demographics and monetary policy shocksnmark/wrkpaper/puc_chile.pdf · 2019. 3. 10. · 4 nar-gbk:...

Demographics and Monetary Policy Shocks

Demographics and MonetaryPolicy Shocks

K.A. Berg?

C.C. Curtis†

S. Lugauer±

N.C. Mark⊕

?Miami University†University of Richmond± University of Kentucky

⊕ University of Notre Dame and NBER

February 2019

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Demographics and Monetary Policy Shocks BLCM

Introduction (1)What we do

Compare consumptionresponse to monetarypolicy shocks of young,middle-aged, and oldhouseholds.

Why?Consumption is 70% ofGDPDemographic profile ischanging. Aging society

Figure: U.S. Pop 65+ divided byPop 25-64: 1950-2050

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 20500.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Will effectiveness ofmonetary policy change?

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Introduction (2)

What we do and useConsumption from CEXFour identified monetarypolicy shocks (fromother people)Two methods: StructuralVAR and local projection

What we findHighest response fromold households

Data also showsNet wealth increaseswith ageWorking age live onlabor incomeHigh income old moreresponsiveHigh and low incomeworking aged the same

How to explain?Life-cycle model thataccentuates differences inwealth, labor supply, anddiscounting the future.

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Literature

Monetary policy transmission under heterogeneity:Gornemann et al. (2012), McKay et al. (2016), and Luetticke (2016).

Monetary policy and inequality:Coibion et al.(2017), Bunn et al. (2018)

NK models with life-cycle aspects:Fujiwara and Teranishi (2007), Bielecki et al. (2018), Doepke andSchneider (2006)

Wealth effects of monetary policy:Krueger and Perri (2006), Sterk and Tenreyro (2018), Glover et al. (2017),Auclert (2019), Storesletten et al. (2007)

Monetary policy and mortgage (re)financeCloyne et al. (2018), Di Maggio et al. (2014), Wong (2018), Eichenbaum etal. (2018)

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Consumption DataConsumption data from CEX

1984Q1 to 2007Q419 real expenditure categories CEX Categories

Young (25-34), Middle (35-64), Old (65+)Urban and ruralPer capita

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The Monetary Policy Shocks

1 HFI-TRM: (Barakchian and Crowe (2013)). Factor structure for fed fundsfutures Term-structure for current month and at 1 through 5 months ahead.Shock is change in first factor on the day announcement following FOMCmeeting.

2 HFI-3MO: (Gertler and Karadi (2015)). Change in 3-month ahead fed fundsfutures within a 30 minute window of FOMC announcement.

3 HFI-CMO: (Gurkaynak et al.(2005) 1990-1993, Gorodnichenko and Weber(2016) 1994-2007) Change in urrent month fed funds futures within 60 minutewindow of FOMC announcement

4 NAR-GBK: (Romer and Romer (2004), Wieland and Yang (2016)) Residualfrom narrative accounts of FOMC meetings regressed on unemployment,Greenbook estimates of past and future inflation and GDP and revisions.

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Time-Series Properties of the Monetary Policy Shocks

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Structural VAR

1 gc,j,t = 100∆ ln(cj,t ) j = {young, middle,old}2 st monetary policy shock3 rt real fed funds rate

1 a12 a130 1 00 a32 1

gc,tstrt

=8

∑p=1

bp,11 bp,12 bp,130 0 0

bp,31 bp,32 bp,33

gc,t−pst−prt−p

+

uc,tus,tur ,t

,

(1)Structural error terms serially uncorrelated, diagonal covariance matrix, E (ut u′t ) = D.

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Cumulated consumption growth impulse response

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VAR Impulse ResponseYoung (24-34) Middle (35-64) Old (65+)

A. HFI-TRM

B. HFI-3MO

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VAR Impulse ResponseYoung (24-34) Middle (35-64) Old (65+)

C. HFI-CMO

D. NAR-GBK

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Old minus Young

Old minus Middle

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OMY and OMM VAR Impulse ResponseOld minus Young Old minus Middle

A. HFI-TRM

B. HFI-3MO

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OMY and OMM VAR Impulse ResponseOld minus Young Old minus Middle

C. HFI-CMO

D. NAR-GBK

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Impulse Response of Aggregate Consumption (NAcA)at Different Points in Time: 1990, 2010, 2030

NA∆ ln (cA,t ) = Nycy

cA∆ ln (cy ,t ) + Nm

cm

cA∆ ln (cm,t ) + No

co

cA∆ ln (co,t ) ,

(2)

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Impulse Response of Aggregate Consumption 2010minus 1990, 2030 minus 1990

Aggregate Response in 2010 minus 1990 and 2030 minus 1990HFI-TRM HFI-3MO HFI-CMO NAR-GBK

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Response of Aggregate Consumption 2010 minus1990, 2030 minus 1990B. Cumulated 20 Quarter Difference in Aggregate Response2010 minus 1990 and between 2030 minus 1990

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Local ProjectionsFor h = 1, . . . ,20, look at (−βh) from

ln

(ct+h

ct

)= βhst + ah,1 ln

(ct

ct−3

)+ ah,2 ln

(ct−4

ct−7

)+

7

∑j=0

bh,j rt−j + ut+h

(3)

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Local ProjectionsYoung (24-34) Middle (35-64) Old (65+)

A. HFI-TRM

B. HFI-3MO

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Local ProjectionsYoung (24-34) Middle (35-64) Old (65+)

C. HFI-CMO

D. NAR-GBK

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Old minus Young

Old minus Middle

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Local Projections OMY and OMMOld minus Young Old minus Middle

A. HFI-TRM

B. HFI-3MO

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Local Projections OMY and OMMOld minus Young Old minus Middle

C. HFI-CMO

D. NAR-GBK

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Nondurable Consumption

Old minus Young

Old minus Middle

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NondurablesOld-Young Old-Middle Old-Young Old-Middle

A. SVAR HFI-TRM LP HFI-TRM

B. SVAR HFI-3MO LP HFI-3MO

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NondurablesOld-Young Old-Middle Old-Young Old-Middle

C. SVAR HFI-CMO LP HFI-CMO

D. SVAR NAR-GBK LP NAR-GBK

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Robustness

Six age groupsDurable goodsConsumption minushousingCEX weights in agegroupVAR in levels

Six lags in VARLocal projectionswithout controlling forlagged consumptionRegressions with microdata Micro Data Regressions

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Looking for Evidence on a Mechanism

Motivating(i) Heterogeneous Wealth Effects

(ii) Heterogenous Funding for Consumption

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Income, Wealth, Portfolio Composition by AgeThe next three slides show,

1 Most people ‘retire’ before 65.Therefore, old eat mostly from non-labor income

2 Old people’s portfolios tilted towards long-term assetsPortfolio value more sensitive to interest rates.

3 Working age people eat mainly from labor income.They look like they hand-to-mouth

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Income, Wealth, Portfolio Composition by Age

Figure: Median and Mean Wage Income as a Share of Total Income by Age:1989, 1998, and 2007

25 30 35 40 45 50 55 60 65 70 75 80+

Age

0

0.2

0.4

0.6

0.8

1

198919982007

25 30 35 40 45 50 55 60 65 70 75 80+

Age

0

0.2

0.4

0.6

0.8

1

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Measures of Net Worth

1 Net Worth is total assets as stated by the SCF minus total debts.2 Net Long Term 1, is the sum of net property equity (value of

properties, including own residence, less outstanding debt on theproperties), stock holdings, stock mutual funds, and annuities - ameasure of long term, interest sensitive assets.

3 Net Long Term 2 is Net Long Term 1 plus non-stock mutual funds(but not money market funds) plus directly held bonds of alltypes.

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Measures of Net Worth

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Wealth and Income1 I want to run VARs with wealth, but I only have household

income data.2 Income and wealth are positively correlated.

Figure: Log Net Wealth and Log Income in 1989, 1998, and 2007

0

5

10

15

20

Log

Net

Wor

th

5 10 15 20

Log Income

1989

0

5

10

15

20

Log

Net

Wor

th

5 10 15 20

Log Income

1998

0

5

10

15

20

Log

Net

Wor

th5 10 15 20

Log Income

2007

Notes: Data is from the Survey of Consumer Finances.33 / 54


VAR High minus Low IncomeYoung (24-34) Middle (35-64) Old (65+)

A. HFI-TRM

B. HFI-3MO

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VAR High minus Low IncomeYoung (24-34) Middle (35-64) Old (65+)

C. HFI-CMO

D. NAR-GBK

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Local Projections High minus Low IncomeYoung (24-34) Middle (35-64) Old (65+)

A. HFI-TRM

B. HFI-3MO

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Local Projections High minus Low IncomeYoung (24-34) Middle (35-64) Old (65+)

C. HFI-CMO

D. NAR-GBK

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Modelling the Mechanism

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ModelWhat is a model that explains

1 Old consumption responds the most to monetary policy shock?2 Young consuumption more responsive than middle but less than

old?3 Emphasize wealth effect for old people, labor-leisure choice for

young people.

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A ModelMain Properties

1 Quarterly OLG model.2 Idiosyncratic income risk3 People can live 86 years4 Age 25-64

WorkUse labor income to eat and buy long-term bond.

5 Age 65-86Retired,Can die prematurely,Live off of accumlated bonds and pension incomeBequest motive

6 Epstein-Zin recursive utility

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Income Process: individual i cohort zLife-cycle of permanent income

Yi,z,t =

Yi,z−1,t−1MgGzeni,z,t z < z∗

prr Yi,z∗,t−1 z = z∗

Yi,z−1,t−1eni,z,t z > z∗.

Transitory income shock

ui,z,t =

{N(µu, σ2

u)

with probability (1− p)−∞ with probability p

Wage incomeWi,z,t = Yi,z,teui,z,t for z < z∗

Pension income

Si,z,t =

{Yi,z,t z = z∗

Yi,z,teui,z,t z > z∗.

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Income Process Parameter Estimates

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Long-Term Bond and Interest RateBond

1 Bond is real perpetuity (consol)2 Pays 1 unit consumption per bond3 Price Pa

t = 1rt

Interest rate1 AR(1) for Federal funds rate2 Expectations theory plus 1.309% term premium to get 10 year

bond rate.3 Five interest rate states: 3.08, 3.27, 3.44, 3.61, and 3.78 percent per

annum

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Utility and budget constraintsWorking aged

Vi,z,t =

{(1− β)

(Cν

i,z,t (1− Li,z,t )1−ν)(1−ρ)

+ β

[(Et

[V 1−γ

i,z+1,t+1

]) (1−ρ)(1−γ)

]} 1(1−ρ)

Pat Ai,z+1,t+1 = Ai,z,t + Pa

t Ai,z,t + Li,z,tWi,z,t −Ci,z,t ,

Rewrite as

Ai,z+1,t+1 = Ai,z,t + rt (Ai,z,t + Li,z,tWi,z,t −Ci,z,t ) .

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Utility and budget constraintsRetired

Vi,z,t =

(1− βδz,t )(

Cνi,z,t

)(1−ρ)+ β

[δz,t Et

(V 1−γ

i,z+1,t+1

)+

(1− δz,t )

1− γEt

((1b

Ai,z+1,t+1rt

)ν)1−γ] 1−ρ

1−γ

1

1−ρ

,

Ai,z+1,t+1 = Ai,z,t + rt (Ai,z,t + Si,z,t −Ci,z,t )

Last possible period (quarter 344)

Vi,Z ,t =

(Cνi,z,t)(1−ρ)

+ β

[1

1− γEt

((1b

Ai,z+1,t+1rt

)ν)1−γ] 1−ρ

1−γ

1

1−ρ

,

Parameters used

prr = 0.4

β = 0.9962 , ρ = 5

γ = 12, ν = 0.5.

b = 2.5

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Impulse ResponseExperiment Federal Funds Rate Impulse Response

1 Simulate economy until there are 344 cohorts2 10,000 individuals per cohort3 Drop interest rate from 3.44 % to 3.08% and hold for 8 quarters4 Interest rate rises to 3.27% for 7 quarters5 Interest rate resumes stochastic evolution6 Observe consumption response by age groups

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Impulse ResponseA. Log Consumption B. Number of Assets

C. Value of Assets D. Log Labor

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Net Asset HistogramsA. Young (25-34) B. Middle (35-64)

C. Old (65+)

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Impulse Response by Age and WealthA. Young (25-34) B. Middle (35-64)

C. Old (65+)

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Alternative Utility SpecificationsA. No Labor B. ψ = 1.5

C. ψ = 0.1,γ = 2 D. CRRA, γ = 12

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Conclusion

1 Largest response: Old consumption2 Second highest response (it seems): Young consumption3 Weath effect for the old4 Hand-to-mouth consumption of working aged5 Model qualitatively explains (most of) data features

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CEX Categories

Back To Orig

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Federal Funds Rate Impulse Response

Back

A. HFI-TRM B. HFI-3MO

C. HFI-CMO D. NAR-GBK

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Micro Data Regressions

Back

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demographics and monetary policy shocksnmark/wrkpaper/puc_chile.pdf · 2019. 3. 10. · 4 nar-gbk:...

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