EH447, 08/09, Week 3-1 Great Depressions in Economic History Did Monetary Forces Cause the Great...

EH447, 08/09, Week 3-1 Great Depressions in Economic

History

Did Monetary Forces Cause the Great

Depression?

Did Monetary Forces Cause the Great Depression?

Albrecht RitschlUniv. of Pennsylvania and Humboldt Univ. of

Berlin

Ulrich WoitekUniv. of Zurich

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Conventional wisdom

Monetary hypothesisHayek (1931): money too looseFriedman/Schwartz (1963): money too

tight

Investment/spending hypothesisKeynes (1937), Hansen (1938): declines

in fertility, immigrationTemin (1976): Residential investment

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Later additions to monetary paradigm on Great Depression

Financial accelerator/banking transmission:Bernanke (1983, 1995), Bernanke/Gertler (1990)

Nominal wage stickiness:Bernanke/Carey (1996)

Money in DSGE models of Great Depr.:Bordo/Erceg/Evans (2000) (nominal wage stickiness)Christiano/Motto/Rostagno (2003) (no exogenous monetary policy shock, but yes liquidity preference shock, which the Fed should have accommodated)

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Critics of monetary paradigm

Productivity shocks, little role for money : Cole/Ohanian (2000), Cole/Ohanian/Leung

(2005)

Slow recovery, related to wages: Cole/Ohanian (2005)

Rise in preference for leisure:Chari/Kehoe/McGrattan (2002)

Investors’ animal spirits/sunspots:Harrison/Weder (2004)

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Evaluating monetary paradigm

Little consensus in existing literature Overfitting properties of DSGE:

what you want is what you get But: Sims (1999)

– VAR approach, closest to what we do here– Monetary policy shocks throughout 20th

century explain < 20% of output variance– holds also for Great Depression

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What this paper does

Submit Friedman/Schwartz et al. hypothesis to rigorous test

the only other work doing this (in passing) seems to be Sims (1999)

Use off-the-shelf specifications of monetary policy channels

Account for Lucas critique Account for time-varying volatility

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What this paper does (continued)

We employ VARs– Bayesian (non-random, non-sample dataset)– Time-varying coefficients (structural breaks)– Time-varying VCV matrix (TVAR volatility)

Two exercises on policy effectiveness– Granger causality: policy effectiveness as

forecast improvement (Lucas-proof)– Identifying assumptions: time-varying

impulse-response functions (partly prone to Lucas critique)

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MCMC: How we currently extend this research

Cogley/Sargent’s (2005) VAR/GARCH methodology (first results confirm ours)

With different sets of coauthors:– Replace VAR with factor-augmented VAR a la

Bernanke/Boivin/Eliasz (2005) (same result)– Replace VAR with Bayesian Dynamic Factor

Analysis of International Business Cycle a la Otrok/Whiteman (2003, 2004) (same result)

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Preview of Results: Did Monetary Forces Cause the Great

Depression?

No.Even less so than in

Sims (1999).And we tried really hard ..

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Estimation strategy

Assume a VAR of order p :

with time-dependent coefficient matrices At-1

where Var(ut)=Qt obtained from OLS estimate

ttt

t

p

jjtjtt

uZA

uxAcx

11

11

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Coefficient matrices are time-varying according to:

or, for every equation i :

ttt VAAA )1(1

ttt vaaa )1(1

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Priors

We assume a Litterman prior with:

where – for every equation i, , all others zero – the initial VCV matrix is diagonal with a

number of restrictions on diag. elements

),(~ 0|11 PaNa

1ia

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Motivation for Bayesian Approach

Bayesian unit roots: we have given dataset of (very short) length, do not want to fuzz about asymptotic properties, any classical unit root test would have very little power

Hence, won’t exploit asymptotic characteristics / cointegration properties for identification

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Implementation In this version:

– Kalman filter updating to time t, t = 1928:1,…,1935:6

– Obtain and Cholesky-decompose for every t– Back out IRFs and VarDecs for every t

Next version: MCMC– Run Monte Carlo simulations on posterior

marginal distributions of coefficients– Obtain convergence through Markov Chain

properties (ca. 50-500 000 iterations)– Discard realizations with explosive roots

tQ̂

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Two exercises

First exercise: Granger causality of policy instrument for output– VAR w/ policy instrument should improve

output forecast– If output forecast from VAR including policy

instrument is “bad”, conclude that policy is ineffective (provided transmission mechanism is correctly specified)

– No counterfactual exercises: have Lucas critique on board

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Two exercises Second exercise: impulse response functions

– Traditional Cholesky decomposition– Two runs: order policy instrument both first and

last (the latter implicitly assumes policy reaction functions)

But: TVAR coefficients and VCV– IRF takes on different values every period– So does variance decomposition– Plot IRFs and VarDecs at given intervals as time

series graphs– Great way of visualizing structural breaks– Hello, Lucas …

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VCV decomposition: a quick refresher

General VCV matrix:

Response of xi,t+s cannot easily be attributed to shock uj,t in any variable xj,t.

I'ˆˆˆ uu

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Recursive VCV matrix

t

t

t

t

t

t

tt

u

u

u

cc

c

uCC

3

2

1

3

2

1

2231

21

1

1

1

:and such that , Find

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Finding

There always exists matrix decomposition:

such that

where D is a diagonal matrix and where has the desired orthogonality properties:

tt uC

CDC

1

'

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Orthogonality of

DCCDCC

CC

CuuECE

''

'

')'()'(

11

11

11

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Four specifications

Money / income causality:– M1 (alternatively: High Powered

Money)– Non-borrowed reserves– Total reserves– Wholesale prices– CPI– Output

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Four specifications

Interest rate targeting:– Federal Discount Rate– Non-borrowed reserves– Total reserves– Wholesale prices– CPI– Output

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Four specifications

Nominal wage rigidity:– Wages– Federal Funds Rate– Wholesale prices– CPI– Hours– Output

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Four specifications

Credit crunch / financial accelerator:– Federal Funds Rate– Total reserves– Deposits in failed banks– Wholesale prices– CPI– Output

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Exercise 1: Forecasts

Take forecasts from each of four specifications at 3 critical junctures:

Late 1928 (when FED contracts) After Oct 1929 (NYSE crash) After Dec 1930 (banking panics)

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Forecasting the Great Depression (Out of Sample)

Interest rate model

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Interest rate model

Late 1928: predicts cyclical downturn of output

Late 1929: much less pessimistic outlook!

1930/31: predicts imminent recovery, as did many contemporaries (but wide error bands)

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Forecasting the Great Depression (Out of Sample)

Monetarist model

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Monetarist model:Same conclusions

Late 1928: predicts cyclical downturn of output

Late 1929: much less pessimistic outlook!

1930/31: predicts imminent recovery, as did many contemporaries (but wide error bands)

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Forecasting the Great Depression (Out of Sample)

Bernanke banking model

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Bernanke banking model:Slightly more mileage

Late 1928: predicts slide into mild recession

Late 1929: again,fails to predict anything

1930/31: some further decline predicted; banking has some but limited forecasting power for 1932

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Forecasting the Great Depression (Out of Sample)

Wage rigidity model

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Wage rigidity model

No predictive power for anything in 1928, 1929

1930/31: better than money or interest rates, not as good as banking model

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Conclusions from First Exercise

Tried to evaluate policy effectiveness through forecasting power in VAR– which avoids counterfactual exercises and

hence Lucas flogging Very little predictive power

– Models almost indistinguishable– Banking, wages do slightly better job at

deepening of depression– No evidence for monetary causation of

beginning of recession

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But …

… maybe we asked the model too much?

Very few observation periods No history of prior business cycles Maybe VAR simply doesn’t pick up time

series dynamics fast enough? [rubbish..] Repeat exercise in-sample with

Kalman filter smoothed parameters

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Forecasting the Great Depression (In-Sample)

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In-sample forecasts

1928 results unchanged 1929 results capture turning point

better, all “predict” mild recession 1930 results show continuing

recession, still fail to capture further slump

Even after-the-fact predictions not satisfactory

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Second Exercise

Impulse Response Functions TVAR coefficients TVAR IRFs

– Graph them for given lags as time series

TVAR coeff’s vs. TVAR volatility– Obtain IRFs from Q t at every t.

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Monetarist model

Response of output to M1 shock

- +

Structural breaks in 1929:10, 1933:1

Explained forecast error var. of output below 6%

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Interest targeting model

Response of output to Fed Rate shock

- +Explained forecast error var. of output below 6%

Structural breaks in 1929:10, 1933:1

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Response of output to Fed Rate shock

Wage rigidity model

Banking model

- +

+

Structural breaks in 1928:10, 1932:1

Would explain a lot, but sign mistake!

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Conclusions from Second Exercise

Lots of sign problems in IRFs Very little explanatory power

– Typically 2%, max 6%

IRFs and VarDecs susceptible to structural breaks as monetary regime changes– particularly 1929:10 and 1933:1

Lucas was right No evidence for Friedman/Schwartz

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So … which Forces Caused the GD?

Still have to prove that VARs not entirely useless for the purpose

Are the alternative sets of indicators?

Bad news: no predictability of GD from leading indicatorsShapiro/Fair/Dominguez (1988)Klug/White (1997)

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Predicting the GD from leading indicators

Temin (1976): declining real investment– Residential construction– Equipment investment

Hence look for leading investment indicators– Output, residential building permits,

production of steel sheets, of steel ingots, shipments of new machinery

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Forecasts mostly in line with realized output data, already by late 1928!

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Conclusion: Towards A Nonmonetary View of GD

Early, catastrophic slump in real investment

Itself not predictable by stock market (we tried, it leads the stock market)

Monetary policy largely passive No monetary panacea to avoid or

dampen the slump

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Conclusions: bonus material from new version

New version:– Allow for TVAR VCV matrix– This implements TVAR simultaneous restrictions– And TVAR CB policy feedback rule– Then, model cannot be estimated with

restricted OLS anymore.– Parameters follow nonstandard joint

distribution– Have to iterate over marginal distributions,

using simulation methods

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A Taylor Rule of Gold? Time-Varying Fed M1 Reaction Functions to Output and Inflation

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RF_output*

RF_CPI*

RF_P*