HuoRiosRullDiscussion

(1)

Financial Frictions, Asset Prices,

and the Great Recession

Zhen Huo and Victor Rios-Rull

Discussion by Grey Gordon

Indiana University

(2)

Research Q+A

Q: What model elements are needed to generate the last recession?

A: Changes in collateral requirements and mortgage interest rates

can generate it provided there is a

I

Goods market friction so falls in demand generate falls in

output.

I

Mismatch between wage and labor supply (to generate

persistent unemployment).

I

Adjustment costs so unwanted production capabilities in

nontradables are not switched to tradables.

(3)

Fed’s Fault? Fannie’s Fault?

Was the Fed to blame for “loose” interest rates generating too

much appreciation in the price of housing? Was Fannie to blame

for loose lending standards?

_{Figure 17: Aggregate Economy Response: Boom and Bust Cycle}

0 1 2 3 4 5 6 7 8 9 10

−1.5

−1

−0.5 0 0.5 1

0 1 2 3 4 5 6 7 8 9 10 4.5

5 5.5 6 6.5 7 7.5 8

Output Unemployment

0 1 2 3 4 5 6 7 8 9 10

−2 0 2 4 6 8 10 12

0 1 2 3 4 5 6 7 8 9 10

0 5 10 15 20 25 30

Housing price Debt

36

(4)

Heterogeneity and a Middle-Class Recession

Huo and Rios-Rull “Paradox of Thrift Recessions” generates

recessions via reduced-form demand shock, rep agent; “Tightening

Financial Frictions” has heterogeneity, but endowments.

Here, recession from lower LTV and higher interest rates.

Heterogeneity has interesting effects:

Figure 7: Cross-Sectional Change of Unemployment Rate

0 1 2 3 4 5 6 7 8 9 10

0 2 4 6 8 10 12 14 16

Skill typeϵ= 1

Skill typeϵ= 2

Skill typeϵ= 3

Skill typeϵ= 4

20070 2008 2009 2010 2011 2012 2013 2014 2015 2016 2 4 6 8 10 12 14 16

Less than high school High school Some college Bachelor and higher

Model U.S. economy

firms, but the magnitude of this channel is relatively small, which limits the shrinkage of the values of financial assets.

Figure 8: Change of Asset and Consumption for Diﬀerent Skill Types

0 1 2 3 4 5 6 7 8 9 10

−14 −12 −10 −8 −6 −4 −2 0

Skill typeϵ= 1 Skill typeϵ= 2 Skill typeϵ= 3 Skill typeϵ= 4

0 1 2 3 4 5 6 7 8 9 10

−10 −8 −6 −4 −2 0 2 4

Skill typeϵ= 1 Skill typeϵ= 2 Skill typeϵ= 3 Skill typeϵ= 4

Asset Consumption

6.3 Decomposition: Only Collateral Constraint or Borrowing Surcharge Changes

In this section, we explore the relative contribution of the change of the collateral constraint and the change of the borrowing surcharges. As discussed in Section??, these two shocks aﬀect households’ consumption decision in a similar way, both of which make households cut consumption expenditures.

Figure9compare the baseline model with the other two cases with one shock at each time. In terms of the contribution to output loss, the collateral constraint shock plays a bigger role then the borrowing surcharge shock. The collateral constraint shock force households to reduce their debt to meet the new requirement, and consequently, households have to reduce their housing demand. The increase of borrowing surcharge makes borrowing much

27

(5)

Getting the Right Portfolios

The authors generate this by matching household portfolios

correctly.

Campbell (2007) shows its the middle class who have housing as

their primary asset.

Housing Utility Function

Housing utility function

Engel Curve: consumption vs housing

0 1 2 3 4 5

−1.2 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 Housing Ho u si n g u ti li ty

1 2 3 4 5 6 7 8

2.5 3 3.5 4 4.5 5 Consumption Ho u si n g

Huo&R´ıos-Rull, NYU, Penn, UCL, CAERP Financial Frictions, Asset Prices, & the Great Recession UBC, October 26, 201528/57

We calibrate the endowment process to capture the earnings and wealth distribution in U.S. We use four discrete endowment levels, i.e.,✏2 {✏1,✏2,✏3,✏4}. We interpret the first three endowments as earnings for the majority of households, with type 1 being poor, type 2 being normal and type 3 being rich, and the fourth endowment level is intended to capture the super rich households in the economy as inCastañeda, Díaz-Giménez, and Ríos-Rull

(2003) orDíaz, Pijoan-Mas, and Ríos-Rull(2003). The transition probability and the levels of the first three states are calibrated to approximate an AR(1) process using the method byTauchen(1986). FollowingNakajima

(2012) andDomeij and Heathcote(2004), we set the persistence for the endowment process to be 0.91, and the standard deviation for the innovation term to be 0.20. We assume the first three types of households have the same probability of becoming the type 4 households who are super rich,⇡entry= ⇧✏i,4,i2 {1, 2, 3}, and that type 4 households return to one of the first three states with the same probability⇡exit= ⇧✏4,i,i2 {1, 2, 3}. We calibrate

the rest of the parameters to match the earning Gini index, 0.64 and the wealth Gini index, 0.82 (calculated from 2007 SCF data). Figure3show the comparison of the networth and housing Lorenz curve between our model and the U.S. economy.

Figure 3: Cross-Sectional Distribution

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Model Data

Wealth Lorenz curve Housing Lorenz curve

As shown inFang and Nie(2013), the job finding rate for diﬀerent education groups are similar to each other, but the job separation rate is substantially larger for workers with a low education level. To capture the heterogeneity in job separation rates, we choose the separation rate for type 1 workers to target an average job duration of 1.5 year and the separation rate for type 3 and type 4 workers to target an average job duration of 5 years. The job separation rate for type 2 workers are set to match the total unemployment rate, which implies a 2.5 year job duration.

4.4 Dynamic Parameters

The last group of parameters have less steady-state implications, and we set them according to their dynamic implications.

20

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Drop in Housing Prices

The model’s drop in house prices:

Figure 6: Aggregate Economy Response in the Baseline Model

0 1 2 3 4 5 6 7 8 9 10

−3.5 −3 −2.5 −2 −1.5 −1 −0.5 0 Baseline

0 1 2 3 4 5 6 7 8 9 10

6 6.5 7 7.5 8 8.5 9 Baseline

Output

Unemployment

0 1 2 3 4 5 6 7 8 9 10

−7 −6 −5 −4 −3 −2 −1 0 Baseline

0 1 2 3 4 5 6 7 8 9 10

−30 −25 −20 −15 −10 −5 0 5 Baseline

Consumption

Investment

0 1 2 3 4 5 6 7 8 9 10

−18 −16 −14 −12 −10 −8 −6 −4 −2 0 Baseline

0 1 2 3 4 5 6 7 8 9 10

−8 −7 −6 −5 −4 −3 −2 −1 0 Baseline

Housing price

Wealth

0 1 2 3 4 5 6 7 8 9 10

−35 −30 −25 −20 −15 −10 −5 0 Baseline

0 1 2 3 4 5 6 7 8 9 10

−1.5 −1 −0.5 0 Baseline

Debt

TFP

0 1 2 3 4 5 6 7 8 9 10

−1.5 −1 −0.5 0

Baseline

0 1 2 3 4 5 6 7 8 9 10

0 0.2 0.4 0.6 0.8 1 1.2 1.4 Baseline

Labor producitvity

Labor quality

26

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Drop in Housing Prices

Case-Shiller drop is 30%, but non-representative, weights by value.

.5

.6

.7

.8

.9

1

2000q1 2005q1 2010q1 2015q1

Time

Home Price Index (FHFA) Home Price Index (Case-Shiller)

(8)

Drop in Housing Prices

The house price change is the main driver of the model recession.

housing holding, and save more in financial assets. In addition, with changing housing price, the financial constraint will be further tightened, a channel which does not take place here neither. Over time, households accumulate more wealth and gradually adjust to their desired income-to-saving ratio. Higher wealth level allows households to consume more than the initial steady state, and the economy experiences a mild boom.

Figure 10: Aggregate Economy Response: Fixed Housing Price

0 1 2 3 4 5 6 7 8 9 10

−3.5 −3 −2.5 −2 −1.5 −1 −0.5 0 0.5 Baseline

Constant housing price

0 1 2 3 4 5 6 7 8 9 10

5.5 6 6.5 7 7.5 8 8.5 9 Baseline

Output Unemployment

0 1 2 3 4 5 6 7 8 9 10

−7 −6 −5 −4 −3 −2 −1 0 1 Baseline

0 1 2 3 4 5 6 7 8 9 10

−30 −25 −20 −15 −10 −5 0 5 Baseline

Consumption Investment

0 1 2 3 4 5 6 7 8 9 10

−8 −7 −6 −5 −4 −3 −2 −1 0 1 Baseline

0 1 2 3 4 5 6 7 8 9 10

−40 −35 −30 −25 −20 −15 −10 −5 0 Baseline

Wealth Debt

This exercise also implies that in an economy with an ad hoc borrowing constraint à la Aiyagari, the tightening of the borrowing constraint will only have limited effects on the real economy (seeGuerrieri and Lorenzoni(2011),Huo and Rios-Rull(2015), andPhillips and Herkenhoff(2015) for example). The change of ad hoc borrowing constraint only has direct effects on the very poor households (10% to 15% of households have negative net worth), while the change of collateral constraint affects a much larger fraction of people (70% of households have some mortgage debt). More importantly, the drop of housing price has significant effects on households’ wealth level.

29

(9)

Drop in Housing Prices and the Consumption Response

Like in emerging markets, consumption more volatile than output.

The consumption drop is 6%, output is 3%. Y and C follow similar

paths in data:

-.

0

5

0

.05

.1

.15

2006q1 2008q3 2011q1 2013q3 2016q1

Time

(10)

Goods Market Frictions

While the housing price change drives the recession,

goods market

frictions are a necessary ingredient

. Measured TFP drops.

Experiment 4.3: Goods Market Frictions

0 1 2 3 4 5 6 7 8 9 10

−3.5 −3 −2.5 −2 −1.5 −1 −0.5 0 Baseline

Low matching elasticity:µ= 0.05

0 1 2 3 4 5 6 7 8 9 10

6 6.5 7 7.5 8 8.5 9 Baseline

Real output Unemployment rate

0 1 2 3 4 5 6 7 8 9 10

−1.6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 Baseline

0 1 2 3 4 5 6 7 8 9 10

−18 −16 −14 −12 −10 −8 −6 −4 −2 0 Baseline

TFP Housing price

(11)

Goods markets frictions show up on household and firm side.

Households:

max

cN,cT,I,d

u

(

v

(

c

N

I

ρ

,

c

T

)

−

G

(

d

))

s.t.

pc

N

I

+

c

T

=

...

I

=

d

Ψ

d

ρ >

1: Need search disutility or

I

would go to

∞

.

Say instead of Armington aggregator for

v

, have Cobb-Douglas

v

(

x,

y

) =

x

γ

y

1−γ

. Then optimal search choice satisfies

Ψ

d

=

G

0

(

d

)

I

(12)

Equilibrium search intensity:

Mechanically, probability of goods market match Ψ

d

satisfies

Ψ

d

=

M

g

(

D

,

1)

/D.

In equilibrium, this must equal optimal search intensity

Ψ

d

=

G

0

(

d

)

I

γ

(

ρ

−

1)

v

.

(13)

Goods Market Frictions

0 0.2 0.4 0.6 0.8 1

D

Psi^d

Equilibrium in search

Matching probability, M^g(D,1)/D

Search intensity, G’(d)I/(gamma(rho-1)v)

(14)

0 0.2 0.4 0.6 0.8 1

D

Psi^d

Equilibrium in search after positive wealth effect (v increase)

Matching probability, M^g(D,1)/D

Search intensity, G’(d)I/(gamma(rho-1)v)

Search intensity, higher v

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Firms (nontradables):

Capital and total labor predetermined. Revenue/output is

Ψ

f

p

Z

c

N

where Ψ

f

=

M

g

(

D,

1) is an increasing function of

D

.

Positive wealth effect generates increased search intensity leading

to more output, revenue, and profit.

Solow residual is

z

=

Ψ

f

p

R

c

N

K

α

N

1−α

Positive wealth effects generate higher Ψ

f

, higher measured TFP.

(16)

Labor Force Participation

Every unemployed person prefers to be employed (ratio of home

production to lowest earnings is .5).

What if chose home production to match LFP rate?

(17)

Questions for future research

Services as a share of total consumption:

.45

.5

.55

.6

.65

.7

Se

rvi

ce

sh

a

re

o

f

to

ta

l

co

n

su

mp

ti

o

n

1960q1 1980q1 2000q1 2020q1

Time