lecture 6: news, recessions and liquidations · roadmap 1.cycles without in ation 2.the news view...

2017-2018 – DEEQA – TSE – Advanced Macroeconomics

Lecture 6: News, Recessions and Liquidations

Franck [email protected]

Version 1.107/03/2018

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[email protected]

Roadmap

1. Cycles Without Inflation

2. The News View

3. Liquidations

4. Economic Cycle: Some Further Evidence

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Part I : Cycles Without Inflation

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Roadmap of Part I

1. Motivations

2. A New Keynesian Model with Gains From Trade

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Roadmap

1. Motivations


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I.1. MotivationsThe modern approach to business cycles fluctuations: Shocks

I The economy is hit by “shocks”,

I Realistic shocks are either “supply” or “demand”,I Supply:

× Technology,× Oil price,× Taxes.

I Demand:

× Monetary shocks,× Fiscal,× Investors mood.

I Empirical work (Smets and Wouters) brings a lot of shocks (not always realistic)(preference shocks, markup shocks, shocks to arbitrage equations, etc...).

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I.1. MotivationsThe modern approach to business cycles fluctuations: Models

I Models are of two types: “Real Business Cycles” Models and “New-Keynesian”ones:

I Real Business Cycles:

× Flexibles Prices,× Supply shocks,

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I.1. MotivationsReal Business Cycles Models

Y

P

Agg

rega

teS

up

ply

Aggregate Demand

Y

Aggregate Demand

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I.1. MotivationsReal Business Cycles Models

Y

P

Agg

rega

teS

up

ply

Aggregate Demand

Y

Aggregate Demand

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I Models are of two types: “Real Business Cycles Models” and “New-Keynesian”ones:

I New-Keynesian Models:

× Prices are sticky,× Monetary rules (Taylor rules) matter,× Demand shocks.

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I.1. MotivationsNew Keynesian Models

Aggregate Demand

Y

P

Aggregate Supply

Aggregate Demand

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I.1. MotivationsNew Keynesian Models

Aggregate Demand

Y

P

Aggregate Supply

Aggregate Demand

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I Both models and shocks have hard time to explain the recent periods (last 30years).

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I.1. MotivationsSome Intriguing Facts over the last 3 cycles

I Over the last 30, the US economy (can be extended to the Eurozone) hasexperienced an intriguing type of business cycle.

I Let’s look at

× Detrended hours H× Detrended Y× Raw core inflation

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I.1. MotivationsSome Intriguing Facts over the last 3 cycles: Non inflationary business cycles

%

Hours

1960 1970 1980 1990 2000 2010−5

0

5

%

GDP

1960 1970 1980 1990 2000 2010−5

0

5

%

Core CPI inflation

1960 1970 1980 1990 2000 2010−2

0

2

4

15 / 144

I.1. MotivationsIntriguing Facts for Usual Shocks and Models

I I discuss the following points:I Demand shocks?

× Should be inflationary if they move output.× Should not move output if not inflationary.× If flex prices, C and I move in opposite direction.

I Supply shocks?

× Productivity should be procyclical× Investment Specific Technology shocks: investment price should be countercyclical

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I.1. MotivationsThe Trouble with NK Models

I If output moves because the output gap moves (demand shocks), then we shouldexpect inflation response.

I The core NK model is quantitatively off target.I Take the Jordi Galı’s textbook New Phillips curve

πt = βEtπt+1 + κyt + ut

I Assume that the output gap is AR(1) with persistence ρ.

πt =κ

1− βρyt + ut

I Take Jordi’s textbook calibration (including a mean duration of prices of 3quarters).

I Assume that the output gap is measured by the HP cycle of output.I Feed it into this last equation and deduct the implied inflation, killing cost-push

shocks.17 / 144

I.1. MotivationsThe Trouble with NK Models

%

1990 1995 2000 2005 2010−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

Actual inflation

NPC predicted

I Post Volcker, NPC implies that s.d. of inflation is 350% of the actual one18 / 144

(Long) IntroductionThe Trouble with NK Models - Output gap is AR(2)

%

1990 1995 2000 2005 2010−1.5

−1

−0.5

0

0.5

1

1.5

Actual inflation

NPC predicted

I Post Volcker, NPC implies that s.d. of inflation is 177% of the actual one19 / 144

I.1. MotivationsThe Trouble with RBC Models

I This pattern is hard to explain in the RBC tradition :

I No procyclical TFP shocks.

I No pro cyclical Investment Specific Technology shocks: investment relative pricesare not countercyclical.

I Demand shocks typically cause negative consumption - investment co-movements.

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I.1. MotivationsThe Trouble with RBC Models: TFP

%

1960 1970 1980 1990 2000 2010−6

−4

−2

0

2

4

Hours

TFP%

1960 1970 1980 1990 2000 2010−6

−4

−2

0

2

4

GDP

TFP

I Post-Volcker, correlation between hours worked and TFP is -.64, correlationbetween GDP and TFP is -.23.

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I.1. MotivationsThe Trouble with RBC Models: IST shocks

%

1960 1970 1980 1990 2000 2010

−4

−2

0

2

4

Hours

Qual.Adj.Rel. I price

I Post-Volcker, correlation between hours worked and relative price of investment is.56.

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I.1. MotivationsThe Trouble with RBC Models: IST shocks

Table 1: Various measures of the relative price of investment, deflating with core CPI,correlations with Hours

Variable 1960Q1-2012Q3 Post-Volcker

Qual.Adj.I -0.07 0.56Fixed I 0.42 0.76Non Res.I 0.09 0.63Struct.I 0.44 0.75Equip.I -0.25 0.17PPI Equip. -0.24 0.11Resid.I 0.70 0.80SP500 0.31 0.56

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I.1. MotivationsThe Trouble with RBC Models: Demand Shocks

%

1990 1995 2000 2005 2010−4

−2

0

2

4

GDP

C

%

1990 1995 2000 2005 2010

−10

0

10

GDP

I

I Post-Volcker, correlations with HP filtered output are .92 for C and .91 for I .24 / 144

I.1. MotivationsThe Trouble with RBC and NK Models

I Possible to ”fix” these commonly used RBC or NK models to fit facts: “MarginalEfficiency of Investment” shocks, preference shocks, fixed price(“backward-looking” Phillips curve), adjustment costs to the investment rate,in-sample correlation of shocks, etc...

I Those explanations in our opinion are not very compelling, intuitive or robust.

I I will review some. models that propose different mechanisms to interpret thedata.

I One is related to the role of trade between agents.

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I.1. MotivationsA Story

I Spain

I Two types of households

I Carpenters and Farmers

I Houses (capital good) and tomatoes (consumption good)

I In the short run, specialization is given.

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I.1. MotivationsA Story (continued)

I The carpenter needs to eat, the farmer needs a shelter

I Static Gains from Exchange (from Trade) between the two.

I Assume that the perceived value of houses decreases (downward revision ofexpectations, bad news, pessimism, ...)

I The relative price of houses in terms of tomatoes p will go downI The carpenter will work less for two reasons

1. he does not want as many houses as before2. he cannot trade as many houses as before

I LI and I I The farmer does not want to buy as many houses as before, and does not need to

produce as many tomatoes for the trade: LC and C

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I.1. MotivationsA Story (continued)

I C, I, L in both sectors, Y = C + pI I If reallocation of workers take some time, the recession is likely to be protracted.

I Changes in perceptions about the future are affecting the width of Gains fromTrade today

I Fluctuations are here related with variations in the amount of Gains from Tradebetween agents.

I This is about natural output fluctuations → does not move inflation.

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I.1. MotivationsTo Be Compared To The Traditional Story

I The same household produces both houses and tomatoesI If the perceived value of houses goes down, it is time to

× work less in constuction× work more in farming

I C, I

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Roadmap of Part I

1. Motivations


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I.2. A New Keynesian Model with Gains From TradeThe basic model

I Let’s start with the textbook New Keynesian model.

I ct =

(∫ 10 c

ε−1ε

jt dj

) εε−1

I∑βt (ln(cct) + Φ(1− `Ct)) .

I Monopoly j :Cjt = AtLjtI Calvo price setting

I Monetary authorities follow a Taylor rule.

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I This basic model collapses to a Phillips curve and a dynamic IS equation + Taylorrule

yt = − (ıt − Et πt+1 − ρ nt ) + Et yt+1

πt = βEt πt+1 + λ (yt − y nt )

+ Taylor rule

I Output gap: yt = yt − y nt

I y nt = At

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yt = − (ıt − Et πt+1 − ρ n

t ) + Et yt+1


+ Taylor rule

I What happens in this environment if agents expect At+1 to be high?

I It is a demand shock to current consumption.

I Absent of an active Taylor Rule, inflation.

I The increased expectation of At+1 does not directly enter into the Phillips curve.

I Therefore, if it creates an output boom, it will create inflation (no change in thenatural output)

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yt = − (ıt − Et πt+1 − ρ n

t ) + Et yt+1


+ Taylor rule

I What happens in this environment if agents observe high At?

I It is a supply shock .

I It shows up directly in the the Phillips curve (y nt = At).

I Therefore, if it creates an output boom, it will not create inflation (changes in thenatural output)

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I Textbook prescription: to keep stable inflation, monetary authorities need tostrongly counteract demand shock but need to accommodate supply shocks.

I If the economy goes into recession due to a fall in demand – as opposed to areduction in supply capacity – this should put substantial downward pressure onprices.

I Let’s contrast those results with the one of a gains-from-trade model.

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I.2. A New Keynesian Model with Gains From TradeThe gains-from-trade model

I Add a mass 1 of agents (type 2)

I Type 2 individuals produce some capital good (full depreciation): Kt+1 = L2t .

I Preferences: ln(C2t + ψ2

2 (1− L2t)2)

.

I Capital market is flex-price and competitive.

I Production of consumption good: Cjt = ΘtKjt + L1jtI If then mass of type 2 individuals is 0, then there is no capital supply and the

model is the previous one.

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I.2. A New Keynesian Model with Gains From TradeThe gains-from-trade model

I The log linear approximation of the model isyt = −ζ (ıt − Et πt+1 − ρ n

t ) + Et yt+1

πt = βEt πt+1 + λζ−1 (yt − y nt )

+ Taylor rule

I Natural or non-inflationary level of output y nt = φ2At + φ1Et

[Ωt+1

]where

Ωt+1 = Θt+1 − At+1

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I.2. A New Keynesian Model with Gains From TradeA new new Phillips curve

πt = βEt πt+1 + λζ−1(yt − φ2At − φ1Et

[Ωt+1

])I Consider believes that Ωt+1 will be high.

I Agents of type 1 to want to by capital as it return is expected to be high.

I They will trade more with agents of type 2.

I Type 2 agents will also want more capital, but also more consumption because ofan income effect.

I This would look like a demand shock.

I But it does not put any pressures on prices, as the natural or non-inflation rate ofoutput has also changed.

I Same thing with downward revisions: recession but no deflation.

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I.2. A New Keynesian Model with Gains From TradeA useful framework

I Useful model to think of the last episodes of non-inflationary expansions andrecessions.

I Booms and busts are mainly driven by expectations on investment return.

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Part II : The News View

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Roadmap of Part II

1. The News View

2. Informal and VAR Evidence

3. An Analytical Framework

4. Discussion / Extensions

5. Applications

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Roadmap of Part II

1. The News View




5. Applications

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II.1. The New view

I Here I present a set of facts/ideas that I think might be relevant

× to understand the three recent business cycles in the developed world (US, Europe)× to understand some previous episodes like the 1997 asian crisis× to design policies when looking forward at emerging economies

I It could be an interesting framework to discuss/work on issues like housingbubbles, waves of investment craze driven by expectations, etc...

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II.1. The New viewThe news view of business cycles

I Boom/bust are mainly the result of agents having incentives to continuouslyanticipate the economy’s future demands.

I Properly anticipating a future need , gains by trying to preempt the market andinvest early as to make goods readily available when the predicted needseventually appear.

I If many agents adopt similar behavior, because they receive related news aboutfuture developments, a boom period.

I However, by the very fact that such behavior involves speculation it will besubject to error.

I In the cases of error, the economy will have over-invested as the anticipateddemand will not materialize.

I This will cause a recession and a process of liquidation.I Both the boom and the bust are direct consequences of people’s incentive to

speculate on information related to future developments of the economy.

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II.1. The New viewAn example: the satellite industry

I 1990s: anticipation of high demand for bandwidth (related to the internet boom)

I The launching of telecommunication satellites exploded.

I In 1998, the satellite industry launched 150 satellites, 300 percent growthcompared to 1993.

I The boom was not caused by a technological improvement nor an increase ofcurrent demand for bandwith.

I The growth rate for demand for satellite bandwidth has been 31% between 1995and 2003, while the supply of satellite bandwidth grew by 54% during this sametimeframe

I At the beginning of the new millenium, demand failed to materialize transponder glut.

I The number of satellites launched went down to 75 in 2001 and 69 in 2003,against 150 in 1998.

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II.1. The New viewAn example: the satellite industry (continued)

I Variety, March 25, 1998 : “Sat glut in Arab skies: With a glut of satellitetransponder space available in the crowded Mideastern skies, the region looks setfor a cutthroat price war.Arabic channels already have the multinational Arabsat as well as Eutelsat andIntelsat to choose from, and in late April these will be joined by Egypt’s Nilesat,which will have 72 transponders available for rental.”

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II.1. The New viewAn example: the satellite industry (continued)

I Satellite News, Nov 4, 2002 : “Companies that purchased satellite transpondercapacity during the industry’s halcyon days are now struggling to resell thecapability in the face of falling market prices.One example is Globecomm Systems [GCOM], a Hauppauge, N.Y.-based providerof end-to-end satellite-based communications services. To rid itself of carrying$400,000 in unused capacity each month, Globecomm reached a broad-basedagreement with one of its vendors to cut the company’s future obligations forsatellite bandwidth capacity.”

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II.1. The New viewNews?

I Let’s consider an exogenous variable, say productivity θ

I Y = θF (K ,H)

I θt = ρθt−1 + εt

I θt = ρθt−1 + εt−q

I Signal St = εt + ηt , where ηt is a Gaussian white noise error term.

I Note : This is an example, News need not to be only about technology.

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Roadmap of Part I

1. The News View




5. Applications

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II.2. Informal and VAR Evidence

I Here I display some informal evidence showing that there is evidence of newsdriven BC and liquidation cycles

I By liquidation cycle I mean the following sequence

× First phase of capital accumulation caused by rosy expectations.× Second phase of downwards expectations revision.

I Then I show evidence for VARs

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I Think of some stochastic G.E. model in which shocks to TFP are random walk.

θt = θt−1 + εt

I The state of the economy is summarized byKθ

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Figure 1: A surprising increase in θ

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Figure 2: A surprising increase in θ

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Figure 3: A surprising decrease in θ

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II.2. Informal and VAR EvidencePredictions for K

θ

I If Kθ is low, the model predicts that employment should be high

I If Kθ is high, then employment should be low as there are low returns to capital

accumulation + wealth effect.

I According to a “surprise” view, employment and Kθ should be strongly negatively

correlated.

I That predictions holds in the same way if nominal rigidities are added (although itdepends on the monetary rule)

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II.2. Informal and VAR EvidenceThe News view

I The news view of business cycles suggest that employment and Kθ should have a

modest positive correlation

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Figure 4: A news about future increase in θ

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Figure 5: A downward revision of θ

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II.2. Informal and VAR EvidencePredictions for K

θ

I If employment booms arise because capital accumulation anticipates growth inTFP, then employment and K

θ should be positively correlated in booms.

I Major recessions in the news view of business cycles arise when Kθ is high and

expectations no longer support such a high capital stock and this leads to arecession

I The second force should contribute to a negative correlation between employmentand K

θ

I Since on average agents should be right more often then wrong, it suggests thatemployment and K

θ should be positively correlated according to the news view.

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Figure 6: Kθ and H, US 1948-2012

%

1950 1960 1970 1980 1990 2000 2010ï5

0

5

Capital Stock/ TFPHours

I correlation = .30 60 / 144

II.2. Informal and VAR EvidenceRecession episodes

I In the “surprise” view, recessions are caused by negative technological shock.

I Major recessions are therefore caused by major shocks to TFP, that are notcorrelated with the level of K

θ .

I In the news view, major recessions occur when Kθ is high and expectations revised.

I On should expect Kθ to be high the periods before a major recession.

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Figure 7: Mean HP deviation of Kθ the year of a NBER peak

1940 1950 1960 1970 1980 1990 2000 2010−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

Date

%

I Kθ is high the year before a recession starts.

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II.2. Informal and VAR EvidenceShocks

I Why are News/Revision shocks not only possible but also of interest?

I Because technological surprises are not the story (and no obvious observed“demand shocks”)

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II.2. Informal and VAR EvidenceTypical TFP shocks are not good candidates for the post Volcker period

1950 1960 1970 1980 1990 2000 2010ï0.5

0

0.5

1

date

corrected TFPuncorrected TFP

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1950 1960 1970 1980 1990 2000 2010ï0.5

0

0.5

1

date

Kydland−PrescottTime to Build


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1950 1960 1970 1980 1990 2000 2010ï0.5

0

0.5

1

date

Kydland−PrescottTime to BuildKydland−PrescottTime to BuildKydland−PrescottNobel Prize


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II.2. Informal and VAR EvidenceNeither are IST shocks

I If IST shocks, investment is cheap in booms, expensive in recessions.

I Various measures of the relative price of investment, deflating with core CPI,correlations with Hours:

Variable 1960Q1-2012Q3 Post-Volcker

Fixed I 0.42 0.76Struct.I 0.44 0.75Equip.I -0.25 0.17Resid.I 0.70 0.80SP500 0.31 0.56

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II.2. Informal and VAR EvidenceIdentification of a News Shock in VARs

I View the data as being generated by many shocks among which:

× Only 2 have a permanent effect on TFP (surprise and news)× Only 1 has a impact effect on TFP (surprise)

I A news shock is therefore a shock

× That has a permanent effect of TFP× That has no impact effect on TFP

I What is the response of the economy to such a shock (US data)

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II.2. Informal and VAR EvidenceResponse to a TFP news

(a) TFP (b) Stock Prices (c) Consumption

0 10 20 30 40 50ï0.4

ï0.2

0

0.2

0.4

0.6

0.8

1

Periods

%

0 10 20 30 40 500

2

4

6

8

10

Periods

%

0 10 20 30 40 500

0.5

1

1.5

Periods

%

(d) Investment (e) GDP (f) Hours

0 10 20 30 40 50ï1

0

1

2

3

4

Periods

%

0 10 20 30 40 500

0.5

1

1.5

Periods

%

0 10 20 30 40 50ï1

ï0.5

0

0.5

1

1.5

Periods

%

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Roadmap of Part I

1. The News View




5. Applications

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II.3. An Analytical FrameworkOverview

I A quite simple analytical model

I I show that investment is explicitly driven by future profit opportunities

I I show how boom/bust cycles can be generated

I The discipline for some assumptions and the choice of functional forms:tractability, not quantitative relevance.

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II.3. An Analytical FrameworkHouseholds

I

E0

∞∑t=0

βt[ln (Ct) + ν ·

(L− LTt

)]I The household can:

× buy some or all of its consumption on the market× supply labor time to the market× produce household consumption× buy one-period bonds× trade in firm shares.

I

CMt + Bt+1 +

N∑i=1

PitZit+1 = wtLMt + Bt(1 + rt) +

N∑i=1

PitZit +N∑i=1

ditZit+1

I Home production: CHt = αLHt

I Ct = CMt + CH

t and LTt = Lt + LHt72 / 144

II.3. An Analytical FrameworkProduction side

I Set of intermediate sectors i = 1, . . . ,N

I final good sector that aggregates the intermediate goods into a marketconsumption good according to:

CMt =

[N∑i=1

1

N(θitXit)

φ

] 1φ

, φ < 1

I Intermediate sectors and final market consumption good sector are competitive.

I Market consumption good is the numeraire

I Price of the intermediate goods: Pit

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II.3. An Analytical FrameworkIntermediate goods (1)

I The representative firm in sector i produces the intermediate good Xit :

Xit = Kγit (LPit )1−γ , 0 < γ ≤ 1

I The firm also hires labor LIit to build up its capital stock:

Kit+1 = Iit + (1− δ)Kit ,

with Iit = ln(LIit)

I Let dividends be dit = PitXit − wtLit

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II.3. An Analytical FrameworkIntermediate goods (2)

I The firm maximizes expected discounted stream of dividends:

maxLPt ,L

It

E0

∞∑t=0

1

1 + R0,t(PitXit − wtLit)

subject toXit = Kγ

it (LPit )1−γ , 0 < γ ≤ 1

Kit+1 = ln(LIit) + (1− δ)Kit

I Firms are assumed to finance themselves by retained earnings

I Shares in each sector are normalized to 1.

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II.3. An Analytical FrameworkWalrasian equilibrium

Result 1

The equilibrium is characterized by a constant wage rate wt = α and a constantinterest rate (1 + rt) = 1

β .

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II.3. An Analytical FrameworkFirm Behavior (1)

I This result makes the problem of the intermediate firm easier to solve.

I Optimal investment:

Iit = ln

βαγ(1− γ)

1−γγ Et

∞∑j=0

(β(1− δ))jP1γ

it+j+1

I Investment is purely forward looking

I Need to build capacity in anticipation of the economy’s future needs.

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I Optimal labor demand:

Lit = LPit + LIit

= Kit

((1− γ)Pit

α

) 1γ

+β

αγ(1− γ)

1−γγ Et

∞∑j=0

(β(1− δ))jP1γ

it+j+1

I Employment itself is partially determined by expectations of future capital prices,that signal future (expected) needs of capital.

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I Intermediate firms need to form expectations about the future price of theiroutput Pit+j :

Pit+j =1

N

(CMt+j

Xit+j

)1−φ

θφit+j

I To form expectations of future prices, firms will want to have information relevantfor predicting aggregate consumption, aggregate production in their sector andtechnological change in their sector.

I Some of this information may be contained in current news (for example signalsabout future changes in the θ’s).

I This is in general a hard problem to solve (see later), but tractable in the caseφ = 0.

CMt =

[N∑i=1

1

N(θitXit)

φ

] 1φ

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II.3. An Analytical FrameworkThe case φ = 0

CMt =

[N∑i=1

1

N(θitXit)

φ

] 1φ

I When φ = 0, Pkt+j writes

Pkt+j =1

N

CMt+j

Xkt+j=

1

N

(ΠNi=1(Xit+j)

1N

)Xkt+j

(ΠNi=1(θit+j)

1N

)I The firm needs predict future market consumption and the future output level

that will be offered in his sector (its market share, the demand for its own good).I However..., at the symmetrical equilibrium

Pkt+j = Θt+j

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II.3. An Analytical FrameworkNews and noise

I Technology index Θ follows an autoregressive process:

Θt = Θ + ρΘt−1 + εt−q, 0 ≤ ρ < 1

I εt is a Gaussian white noise process.

I SignalSt = εt + ηt

I ηt is a Gaussian white noise error term.

I Let’s study the response of the economy to news and noise in that tractable casewhere φ = 0 and γ = 1, so that L is fully forward looking.

Xit = Kγit (LPit )1−γ

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II.3. An Analytical FrameworkWhen γ = 1 (1)

I Labour demand writes:

Lit =β

αEt

∞∑j=0

(β(1− δ))jPit+j+1

I Let ψ = σ2ε

σ2η+σ

2ε

captures the information content of the signal St with respect to εt

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II.3. An Analytical FrameworkWhen γ = 1 (2)

I Relative deviation of L from SS:

Lt =(1− (1− δ)β)

(1− ρ(1− δ)β)×q−1∑

j=0

((1− δ)β)q−j−1ψεt−j +∞∑j=q

ρj−q+1εt−j

+

q−1∑j=0

((1− δ)β)q−j−1ψηt−j

I In comparison

Θt =∞∑j=0

ρjεt−q−j

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II.3. An Analytical FrameworkIRF (1)

I Numerical illustration: β = .99, δ = .025, ρ = .999, q = 8, ψ = .8, α = 25 andκ = 0.1.

I Consider successively news and noise shocks.

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II.3. An Analytical FrameworkIRF to news Figure 2:

0 5 10 15 20

0

2

4

6

8

10

x 10!3 Response of Lt+j to !t

j0 5 10 15 20

0

2

4

6

8

10

x 10!3 Response of !t+j to !t

j

0 5 10 15 20

0

2

4

6

8

10

x 10!3 Response of Lt+j to "t

j0 5 10 15 20

0

2

4

6

8

10

x 10!3 Response of !t+j to "t

j

51

Figure 2:

0 5 10 15 20

0

2

4

6

8

10


j0 5 10 15 20

0

2

4

6

8

10


j

0 5 10 15 20

0

2

4

6

8

10


j0 5 10 15 20

0

2

4

6

8

10


j

51

I On receiving the news employment starts to increase immediately, while Θ doesnot change

85 / 144

II.3. An Analytical FrameworkIRF to noise

Figure 2:

0 5 10 15 20

0

2

4

6

8

10


j0 5 10 15 20

0

2

4

6

8

10


j

0 5 10 15 20

0

2

4

6

8

10


j0 5 10 15 20

0

2

4

6

8

10


j

51

Figure 2:

0 5 10 15 20

0

2

4

6

8

10


j0 5 10 15 20

0

2

4

6

8

10


j

0 5 10 15 20

0

2

4

6

8

10


j0 5 10 15 20

0

2

4

6

8

10


j

51

I A key feature of this model is that it creates potentially large reversals inemployment as the result of agents re-evaluating their information.

86 / 144

II.3. An Analytical FrameworkAsset prices

I Define

Pit = Et

∞∑j=0

βidit+j = Et

∞∑j=0

βj(Pit+jKit+j − wtLit+j)

I First-order approximation:

Pt =

q−1∑k=0

K

βq−k

1− ρβ ψ +[1 + α (1− δ) L

]Bk−1ψ

εt−k

+∞∑k=q

K

ρk−q

1− ρβ +[1 + α (1− δ) L

] [Ak−1 + Bk−1 (1− δ)k−q ψ

]εt−k

+

q−1∑k=0

K

βq−k

1− ρβ ψ +[1 + α (1− δ) L

]Bk−1ψ

ηt−k

+∞∑k=q

[1 + α (1− δ) L

]Bq−1 (1− δ)k−q ψ

ηt−k

87 / 144

II.3. An Analytical FrameworkStock prices

Figure 3:

0 5 10 15 20

0

1

2

3

4

5

6

7

8

9

Response of P st+j to !t

j0 5 10 15 20

0

1

2

3

4

5

6

7

8

9

Response of P st+j to "t

j

Figure 4:

0 5 10 15 20!1

!0.5

0

0.5

1

1.5

2

2.5

3

3.5x 10

!3 Response of It+j to !t

j

52

Figure 3:

0 5 10 15 20

0

1

2

3

4

5

6

7

8

9


j0 5 10 15 20

0

1

2

3

4

5

6

7

8

9


j

Figure 4:

0 5 10 15 20!1

!0.5

0

0.5

1

1.5

2

2.5

3

3.5x 10


j

52

I Remark: If L bears adjustment costs, the stock price would be the best indicatorof the agents’ information and would be the first variable to move in response tonews. (key insight for identification of news)

88 / 144

Roadmap of Part I

1. The News View




5. Applications

89 / 144

II.4. Discussion / ExtensionsThe non-trivial information processing problem, and the interpretation of errors (1)

CMt =

[N∑i=1

1

N(θitXit)

φ

] 1φ

I With φ = 0, the model boils down to a boring expectation of an exogenousvariable Θ.

I When φ 6= 0, the prediction problem faced by firms becomes much more involved.I To see this, let’s consider another specific version of the model:

× φ 6= 0× δ = 1× θjt = θ + εjt−1 (iid)× Sit = εjt + ηit (one period news)

90 / 144

II.4. Discussion / ExtensionsThe non-trivial information processing problem, and the interpretation of errors (2)

I We obtain

EjtPjt+1 = Ejt

∑N

i=1(εit+1 ln(Eit [

βαPit+1]

)φXjt

1−σ

εφjt+1

I Non linear problemI Complementarity structure: firms in one sector will want to increase their

production if they expect others to increase their production, regardless of theactual news received.

I There is a rational expectation solution with common informationI Errors by some firms lead to errors by other firms: if limited ability to process

information, cycles can occur.I News shocks generally coined as “augmented TFP” shocks, but this is a restricted

view91 / 144

II.4. Discussion / ExtensionsRecessions as Liquidation Periods (1)

I Booms are periods in which expectations might be rosy (because of the noise).

I The post-bust periods (after expectation revisions) are periods of liquidation, asinvestment falls and capital is depleted.

I However, I and L simply return to SS because we have a simplified setup.I Model modification:

× CMt = ΘtXt − κX 2

t

2 (DRS)× Keep noisy signals on Θ× Iit = 2(LIit)

12

92 / 144


I Solution is

Kt+1 = λKt +βλ

1− δ

∞∑j=0

(βλ)jEtΘt+1+j

I and

It = Ω∞∑j=0

φjηt−j

where

Ω ≡ β (βλ)q+1

(1− δ) (1− ρβλ) (1− βλ2)

φj ≡

λjλ[(βλ2

)−1−j − 1]− (1− δ)

[(βλ2

)−j − 1]

ψ if j < q

− (1− δ − λ)[(βλ2

)−q − 1]λjψ if j ≥ q

93 / 144


Figure 3:

0 5 10 15 20

0

1

2

3

4

5

6

7

8

9


j0 5 10 15 20

0

1

2

3

4

5

6

7

8

9


j

Figure 4:

0 5 10 15 20!1

!0.5

0

0.5

1

1.5

2

2.5

3

3.5x 10


j

52

94 / 144

II.4. Discussion / ExtensionsThe efficiency of news-driven booms and busts

I In that basic model, fluctuations are constrained-efficient.

I But our core mechanism does to rely on constrained efficiency.

I Assume a constant labor market wedge: w = α(1 + τ), τ > 0

I Same positive implictions

I Different normative ones: in a noise driven boom, more employment andconsumption the economy gets closer to an efficient outcome.

I The boom is a good period even if expectations are not met eventually.

I (This is not an unconditional statement)

I But one can think of a wedge that varies with the business cycle.

95 / 144

II.4. Discussion / ExtensionsExpectations of new markets as a form technological news

I News need not to be about future productivity.

I Assume

Ct = Nξt

(Nt∑i=1

X σit

) 1σ

, σ < 1

I Nt is now the driving force, and expands over time, with news and noise.

I Symmetric equilibrium:

Ct = Nξ+ 1

σt Xt

I Hence, much of the literature on technological news can be re-interpreted asmodels with news of expanding market.

I Assume ξ = − 1σ , monopoly power for each intermediate and a setup cost to

implement a new variety: totally inefficient business cycles driven by changes inNt .

96 / 144

II.3. To Go Further

97 / 144

Roadmap of Part I

1. The News View




5. Applications

98 / 144

II.5 Applications: Japan Lost Decade

I How to explain the “bubble burst” of the early 90’s, then the slow growth of the90’s

I A downward revision of TFP growth (that is left to be explained)

I This is what the data suggest.

99 / 144

II.5 Applications: Japan Lost DecadeEstimated News Shocks

1965 1970 1975 1980 1985 1990 1995 2000 2005−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

ε2

ε1 tilde

100 / 144


I What do we get from this figure?

I Two stock market shocks at the beginning of the 1990s, that where possibly theconsequence of bad news about future TFP, explain most of TFP changes in the1990s and about half of the stock market variations.

I Gives some rationale to Hayashi and Prescott (2002) intuition:

101 / 144


“ In examining the virtual stagnation that Japan began experiencing in the early 1990s,we find that the problem is not a breakdown of the financial system [...]. The problemis low productivity growth. [...] We said very little about the “bubble” period of thelate 1980s and early 1990s, a boom period when property prices soared, investmentas a fraction of GDP was unusually high, and output grew faster than in any otheryears in the 1980s and 1990s. We think the unusual pickup in economic activities,particularly investment, was due to an anticipation of higher productivity growth thatnever materialized. To account for the bubble period along these lines, we need tohave a model where productivity is stochastic and where agents receive an indicatorof future productivity.” (italics added by myself)

(page 227-228), Hayashi & Prescott, Japan’s Lost Decade, RED, 2002

102 / 144

II.5 Applications: Japan Lost DecadeHistorical Decomposition of the 1990s

1988 1990 1992 1994 1996 1998 20000

5

10

15

20

25

TFP

, %

No Shocks after 1989

ActualPredicted

1988 1990 1992 1994 1996 1998 20000

5

10

15

20

25

TFP

, %

No Shock ε2 or ε

1 tilde in 1990 & 1992

No ε2

No ε1 tilde

103 / 144

Historical Decomposition of the 1990s

1988 1990 1992 1994 1996 1998 2000−120

−100

−80

−60

−40

−20

0

20

SP, %

No Shocks after 1989

ActualPredicted

1988 1990 1992 1994 1996 1998 2000−120

−100

−80

−60

−40

−20

0

20

SP, %

No Shock ε2 or ε

1 tilde in 1990 & 1992

No ε2

No ε1 tilde

104 / 144

II.5 Applications: A theory of international comovementsInternational Business Cycle Comovements

I At business cycle frequencies, Y , C , I , H are:- positively correlated within countries,- pairwise positively correlated among countries.

I Local technology shocks imply reallocation of mobile inputs negative comovements unless almost perfectly correlated shocks.

I As technology shocks are not ’global enough’, ’Demand’ shocks and frictions areneeded for synchronization business cycles.

105 / 144

II.5 Applications: A theory of international comovements

I We show theoretically that shocks to expectations (news) are indeed creatinginternational comovements. (even if the realization is local, information is global)

I We identify in the data a particular news (TFP news), and show that it isinternationally transmitted (US/Canada, Germany/Austria).

I We propose a model that is able to replicate the qualitative features of the data.

106 / 144

II.5 Applications: A theory of international comovementsA news shock triggers an expansion in the US...

Response to a news shock, USA

0 10 20 30 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

C

0 10 20 30 400

0.5

1

1.5

2

2.5

I

0 10 20 30 40−0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

N

0 10 20 30 40−1

−0.5

0

0.5

1

1.5

X

0 10 20 30 40−0.5

0

0.5

1

1.5

2

2.5

3

M

0 10 20 30 400.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

C+I+X−M

107 / 144

II.5 Applications: A theory of international comovements...as well as in Canada.

Response of Canadian aggregates to a news on US TFP

0 10 20 30 40−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

C

0 10 20 30 400

0.5

1

1.5

2

I

0 10 20 30 40−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

N

0 10 20 30 40−1

−0.5

0

0.5

1

1.5

2

2.5

X

0 10 20 30 40−1

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

M

0 10 20 30 40−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

C+I+X−M

108 / 144

II.5 Applications: A theory of international comovementsAgain a news shock triggers an expansion in Germany...

Response to a news shock, Germany

0 10 20 30 40−0.5

0

0.5

1

1.5

2

2.5C

0 10 20 30 40−0.5

0

0.5

1

1.5

2

2.5I

0 10 20 30 40−2

−1

0

1

2

3

4

5X

0 10 20 30 40−0.5

0

0.5

1

1.5

2

2.5

3

3.5

4M

0 10 20 30 40−0.5

0

0.5

1

1.5

2

2.5C+I+X−M

0 10 20 30 40−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15N

109 / 144

II.5 Applications: A theory of international comovements... as well as in Autria.

Response of Austrian aggregates to a German News Shock

0 10 20 30 40−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8C

0 10 20 30 40−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1I

0 10 20 30 40−0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6X

0 10 20 30 40−1.5

−1

−0.5

0

0.5

1M

0 10 20 30 400.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9C+I+X−M

0 10 20 30 40−0.25

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2N

110 / 144

Part III : Liquidations

111 / 144

III.1. Motivations

Recessions

I Recessions often happen after periods of rapid accumulation of houses, consumerdurables and business capital.

I Obvious examples: The Great Depression, The dot com crash, the Asian crisis of1997, etc...

I Systematic look at the US business cycle:

112 / 144

III.1. MotivationsRecessions

Figure 8: Depth of recess. and length of recov. vs. cumulated invest., Non ResidentialInvestment

0 2 4 6 8ï1

0

1

2

3

4

5

N R/T F P at peak (%)

Dep

thof

the

rece

ssio

n(%

)

1957.5

1960.251969.75

1973.75

1980

1981.5

1990.5

2001

2007.75corr: 0.77, P-value : 0.01

0 2 4 6 80

2

4

6

8

10

12

14

16

N R/T F P at peak (%)L

ength

of

reco

ver

y

1957.51960.25

1969.75

1973.75

1980

1981.51990.5

2001

2007.75corr: 0.55, P-value : 0.13

113 / 144


Figure 9: Depth of recess. and length of recov. vs. cumulated invest., Residential Investment

ï5 0 5 10 15ï1

0

1

2

3

4

5

6

R/T F P at peak (%)

Dep

thof

the

rece

ssio

n(%

)

1957.5

1960.251969.75

1973.75

19801981.5

1990.5

2001

2007.75

corr: 0.81, P-value : 0.01

ï5 0 5 10 150

2

4

6

8

10

12

14

16

R/T F P at peak (%)L

ength

of

reco

ver

y

1957.51960.25

1969.75

1973.75

1980

1981.51990.5

2001

2007.75corr: 0.74, P-value : 0.02

114 / 144


Figure 10: Depth of recess. and length of recov. vs. cumulated invest., Durable Goods

ï4 ï2 0 2 4 6 8 10ï1

0

1

2

3

4

5

D/T F P at peak (%)

Dep

thof

the

rece

ssio

n(%

)

1957.5

1960.251969.75

1973.75

1980

1981.5

1990.5

2001

2007.75corr: 0.89, P-value : 0

ï5 0 5 100

2

4

6

8

10

12

14

16

D/T F P at peak (%)L

ength

of

reco

ver

y

1957.51960.25

1969.75

1973.75

1980

1981.51990.5

2001

2007.75corr: 0.67, P-value : 0.05

115 / 144


I Two opposite views of economic policy in recessions

× Hayek× Keynes

116 / 144

III.1. MotivationsThe Liquidationist View (Friedrich Hayek)

I Recessions are needed to cleanse the economy.

I Gvt spendings, aggregate demand management only delays necessary adjustmentI J. Schumpeter, The Economics of the Recovery Program, 1934 :

× “Any revival which is merely due to artificial stimulus leaves part of the work ofdepressions undone”,

117 / 144

III.1. MotivationsThe Aggregate Demand View (John Maynard Keynes)

I Recessions are periods of insufficient demand

I Activist fiscal policy is neededI J.M. Keynes, The General Theory of Employment, Interest and Money, 1936:

× “If the Treasury were to fill old bottles with bank-notes× bury them at suitable depths in disused coal-mines which are then filled up to the

surface with town rubbish,× and leave it to private enterprise on well-tried principles of laissez-faire to dig the

notes up again [...],× there need be no more unemployment× and, with the help of repercussions, the real income of the community, and its

capital wealth, would probably become a good deal greater than it actually is”

118 / 144

III.2. Another Perspective

I The two views are not mutually exclusive

I Liquidations can produce periods where the economy functions particularlyinefficiently.

I Many socially desirable trades between individuals may remain unexploited.

I In this sense, liquidations can cause recessions characterized by deficientaggregate demand.

I Some stimulative policies may remain desirable even if they postpone a recovery.

119 / 144

III.2. Another PerspectiveMain Ingredients

I Environment with decentralized markets, flexible prices and search frictions.I Two imperfections:

× Labor market matching friction in the spirit of Diamond-Mortensen-Pissarides,× Adverse selection in the insurance market : unemployment risk is not insurable.

120 / 144

III.2. Another PerspectiveMain Mechanism

I Environment with decentralized markets, flexible prices and search frictions.I If the economy finds itself with an excess of accumulated goods (durables and/or

capital goods):

× Consumers and firms will spend less because they already have a lot,× Firms will hire less as demand is low× Consumers will consume less by fear of being unemployed,× Spendings will therefore be low× etc...

I There will be socially excessive precautionary savings

I Government spending can boost mutually beneficial trades ...

I ... but it will postpone the recovery by slowing down the liquidation process.

121 / 144

III.2. Another PerspectiveMain Result

Figure 11: Consumption as function of X .

0 0.2 0.4 0.6 0.8 10.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

X⋆

X⋆⋆

X

c

122 / 144

Part IV : Economic Cycle: Some Further Evidence

123 / 144

IV. Further EvidenceCommon Way of Looking at the Data

Figure 12: Total Hours

1940 1950 1960 1970 1980 1990 2000 2010 2020

-7.25

-7.2

-7.15

-7.1

-7.05

-7

-6.95

124 / 144


Figure 13: Total Hours and Trend (High-pass filter, 80 quarters)

1940 1950 1960 1970 1980 1990 2000 2010 2020

-7.25

-7.2

-7.15

-7.1

-7.05

-7

-6.95

125 / 144


Figure 14: Cyclical Component of Total Hours

1960 1970 1980 1990 2000 2010 2020

-6

-4

-2

0

2

4

6

126 / 144


I Fit an AR(2) (standard AIC-BIC criteria)I Ordinary Least-squares Estimates

Dependent Variable = x

R-squared = 0.9362

Durbin-Watson = 2.2217

Nobs, Nvars = 220, 3

***************************************************************

Variable Coefficient t-statistic t-probability

constant -0.000712 -0.017705 0.985891

x(-1) 1.418969 23.746422 0.000000

x(-2) -0.481165 -8.054144 0.000000

Max Eigenvalue Modulus

AR :0.85849 [0.85849]

Autocor : .95

127 / 144


I Xt = AXt−1 + εt

I λ1, ..., λn eigenvalues of A

I Asymptotic behavior of X given by the largest (in modulus) eigenvalue λmaxI |λmax| < 1 : stable

I |λmax| > 1 : explosive

I λ complex : oscillations

128 / 144


I xt = α0 + α1xt−1 + α2xt−2 + εt

I Dynamic simulation :

x1 = x1

x2 = x2

xt = α0 + α1xt−1 + α2xt−2

I Simulation as of date T0:

xS1 = x1

xS2 = x2

xSt = α0 + α1xSt−1 + α2x

St−2

129 / 144


Figure 15: Dynamic Simulation, AR(2) Model

1960 1970 1980 1990 2000 2010 2020

-6

-4

-2

0

2

4

6

130 / 144


Figure 16: Forecasting as of 1961, Linear Model

1960 1970 1980 1990 2000 2010 2020

-6

-4

-2

0

2

4

6

131 / 144

IV. Further EvidenceFull equation

xt = α0 + α1xt−1 + α2xt−2 + α3Xt−1 + α4x

3t−1 + εt

Xt =∑N

j=0(1− δ)jxt−j

132 / 144

IV. Further EvidenceTotal Hours

Ordinary Least-squares Estimates

Dependent Variable = x

R-squared = 0.9424

Durbin-Watson = 2.1163

Nobs, Nvars = 220, 5

***************************************************************

Variable Coefficient t-statistic t-probability

constant -0.020394 -0.526499 0.599084

x(-1) 1.391289 19.728102 0.000000

x(-2) -0.342470 -5.121542 0.000001

X(-1) -0.013430 -4.377008 0.000019

x(-1)^3 -0.006238 -2.386330 0.017884

Max Eigenvalue Modulus

Full :1.0178 [1.01+0.12547i]

AR :0.85849 [0.85849]

133 / 144


Figure 17: The Limit Cycle - Simulation as of T0 = 1961

-3

20

-2

-1

10

0

xt

1

0

2

Xt−1

3

-103

2

xt−1

-20 10

-1-2-30

-3

134 / 144


Figure 18: The Limit Cycle

-3

-2

30

-1

0

20

1

2xt

3

10

4

Xt−1

5

6

06

54

xt−1

-10 32

10

-1-20 -2-3

135 / 144



-3

-2

30

-1

0

20

1

2xt

3

10

4

Xt−1

5

6

06

54

xt−1

-10 32

10

-1-20 -2-3

136 / 144



-3

-2

30

-1

0

20

1

2xt

3

10

4

Xt−1

5

6

06

54

xt−1

-10 32

10

-1-20 -2-3

137 / 144


Figure 21: The Limit Cycle - Simulation as of T0 = 1961

-3 -2 -1 0 1 2 3

xt

-25

-20

-15

-10

-5

0

5

10

15

20

Xt

138 / 144


Figure 22: Dynamic Simulation, Non Linear Model

1960 1970 1980 1990 2000 2010 2020

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

139 / 144


Figure 23: Dynamic Simulation, Full Model

1960 1970 1980 1990 2000 2010 2020

-6

-4

-2

0

2

4

6

140 / 144


Figure 24: Forecasting as of 1961, Full Model

1960 1970 1980 1990 2000 2010 2020

-6

-4

-2

0

2

4

6

141 / 144


Figure 25: Forecasting as of 1961, AR(2) and Full Model

1960 1970 1980 1990 2000 2010 2020

-6

-4

-2

0

2

4

6

142 / 144


Figure 26: Visual Inspection of Non Linearities

xt = α0 + α1xt−1 + α2xt−2 + α3Xt−1 + α4x3t−1 + εt

-8 -6 -4 -2 0 2 4 6 8

α1xt−1 + α4x3t−1

-8

-6

-4

-2

0

2

4

6

8

xt

143 / 144

144 / 144

lecture 6: news, recessions and liquidations · roadmap 1.cycles without in ation 2.the news view...

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