Transcript blog.zhaw.ch

Calling Recessions in Real Time
By J.D. Hamilton
Paper version : Econbrowser May 23 2010
Marc Wildi
[email protected]
IDP-ZHAW
Zurich University of Applied Sciences
Emphasize of a True Challenge
• “The paper stresses … actual real-time
analysis, in which a researcher stakes
his/her reputation on publicly using the
model to generate out-of-sample, real-time
predictions”.
– Facing the `true’ future puts oneself in a
situation which differs from `feigning to ignore
withdrawn data’.
• USRI: http://www.idp.zhaw.ch/usri
Topics
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Simplicity, robustness, continual changes
Statistical Revisions: Filter or Smooth?
Replicability
Data-Revisions
Mixed Frequency Approaches
Simplicity, Robustness,
Continual Changes
Trade-Off
• Abstract: “The paper … emphasizes the
fundamental trade-off between parsimony
– trying to keep the model as simple and
robust as possible – and making full use of
available information.”
Precision
• One may ask: “as simple as possible”
under which circumstance?
– Optimization criterion
Shift of Perspective
• Proposal: instead of emphasizing the
(simplicity of the) model one could stress
the (structure of the) optimization criterion.
– What do we want to emphasize?
One-Step Ahead Mean-Square Fit
• Black-box mechanism:
– Anything to improve the in-sample fit
– No control on the outcome
• Back door: parsimony
– Contain `anything’ within assigned limits
Customized Criteria
• Account for additional or alternative userrelevant aspects of the estimation problem
directly in the optimization criterion
User Requirements
• Expectations:
– Reliability
– Timeliness
• Fitting user-needs enhances performance
• Intrinsic out-of-sample quality
• Address a fundamental uncertainty-principle
Parsimony and Relativity
• yt = 1.1221yt−1 + 0.6462yt−2 − 2.0422yt−3 +
1.2562yt−4 + 0.6435yt−5 −1.8344yt−6 +
1.1496yt−7 + 0.5889yt−8 − 1.6788yt−9 +
1.0521yt−9 +0.5390yt−10 − 0.8349yt−11 +
0.1755yt−12 + 0.1758yt−13 + 0.0400yt−14
+0.0264yt−15 +0.3511xt − 0.1695xt−1 −
0.3195xt−2 + 0.5272xt−3 − 0.1755xt−4
−0.3104xt−5 + 0.5174xt−6 − 0.1755xt−7 −
0.3104xt−8 + 0.5174xt−9 −0.1755xt−10 −
0.3104xt−11 + 0.1662xt−12 − 0.006xt−13 +
0.0091xt−14 −0.0099xt−15
USRI:
ARMA(10,10) for each time series
AR (IP)
1
-4.78008407
9.0014996
-7.96890106
2.57688672
0.75636028
-0.69311417
0.11731979
-0.01372376
-0.0037665
0.00753421
AR(nm)
1
-3.06631134
3.37060102
-1.28215875
-0.52360667
0.87332593
-0.47145616
0.09535759
0.03551554
-0.04621295
0.01727007
AR(pa)
1
-4.03326512
6.26306973
-4.42747239
0.97718086
0.66116987
-0.76908487
0.42720126
-0.05806605
-0.06665926
0.02601346
AR(tc)
1
-3.38419728
3.13108258
1.49040313
-4.36535188
2.58359665
-0.1714782
-0.75352367
0.82221808
-0.4463622
0.09364743
AR(mean)
1
-4.35464419
6.92894725
-4.03949906
-1.04436046
2.54503417
-1.44684261
0.46079938
0.10825122
-0.23331143
0.07564891
MA (IP)
0.00631533
-0.02089974
0.01709169
0.01598015
-0.03624964
0.02298505
-0.00236769
-0.01030672
0.01354595
-0.00775837
0.00166473
MA(nm)
0.069996738
-0.088157798
-0.000227985
0.104777438
-0.128594641
0.062416127
0.012511834
-0.05244012
0.048645871
-0.020867429
0.003396401
MA(pa)
0.054209565
-0.124133325
0.041932612
0.118487776
-0.162869025
0.10404881
-0.011152528
-0.066494306
0.072678036
-0.031887287
0.005223008
MA(tc)
0.027308377
-0.049760403
0.011992236
0.037183307
-0.063845941
0.063352327
-0.013679473
-0.03706134
0.036659531
-0.013469957
0.001809204
MA(sum)
0.107450468
-0.355337295
0.360580086
0.021354045
-0.316171691
0.312593152
-0.172656935
-0.009808104
0.106656545
-0.068613016
0.014006738
Performances USRI
• Inexperienced young child: on-line since
March 2009
• Troughs of acceleration-, classical- and
growth-cycles were detected in December
2008, April 2009 and June 2009 without
subsequent revisions.
Conclusion (Section 6)
• “Averaging the inference from alternative
specifications or using Bayesian
approaches to constrain more richly
parameterized specifications are worth
exploring.”
– Customized criteria
• “A particularly promising approach is to
combine data of different frequencies”.
– I’m more skeptical
Mixed-Frequency
Market Needs
• `Consumer eagerness’ for fresh news
• Observe a growing tendency to account
for high-frequency (daily/weekly) data
– ADS (Philadelphia Fed)
– Camacho and Perez Quiros (EU)
• Mix `high-frequency’ with monthly and
quarterly data
Questions
• What additional information might we
expect to appear in (and hopefully extract
– in real time - from) disaggregated noisy
`high-frequency’ data that is not already
disclosed in monthly aggregates?
– Up-date GDP on a `high-frequency’ scale?
• How stable/robust are the relations?
ADS-Index
Revisions in the `Eye of the Storm’
Upshot
• How much reliability are `consumers’
willing to trade against (alleged)
freshness?
Data Revisions
Own Experience: USRI
• USRI works with standardized log-returns
of monthly data
• In order to be effective, revisions of time
series should affect standardized logreturns
USRI-Revisions due to DataUpdate (Different Vintages)
Industrial Production
•Frequently fast convergence
•Sometimes larger revisions stretch over the whole history
•Medium weight associated by filter
Manufacturing and trade sales
•Frequently small revisions (fast convergence)
•Sometimes larger revisions on whole history
•Medium weight assigned by filter
Employment on non-agricultural
payroll
•Often fast convergence. Sometimes larger revisions but dynamics remain
`consistent’ (turning-points)
•Important time series
Personal Income (Less Transfer
Payments)
•Large revisions (may affect dynamics)
•Small weight attributed by the filter
Total civilian employment
•Frequently unrevised (unfrequent revisions on whole history)
•Most important time series (largest weight attributed by filter)
Filter or Smooth?
Smoothing
• Smoothing the history of an Indicator
hides/masks real-time performances
– Smoothing cosmetics
– USRI: WYSIWYG-design
• Never calibrate a model on the history of a
revised indicator (almost all of them)
– Exception: USRI
Replicability
Replicability vs. Subjective
Adjustments
• Do index-providers discuss/adjust indicator
values prior to release or is the release
automated?
– An important real-time indicator published by a
national agency?
– Do we have to attribute observed performances to
improved statistics or to a clever economic staff
relying on `insider’-knowledge (proprietary data)?
• Can we (users) replicate an indicator?
– Excel-replication of USRI
– The Econbrowser-index can be replicated
straightforwardly
Miscellaneous
Self-Fulfilling Prophecy
• I firmly believe that it is possible to publish
a `perfect‘ real-time indicator without
affecting the course of the announced
recession in any relevant way.
Continual Change of Economic
Relations
• We don‘t need necessarily fixed or stationary
economic relations
• What we need is a consistent/stable recession
definition (by the NBER)
– The USRI relies on `NBER-data‘
– Detecting peaks in Industrial Production,
Manufacturing, Employment
– Customized real-time filters are optimized to detect
the relevant features (turning-points in these series)
– As long as the NBER defines recessions this way, the
USRI will be pretty `robust‘.
Uncertainty Principle
• Reliability and timeliness are conflicting
requirements
• Address uncertainty-principle explicitly by
customized criterion
– USRI
– MDFA
An Inconvenient Truth
• The venerated maximum likelihood
principle serves as a formal justification for
a capricious black-box mechanism