Download Monetary policy and business fluctuations

Steinar Holden
Lecture 1
Monetary policy and business fluctuations
Course content
knowledge and understanding of recent research in
monetary policy and business fluctuations:
The course focuses on the following
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Business cycles - theory and evidence.
Why and how does the activity level of the
economy vary over time,
what can the policymakers do to stabilise the
cycles?
real business cycle theory and New-Keynesian
theory
Inflation and monetary policy.
Nominal rigidities and New-Keynesian models
Targeting regimes and instrument rules in
monetary policy
Inflation targeting in open economies
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Learning outcomes
The course aims at making the students able to
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understand and give an account of the main
theories, including the assumptions, the
mechanisms and analyses, and the conclusions
evaluate the theories in light of empirical
findings
make use of the theories in work on practical
economic problems
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Business cycle regularities
The business cycle is the periodic but irregular upand-down movements in economic activity,
measured by fluctuations in real GDP and other
macroeconomic variables.
Definition Lucas (1977): Understanding business
cycles:
Deviations of aggregate real output from trend
Business cycle regularities: Statistical properties of
the comovements of deviations from trend of various
economic aggregates with those of real output
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A brief and selective history
William Jevons’ 1866) sunspot theories:
Sunspot cycle => weather cycle => harvest cycle =>
price cycle
Overinvestment cycles
Clement Juglar (1856, 1859) Credit cycle.
Recurrence of crisis in monetary data. Cycles were of
variable length and amplitude
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Monetary theory
Hawtrey's Pure Money Cycles (1913, 1926)
 Increase in money stock lowers the interest rate,
leading wholesalers and middlemen to borrow
from banks and increase their demand from
firms, so as to increase inventories.
 Increasing production takes time,
 also consumers increase their demand due to
lower interest rates, reducing wholesalers’
inventories so they increase their demand further,
by borrowing more money.
 Increased money stock amplifies cycle
 Eventually banks will close off credit
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Expectational cycles
Irving Fisher's (1907) cycle theory
 leads and lags of adjustment cause cycles
 influenced by psychological factors.
 expansions emerge because expected profits on
investment exceed the rate of interest.
 induced by technological improvements which
then lead to expectations of profit.
Fisher (1911)
 Cycles due to expansions in money leading to
sustained rises in aggregate demand
 slow-adjusting interest rates (due to adjustment
costs and uncertainty) permits demand to be
continued.
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Shock-dependent theories:
Frisch (1933). “Propagation problems and impulse..”
Impulses in the form of random shocks
Propagation mechanisms
“when you hit a wooden rocking horse with a club,
the movement of the horse will be very different from
the movement of the club”
Slutsky (1937) “The summation of random causes as
the source of cyclical processes.”
Shows that random shocks may lead to cycles if there
is persistence in the process
et random variable, either 0.5 or -0.5 (equal prob.)
yt+1 = 0.95 yt + et+1
Leads to
yt+1 = et+1 + 0.95 et-1 + 0.952et-2 + 0.953 et-3 + … +
0.95ty0
(see illustration in Kydland & Prescott, 1990, chart 1)
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Burns & Mitchell (1946).
Business cycles consist of sequences of expansions
and contractions
Measuring business cycles
Criticized by Koopmans as “Measurement without
theory”
 No systematic discussion of the theoretical
reasons for which variables to study
 No explicit assumption about the probability
distribution of the variables
Intellectual debate between Cowles commission
(structural macroeconometrics) and NBER (applied
econometrics), both also applying for financing
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Spurious cycles:
Detrending method can lead one to find cycles that
do not exist in the data.
Yule (1927) and Slutsky (1937)
Different detrending methods may give rise to
different cyclical patterns.
By smoothing a series using a summation procedure
(e.g. moving average), in addition to removing the
trend by differentiating, one may generate oscillatory
movement when no oscillations exist in the original
data in the original data (e.g. if data are caused by a
random walk series, cf. below)
Could be explanation of findings of Kontradieff’s 50year cycles, or Kuznet’s 20 year cycles
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Real GDP Mainland-Norway, quarterly data
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Trend constructed as a 36 quarter moving average
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Business fluctuations and unemployment
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Persistent or transitory fluctuations?
Traditional view:
 The economy is growing along a fairly smooth
path (trend)
 Transitory shocks lead to cyclical fluctuations
Different sources of shocks:
 The trend is caused by deterministic
improvement of productivity
 Transitory shocks are due to demand, usually
monetary factors
Cyclical fluctuations can be found by first detrending
the data series
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Illustration – transitory deviations
Autoregressive process yt around a time trend
yt   t  zt ,
where zt   zt 1   t
(1)
εt is white noise, t is deterministic trend
Solving out for yt, we obtain
yt   t   t  a t 1  a  t 2  a  t 3  ...   t  s0  s t s
2
3
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when |α| < 1, (1) is trend stationary.
The effect of a shock will die out, and the process
will return to the deterministic trend
An innovation will not affect long-run forecasts
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Persistent fluctuations
Alternatively, assume that yt is a random walk, i.e. a
non-stationary series
yt  yt 1   t
(3)
εt is white noise,
Solving out for yt-1 , we obtain
yt   t   t 1   t 2   t 3 ....   s o  t s
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(y0 set to zero)
 Any shock εt-s, will have a permanent effect on
yt, i.e. their effect will never die out.
 Fluctuations are not stationary deviations around
trend – the “trend” consists of permanent shocks
 We say that (3) has a unit root, or is integrated
of degree one, I(1)
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Random walk (non-stationarity) and deterministic
trend can be combined
yt  yt 1     t
which solves for
yt   t  s o  t s
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Makes it more difficult to distinguish between
stationary and non-stationary series.
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Many economic variables, like output, consumption,
price levels are non-stationary
Empirical investigation of non-stationary data is
problematic because one may find spurious
relationships, i.e. find a relationship that is significant
by use of conventional statistical measures even if no
relationship exists at all.
As illustrated by Hendry, Economica, 1980, who find
statistical significant relationship between two nonstationary time series, namely the price level in the
UK and the cumulated rain fall in the UK, even if
there obviously is no relationship in reality.
To avoid finding such spurious relationships, one
often first difference the series to obtain a stationary
series, cf. below
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To obtain a stationary series of
yt  yt 1   t ,
we may first difference, i.e.
yt  yt  yt 1  yt 1   t  yt 1   t
Δ yt-1 is stationary, or integrated of order zero I(0)
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Common to test whether series are non-stationary
I(1) or stationary I(0) before analyzing business
cycles
Can be done by Dickey-Fuller test
Autoregressive process of order one AR(1)
yt  ayt 1   t
can be re-written as
yt   yt 1   t ,
where     1
Test for whether μ=1, i.e. whether α = 1
Non-standard distribution of t-values.
Appropriate critical values by Fuller (1976).
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Real business cycle theory – the background
Macroeconometric models dominated
macroeconomic from WWII to mid 1970s
(“behavioral-empirical approach in Keynesian system
of equations”)
Lucas (1977): Understanding business cycles
 Deviations of aggregate real output from trend
 Comovements of deviations of different times
series
Weaknesses of Keynesian theories
- disconnect macro and micro theory (micro based
on optimization behavior)
- empirical failures
- Lucas (1976) critique that behavioral
relationships that depend on expectations of
policy will break down, if policy is changed
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- better to construct models based on optimizing
behavior, which should then be robust to changes
in policy and other variables
Proceed on the basis of the neoclassical growth
model, with stochastic productivity
Kydland & Prescott (1982) Time to build and
aggregate fluctuations
Adds random productivity shocks to neoclassical
growth model, assuming that it takes more than one
period to construct productive capital
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Stylized facts of aggregate activity
To obtain data for deviations from trend, one first
needs to find the trend.
Common to construct trend by use of HodrickPrescott (HP) filter, constructed as follows
min Σ { (yt – ygt)2 + λ [ (ygt+1 - ygt) - (ygt - ygt-1)]2 }
wrt ygt+1, ygt …
i.e. minimize both deviations from trend, and change
in trend growth. Smoothing parameter λ = 1600
common for quarterly data
 λ= ∞ corresponds to linear trend,
 λ = 0 gives original series
Note that HP-filter will generate cycles, even in cases
where there is none (i.e. if non-stationary variable
like a random walk: yt = yt-1 + et)
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Stylized facts of aggregate activity
Which data to choose?
Look at data that is
Consumer durables purchases are more volatile than
output
 Investment is three times more volatile than
output
 Government expenditure are less volatile than
output
 Total hours worked has about the same volatility
as output
 Capital is much less volatile than output, but
capital utilization in manufacturing is more
volatile than output
 Employment is as volatile as output, while hours
per worker are much less volatile than output, so
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that most of the cyclical variation in total hours
worked stems from changes in employment
 Labor productivity (output per man-hour) is less
volatile than output
 The real wage is much less volatile than output
 Comovement: Most variables are pro-cyclical,
i.e. show positive contemporaneous correlation
with output
 Wages, government expenditure and the capital
stock essentially acyclical
 All macroeconomic variables display substantial
persistence (first order serial coefficient for
detrended quarterly variables 0.6 to 0.9)
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