUSION

Accordingtothisstudy,itcanbeconcluded,asfollows:

1. The irelationship ibetween itrade ibalanceand iexchange irateinIndonesiaisone-way.Only

the iexchange irateaffects ithe itrade ibalance iinIndonesia.Therefore,thisstudycannot

provethehypothesis “thereisabi-directional irelationship ibetween iexchange irateand itrade ibalance”.”

2. Ifthereisashock(impulse)fromexchangerateaboutoneunitofstandarddeviation,itwill leadtoadecreaseoftradebalanceabout0.45percentintheshortrun.

3. iThe itrade ibalancewilldeteriorateuntil6months,thenitwillbestableafter6months.

4. ThereisnoJ-curveconditioninIndonesiabecausethereisnolong-runrelationshipatthe national level. Therefore, this studycannot prove the hypothesis “The depreciation of exchangeratewilldeteriorate itrade ibalancein ishort-run,butitwillimprove itrade ibalance

in ilong-run”

5. In the commodity level, agricultural commodity gets higher deteriorationcompared to

manufactured commodity. However, mining commodity will increase if there is

depreciationofthe iexchange irate.

Furthermore,in iaccordancewiththetradecharacteristicinIndonesia,the idepreciationof ithe ireal iexchange iratewillgivegreaterimpact,ifthevolumeofimportislargerthanexport.

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V.2 POLICYRECOMMENDATION

Inresponsetotheconclusionofthisstudy,wecanconcludesomepolicy recommendation,asfollows:

1. TheCentralBankshouldfocusonovercomingtheimpactonthevolatilityofexchangerate inshort-term,suchasinterferingassetandmoneymarketduetotheimpactislargerinthe shortrun.

2. Hedgingcouldbethealternativefortheexportertomitigate ithe ivolatilityofthe iexchange irateintheshortrun.

3. Inregardtocommoditylevel,it isnecessarytohaveimport-substitutiongoodsinagriculture andmanufactureandincreasinglocalproductiononagriculturebyempoweringMSMEs.In miningcommodity,itneedstoincreasevalueaddedtotheminingcommodityinordertogain highertradebalanceinthelong-term.

V.3 RECOMMENDATIONFORFURTHERSTUDIES

Thisstudyhasfilledthegapfromthepreviousstudieswhereasaddsthe ianalysis iof ithe iimpact iof iexchange iratetoward itrade ibalanceincommoditylevel. iIncontrast,thisstudystill

hasalimitation,anditcanbeenhancedinfurtherstudy.

Forthefurther studywhich hasthesamescopeof analysis,furtherstudiescanconsider addingthepriceoftheimport.Therefore,itcanidentifytheexchangeratepass-through,andit canmeasurethevalueand volumeeffect.Furthermore, thefurther studyalsocanconsider addingworldGDPasacontrolledvariable.

Moreover,furtherstudycanconsideranalysingtheimpactofdepreciationexchangerate beforeandafterthecrisis byadding ia idummy ivariableinthe model. Anothertimeseries

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34

REFERENCE

“Ahmad,J.&YangJ.(2004).EstimationofJ-CurveinChina.East-WestWorkingPapers,No.67.”

Bank Indonesia. (2002). Laporan tahunan perekonomian indonesia 2001 [Indonesia yearly

economicreport2001].Retrievedformhttps://www.bi.go.id.

Bank Indonesia. (2009). Laporan tahunan perekonomian indonesia 2008[Indonesia yearly

Bank Indonesia. (2013). laporan tahunan perekonomian indonesia 2012 [Indonesia yearly

Carniero,F.(1998).“Temporalcausalitybetweentheexchangerateandthetradebalance:The caseofbrazil”.SeminariosdePesquisaEconomica[EconomicResearchSeminar]. CentralStatisticsBureauofIndonesia.(2018).Analisiskomoditiekspor2011-2017[Analysisof

exportcommodities].Catalog,No.8202005,ISSN:2085–6008.

“Engle, R.F, & Granger, W.J. (1987). Co-integration and error correction: Representation,

estimation,andtesting.Econometrica,Vol.55,251–276.”

“Enders,W.(2004).Applied econometrictime seriessecondedition.Boston:JohnWileyand Sons.”

Gujarati,D.(2007).IntroductionofEconometric.Jakarta(ID):Erlangga.

Hook, L.S., &Boon, T.H.(2000).RealexchangeratevolatilityandMalaysianexporttoitsmajor

(43)

35

Krugman.(2012).Internationaleconomics:Theoryandpolicy.Boston:Pearson.

Mankiw.(2012).Macroeconomics.NewYork:WorthPublisher.

McKinnon.(1990).Theexchangerateandthetradebalance:Insularversusopeneconomies.Open

EconomicReview,Vol.1,No.17-37.

Ministry of Finance of Indonesia. (2014). Dealing with current account deficit: Indonesia’s

Experience for OECD Development Center. Retrieved from

http://www.oecd.org/eco/surveys/CAD-Indonesia.pdf.

MinistryofIndustryofIndonesia.(2018).Ringkasaneksekutifperkembanganekspordanimport

industrypengolahan.[Executivesummaryofthedevelopmentofexportandimport

in manufacturing industry]. Retrieved from

http://www.kemenperin.go.id/download/19434/Laporan-Ekspor-Impor-Hasil-Pengolahan-2018-Juni.

Mostofa, G.,&Rashid. A.(2014).“ExchangerateandtradebalanceofBangladesh:Causalityand

co-integration analysis. The 1st Conference on Entrepreneurship and SMEs’s

DevelopmentandManagement(ICESDM2013)”,Vol.9No.02.

Onafora, O.(2003).ExchangerateandtradebalanceinEastAsia:isthereaJ-curve.Economic

Bulletin,Vol.5,No.18pp.1−13.

Oskooee, M.B,&Baek, J.H.(2016).“Doexchangeratechangeshavesymmetricorasymmetric effectsonthetradebalance?EvidencefromU.S.–Koreacommoditytrade.Journalof

AsianEconomics,Vol.45,pp.15-30.”

“Oskooee, M.B.,&Harvey H.(2009).TheJ-curve:Indonesia vs.HerMajortradingpartners.

(44)

36

Ousseini, A.M, Hu, X.,&Aboubacar, B.(2017).WAEMUtradeandcurrentaccountbalance deficitanalysis:Apanelvarapproach.Theoreticaleconomicsletters,Vol.7,

pp.834-861

Ramadona. (2016).Pengaruhperubahannilaitukarterhadap neracaperdaganganindonesia

[theimpactofexchangeratevolatilityonIndonesiantradebalance](Master’sThesis). RetrievedfromIPBRepositoryhttp://repository.ipb.ac.id/handle/123456789/82685. Sanadheera,Y.W.(2015).ImpactoftheeffectiveexchangerateonthetradebalanceofSriLanka:

Evidencefrom2000to2013.TheJournalofAppliedEconomicResearch,Vol.9No.2.

Sezer,S.(2017).Theeffectsofrealexchangeratesandincomeonthetradebalance: Asecond generationpaneldataanalysisfortransitioneconomiesandTurkey.Theoreticaland

AppliedEconomics,Vol.24,No.2(611).

Shao, Z.(2008).Exchangeratechangeandtradebalance:Anempiricalstudyofcaseofjapan.

(Master Thesis). Retrieved from library Singapore Management University

http://ink.library.smu.edu.sg/etd_coll.

Stock, J.H, & Watson, M.W..(2014).Introductiontoeconometrics:Thirdeditionupdate.New

York.Pearson.

Stucka, T. (2004). The effect of exchange rate change on the trade balance in Croatia. IMF

WorkingPaper/04/65.

Taylor, M.P,&Sarno, L.(1998).Thebehaviourofrealexchangeratesduringthepost-bretton woodsperiod.JournalofInternationalEconomics,No.46.

The Federal Reserve. (2018). Selected interest rate. Retrieved from

https://www.federalreserve.gov/releases/h15/

Thorbecke, W.. (2006). The Effect of exchange rate changes on trade in East Asia. RIETI

(45)

37

Thirlwall, A.P, & Gibson H.D..(1986). Balanceof payments theory and the united kingdom

experience4thedition.London:TheMaculusalanPressLtd.

UNCTAD. (2018). UNCTAD’s Trade Indicator. Retrieved from

https://unctadstat.unctad.org/wds/ReportFolders/reportFolders.aspx.

Wooldrige, J..(2018). Introductory econometrics: A modern Approach. Boston: Cengage

Learning.

World Bank.(2018). Global financial markets and the rupiah exchange rate. Retrieved

from https://www.worldbank.org/en/news/opinion/2018/10/03/global-financial-markets-and-the-rupiah-exchange-rate.

World Trade Organization.(2018).World trade statistical review 2018. Retrieved form

https://www.wto.org.

Vural, B.M.(2015).Effectofrealexchangerate ontradebalance:Commoditylevelevidence fromTurkishbilateraltradedata.ProcediaEconomicsandFinance,Vol.38,

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APPENDIX 1. The Stationarity Test

a) LN_REER (LEVEL)

i. Intercept and Trend

ii. Non-Constant, Regress

.

L1. .0003953 .0011008 0.36 0.721 -.001798 .0025887 ln_reer

D.ln_reer Coef. Std. Err. t P>|t| [95% Conf. Interval] Z(t) 0.359 -2.610 -1.950 -1.610 Statistic Value Value Value Test 1% Critical 5% Critical 10% Critical Interpolated Dickey-Fuller Dickey-Fuller test for unit root Number of obs = 75 . _cons .4665482 .2439029 1.91 0.060 -.0196633 .9527598 _trend .0002114 .000297 0.71 0.479 -.0003806 .0008033 L1. -.1057983 .056217 -1.88 0.064 -.2178649 .0062682 ln_reer D.ln_reer Coef. Std. Err. t P>|t| [95% Conf. Interval] MacKinnon approximate p-value for Z(t) = 0.6640

Z(t) -1.882 -4.095 -3.475 -3.165 Statistic Value Value Value Test 1% Critical 5% Critical 10% Critical Interpolated Dickey-Fuller Dickey-Fuller test for unit root Number of obs = 75

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iii. LN_REER (First Different)

b) LN_TB :

i. Intercept and Trend

ii.. Non-constant _cons .1354808 .0409886 3.31 0.001 .0537715 .2171901 _trend -.0020514 .0006702 -3.06 0.003 -.0033875 -.0007153 L1. -.350078 .0899664 -3.89 0.000 -.5294227 -.1707332 ln_tb D.ln_tb Coef. Std. Err. t P>|t| [95% Conf. Interval] MacKinnon approximate p-value for Z(t) = 0.0125

.

L1. -.0697659 .0334396 -2.09 0.040 -.1363957 -.003136 ln_tb

D.ln_tb Coef. Std. Err. t P>|t| [95% Conf. Interval] Z(t) -2.086 -2.610 -1.950 -1.610 Statistic Value Value Value Test 1% Critical 5% Critical 10% Critical Interpolated Dickey-Fuller Dickey-Fuller test for unit root Number of obs = 75

_cons .0031675 .0048576 0.65 0.516 -.0065158 .0128509 LD. -.9626409 .113939 -8.45 0.000 -1.189774 -.7355077 ln_reer D2.ln_reer Coef. Std. Err. t P>|t| [95% Conf. Interval] MacKinnon approximate p-value for Z(t) = 0.0000

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iii. First Different

c) LN_GDP:

i. Intercept dan trend

ii. No constant regress

. _cons -.1105269 .5537461 -0.20 0.842 -1.2144 .9933458 _trend -.0011682 .0015893 -0.74 0.465 -.0043365 .0020001 L1. .0131578 .0437619 0.30 0.765 -.0740799 .1003956 ln_gdp D.ln_gdp Coef. Std. Err. t P>|t| [95% Conf. Interval] MacKinnon approximate p-value for Z(t) = 0.9963

Z(t) 0.301 -4.095 -3.475 -3.165 Statistic Value Value Value Test 1% Critical 5% Critical 10% Critical Interpolated Dickey-Fuller Dickey-Fuller test for unit root Number of obs = 75

L1. .0020304 .0004936 4.11 0.000 .0010469 .0030139 ln_gdp

D.ln_gdp Coef. Std. Err. t P>|t| [95% Conf. Interval] Z(t) 4.114 -2.610 -1.950 -1.610 Statistic Value Value Value Test 1% Critical 5% Critical 10% Critical Interpolated Dickey-Fuller Dickey-Fuller test for unit root Number of obs = 75

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iii. First Different

2. Co-Integration Test . _cons .0286058 .0078508 3.64 0.001 .0129554 .0442562 LD. -.9804669 .1181566 -8.30 0.000 -1.216008 -.744926 ln_gdp D2.ln_gdp Coef. Std. Err. t P>|t| [95% Conf. Interval] MacKinnon approximate p-value for Z(t) = 0.0000

3 21 348.33917 0.06820

2 20 345.72556 0.09738 5.2272 3.76 1 17 341.93461 0.19644 12.8091 15.41 0 12 333.8424 . 28.9936* 29.68 rank parms LL eigenvalue statistic value maximum trace critical 5%

Sample: 2000q3 - 2018q4 Lags = 2 Trend: constant Number of obs = 74 Johansen tests for cointegration

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42 3. Maximum-Lag 4. VAR-Stabilization Exogenous: _cons Endogenous: ln_tb ln_reer ln_gdp 10 398.024 21.156* 9 0.012 2.5e-08 -9.24317 -8.02397 -6.15774 9 387.447 37.462 9 0.000 2.4e-08 -9.19536 -8.09414 -6.40852 8 368.716 19.705 9 0.020 3.1e-08 -8.90048 -7.91726 -6.41224 7 358.863 8.7526 9 0.460 3.0e-08 -8.87464 -8.0094 -6.68498 6 354.487 17.574 9 0.040 2.6e-08 -9.01475 -8.2675 -7.12369 5 345.7 9.0924 9 0.429 2.5e-08 -9.02121 -8.39195 -7.42873 4 341.154 10.217 9 0.333 2.2e-08 -9.15617 -8.6449 -7.86229 3 336.045 16.3 9 0.061 1.9e-08 -9.2741 -8.88081 -8.2788 2 327.895 6.9704 9 0.640 1.8e-08 -9.29986 -9.02456 -8.60315 1 324.41 447.23 9 0.000 1.6e-08* -9.46697* -9.30966* -9.06886* 0 100.793 .00001 -2.96341 -2.92408 -2.86388 lag LL LR df p FPE AIC HQIC SBIC Sample: 2002q3 - 2018q4 Number of obs = 66 Selection-order criteria

. varsoc ln_tb ln_reer ln_gdp , maxlag(10)

.

VAR satisfies stability condition.

All the eigenvalues lie inside the unit circle. -.1922862 .192286 -.07223895 - .1938175i .206842 -.07223895 + .1938175i .206842 .3191547 .319155 -.05755388 - .4477387i .451423 -.05755388 + .4477387i .451423 Eigenvalue Modulus Eigenvalue stability condition

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5. VAR Short-run Estimation

_cons .0291166 .0084957 3.43 0.001 .0124654 .0457679 L2D. -.0437898 .117686 -0.37 0.710 -.2744501 .1868705 LD. .0206063 .1186363 0.17 0.862 -.2119166 .2531292 ln_gdp L2D. .0037076 .1729716 0.02 0.983 -.3353104 .3427256 LD. -.0391239 .175547 -0.22 0.824 -.3831898 .3049419 ln_reer L2D. .0164842 .1021927 0.16 0.872 -.1838097 .2167782 LD. -.0794426 .1061409 -0.75 0.454 -.287475 .1285898 ln_tb D_ln_gdp _cons .0043014 .0058226 0.74 0.460 -.0071107 .0157135 L2D. -.0610655 .0806575 -0.76 0.449 -.2191512 .0970202 LD. .0677215 .0813088 0.83 0.405 -.0916407 .2270837 ln_gdp L2D. .0290832 .118548 0.25 0.806 -.2032667 .2614331 LD. -.0280239 .1203132 -0.23 0.816 -.2638334 .2077856 ln_reer L2D. .0470221 .0700389 0.67 0.502 -.0902516 .1842959 LD. .0841535 .0727449 1.16 0.247 -.0584239 .2267309 ln_tb D_ln_reer _cons -.0092241 .0093149 -0.99 0.322 -.027481 .0090329 L2D. -.0009952 .1290346 -0.01 0.994 -.2538983 .2519079 LD. .1367939 .1300765 1.05 0.293 -.1181513 .3917392 ln_gdp L2D. .4082222 .1896513 2.15 0.031 .0365124 .779932 LD. -.4746956 .1924752 -2.47 0.014 -.8519399 -.0974512 ln_reer L2D. -.1003716 .1120472 -0.90 0.370 -.31998 .1192368 LD. -.1252995 .1163762 -1.08 0.282 -.3533926 .1027936 ln_tb D_ln_tb Coef. Std. Err. z P>|z| [95% Conf. Interval] D_ln_gdp 7 .062337 0.0153 1.136337 0.9799 D_ln_reer 7 .042723 0.0324 2.443268 0.8748 D_ln_tb 7 .068348 0.1818 16.21608 0.0126 Equation Parms RMSE R-sq chi2 P>chi2

Det(Sigma_ml) = 2.25e-08 SBIC = -7.861766 FPE = 4.01e-08 HQIC = -8.258083 Log likelihood = 332.0043 AIC = -8.520665 Sample: 2000q4 - 2018q4 No. of obs = 73 Vector autoregression

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44 6. Impulse Response Engle-Granger -1 -.5 0 .5 1 -1 -.5 0 .5 1 -1 -.5 0 .5 1 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 varbasic, D.ln_gdp, D.ln_gdp varbasic, D.ln_gdp, D.ln_reer varbasic, D.ln_gdp, D.ln_tb

varbasic, D.ln_reer, D.ln_gdp varbasic, D.ln_reer, D.ln_reer varbasic, D.ln_reer, D.ln_tb

varbasic, D.ln_tb, D.ln_gdp varbasic, D.ln_tb, D.ln_reer varbasic, D.ln_tb, D.ln_tb

95% CI impulse response function (irf)

step

Graphs by irfname, impulse variable, and response variable

D_ln_gdp ALL 1.187 2 0.552 D_ln_gdp D.ln_reer .11603 1 0.733 D_ln_gdp D.ln_tb .85983 1 0.354 D_ln_reer ALL 1.3398 2 0.512 D_ln_reer D.ln_gdp .45119 1 0.502 D_ln_reer D.ln_tb .96582 1 0.326 D_ln_tb ALL 5.0948 2 0.078 D_ln_tb D.ln_gdp .85507 1 0.355 D_ln_tb D.ln_reer 4.5983 1 0.032 Equation Excluded chi2 df Prob > chi2 Granger causality Wald tests