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Chapter 12
Monetary Policy and data uncertainty
2
Learning Outcomes
➢Monetary policy & data uncertainty
➢ Data revisions Versus rationality
➢ Choice of Policy instruments
➢ Macro economic frameworks
➢ Poole’s Model
➢ Brainard’s Model
➢ Interest rate smoothing
3
12.1.1 Assumptions upto this point
Ch 12– Monetary policy and data uncertainties
▪ The central bank is assumed to know the true model of the economy,
▪ The central bank observes accurately all relevant variables timely and
accurately,
▪ The central bank knows sources and properties of economic disturbances.
4
12.1.2 Monetary policy in practice
Ch 12– Monetary policy and data uncertainties
▪ In reality there is a lot of uncertainty in the policy environment and on the
impact monetary policy actions would have
▪ Hence there is a need to be cautious when taking policy actions
▪ This calls for smooth and gradual adjustment of interest rates
5
12.2.1 Data uncertainty - Introduction
Ch 12– Monetary policy and data uncertainties
▪ Economic agents formulate their expectations based on information they
possess
▪ They use this information to formulate expectations and forecast in real time
▪ Most macroeconomic data is subject to continuous revisions.
Recent errors in fiscal estimation
5.0%
6.0%
4.4%
6.9%
5.6%
6.0%
2014E Interim Budget 2014 Treasury AR 2015F Interim Budget Finance Minister (16thNov)
FMR 2016 (New base) Budget 2016 (Newbase)
2014 2015
- Jan 2015 - May 2015 - Jan 2015 - 20th Nov - 20th Nov
7
12.2.2 Arouba (2008) - Characterizing
data revisions
Ch 12– Monetary policy and data uncertainties
▪ XP – Initial announcement
▪ Xf – Final value announced
▪ rf – Final revision which is unobserved
8
12.2.3 Reasons for revisions
Ch 12– Monetary policy and data uncertainties
Final revision = Latest observation – Initial announcement
▪ Short run revisions based on additional source data
▪ Benchmark revisions based on structural changes or updating base year.
▪ Measurement errors
Significant change in trend of GDP following the rebasing
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
2010 2011 2012 2013 2014
Old GDP (2002 Base) New GDP (2010 Base)
7.4%
6.3%
7.2%
4.5%
3.4%
9.1%
GDP estimate revisions
5.95
2.89
(1.88)
10.09
4.36
1.57
0.67
6.18
-3.0
-1.0
1.0
3.0
5.0
7.0
9.0
11.0
Total GDP Growth Agriculture Industry Services
Yo
Y G
ro
wth
, %
1Q2015 Provisional 1Q2015 Revised
Source: Deprt.. of Census and Statistics
2Q2015: 6.7%
11
12.2.4 Revisions Vs. rationality
Ch 12– Monetary policy and data uncertainties
▪ Data is said to be well behaved if revisions are done because of new data arrivals
that are not forecastable at the time of the forecast (news)
▪ If, however, future data revisions are forecast able at the time of the forecast
these are not well behaved revisions (noise).
▪ The former is consistent with rationality as agents cannot do anything about it
while the latter is inconsistent with rationality
12
12.2.5 Properties of well behaved revisions
Ch 12– Monetary policy and data uncertainties
▪ Revisions should have a mean zero.
▪ Final revision should be unpredictable given the information set at the time of
the initial announcement
▪ The variance of the final revision should be small compared to the variance of
the final value of the data.
13
12.2.6 Arouba (2008) Data revisions in reality
Ch 12– Monetary policy and data uncertainties
Arouba
▪ Revisions should have a mean zero. -
▪ Indicating that the initial announcements of statistical agencies are biased.
▪ Final revision should be unpredictable given the information set at the time of
the initial announcement
▪ forecast from a forecasting equation is significantly better than a naive
zero-forecast,
▪ The variance of the final revision should be small compared to the variance of
the final value of the data.
▪ Magnitudes of revisions are quite large compared to the original variables.
14Ch 12– Monetary policy and data uncertainties
12.2.6 Arouba (2008) Data revisions in reality
Findings▪ Even in advanced countries none of the conditions are satisfied
▪ The measurement problem is more important for output series than it isfor inflation or employment/unemployment series.
Reasons
▪ With technological progress it makes collecting data more harder due tothe difficulty in adjusting the quality of goods in the economy
▪ Revisions to durables consumption seem to be an important source of theproblem for the results we get regarding the revisions to real output.
15
https://www.youtube.com/watch?v=EryTjln7l2g
Ch 12– Monetary policy and data uncertainties
Video - What is GDP?
16
12.2.6 Monetary Policy Instruments
Ch 12– Monetary policy and data uncertainties
In this context monetary authorities agree that there is broadly three policy
instruments available for the conduct of monetary policy
▪ Money Supply - Monetary policy should set the money stock while letting the
interest rate fluctuate as it will.
▪ Interest Rates - Authorities should push interest rates up in times of boom
and down in times of recession, while the money supply is allowed to fluctuate
as it will.
▪ Fence sitters – those who argue for a combination of the two
17
12.2.6 Monetary Policy Instruments
Ch 12– Monetary policy and data uncertainties
Notation
- Fix real interest rate and change money supply
- Fix money supply and change real interest rate
18
12.2.8 Instruments Versus outcomes
Ch 12– Monetary policy and data uncertainties
In the case where we have a deterministic macro model where there are is no
shocks and no parameter uncertainty, the choice of policy instruments does not
matter
19
12.2.8 Instruments Versus outcomes
Ch 12– Monetary policy and data uncertainties
However in the presence of macro shocks (Shown by shifts in IS and LM curves)
the choice of policy instrument will matter
20
12.3.1 Poole’s additive model -Introduction
Ch 12– Monetary policy and data uncertainties
Poole (1970) argued that data uncertainty can come from both the IS side as well as
the LM side
IS Side
▪ Changes in consumer tastes
▪ Government expenditures shocks
LM Side
▪ stock market crashes
▪ Financial crises
21
12.3.2 Poole’s additive model -Assumptions
Ch 12– Monetary policy and data uncertainties
▪ parameters and structure of the model are known with certainty – unrealistic
assumption
▪ IS and LM schedules are allowed to be subject to zero-mean random errors –
News errors
Model Setup
IS Curve =>
LM Curve =>
22
12.3.4 Poole’s additive model –Key Results
Ch 12– Monetary policy and data uncertainties
The objective of the Central bank is to choose a policy instrument (R or M) that
would minimize the variance of the Output.
The variance in output is caused by IS or LM shocks
▪ Setting Interest rate
▪ Setting money supply
23
12.3.5 Poole’s additive model –Policy
implications
Ch 12– Monetary policy and data uncertainties
Optimal policy responses
▪ No IS Shocks – Change money supply while keeping interest rates unchanged
▪ No LM Shocks – Change Interest rate keeping money supply fixed
24
12.3.6 Poole’s additive model –Graphs
Ch 12– Monetary policy and data uncertainties
25
12.4.1 Brainard’s multiplicative model
Ch 12– Monetary policy and data uncertainties
Poole’sModel assumptions
▪ parameters of the model are known with certainty
▪ The response of target variables to policy instruments is known for certain
Brainard’s model corrects for this and takes into account the possibility that the
model may be misspecified or there may be measurement error
Model Setup
Policy Constraint =>
Monetary policy objective =>
26
12.4.2 Brainard’s multiplicative model
Ch 12– Monetary policy and data uncertainties
Key result
▪ Because of model uncertainty, the authorities will never push aggressively
enough to make average output equal to the target level y *
▪ Doing so would cause output variation to increase to an intolerable level
▪ The policy maker would rather have a stable level of output below the full
employment level than very volatile output – Brainerd Conservatism
27
12.4.3 Brainard’s multiplicative model
Ch 12– Monetary policy and data uncertainties
Graph
28
12.5.1 Key definition – Certainty equivalence
Brainard 1967
▪ This is a situation where the response of target variables to policy instruments
are known for certain
▪ This however does not mean that there are no shocks in the system
▪ Even though the actual target variable may differ from what was expected, the
policy-maker should act on the basis of expected values as if he were certain
they would actually occur
29
12.5.1 The New Keynesian Model & Parameter
uncertainty
Ch 12– Monetary policy and data uncertainties
Cold Turkey Vs. Gradualism
30
12.5.2 The New Keynesian Model & Parameter
uncertainty
Ch 12– Monetary policy and data uncertainties
Key result to be proved
▪ Nomodel uncertainty-
▪ Economy behaving like Poole model
▪ set policy such that output = y* - Certainty equivalent
▪ Cold Turkey
▪ Model Uncertainty
▪ Economy behaves like Brainard model
▪ Conservative approach recommended y < y*
▪ Gradualism
31
12.5.2 The New Keynesian Model & Parameter
uncertainty
Ch 12– Monetary policy and data uncertainties
Model setup
▪ The economy is characterized by the 1st 2
Equations
▪ The economy is subject to non-stochastic shocks
Captured by et
▪ The objective of the Central Bank is to minimize
The loss function characterized by an inflation gap
▪ The policy instrument used is the short term
Interest rate
32
12.5.3 The New Keynesian Model & Parameter
uncertainty
Ch 12– Monetary policy and data uncertainties
Additive uncertainty (Poole’smodel)
▪ The monetary authorities incorporate the fact that the E(e) = 0 into their
decision making process
Certainty equivalent result
▪ policy rate set by the policy maker is the same as the one that it would set if
there were no shocks hitting the economy.
33
12.5.4 The New Keynesian Model & Parameter
uncertainty
Ch 12– Monetary policy and data uncertainties
Parameter uncertainty (Brainard’smodel)
▪ The monetary authorities are uncertain of the parameters that determine real
income
▪ The authorities can however still form expectations (albeit imperfect
expectations) about the value of the parameter b
▪ The shocks to the economy are still additive.
34
12.5.4 The New Keynesian Model & Parameter
uncertainty
Ch 12– Monetary policy and data uncertainties
Key result
▪ The coefficient of variation of the parameter b captures the trade-off faced by
monetary authorities. It captures the tension between bringing inflation back
to target while the increasing uncertainty about inflation depends on the
variance of parameter b
▪ A large coefficient of variation means for a small reduction in the inflation bias
the central bank induces a large variance into future inflation.
35
12.6.1 Graphical representation
Ch 12– Monetary policy and data uncertainties
Scenario 1 – No uncertainty
36
12.6.2 Graphical representation
Ch 12– Monetary policy and data uncertainties
Scenario 2 – Additive uncertainty
37
12.6.2 Graphical representation
Ch 12– Monetary policy and data uncertainties
Scenario 3 – Parameter uncertainty
38
12.7.1 Interest rate smoothing
Ch 12– Monetary policy and data uncertainties
▪ In light of the previous discussion, monetary authorities are more likely to
adopt a gradualist approach to monetary intervention
▪ Hence interest rates are likely to be altered in stages. There seems to exists a
positive correlation whereby changes in the funds rate to be followed by
additional changes in the same direction.
▪ Sack (2000) - The interest-rate smoothing that is observed may reflect the
cautious reaction of the Fed to uncertainty over the dynamic structure of the
economy. – Interest rate smoothing
39
12.7.2 Federal Funds rate
Ch 12– Monetary policy and data uncertainties
“The results indicate that the estimated dynamic structure of the economy can account for the
observed persistence of the directional movements in the funds rate”
40
12.7.2 Actual vs. optimal policy“The optimal policy responds more aggressively to changes in the state of the economy than
the observed policy. As a result, the funds rate path under the optimal policy is more
volatile than the actual funds rate.”
Ch 12– Monetary policy and data uncertainties
41
12.7.3 Loss functions with and without
uncertainty
Ch 12– Monetary policy and data uncertainties
42
Optimal Policy vs. Preferred action
43
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