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Transcript of 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*