石油供需冲击对粮食价格的影响（英文）

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https://wwwtandfonlineAPPLIEDECONOMICS2022,VOL.54,NO.10,1199–1211httpsdoiorg3KEYWORDSFood-KEYWORDSFood-Grainprices;oilprice;Markov-switching;structuralhocksJELCLASSIFICATIONQQ0Impactofoildemandandsupplyshocksonfood-grainprices:aMarkov-switchingapproachMd.AbdurRahmanForhadaandMdRafayetAlambNUSAABSTRACABSTRACTThisstudyexaminestheimpactofstructuraloildemandandsupplyshocksonthepricesoffourfood-grains:corn,rice,soybeanandwheat.Wefirstderivetwooildemandshocks-oneisduetothechangeintheglobaleconomicconditionandtheotherisoil-market-specific-andanoilsupplyshockthatreflectsthechangeinglobaloilproduction.Thestructuralshocksarethenusedinthelinear,andnonlinearMarkov-switchingmodelstoexaminetheireffectsonthefourfood-grainprices.Theresultsshowthatanunanticipatedoilsupplyshockdoesnotaffectpriceofanyofthefourfood-grainssignificantly.Anincreaseinthedemandforoil,whichisnotinducedbythechangeinglobaleconomiccondition,increasesonlythecornpriceinthelessvolatilecornmarket.Animprovementofglobaleconomicconditionimpactswheatandcornpricesinhighvolatilityregimes.However,oilshocksthatareolderthanonemonthusuallydonotaffectthefood-grainprices.I.IntroductionTheaveragepricesoffood-grainshavebeensig-nificantlyhighersince2006comparedtotheirpre-2006levels,andsohasbeenthevolatilityofthefood-grainprices.Recently,inJuly2021,thecostofbulkcarriersthatmovegrainsandoilseedshasroughlydoubledcomparedtotheir2020levels,thankstotherisingoilpriceamongothers.Forexample,inJune2021,thecostoftransportinggrainsfromAustraliatoSoutheastAsiaincreasedto$30atonnefrom$15previousyear.Inthesamemonth,thecostoftransportinggrainsfromU.S.PacificNorthwesttoAsiaincreasedto$55atonnefrom$25previousyear.1Therisingcosttotrans-portfood-grainsisfuelingthealreadydecade-highfoodinflation.WorldfoodpricesroseinMay2021attheirfastestmonthlyrateinmorethanadecade,postinga12thconsecutivemonthlyincreasetohittheirhighestlevelsinceSeptember2011.2InMay2021,FAO’scerealpriceindexrose6.0%month-on-monthand36.6%year-on-year,Maizebeingtheleaderinthepricesurgethatset89.9%abovetheiryear-earliervalueinJune2021.3Likeprices,theincreasingvolatilityinfood-grainpricesisanotherrecentthreat.Benchmarkcornfutureslurchedmorethan10%higherinlastweekofJune2021,beforeslumping10%thefollowingweekasweatherforecastsshiftedmarketsentiment.4Sincefood-grains,suchasriceandwheatarethestaplefoodsinmanycountries,anincreaseintheirpriceandmarket-volatilitytriggersconcernsoffoodinsecurityandpoverty.AWorldBankstudyshowsthatanadditional44millionpeoplefellintopovertyinthedevelopingworldduetohigherfoodpricesin2006–2008(WorldBank2011).Withtherisingcostoffood-grains,theworldisfacingthesimilarthreatsnow.Theshockstothefood-grainpricesmaycomeeitherfromthedemandside,suchasglobalincome,non-traditionaluse,monetarypolicyinmajoreconomies,financialmarketsexposure,orfromthesupplyside,suchasproductionandsupplydisruptions.Oilpricehaslongbeenthoughttoplayanimportantroleonfood-grainCONTACTMdRafayetAlammailtorafayet@DepartmentofFinanceandEconomics,UniversityofTennesseeatChattanooga,Chattanooga,TN37403,USA1/world/double-whammy-food-buyers-freight-costs-spike-amid-high-grain-prices-2021-07-09/.2/world/china/world-food-price-index-surges-may-highest-level-since-2011-fao-2021-06-03/.3/world/china/world-food-price-index-surges-may-highest-level-since-2011-fao-2021-06-03/.4/world/double-whammy-food-buyers-freight-costs-spike-amid-high-grain-prices-2021-07-09/.©2021InformaUKLimited,tradingasTaylor&FrancisGroupgrainpricesincreaseduetoanincreaseinglobalincome,thenthecomovementbetweenoilpriceandfood-grainpricesmaybeamerecorrelation,globalincomebeingthefundamentaldriver.Withthehelpofthisidentificationstrategywearealsoabletoidentifytheoilsupplyshocksseparately.Thesestructuralshocksarethenusedtoexaminetheirimpactsonthepricereturnsofthefourfood-grains.Moreover,whilemodelingoilmarket,we,unlikemanypreviousstudies,avoidthe‘commonmistakes’asmentionedinKilianandZhou(2020).Forexample,weuse24monthslaginthestructuralVARmodel,post-1973eradata,USrefiner’sacqui-sitioncostofcrudeoilimportasaproxyfortheglobalpriceofcrudeoil,andalltherealvariablesinthemodel.Anothersignificantcontributionofourstudyisthatweestimatenon-linearMarkov-switchingmodelstoseeifourfindingsdependonthevolati-lityregimesofthefood-grainmarkets.OnebenefitofMarkov-switchingapproachisthatitcapturestime-dependentcausalityconsideringallthedataasopposedtoseparatelinearmodelforeachregime.Runningseparatelinearmodelforeachregimemaysufferfromthepaucityofdataandmaygenerateunreliableestimates.Markov-switchingmodelsdetermineregimesfromthedatawithoutimposingastrictformulaforswitches,andthisframeworkisespeciallyusefulincaseswheretheadjustmentsarelikelytobedrivenbyexogenousevents(Basher,Haug,andSadorsky2016).Therearenumerousexamplesofsuchexo-genouseventsinoursampleperiod.Forexample,thebiofuelmandatesin2005and2007,productiontargetcutsbyOPECin1999,2009and2020;oilpriceplungein2014,globalfinancialcrisisin2008,thedot-combubblecrisisin2000,theIraqiinva-sionofKuwaitin1990,theterroristattackontheWorldTradeCenterin2001,theIraqWarin2003,etc.5Thehistoricalnon-uniformcorrelationbetweenoilpriceandfood-grainpricesalsojusti-fiestheuseofMarkov-switchingmodel.Weaddi-tionallyperformtheformaltestssuchasBDStestfollowingBrocketal.(1987,1996)andLikelihoodRatiotestsfollowingCharfeddine(2017)andGarcia(1998)tocheckifnon-linearmodelsareAPPLIEDECONOMICS1201required.ThesetestsjustifytheuseofMarkov-switchingmodels.6Furthermore,tokeeptheMarkov-switchingmodelsparsimonious,welimitourselvestotwo-regimeMarkov-switchingmodels.ThisismotivatedbytheworkofBasher,Haug,andSadorsky(2016),EngelandHamilton(1990),Dumas(1992),andEngel(1994).Dumas(1992)showsthat,undertheassumptionofspatiallysepa-ratedcountriesandshippingcosts,therealexchangerateswitchesbetweentwostatesandexhibitsmeanreversionwithineachregime.EngelandHamilton(1990)andEngel(1994)alsoshowthatasimpletwo-stateMarkov-switchingrandomwalkmodelisafittingrepresentationofnominalexchangerateregimes.Themainmotivationofthisstudyistounder-standhowfood-grainpricesrespondtotheoildemandandsupplyshocks,andwhethertheresponsesdependonthevolatilityofthefood-grainmarkets.Usually,oilpriceandglobaleco-nomicconditionmovetogether.Therefore,it’simportanttoisolatetheoilshocksfromglobaleconomicshockstounderstandthetrueeffectsoftheoilshocks.Thisisalsoimportanttounder-standwhetheranincreaseinoilpriceisduetoademandshockorsupplyshockasthepolicyresponsecouldbedifferentbasedontheunder-lyingreasonofthepricechange(Kilian2009).Previousstudiesthatshowtheimpactofchangeinoilpriceonfood-grainpricesfailtoconsidertheunderlyingcausesofpricechange.However,iffoodpricesincreaseduetoanabruptcutinoilproductionbyOPEC(supplyshock),thepolicyresponseshouldbedifferentfromthepolicyresponseiftheoilpricerisesduetoanoverallimprovementoftheglobaleconomywhichislikelytoincreasethedemandforfood-grainsaswell.Likewise,ifthechangeinoilpriceisduetothechangeinoil-market-specificdemand,suchastheoneduetoclean-energyregulations,thepolicyresponseshouldbedifferentfromthepolicyresponseifthechangeinoilpriceisduetothechangeinglobaleconomiccondition.Furthermore,ourstudy,unlikepreviousstudies,documentstheimpactsoftheoilshocksonfood-grainpricesintwodifferentvolatilityregimesof5TheseeventsaretreatedasexogenousonlywithrespecttotriggeringaregimeswitchfortheMarkovprocess,or,inotherwords,theMarkovregimegeneratingprocessisexogenous.6Theresultsofthesetestsareavailableuponrequest.1202MD.ABDURRAHMANFORHADANDM.R.ALAMthefood-grainmarkets.Thishelpsusunderstandhowtherelationshipsbetweenoilpriceandfood-grainpricesdependonthevolatilityofthefood-grainmarkets,andprovidesfurtherinsightswhichmayhelppolicymakersformulateappropriatepolicyresponsesindifferentmarketconditions.Ourfindingsshowthatanunanticipatedoilsup-plyshockdoesnotaffectpriceofanyofthefourfood-grainssignificantly.Anincreaseindemandforoil,whichisnotinducedbythechangeinglobaleconomiccondition,increasesonlythecornpriceinthelowvolatilitycornmarket.Animprovementofglobaleconomicconditionimpactswheatandcornpricesinthehighvolatilityregimes.However,oilshocksthatareolderthanonemonthusuallydonotaffectthefood-grainprices.Contributionsofourstudiesaremanifold.Weshowthattheeffectsofdifferentoilshocksonfood-grainpricesaredifferent.Whereasoilsupplyshocksdonothaveanyeffect,oil-market-specificdemandndwheatprices.Unlikepreviousstudies,ourstudyalsoshowsthattheeffectsofoilshocksonfood-grainpricesdependsonthelevelofvolatilityofthefood-grainmarkets.Aswehavealreadydiscussed,aware-shocksindifferentmarketconditionsisimportantpThispaperisorganizedinthefollowingway.SectionIIdiscussestheeconometricmethodandidentificationstrategies,sectionIIIdescribesdata,theempiricalresultswhilesectionVchecksrobust-nessofthefindings,sectionVIprovidesthediscus-sionofthefindings,andfinally,sectionVIIconcludes.II.MethodologyWefollowatwo-stageapproachtoexaminetheeffectsofoildemandandsupplyshocksonthefoodgrainprices.Inthefirststage,wefollowKilian(2009)toidentifystructuraloildemandandsupplyshocks.Inthesecondstage,weexaminetheeffectsofthestructuralshocksontheprice-returnsofthefourfood-grains.Toconstructthestructuraloildemandandsup-plyshocks,wefirstconsiderthefollowingstruc-turalvectorautoregression(SVAR)modelwhichisestimatedusing24lags:BytBLyt1þ2t(1)whereytisa31vectorofpercentagechangeinglobalcrudeoilproduction,anindexofcyclicvariationinglobalrealeconomicactivityderivedfrombulkdrycargooceanfreightrates(originallyproposedbyKilian2009),andthelogoftherealpriceofoil.The2tisavectorofseriallyandmutuallyuncorrelatedstructuralinnovations.Equation(1)canberewrittenas(2)(2)Thestructuralinnovationsarederivedbyimpos-ingexclusionrestrictionsonB1inet=B12t,whereetisavectoroferrorsinareducedformVAR.Specifically,thestructuralshocksare(1)ashocktotheamountofoilpumpedoutoftheground(‘oilsupplyshock’),(2)ashocktothedemandforallindustrialcommodities,includingcrudeoil(‘aggre-gatedemandshock’)and(3)aresidualoildemandshock(‘oil-market-specificdemandshock’)(Kilian2009;KilianandZhou2020).FollowingKilian(2009),wepostulatethatB1hasarecursivestruc-turesuchthatthereducedformerrorsetcanbedecomposedaccordingtoet=B12t:epob31b32b332il一specificdemandshockwhereΔprodisthepercentagechangeinglobalcrudeoilproduction,reatisanindexofcyclicvariationinglobalrealeconomicactivityderivedfrombulkdrycargooceanfreightrates;andrpotrefersto(thelogof)therealpriceofoil.Therestrictionrepresentedbythefirstrowoftheabovematriximpliesthatcrudeoilproductiondoesnotrespondcontemporaneously(withinthesamemonth)eithertoaggregatedemandshocksortooil-market-specificdemandshocks.Oil-producingcountrieswillbeslowtorespondtodemandshocks,giventhecostsofadjustingoilproductionandtheuncertaintyaboutthestateofthecrudeoilmarket(Kilian2009).Asaresult,followingKilian(2009),wedefineoil-supplyshocks(2)asunpredictablechangesinglobaloilproduction.Therestrictionrepresentedbythesecondrowimpliesthatglobalrealeconomicactivitiesrespondimmediately(withinamonth)tooilsupplyshocks,butnottooil-market-specificdemandshocks.Theexclusionrestrictionismotivatedbythefactthatincreasesintherealpriceofoildrivenbyshocksthatarespecifictotheoilmarketdonotlowerglobalrealeco-nomicactivitiesimmediately,butwithadelayofatleastamonth.Thisexclusionrestrictionisconsistentwiththesluggishbehaviorofglobalrealeconomicactivitiesaftereachmajoroilpriceincreases(Kilian2009).Insum,followingKilian(2009),wedefinetheinnovationtochangesinglobalrealeconomicactivitythatcannotbeexplainedbycrudeoilsupplyshocksasshockstotheglobaldemandforindustrialcommodities(oraggregatedemandshocksforshort(2).Finally,thethirdrowofthematriximpliesthatoilpricesrespondcontemporaneouslytooilsupplyandaggregatedemandshocks.Innovationstotherealpriceofoilthatcannotbeexplainedbyeitheroilsupplyshocksoraggregatedemandshockswillreflectoil-market-specificchangeindemandforoil(oroil-specificdemandshocksforshort[2]).AfterestimatingtheSVARmodelwiththerestrictionsmentionedabove,wehaveobtainedtheoilsupplyshocks(2Þ,aggregatedemandshocks(2Þ,andoil-specificdemandshocks(2ÞandusetheshocksinthelinearandnonlinearMarkov-switchingmodels.Wefirststartwithalinearregressionmodelforeachcountry,asshownbyEquation(5).4Pit=α0iþα1i2þα2i2þα3i2þα4i4Pit-1þuit.(5)where4Pitisthefirstdifferenceofthelogofthenominalpriceforfoodcommodityiatperiodt.7APPLIEDECONOMICS1203Assumingthattherelationbetweenoilshocksandfoodcommoditypricesmaybestate-dependent(i.e.nonlinear),oursecondmodelisastate-dependentMarkov-switchingmodel,asshownbyEquation(6)4Pit=α0istþα1ist2þα2ist2þα3ist2þα4ist4Pit-1þuit.(6)InEquation(6),weassumethatthetransitionprobabilityfromstate1attimeperiodttoanotherstate,forexample,statem,attimeperiodtþ1dependsuponthestateattimeperiodt,whichdoesnotdependonanyotherstate.Additionally,theMarkov-switchingisconditionallylinearwithineachregime,andtheswitchingbetweenregimesisinherentlystochastic.Theswitchingisalsoassumedtobestochasticdependingonthetime-varyingtransitionprobabilitymatrix.Itisalsoassumedthatthestochasticregimegeneratingprocessfollowsanergodic,homogeneous,andfirst-orderMarkovchain,wheretransitionprob-abilitiesareconstantandthenumberofregimesarefinite.Thechangeintransitionprobabilitymatrixalsodependsonintercept,variance,threetypesoftheoilshocks,andoneperiodlagofthereturnsoncommodityprices.Thistransitionmatrixisgivenasfollows:X MXplm=Prðstþ1=mstþ1=lÞ;plm0;plm=1.m=1(7)WeestimatetheMarkov-switchingmodel(Equation(6))foreachfood-grainfollowingBasher,Haug,andSadorsky(2016)andPerlin(2008).Inthiscase,twostates,suchasstate-dependentregressioncoefficientsandstate-dependentvolatilityfortheerrorprocess,areconsidered.AstheMarkovchainisunobservable,theesti-matedoutputsincludeprobabilitiesforaspecificstate.AgoodfittingMarkov-switchingmodelusuallyprovidesasharpclassificationofregimes,andithassmoothprobabilitiesthatareeitherclosesmonthlyfoodpriceinperiodtmaydependonthepricesinperiodtwefollowBasherHaugandSadorskyandincorporatethelaggedfoodpricesinourbaselinemodel.1204MD.ABDURRAHMANFORHADANDM.R.ALAMtozeroorone.TochecktheaccuracyofresultsfromtheMarkov-switchingmodel,weemploythefollowingregimeclassificationmeasure(RCM).t=1j=1RCMðSÞ=100*S2**XTYSj;t=1j=1whereSisthenumberofregimesorstates,andisthesmoothedprobabilities.TheRCMisusuallycomputedastheaverageoftheproductofsmoothedprobabilities,:Inthiscase,theswitch-ingvariablesfollowaBernoullidistribution.Thisultimatelyallowsustofindanestimateforthevariance.ThevaluesforRCMliebetweenzeroand100,wherezeroindicatestheperfectregimeclassification,and100indicatesthefailuretodetectanyregimeclassification.WhenamodelhasanRCMvalueclosetozeroandsmoothedprobabilityindicatorclosetoone,thecorrespondingregimesareconsideredtobesignificantlydifferent.III.Data,variablesanddescriptivestatisticsWeusemonthlydataonrealoilprice,globalproductionofcrudeoil,andcyclicvariationofglobalrealeconomicactivity.Weusemonthlypricesofthefourfood-grains:corn,wheat,soy-bean,andrice.TherealoilpricesindollarsperbarrelaremeasuredusingU.S.refiners’acquisitioncostofcrudeoildeflatedbytheUSConsumerPriceIndex.8Globalproductionofcrudeoil(measuredinmillionsofbarrels)andoilpricesareobtainedfromtheUSEnergyInformationAdministration(USEIA).9Theindexofglobalrealeconomicactivity(Kilianindex)isretrievedfromProfessorLutzKilian’swebsite.10Themonthlypricesofthefood-grainsarecollectedfromtheIndexMundi.11ThetimecoveredisfromOctober1990toApril2020whichisdictatedbytheavailabilityofdata.Inthefirststage,forthethreevariableSVARmodel,we,followingKilian(2009),usethefirstdifferenceofthenaturallogarithmofglobaloilpro-duction,theKilianindex12ofglobalrealeconomicactivity,andthenaturallogarithmofrealoilprice.Inthesecondstage,returnsonthefood-grainpricesareregressedonthestructuralshocksderivedfromthefirststage.Wecalculatereturnsonfood-grainpricesbyusingrt=100*lnPt/lnPt_1,wherertisthereturns;andlnPtandlnPt_1arethelogoftheeachfoodpriceinperiodstandt_1;respectively.Figure1presentsthetrendsinpricesofthefourfood-grains.Itshows,onaverage,thepricesarehigherafter2006thanbefore2006.Therearealsomoresharpupsanddownsafter2006.Somesortofcomovementamongthecommoditypricesisalsonoticeablefromthechart.http//petroleum/data.cfm#prices.9/totalenergy/data/monthly/index.cfm.10/site/lkilian2019/research/data-sets.FordetailsaboutthisindexseeKilian(2009).11/commodities/.12WeusetheupdatedversionofKilianindexwhichdoesnothaveanytrend.APPLIEDECONOMICS1205nskas1206MD.ABDURRAHMANFORHADANDM.R.ALAMCornreturnsreturnsSoybeanreturnsWheatreturnsOilSupplyShockAggregateDemandShockOildemandShock−24.486590−24.241900−25.609710−21.917580−3.022790−4.4036060devwness−0424715−0402697−0141095−0378308−0550587estpNNote.ThisTableshowsthedescriptivestatisticsofpricereturnsofthefood-grainsandthethreestructuralshocks.Figure2showsthereturnsonthepricesofthefourfood-grains.Thepricereturnsshowasignificantamountofvariabilityandconvergetozeroovertime.Figure3showsthestructuralshocks:oilsupply,aggregatedemand,andoildemandshocksobtainedfromtheSVARmodel.Theoilsupplyshocksshowasignificantvariabilitythroughoutthesampleperiod,whileoil-demandshocksshowsignificantvariabilityatthelaterpartofthesample.Table1showsthesummarystatisticsforreturnsonfood-grainpricesandforthethreestructuralshocks.Themeanofpricereturnsishighestforriceandlowestforsoybean.Thepricereturnofwheathasthehigheststandarddeviationwhilethatofsoybeanhastheloweststandarddeviation.Themeanvaluesforthethreestructuralshocksarezero.However,oil-demandshockshavehighestskewnessandaggregatedemandshockshavehigh-estkurtosisamongthethreeshocks.Thelasttworowsshowresultsfromthenormalitytestswhichconfirmthenormalityofalltheseries.IV.EmpiricalresultsBeforeusingtheseriesinthemodel,weperformunitroottestsonthemusingtheElliot,Rothenberg,andStock(1996)Dickey–FullerunitTable2.UnitRoottest.DF-GLSDF-GLS(withtrend)nSoybeanSupplyshock−22445*283**AggregateDemandshockOildemandshockNote:***,**,*representrejectionofthenullhypothesisofunitrootat1%,5%and10%levelofsignificance.Table3.Theimpactofoilshocksonfood-grainprices(linearablesnSoybeanOilsupplyshock−0.52712−0.52269−0.26738−0.21994Agg.demand−0.00526shockOildemandshock−002724L.pricereturnsConstantervationsdNote:Robuststandarderrorsareinthebrackets.***,**,*representsignifi-cantat1%,5%and10%levelofsignificance.roottest(DF-GLS)withtrends.Table2showsthatalltheprice-returnsandstructuralshocksaresta-tionary.TheseresultsareexpectedasFigure2and3shownotrendsineitherpricereturnsorstruc-turalshocks.Toexaminetheimpactsofoilshocksonfood-grainpricereturns,wefirstestimatethelinearmodel(Equation5)andreporttheresultsinTable3.Oil-market-specificoildemandshocksoroilsupplyshocksdonotaffectthepricereturnsofanyofthefourfood-grains.However,anincreaseindemandforoilduetonon-oil-market-specificshock(aggregatedemandshock)increasesthewheatprice.Specifically,thecoefficientofaggre-gatedemandshockforwheatis1.08,anditisstatisticallysignificantat5%levelofsignificance.Additionally,laggedvaluesofpricereturnsarestatisticallysignificantimplyingpersistenceinthepricereturns.Wediscusstheresultsin-detailinsectionVI.Wethenestimatethenon-linearMarkov-switchingmodel,asrepresentedbyEquation(6),toseeiftheimpactsofoilshocksonfood-grainAPPLIEDECONOMICS1207nSoybeanState1State2State1State2Stat