[PPT] - Lawrence Christiano Overview RecentDevelopmentsinMonetary p y PowerPoint Presentation

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Recent Developments in Monetary Economics

Lawrence Christiano

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RecentDevelopmentsinMonetary p y Economics

LawrenceChristiano Northwestern University NorthwesternUniversity

Overview

A new consensus has emerged about the rough outlines of a model
Anewconsensushasemergedabouttheroughoutlinesofamodel

fortheanalysisofmonetarypolicy.

– Consensusinfluencedheavilybyestimatedimpulseresponse functionsfromStructuralVectorAutoregression (SVARs)

DescribeempiricalSVARresults.
ConstructionoftheconsensusmodelsbasedonresultsfromSVARs.

– Christiano,EichenbaumandEvansJPE(2005) – Smets andWouters,AER(2007)

Furtherdevelopmentsoftheconsensusmodel

– Labormarket – Financialfrictions – Openeconomy

P li l i h t li i d t dl t ib t t

Policyanalysis:howmonetarypolicymayinadvertedly contributeto

excessassetmarketvolatility.

VectorAutoregressions

d b h i i i

ProposedbyChrisSimsin1970s,1980s
Majorsubsequentcontributionsbyothers(Bernanke,BlanchardWatson,

BlanchardQuah) Blanchard Quah)

UsefulWaytoOrganizeData

– VARsserveasa‘Battleground’betweenalternativeeconomictheories – VARscanbeusedtoquantitativelyconstructaparticularmodel

Questionthatcan(inprinciple)beaddressedbyVAR:

‘How does the economy respond to a particular shock?’ – Howdoestheeconomyrespondtoaparticularshock? – Currentconsensusmodelheavilyguidedbyanswerstothisquestion

VARscan’tactually addresssuchaquestion

y q

– Identificationproblem – Needextraassumptions….StructuralVAR(SVAR).

OutlineofSVARdiscussion

WhatisaVAR?
TheIdentificationProblem
Identificationrestrictions
Results
Results
HistoricalDecompositionsofData

SLIDE 4

Shocks and Identification Assumptions ShocksandIdentificationAssumptions

MonetaryPolicyShock
NeutralTechnologyShock
CapitalEmbodied Shock to Technology

Capital EmbodiedShocktoTechnology

SLIDE 5

IdentifyingMonetaryPolicyShocks

Onestrategy:estimateparametersofFed’sfeedback

rule

Rule that relates Fed’s actions to state of the economy: – RulethatrelatesFed sactionstostateoftheeconomy:

Policy shock Fed information set

Rt =f(t)+et

R

f linear – f linear – et

R orthogonaltoFedinformation,t

– tcontainscurrentpricesandwages,aggregatequantities,

t

p g , gg g q , laggedstuff – et

R estimatedbyOLSregression

Regress X on e R e

R e R

– RegressXtonet

R,et1 R,et2 R ,…

IdentificationofTechnologyShocks(Blanchard Q h Fi h JPE 2007) Quah,Fisher,JPE2007)

Therearetwotypesoftechnologyshocks:neutral

and capital embodied andcapitalembodied

Xt ZtFKt,Lt

These are only shocks that can affect labor

Kt1 1 Kt VtIt

Theseareonlyshocksthatcanaffectlabor productivityinthelongrun.

Theonlyshockwhichalsohasalongruneffecton

therelativepriceofcapitalisacapitalembodied t h l h k (V ) technologyshock(Vt). VAR estimation with the following data:

The data have been transformed to ensure stationarity Sample period: 1959Q1-2007Q1

SLIDE 6

Results

Results…..

InterestingPropertiesofMonetaryPolicyShocks

Plentyofendogenouspersistence:

– moneygrowthandinterestrateoverin1year,butothervariableskeep i going….

Inflationslowtogetofftheground:peaksinroughlytwoyears

– Ithasbeenconjecturedthatexplainingthisisamajorchallengeforeconomics – ChariKehoeMcGrattan (Econometrica),Mankiw. – KillsmodelsinwhichmovementsinP arekeytomonetarytransmission h i (L i ti d l ti k d l) mechanism(Lucasmisperceptionmodel,purestickywagemodel) – Hasbeenattheheartoftherecentemphasisonstickyprices.

Output, consumption, investment, hours worked and capacity utilization

Output,consumption,investment,hoursworkedandcapacityutilization humpshaped

Velocitycomoves withtheinterestrate

SLIDE 7

Observations on Neutral Shock ObservationsonNeutralShock

Generally,resultsare‘noisy’,asoneexpects.

– Interest,moneygrowth,velocityresponsesnotpinneddown.

Interestingly,inflationresponseisimmediateandprecisely

estimated.

Doesthisraiseaquestionabouttheconventional

interpretationoftheresponseofinflationtoamonetary shock?

Alternative possibility: information confusion stories.

Alternativepossibility:informationconfusionstories.

– AvariantofrecentworkbyRhysMendesthatbuildsonGuido Lorenzoni’s work.

HistoricalDecompositionofDatainto h k Shocks

We can ask:

Wecanask:

– Whatwouldhavehappenedifonlymonetary policy shocks had driven the data? policyshockshaddriventhedata? – We can ask this about other identified shocks or – Wecanaskthisaboutotheridentifiedshocks,or aboutcombinationsofshocks – Wefindthatthethreeshockstogetheraccount for a large part of fluctuations foralargepartoffluctuations

Dark line: detrended actual GDP Thin line: what GDP would have been if there had only Thin line: what GDP would have been if there had only been one type of technology shock, the type that affects only the capital goods industry Th h k h ff t b t t t ibl i t t These shocks have some effect, but not terribly important

SLIDE 8

Type of technology shock that affects Type of technology shock that affects all industries This has very large impact on broad trends in the d t d ll i t b i l data, and a smaller impact on business cycles. Has big impact on trend in data, and 2000 boom-bust Monetary policy shocks have a big impact on 1980 ‘Volcker big impact on 1980 Volcker recession’ All three shocks together account for large part of business cycle

VarianceDecomposition

Variable BP(8,32) Output 86 Output

18

86 Money Growth

11

23 Inflation 33

17

Fed Funds

16

52 Capacity Util.

16

51

Avg. Hours

17

76 Real Wage

16

44 Consumption

21

89 Investment

16

69 Velocity 29 Velocity

16

29 Price of investment goods

16

11

SLIDE 9

Now to the construction of a monetary

Now,totheconstructionofamonetary equilibriummodel,basedontheprevious impulse response functions impulseresponsefunctions…. B d

Basedon

– ChristianoEichenbaumEvansJPE(2005) – AltigChristianoEichenbaumLinde

Objectives

d d (‘ ’) d l

Constructingastandard(‘consensus’)DSGEModel

– Modelfeatures. – EstimationofmodelusingimpulseresponsesfromSVAR’s.

Determineifthereisaconflictregardingpricebehavior

betweenmicroandmacrodata.

– MacroEvidence:

Inflationappearssluggish

pp gg

Inflationrespondsslowlytomonetaryshock

– MicroEvidence:

Bil Kl N k St i t id f f i

BilsKlenow,NakamuraSteinssonreportevidenceonfrequencyofprice

changeatmicrolevel:511months.

SLIDE 10

Description of Model DescriptionofModel

TimingAssumptions

g p

Firms
Households
MonetaryAuthority
GoodsMarketClearingandEquilibrium

Timing

TechnologyShocksRealized.

A t M k P i /W S tti C ti

AgentsMakePrice/WageSetting,Consumption,

Investment,CapitalUtilizationDecisions.

MonetaryPolicyShockRealized.
HouseholdMoneyDemandDecisionMade.
Production Employment Purchases Occur and
Production,Employment,PurchasesOccur,and

MarketsClear.

Note:Wages,PricesandOutputPredeterminedRelativetoPolicyShock.

Firm Sector

Final Good, Competitive Fims

Intermediate Good Producer 1 Intermediate Good Producer 2 Intermediate Good Producer infinity … … … … .. Competitive M arket Competitive M arket for Homogeneous Labor For Homogeneous Capital Homogeneous Labor Input Household 1 Househo ld infinity Househo ld 2

SLIDE 11

IsCalvoaGoodReducedFormModel f k

fStickyPrices?
Evidence on relative frequency of large and

Evidenceonrelativefrequencyoflargeand smallpricechangessuggests‘yes’

Evidenceofprobabilityofpricechange

di i l i i l h conditionalontimesincelastchangesuggests ‘yes’

Evidence from Midrigan, ‘Menu Costs, Multi-Product Firms, and Aggregate Fluctuations’ Lot’s of small changes Hi t f l (P /P ) diti l i dj t t f t d t t Histograms of log(Pt/Pt-1), conditional on price adjustment, for two data sets pooled across all goods/stores/months in sample.

SLIDE 12

Households:SequenceofEvents

Technologyshockrealized.
Decisions:Consumption,Capitalaccumulation,Capital

Utilization.

Insurancemarketsonwagesettingopen.
Wagerateset.
Monetary policy shock realized
Monetarypolicyshockrealized.
Householdallocatesbeginningofperiodcashbetweendeposits

at financial intermediary and cash to be used in consumption atfinancialintermediaryandcashtobeusedinconsumption transactions.

SLIDE 13

DynamicResponseofConsumptiontoMonetary PolicyShock

In Estimated Impulse Responses:
InEstimatedImpulseResponses:

– RealInterestRateFalls

Rt/t1

– ConsumptionRisesinHumpShapePattern:

c t

Consumption‘Puzzle’

IntertemporalFirstOrderCondition:

ct1 ct MUc,t MU

1 Rt/t1

‘Standard’ Preferences

With Standard Preferences:

ct MUc,t1 WithStandardPreferences:

c c Data! t t

OneResolutiontoConsumptionPuzzle O e eso ut o to Co su pt o u e

ConcaveConsumptionResponseDisplays:

– RisingConsumption(problem) F lli Sl f C ti – FallingSlopeofConsumption

Habit Persistence in Consumption

Habit parameter

HabitPersistenceinConsumption

Uc logc b c1

– MarginalUtilityFunctionofSlope ofConsumption – HumpShapeConsumptionResponseNotaPuzzle

EconometricEstimationStrategyGiventheOption,b>0

DynamicResponseofInvestmentto MonetaryPolicyShock

InEstimatedImpulseResponses:

– InvestmentRisesinHumpShapedPattern:

I t

SLIDE 14

One Solution to Investment Puzzle… OneSolutiontoInvestmentPuzzle…

CostofChange Adjustment Costs:

Cost of ChangeAdjustmentCosts:

k 1 k F

I

Thi D P d H Sh I t t

k

1 k F I 1 I
ThisDoesProduceaHumpShapeInvestment

Response

Other Evidence Favors This Specification – OtherEvidenceFavorsThisSpecification – Empirical:Matsuyama,SmetsWouters. Theoretical: Matsuyama David Lucca – Theoretical:Matsuyama,DavidLucca

WageDecisions

Eachhouseholdisamonopolysupplierofa

specialized,differentiatedlaborservice.

– SetswagessubjecttoCalvo frictions. Given specified wage household must supply – Givenspecifiedwage,householdmustsupply whateverquantityoflaborisdemanded.

Householddifferentiatedlaborserviceis

aggregatedintohomogeneouslaborbya competitive labor ‘contractor’ competitivelabor contractor .

lt

1ht j

1 w dj

w 1

lt

0ht,j w dj

, 1 w .

Firm Sector

Final Good, Competitive Fims

Intermediate Good Producer 1 Intermediate Good Producer 2 Intermediate Good Producer infinity … … … … .. Competitive M arket Competitive M arket for Homogeneous Labor For Homogeneous Capital Homogeneous Labor Input Household 1 Househo ld infinity Househo ld 2

L b l Nominal Labor supply Nominal wage, W Shock Firms use a lot of Labor because it’s ‘cheap’ cheap . Households must supply that labor Labor demand Quantity of labor

SLIDE 15

Econometric Methodology EconometricMethodology

Choose parameters of economic model so

Chooseparametersofeconomicmodel,so thatthedynamicresponsetoshocks resembles as closely as possible the impulse resemblesascloselyaspossibletheimpulse responsesestimatedfromSVARs.

Makesurethatidentifyingassumptionsused

i h SVAR i fi d i h d l intheSVARaresatisfiedinthemodel.

SLIDE 16

Parameter estimates
Parameterestimates

TABLE 2: ESTIMATED PARAMETER VALUES 1 Model f w

a

b S

f
Benchmark

0.17

1.35

0.06

.75

0.32

.32

0.18

0.06

0.04

0.80

2.15

4.85

0.27

0.77

Parametersaresurprisinglyconsistentwithestimates

reportedinJPE(2005)basedonstudyingonlymonetary policy shocks policyshocks

Pointestimatesimplypricesrelativelyflexibleatmicrolevel

1

– Atpointestimates:

p 0.58, 1 1 p 2.38 quarters

Otherparameters‘reasonable’:estimationresultsreally

wantstickywages!

Parameters of exogenous shocks:

Parametersofexogenousshocks:

TABLE 3 ESTIMATED PARAMETER VALUES TABLE 3: ESTIMATED PARAMETER VALUES 2 M M z z xz cz cz

p

x

c c

p

Benchmark Model

0.12

0.10

0.31

0.03

.91

0.02

0.05

0.22

0.36

1.55

3.68

1.22

2.49

0.52

0.24

0.06

0.17

0.07

0.91

0.57

0.10

0.65

0.63

Neutraltechnologyshock,,ishighly

persistent.

z

Monetary Policy Shock MonetaryPolicyShock

Key findings:

Keyfindings:

C t f l i h t t – Canaccountforsluggishaggregateresponseto monetarypolicyshockwithoutalotofprice stickiness stickiness Can account for the observed effects of monetary – Canaccountfortheobservedeffectsofmonetary policyonconsumption,investment,output,etc.

SLIDE 17

troublesome

Furtherworkwiththismodel

Policyquestions:

– roleofmonetarypolicyintransmissionoftechnology shocks shocks – Roleofmonetarypolicyinassetpricevolatility

Canconstruct‘micropaneldatasets’impliedby

model:

– Gainpowertotestmodelbydevelopingitsmicro implications implications. – Whatarecrosssectionalimplicationsofmodelfor pricesandquantitiesatthefirmlevel?

SLIDE 18

Implications for Panel Data ImplicationsforPanelData

‘Demand shocks’ for intermediate good firms:

Demandshocks forintermediategoodfirms:

Yt

1

itYit

1 f

f

1

di 1 iid i

‘Supply shocks’ for intermediate good firms:
0 itdi 1, it~iid across i

Supplyshocks forintermediategoodfirms:

Yit itKit

ztLit1

it~iid across i

Conclusionof‘Consensus’Model Construction and Estimation ConstructionandEstimation

Identifiedfeaturesofamodel(variablecapital

utilization,habitpersistence,adjustmentcostsinthe , p , j changeofinvestment)thatallowittoaccountfor estimatedSVARimpulseresponses.

Theestimationstrategyfocusedonasubsetofmodel

implications.

Fullinformationmethodshavebeenusedtoestimate

versionofthemodelwithafullsetofshocksonthe rawdata(Smets andWouters).

A future phase of empirical work will draw out the
Afuturephaseofempiricalworkwilldrawoutthe

implicationsofmacromodelsforpaneldatasets.

Additionalmodeldevelopment

Labormarket

– Modelhasnoimplicationsforunemployment, p p y , vacancies,hoursworked,peopleemployed, separations,etc. – Stickywagesinmodelsubjectto‘Barrocritiqueof stickywages’ y g

Financialmarkets

– Financialmarketsarenotasourceofshocksor propagation. C t k ‘ h t h ld t th it d i – Cannotask:‘whatshouldmonetaryauthoritydoin responsetoincreaseininterestratespreads?’

‘Barrocritique’

k f l h l d l k l

Mostworkerfirmrelationshipsarelongterm,andunlikely

tobestronglyaffectedbydetailsofthetimingofwage setting.

Standardstickywagemodelimplausible.
Recentresultsinsearchmatchingliterature:

M st disting ish bet een intensi e (ho rs) and e tensi e – Mustdistinguishbetweenintensive(hours)andextensive (employment)margin. Barro critique applies to idea that wage frictions matter in the – Barrocritiqueappliestoideathatwagefrictionsmatterinthe intensivemargin. – Does not apply to idea that wage frictions matter for extensive Doesnotapplytoideathatwagefrictionsmatterforextensive margin.

SLIDE 19

Papers Papers

Mortensen and Pissarides

MortensenandPissarides

Shimer

G l i i G l S l i i

GertlerTrigari,GertlerSalaTrigari
Hall
DenHaan,RameyandWatson
Christiano Ilut Motto Rostagno

Christiano,Ilut,Motto,Rostagno

Christiano,Trabandt,Walentin

Firm Sector

Final Good, Competitive Fims

Intermediate Good Producer 1 Intermediate Good Producer 2 Intermediate Good Producer infinity … … … … .. Competitive M arket Competitive M arket for Homogeneous Labor For Homogeneous Capital Homogeneous Labor Input Household 1 Househo ld infinity Househo ld 2

AddingLaborMarketFrictions

Firms Employment agency Employment Labor Market Employment agency LaborMarket Undirected search Unemployment Employment agency Undirected search endogenous vacancies Households Unemployment g y Endogenous and exogenous separation

MoreontheLaborMarket

N b f l d

HouseholdPreferences

Number of employed workers in cohort i

Et

l

lttl

c logCtl bCtl1 tl h AL N1 i,tl1L

1 L Ltl

i

,

hours per worker in cohort i

l0 i0 L

Worker finances

hours per worker in cohort i

Workerfinances

1 LtPt

cbuzt N1

Wt

iLt ii,t 1 t y

1 w

i0

1 t

SLIDE 20

Timeline– labormarket

E h k i Stock of employees in each agency reduced by exogenous separations increased by new arrivals Each worker experiences idiosyncratic, iid productivity shock. Least efficient are cut: Agency employees Vacancies posted increased by new arrivals Shocks realized

Unilateral firm decision
Cut determined by total

surplus criterion Agency employees sent to work t t+1 t Wages set

If it’s a time to bargain, choose wage to

Hours worked set according to an efficiency criterion: solve

max

wt V0wt UttJwt1t

y Marginal value of worker to agency = marginal cost of labor for worker

Otherwise, do simple updating

labor for worker

Timeline– labormarket

E h k i Each worker experiences idiosyncratic, iid productivity shock. Least efficient are cut:

Bargaining internalizes

Unilateral firm decision
Cut determined by total

surplus criterion

internalizes natureofthe job

t t+1 t Wages set

If it’s a time to bargain, choose wage to

Hours worked set according to an efficiency criterion: solve

max

wt V0wt UttJwt1t

y Marginal value of worker to agency = marginal cost of labor for worker

Otherwise, do simple updating

labor for worker

ExtensiontoIncorporateFinancial Frictions Frictions

Generalidea:

– Standardmodelassumesborrowersandlenders arethesamepeople..noconflictofinterest – Financialfrictionmodelssupposeborrowersand lenders are different people, with conflicting lendersaredifferentpeople,withconflicting interests – Financialfrictions:featuresoftherelationship betweenborrowersandlendersadoptedto mitigate conflict of interest mitigateconflictofinterest.

StandardModel Firms

consumption Investment goods

Firms

Investment goods Supply labor Rent capital

Households

Backyardcapitalaccumulation:

Kt1 1 Kt GKt,It.

Saversandinvestorsarethesame:NOFRICTIONS!

u t Etu t 1 Rt1

k

Rt1

k

rt1

k 1Pk,t1

uc,t Etuc,t1 t1 Rt1

Pk,t

SLIDE 21

FrictionsinFinancingofPhysicalCapital

Money

Savers Have money, but Investors (‘entrepreneurs’) a e

ey, bu

no ideas Have ideas, but not enough money.

FrictionsinFinancingofPhysicalCapital

Money

Savers Have money, but Investors (‘entrepreneurs’) a e

ey, bu

no ideas Problem: ‘stuff’ happens.

Incentive of entrepreneurs to under-report earnings

Vt1 real earnings on capital (rent plus capital gains)t i l t f i t t nominal rate of interestt1 t real debt to bankst1 Net Wortht1 Vt1 Wt1

e 1 Wt1 e

ModelwithFinancialFrictions

Firms Labor L K Entrepreneurs Labor market Capital Producers C I Producers household

SLIDE 22

ModelwithFinancialFrictions

Firms Labor Entrepreneurs Labor market Capital Producers K ’ Producers household banks Loan s

Prediction of financial friction model: Predictionoffinancialfrictionmodel:

Shocks that drive output and price in the same

Shocksthatdriveoutputandpriceinthesame direction(‘demand’)acceleratedbyfinancial frictions.

– Fisherandearningseffectsreinforceeachother.

Shocksthatdriveoutputandpriceinopposite

directions(‘supply’)notmuchaffectedby ( pp y ) y financialfrictions.

– Fisherandearningseffectscanceleachother.

EmpiricalAnalysisofFinancialFriction d l Model

Ch i i (2008) b d

ChristianoMottoRostagno (2008),basedon

BernankeGertlerGilchrist(1999)modelof fi i l f i i financialfrictions.

Risk Shock and News RiskShockandNews

Assume

iid i i t i ti t

Assume

h d i f i b

t 1

t1

iid, univariate innovation to t

ut
Agentshaveadvanceinformationabout

piecesofut

ut t

0 t1 1

...t8

8

ti

i

~iid, Eti

i 2 i 2

ti

i

~piece of ut observed at time t i

SLIDE 23

Estimation

EAandUSdatacovering1985Q12007Q2

log

Nt1 Pt

t logper capita hourst l

per capita creditt

log

p p

t

Pt

logper capita GDPt log

Wt Pt

logper capita It Xt log

per capita M1t Pt

log

per capita M3t Pt

logper capita consumptiont E t l Fi P i , External Finance Premiumt Rt

long Rt e

Rt

e

logPI,t logreal oil price

StandardBayesianmethods

logreal oil pricet log

per capita Bank Reserves t Pt

Weremovesamplemeansfromdataandsetsteady

stateofXtozeroinestimation.

SummaryofEmpiricalResultsWith Fi i l F i ti FinancialFrictions

Riskshocks:

– importantsourceoffluctuations. – newsontheriskshockimportant

h h b fl h l h b l

TheFisherdebtdeflationchannelhasasubstantialimpacton

propagation. M d d d h i f d i i id

Moneydemandandmechanismofproducinginsidemoney:

– relativelyunimportantasasourceofshocks – modestcontributiontoforecastability

Modelaccountsorsubstantialfractionoffluctuationsinterm

structure.

OutofSampleRMSEsofthemodelperformwellcomparedwith

BVARandsimplermodels.

Risk Shocks are Important RiskShocksareImportant

Actual data versus what actual data would have been if there were only risk Shocks: Note: (1) as suggested by the picture, risk shocks are relatively important at the lower frequencies (2) We find that they are the single most important source of low frequency (2) We find that they are the single most important source of low frequency fluctuation in the EA, and a close second (after permanent tech shocks) in the US

Table: Variance Decomposition, HP filtered data, EA x shock

utput

consumption investment hours inflation labor productivity interest rate f 15.02 23.05 2.63 16.37 35.74 1.40 20.46 x b 0.59 1.29 0.02 0.44 0.52 1.44 0.24

0.32

0.01 0.12 0.18 0.08 0.01 0.04

Markup Banking tech Capital tech

0.32

0.01 0.12 0.18 0.08 0.01 0.04

0.02

0.06 0.00 0.02 0.00 0.00 0.00 g 3.26 3.11 0.00 3.34 0.87 0.21 0.48 z

3.72

1.16 0.24 1.42 1.07 10.29 0.72

0 43

0 06 0 92 0 80 0 24 1 52 0 30

Capital tech Money demand Government Permanent tech Gamma shock

0.43

0.06 0.92 0.80 0.24 1.52 0.30

10.54

21.68 0.49 7.46 16.10 27.52 8.56 policy 6.22 11.27 1.01 4.14 5.40 0.10 33.15

2.88

0.19 5.11 6.57 0.88 13.17 1.08

20 09

1 81 38 09 15 96 9 22 38 24 9 80

Gamma shock Temporary tech Monetary policy Risk, contemp Si l i k

signal 20.09 1.81 38.09 15.96 9.22 38.24 9.80 and signal 22.96 2.00 43.20 22.53 10.09 51.41 10.88 c 11.68 32.75 0.15 12.20 11.26 0.83 10.15 i 24.57 1.72 51.14 30.69 10.17 5.22 11.56

Signals on risk Risk and signals Discount rate Marginal eff of I P i f il

il

0.42 1.39 0.03 0.24 2.21 0.04 1.32 long 0.00 0.00 0.00 0.00 0.00 0.00 0.00 measurement error 0.00 0.00 0.00 0.00 0.00 0.00 1.26 inflation target 0.24 0.43 0.05 0.16 6.23 0.01 0.87

Price of oil Long rate error

all shocks 100.00 100.00 100.00 100.00 100.00 100.00 100.00

SLIDE 24

Table: Variance Decomposition, HP filtered data, EA x shock

utput

consumption investment hours inflation labor productivity interest rate f 15.02 23.05 2.63 16.37 35.74 1.40 20.46 x b 0.59 1.29 0.02 0.44 0.52 1.44 0.24

0.32

0.01 0.12 0.18 0.08 0.01 0.04

Markup Banking tech Capital tech

0.32

0.01 0.12 0.18 0.08 0.01 0.04

0.02

0.06 0.00 0.02 0.00 0.00 0.00 g 3.26 3.11 0.00 3.34 0.87 0.21 0.48 z

3.72

1.16 0.24 1.42 1.07 10.29 0.72

0 43

0 06 0 92 0 80 0 24 1 52 0 30

Capital tech Money demand Government Permanent tech Gamma shock

0.43

0.06 0.92 0.80 0.24 1.52 0.30

10.54

21.68 0.49 7.46 16.10 27.52 8.56 policy 6.22 11.27 1.01 4.14 5.40 0.10 33.15

2.88

0.19 5.11 6.57 0.88 13.17 1.08

20 09

1 81 38 09 15 96 9 22 38 24 9 80

Gamma shock Temporary tech Monetary policy Risk, contemp Si l i k

Markup shock uncomfortably important as a source of fluctuations in inflation

signal 20.09 1.81 38.09 15.96 9.22 38.24 9.80 and signal 22.96 2.00 43.20 22.53 10.09 51.41 10.88 c 11.68 32.75 0.15 12.20 11.26 0.83 10.15 i 24.57 1.72 51.14 30.69 10.17 5.22 11.56

Signals on risk Risk and signals Discount rate Marginal eff of I P i f il

il

0.42 1.39 0.03 0.24 2.21 0.04 1.32 long 0.00 0.00 0.00 0.00 0.00 0.00 0.00 measurement error 0.00 0.00 0.00 0.00 0.00 0.00 1.26 inflation target 0.24 0.43 0.05 0.16 6.23 0.01 0.87

Price of oil Long rate error

all shocks 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Table: Variance Decomposition, HP filtered data, EA x shock

utput

consumption investment hours inflation labor productivity interest rate f 15.02 23.05 2.63 16.37 35.74 1.40 20.46 x b 0.59 1.29 0.02 0.44 0.52 1.44 0.24

0.32

0.01 0.12 0.18 0.08 0.01 0.04

Markup Banking tech Capital tech

0.32

0.01 0.12 0.18 0.08 0.01 0.04

0.02

0.06 0.00 0.02 0.00 0.00 0.00 g 3.26 3.11 0.00 3.34 0.87 0.21 0.48 z

3.72

1.16 0.24 1.42 1.07 10.29 0.72

0 43

0 06 0 92 0 80 0 24 1 52 0 30

Capital tech Money demand Government Permanent tech Gamma shock

0.43

0.06 0.92 0.80 0.24 1.52 0.30

10.54

21.68 0.49 7.46 16.10 27.52 8.56 policy 6.22 11.27 1.01 4.14 5.40 0.10 33.15

2.88

0.19 5.11 6.57 0.88 13.17 1.08

20 09

1 81 38 09 15 96 9 22 38 24 9 80

Gamma shock Temporary tech Monetary policy Risk, contemp Si l i k

Banking and money demand sector not a source of shocks

signal 20.09 1.81 38.09 15.96 9.22 38.24 9.80 and signal 22.96 2.00 43.20 22.53 10.09 51.41 10.88 c 11.68 32.75 0.15 12.20 11.26 0.83 10.15 i 24.57 1.72 51.14 30.69 10.17 5.22 11.56

Signals on risk Risk and signals Discount rate Marginal eff of I P i f il

il

0.42 1.39 0.03 0.24 2.21 0.04 1.32 long 0.00 0.00 0.00 0.00 0.00 0.00 0.00 measurement error 0.00 0.00 0.00 0.00 0.00 0.00 1.26 inflation target 0.24 0.43 0.05 0.16 6.23 0.01 0.87

Price of oil Long rate error

all shocks 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Table: Variance Decomposition, HP filtered data, EA x shock

utput

consumption investment hours inflation labor productivity interest rate f 15.02 23.05 2.63 16.37 35.74 1.40 20.46 x b 0.59 1.29 0.02 0.44 0.52 1.44 0.24

0.32

0.01 0.12 0.18 0.08 0.01 0.04

0.32

0.01 0.12 0.18 0.08 0.01 0.04

0.02

0.06 0.00 0.02 0.00 0.00 0.00 g 3.26 3.11 0.00 3.34 0.87 0.21 0.48 z

3.72

1.16 0.24 1.42 1.07 10.29 0.72

0 43

0 06 0 92 0 80 0 24 1 52 0 30

0.43

0.06 0.92 0.80 0.24 1.52 0.30

10.54

21.68 0.49 7.46 16.10 27.52 8.56 policy 6.22 11.27 1.01 4.14 5.40 0.10 33.15

2.88

0.19 5.11 6.57 0.88 13.17 1.08

20 09

1 81 38 09 15 96 9 22 38 24 9 80

It’s the

signal 20.09 1.81 38.09 15.96 9.22 38.24 9.80 and signal 22.96 2.00 43.20 22.53 10.09 51.41 10.88 c 11.68 32.75 0.15 12.20 11.26 0.83 10.15 i 24.57 1.72 51.14 30.69 10.17 5.22 11.56

It s the signals!

il

0.42 1.39 0.03 0.24 2.21 0.04 1.32 long 0.00 0.00 0.00 0.00 0.00 0.00 0.00 measurement error 0.00 0.00 0.00 0.00 0.00 0.00 1.26 inflation target 0.24 0.43 0.05 0.16 6.23 0.01 0.87 all shocks 100.00 100.00 100.00 100.00 100.00 100.00 100.00

Table: Variance Decomposition, HP filtered data, EA x shock stock market credit spread term structure real M1 real M3 shock stock market credit spread term structure real M1 real M3 f 1.83 13.15 0.16 12.36 44.28 1.82 x b 0.00 0.14 0.00 0.10 5.04 42.39

0.18

0.07 0.03 0.07 0.03 0.02

Markup Banking tech Capital tech

0.00

0.00 0.00 0.00 13.17 22.63 g 0.03 0.10 0.01 0.07 0.44 0.02 z

0.17

0.07 0.05 0.14 0.42 1.29

5 37

25 82 1 86 0 33 0 13 0 15

Money demand Government Permanent tech Gamma shock

5.37

25.82 1.86 0.33 0.13 0.15

0.10

4.06 0.00 3.40 9.89 0.61 policy 4.89 1.81 0.99 25.76 13.15 1.58

13.94

5.07 20.58 0.97 1.39 0.76

Gamma shock Temporary tech Monetary policy Risk, contemp

signal 68.29 44.23 75.90 6.79 5.98 6.20 and signal 82.22 49.30 96.48 7.76 7.38 6.96 c 0.02 1.72 0.02 3.99 2.46 15.40 1 90 2 54 0 27 8 77 1 18 6 17

Signals on risk Risk and signals Discount rate Marginal eff of I

i 1.90 2.54 0.27 8.77 1.18 6.17

il

0.14 0.94 0.05 0.56 1.87 0.15 long 0.00 0.00 0.00 36.05 0.00 0.00 measurement error 2.89 0.19 0.02 0.32 0.21 0.02

Marginal eff of I Price of oil Error in long rate

inflation target 0.24 0.10 0.05 0.34 0.35 0.80 all shocks 100.00 100.00 100.00 100.00 100.00 100.00

SLIDE 25

Table: Variance Decomposition, HP filtered data, EA x shock stock market credit spread term structure real M1 real M3 shock stock market credit spread term structure real M1 real M3 f 1.83 13.15 0.16 12.36 44.28 1.82 x b 0.00 0.14 0.00 0.10 5.04 42.39

0.18

0.07 0.03 0.07 0.03 0.02

Markup Banking tech Capital tech

0.00

0.00 0.00 0.00 13.17 22.63 g 0.03 0.10 0.01 0.07 0.44 0.02 z

0.17

0.07 0.05 0.14 0.42 1.29

5 37

25 82 1 86 0 33 0 13 0 15

Money demand Government Permanent tech Gamma shock

5.37

25.82 1.86 0.33 0.13 0.15

0.10

4.06 0.00 3.40 9.89 0.61 policy 4.89 1.81 0.99 25.76 13.15 1.58

13.94

5.07 20.58 0.97 1.39 0.76

Gamma shock Temporary tech Monetary policy Risk, contemp

signal 68.29 44.23 75.90 6.79 5.98 6.20 and signal 82.22 49.30 96.48 7.76 7.38 6.96 c 0.02 1.72 0.02 3.99 2.46 15.40 1 90 2 54 0 27 8 77 1 18 6 17

Signals on risk Risk and signals Discount rate Marginal eff of I

i 1.90 2.54 0.27 8.77 1.18 6.17

il

0.14 0.94 0.05 0.56 1.87 0.15 long 0.00 0.00 0.00 36.05 0.00 0.00 measurement error 2.89 0.19 0.02 0.32 0.21 0.02

Marginal eff of I Price of oil Error in long rate Signal matters!

inflation target 0.24 0.10 0.05 0.34 0.35 0.80 all shocks 100.00 100.00 100.00 100.00 100.00 100.00

Importance of Risk Signals ImportanceofRiskSignals

News Specification on Risk and Marginal Likelihood (EA data)

1 2 p

t 1

t1 t0 t1

1

t2

2

...tp

p

p log, marginal likelihood odds (exp(difference in log likelihood from baseline)) 8 (baseline) 4397.487 1 ( ) 6 4394.025 31 1 4325.584

WhyisRiskShocksoImportant?
Accordingtothemodel,externalfinance

premium is primarily risk shock premiumisprimarilyriskshock. T l k f id h i k i h b

Tolookforevidencethatriskmightbe

important,lookatdynamicsofexternal finance premium and gdp financepremiumandgdp. E l fi i i i l di

Externalfinancepremiumisanegativeleading

indicator

SLIDE 26

WhyisRiskShocksoImportant?: d Asecondreason

Ourdatasetincludesthestockmarket

Output stock market investment all procyclical – Output,stockmarket,investmentallprocyclical (surgetogetherinlate1990s) – Thisispredictedbyriskshock. ExplainingtheSlopeoftheTermStructure

Differencebetweentheyieldonthelowestratedcorporatebonds(Baa)andthehighestrated corporate bonds (Aaa) corporatebonds(Aaa)

Actual data Euro Area US Data under the assumption that only the monetary policy shock was

perative.

SLIDE 27

Table: Variance Decomposition, HP filtered data, EA x shock stock market credit spread term structure real M1 real M3 shock stock market credit spread term structure real M1 real M3 f 1.83 13.15 0.16 12.36 44.28 1.82 x b 0.00 0.14 0.00 0.10 5.04 42.39

0.18

0.07 0.03 0.07 0.03 0.02

Markup Banking tech Capital tech

0.00

0.00 0.00 0.00 13.17 22.63 g 0.03 0.10 0.01 0.07 0.44 0.02 z

0.17

0.07 0.05 0.14 0.42 1.29

5 37

25 82 1 86 0 33 0 13 0 15

Money demand Government Permanent tech Gamma shock

5.37

25.82 1.86 0.33 0.13 0.15

0.10

4.06 0.00 3.40 9.89 0.61 policy 4.89 1.81 0.99 25.76 13.15 1.58

13.94

5.07 20.58 0.97 1.39 0.76

Gamma shock Temporary tech Monetary policy Risk, contemp

signal 68.29 44.23 75.90 6.79 5.98 6.20 and signal 82.22 49.30 96.48 7.76 7.38 6.96 c 0.02 1.72 0.02 3.99 2.46 15.40 1 90 2 54 0 27 8 77 1 18 6 17

Signals on risk Risk and signals Discount rate Marginal eff of I

i 1.90 2.54 0.27 8.77 1.18 6.17

il

0.14 0.94 0.05 0.56 1.87 0.15 long 0.00 0.00 0.00 36.05 0.00 0.00 measurement error 2.89 0.19 0.02 0.32 0.21 0.02

Marginal eff of I Price of oil Error in long rate

inflation target 0.24 0.10 0.05 0.34 0.35 0.80 all shocks 100.00 100.00 100.00 100.00 100.00 100.00

ImpactofFinancialFrictionson Propagation

Effectsofmonetaryshocksongdpamplified

by BGG financial frictions because P and Y go byBGGfinancialfrictionsbecauseP andY go insamedirection.

Effectsoftechnologyshocksongdpmitigated

b BGG fi i l f i ti b P d Y byBGGfinancialfrictionsbecauseP andY go inoppositedirections.

Baseline model with no Fisher Effect Baseline model Blue line: baseline model with no financial frictions

Out of Sample RMSEs OutofSampleRMSEs

There is not a loss of forecasting power with

Thereisnotalossofforecastingpowerwith theadditionalcomplicationsofthemodel.

Themodeldoeswelloneverything,except

h i k i theriskpremium.

SLIDE 28

ConclusionofEmpiricalAnalysiswith Fi i l F i i FinancialFrictions

Incorporatingfinancialfrictionschangesinference

aboutthesourcesofshocksandofpropagation

– riskshock. – Fisherdebtdeflation

d l h f l f b d k

Modelswithfinancialfrictionscanbeusedtoask

interestingpolicyquestions:

When there is an increase in risk spreads how should – Whenthereisanincreaseinriskspreads,howshould monetarypolicyrespond? – Howshouldmonetarypolicyreacttocreditvariables y p y andthestockmarket?