Download Short Run Fluctuations

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Short Run Fluctuations
Chapter 5
Neoclassical Dichotomy
• Theory of macroeconomy where output is given
by labor, capital, and TFP.
• TFP is given by R & D (and possibly allocative
efficiency)
• Capital investment is given by real interest rates
which is given by savings which is in turn given
by demographic factors and the dynamics of
future income.
• Labor is given by labor-leisure trade-off, real
labor productivity and turnover in job market.
• No relationship between output between money,
prices or inflation.
Long run output, Y
π
Y
Y
Business Cycles
600,000
500,000
400,000
300,000
200,000
100,000
0
1975
1980
1985
HK GDP
In chained (2009) dollars
1990
Link
1995
2000
2005
2010
2015
Pattern of production.
• GDP is growing over time.
• GDP growth is not smooth. Sometimes GDP is
above and sometimes below the long term
growth path.
• GDP has seasonal pattern with production
consistently concentrated in 4th quarter.
Christmas given as an explanation.
• These movements are so large they hide less
predictable short-term movements in the
economy. Solution: Seasonal adjustment.
Smooth out the average changes associated
with the season.
600,000
500,000
400,000
300,000
200,000
100,000
0
1975
1980
1985
1990
GDP
1995
2000
GDP_SA
2005
2010
2015
GDP_SA
600,000
500,000
400,000
300,000
200,000
100,000
0
1975
1980
1985
1990
1995
2000
2005
2010
2015
Output Gap
• Study of long-term growth focuses on
explaining the secular upward movement
in GDP.
• Business cycles examine fluctuations
around that trend. Object of interest is the
output gap, the % deviation of GDP from
its long-term trend path.
 Yt

GAPt  ln 

 TRENDt 
Measuring Trend
• Simplest way to measure trend is to assume
that it grows at a constant rate over time.
Ln(TRENDt) = α0 + α1∙t →
ΔLn(TRENDt)= Ln(TRENDt)- Ln(TRENDt-1) = α1
• In theory, corresponds with BGP of
neoclassical growth model where α1 is the
growth rate of technology.
Estimating Trend
• Construct Data
• LHS: The natural log of
GDP
• RHS: Index of Time
• Source: FRED Database
• Note: USA Annual Data
used here for
convenience. Can easily
be applied to quarterly
data.
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
GDP
4319.6
4311.2
4540.9
4750.5
5015
5173.4
5161.7
5291.7
5189.3
5423.8
5813.6
6053.7
6263.6
6475.1
6742.7
6981.4
7112.5
7100.5
7336.6
7532.7
7835.5
8031.7
8328.9
8703.5
9066.9
9470.3
9817
9890.7
10048.8
10301
10703.5
11048.6
ln(GDP) t
8.370918
8.368972
8.420881
8.466005
8.520189
8.551285
8.549021
8.573895
8.554354
8.598552
8.667955
8.708425
8.74251
8.775719
8.816216
8.851005
8.869609
8.86792
8.900631
8.927009
8.96642
8.991151
9.027487
9.071481
9.112386
9.155916
9.191871
9.19935
9.215209
9.239996
9.278326
9.310059
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Estimate Regression Model
• Estimate Regression: ln(Yt) = α0 + α1∙t +εt
• Regression coefficient is α1 =.03068
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.997438
R Square 0.994883
Adjusted R Square
0.994713
Standard Error
0.020981
Observations
32
ANOVA
df
Regression
Residual
Total
SS
MS
F
Significance F
1 2.567735 2.567735 5833.186 6.25E-36
30 0.013206 0.00044
31 2.580941
Coefficients
Standard Error t Stat
P-value Lower 95%Upper 95%Lower 95.0%
Upper 95.0%
Intercept
6.952588 0.024981 278.3116 9.51E-53 6.90157 7.003607 6.90157 7.003607
X Variable 1 0.03068 0.000402 76.37529 6.25E-36 0.029859
0.0315 0.029859
0.0315
Output Gap
• The output gap is
the % deviation
from trend ln(Yt)ln(TRENDt) which
corresponds with
εt.
• Use the fitted
residual as a
measure of the
output gap.
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
GDP
4319.6
4311.2
4540.9
4750.5
5015
5173.4
5161.7
5291.7
5189.3
5423.8
5813.6
6053.7
6263.6
6475.1
6742.7
6981.4
7112.5
7100.5
7336.6
7532.7
7835.5
8031.7
8328.9
8703.5
9066.9
9470.3
9817
9890.7
10048.8
10301
10703.5
11048.6
ln(GDP) t
8.370918
8.368972
8.420881
8.466005
8.520189
8.551285
8.549021
8.573895
8.554354
8.598552
8.667955
8.708425
8.74251
8.775719
8.816216
8.851005
8.869609
8.86792
8.900631
8.927009
8.96642
8.991151
9.027487
9.071481
9.112386
9.155916
9.191871
9.19935
9.215209
9.239996
9.278326
9.310059
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
ln(TREND) OutputGap
8.364
0.007
8.395
-0.026
8.425
-0.004
8.456
0.010
8.487
0.034
8.517
0.034
8.548
0.001
8.579
-0.005
8.609
-0.055
8.640
-0.041
8.671
-0.003
8.701
0.007
8.732
0.010
8.763
0.013
8.793
0.023
8.824
0.027
8.855
0.015
8.885
-0.017
8.916
-0.015
8.947
-0.020
8.977
-0.011
9.008
-0.017
9.039
-0.011
9.069
0.002
9.100
0.012
9.131
0.025
9.162
0.030
9.192
0.007
9.223
-0.008
9.254
-0.014
9.284
-0.006
9.315
-0.005
Deviations from Trend
9.4
9.2
9
8.8
8.6
8.4
8.2
4 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04
7
19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20
ln(GDP)
ln(TREND)
US OutputGap
0.04
0.02
0
74 9 76 9 78 9 80 9 82 9 84 9 86 9 88 9 90 9 92 9 94 9 96 9 98 0 00 0 02 0 04
9
-0.02
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
-0.04
-0.06
Y
Estimating Trend HK
GDP
• Construct Data
• LHS: The natural log
of GDP
• RHS: Index of Time
• Source:
• Note: Annual Data
used here for
convenience. Can
easily be applied to
quarterly data.
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
91180
104211
120639
131048
150237
152933
155385
160664
178889
195359
209607
231850
260321
266608
267920
311227
347721
376446
419951
462401
505223
520126
551214
606191
610780
678308
769191
834662
853668
886367
936907
995323
1057044
1120847
1147454
1196318
1257326
1183362
1213025
1305985
1313309
1335066
1375870
1495572
1606067
1719015
1830145
1869088
1823125
1946507
2040225
2074915
2138660
2192153
ln(Y)
t
11.42059
11.55417
11.70056
11.78332
11.91997
11.93776
11.95366
11.98707
12.09452
12.18259
12.25299
12.35385
12.46967
12.49353
12.49844
12.64828
12.75916
12.83853
12.94789
13.04419
13.13276
13.16183
13.21988
13.31495
13.32249
13.42736
13.55309
13.63478
13.6573
13.69489
13.75034
13.81082
13.87099
13.9296
13.95306
13.99476
14.0445
13.98387
14.00863
14.08247
14.08806
14.10449
14.1346
14.21802
14.2893
14.35726
14.41991
14.44096
14.41606
14.48155
14.52857
14.54543
14.57569
14.60039
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
Estimate Regression Model
• Estimate Regression: ln(Yt) = α0 + α1∙t +εt
• Regression coefficient is α1 =.0255
Dependent Variable: LGDP
Method: Least Squares
Date: 11/05/15 Time: 11:27
Sample (adjusted): 1961 2014
Included observations: 54 after adjustments
Variable
C
@TREND
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Coefficien...
Std. Error
t-Statistic
Prob.
11.75217
0.059380
0.045368
0.001476
259.0433
40.23900
0.0000
0.0000
0.968884
0.968286
0.169012
1.485378
20.39681
1619.177
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
13.32573
0.949052
-0.681363
-0.607697
-0.652953
0.068973
Y
Output Gap
• The output gap is
the % deviation
from trend ln(Yt)ln(TRENDt) which
corresponds with
εt.
• Use the fitted
residual as a
measure of the
output gap.
ln(Y)
t
Trend
Output Gap
1961
91180
11.42059
1
11.81155
-0.39096
1962
104211
11.55417
2
11.87093
-0.31676
1963
120639
11.70056
3
11.93031
-0.22975
1964
131048
11.78332
4
11.98969
-0.20637
1965
150237
11.91997
5
12.04907
-0.1291
1966
152933
11.93776
6
12.10845
-0.17069
1967
155385
11.95366
7
12.16783
-0.21417
1968
160664
11.98707
8
12.22721
-0.24014
1969
178889
12.09452
9
12.28659
-0.19207
1970
195359
12.18259
10
12.34597
-0.16338
1971
209607
12.25299
11
12.40535
-0.15236
1972
231850
12.35385
12
12.46473
-0.11088
1973
260321
12.46967
13
12.52411
-0.05444
1974
266608
12.49353
14
12.58349
-0.08996
1975
267920
12.49844
15
12.64287
-0.14443
1976
311227
12.64828
16
12.70225
-0.05397
1977
347721
12.75916
17
12.76163
-0.00247
1978
376446
12.83853
18
12.82101
0.01752
1979
419951
12.94789
19
12.88039
0.067503
1980
462401
13.04419
20
12.93977
0.104418
1981
505223
13.13276
21
12.99915
0.133605
1982
520126
13.16183
22
13.05853
0.103296
1983
551214
13.21988
23
13.11791
0.101968
1984
606191
13.31495
24
13.17729
0.13766
1985
610780
13.32249
25
13.23667
0.085822
1986
678308
13.42736
26
13.29605
0.131307
1987
769191
13.55309
27
13.35543
0.197665
1988
834662
13.63478
28
13.41481
0.219972
1989
853668
13.6573
29
13.47419
0.183108
1990
886367
13.69489
30
13.53357
0.161316
1991
936907
13.75034
31
13.59295
0.157389
1992
995323
13.81082
32
13.65233
0.158493
1993
1057044
13.87099
33
13.71171
0.159277
1994
1120847
13.9296
34
13.77109
0.158505
1995
1147454
13.95306
35
13.83047
0.122586
1996
1196318
13.99476
36
13.88985
0.104909
1997
1257326
14.0445
37
13.94923
0.095268
1998
1183362
13.98387
38
14.00861
-0.02474
1999
1213025
14.00863
39
14.06799
-0.05936
2000
1305985
14.08247
40
14.12737
-0.0449
2001
1313309
14.08806
41
14.18675
-0.09869
2002
1335066
14.10449
42
14.24613
-0.14164
2003
1375870
14.1346
43
14.30551
-0.17091
2004
1495572
14.21802
44
14.36489
-0.14687
2005
1606067
14.2893
45
14.42427
-0.13497
2006
1719015
14.35726
46
14.48365
-0.12639
2007
1830145
14.41991
47
14.54303
-0.12312
2008
1869088
14.44096
48
14.60241
-0.16145
2009
1823125
14.41606
49
14.66179
-0.24573
2010
1946507
14.48155
50
14.72117
-0.23962
2011
2040225
14.52857
51
14.78055
-0.25198
2012
2074915
14.54543
52
14.83993
-0.2945
2013
2138660
14.57569
53
14.89931
-0.32362
2014
2192153
14.60039
54
14.95869
-0.3583
Business Cycles?
RESID01
.3
.2
.1
.0
-.1
-.2
-.3
-.4
65
70
75
80
85
90
95
00
05
10
15
Stochastic Trends
• Trend line may change over time, if longrun technology also changes.
• We want to distinguish between short-run
deviations from trend from long-lasting
changes in the trend path.
• Allow for a smoothly changing trend,
known as a Hodrick Prescott trend.
Examine Data in Growth Rates
HP Trend and ln(GDP)
600,000
500,000
400,000
300,000
200,000
100,000
0
1975
1980
1985
1990
GDP_SA
1995
2000
2005
HPTREND01
2010
2015
HP Filtered Output Gap
GAP
.08
.06
.04
.02
.00
-.02
-.04
-.06
-.08
-.10
1975
1980
1985
1990
1995
2000
2005
2010
2015
Final Exam
• Wednesday, December 16th, 08:30AM 11:30AM. Lecture Theater L.
• Non-cumulative. Consumption, Investment,
Business Cycles: Keynesian, Rational
Expectation, New Keynesian. Similar to midterm and practice exams.
• Bring writing instruments and a calculator.
• Semi-open book – Bring 1 A4 size paper with
handwritten notes on both sides.
• Office Hours: Standard.
Equilibrium
iIBR
SBR
i
i*
i
DBR
Reserves
Expenditure Cycles in the
Closed Economy
• Planned Expenditure is C + I + G.
• Investment is sensitive to the real interest rate.
Consumer spending is sensitive to disposable
income YD = Y – T.
• Draw a planned expenditure curve that shows
response of demand to GDP
IS Curve
• When the real interest rate rises, investment will
fall. This, along with knock-on multiplier effects,
will lead to a contraction in demand.
• IS curve maps out the relationship between real
interest rate and demand for goods (taking into
account the multiplier effect)
• Q: What shifts the IS curve?
• A: Shifts in Fiscal Policy, optimism about future
income, expectations about future MPK, wealth
effects of asset prices.
Planned Expenditure
r
MP
r(π)
IS
Y
Y*
Monetary Policy Reaction Function
• Central bank controls the money supply.
• Central bankers set money supply in
response to economic conditions.
• When economy is booming or prices are
rising to quickly, central banks raise real
interest rates.
r  MP(Y ,  )


How does the central bank set
real interest rates?
• The central bank participates in the money
market to control the real interest rate.
• Central bank has control over the money supply
M. If prices do not respond 1-for-1 with money
(due to pricing stickiness), they can control the
supply of real balances.
• From Baumol-Tobin, the demand for real
balances is determined by nominal interest rates
and output. Nominal interest rates are a function
of real interest rates and expected inflation.
M
P
Y
V (i )
Y
V (r   E )
Money Demand Curve
r(π,Y)
Y
M
M
P
*
V (r   E )
P
Money Market Equilibrium
• Equilibrium under fixed money supply: If real
interest rate is higher than money market
equilibrium, money supply will be higher than
money demand. Households will not want to
hold cash and will deposit it into banks. Banks
with a surplus of liquidity will lower equilibrium
rates.
• Equilibrium under interest target: Under interest
rate targets, if money supply is higher than
money demand at the desired real interest rate,
the central bank must sell some of its holdings of
interest paying assets (typically government
bonds).
Increase in expected inflation
r(π)
Y
M
M
P
*
P
V (r   E )
Increase in Inflation
r(π,Y)
Y
M
M
P
**
M
P
*
V (r   E )
P
Increase in Output
r(π,Y)
Y
V (r   E )
M
M
P
**
M
P
*
P
Planned Expenditure
MP
r
r*(π)
IS
Y*(π)
Y
Inflation Rises
MPʹ
r
MP
r**(πʹ)
r*(π)
IS
Y**(πʹ) Y*(π)
Y
Aggregate Demand Curve
• Negative relationship between inflation
and output generated by monetary policy
response to inflation.
Q: What causes AD curve to shift?
Answer
• Shifts in the IS curve
• Shifts in Monetary Policy
AD Curve
π
Y
Aggregate Supply Curve
• Keynesian model: Firms will increase
output if the inflation rate is high.
• Positive relationship between inflation
level and output is called AS curve.
• Fluctuations in output are caused by
fluctuations in the AD curve which are in
turn caused by fluctuations in the
Equilibrium
SRAS
π
π*
AD
Y
Y*
AD Curve Shifts
SRAS
π
π**
π*
AD’
AD
Y
Y*
Y**
Monetary Policy and the Demand
Curve
Weak Stance
π
S
π*
Strong Stance
Y
Y
Demand Curve/Supply Curve
Weak Stance
π
π*
S
Strong Stance
Y
Supply Curve
• The supply curve is based on the notion
that wages are sticky.
• So far, we have thought of real wages as
being determined in a competitive market
with a supply curve and a demand curve for
labor.
• In Keynesian theory, wages are set by
contract. Workers choose a wage and firms
hire as many workers as desired at that
wage rate.
Inflation and Labor Demand
• Workers base wage demands on what
they believe the price level will be.
Workers are backward looking in their
expectations Wt = w×PtE = w×Pt-1.
• Firms choose a quantity of workers so
Wt = Pt×MPLt=w×Pt-1.
• In equilibrium,
w
w
MPLt 

 MPLt
Pt
1 t
Pt 1
Equilibrium Labor
W
P
w
1 
MPL
L
L*
Inflation Rises, Real Wages Fall
W
P
w
1 
MPL
L
L*
L**
Inflation/Employment Tradeoff
• Keynesian economists perceived that the
government faced a choice between high
inflation on the one hand and unemployment on
the other.
• If the government wanted to push up
employment, they could (through expansionary
monetary or fiscal policy) push out the demand
curve if they were willing to bear the
consequences in terms of inflation.
Tradeoff Inflation & Unemployment
USA 1948-1969 Source: St. Louis Fed Database
8
Unemployment Rate
7
6
5
4
3
2
1
0
-2
0
2
4
Inflation
6
8
Critics
• Critics of this business cycle theory pointed out
that the ability of the government to increase
employment was based on the notion that
workers would expect zero employment.
• Phelps and Friedman suggested that it might be
more realistic to imagine that workers would
have some forecast of inflation πEt. Then they
would demand wages that would maintain their
standard of living in the face of this inflation. Wt
= w×PtE = w×(1+ πEt ) ×Pt-1.
Inflation and inflation expectations
• Firms would hire workers up until that point where
MPL = real wage.
1   tE
w
MPLt 

w  MPLt
Pt
1 t
Pt 1
• Inflation reduces real wages and increases
employment only to the extent that it stays ahead
of workers expectations.
• There is some level of employment (referred to as
the natural level) and corresponding level of
output (referred to as potential output) which
prevails when output inflation equal expectation.
IA Curve
YP
π
SRAS
πE
AD
Y
Y*
Supply Curve
• A more realistic supply curve rule, might say that
output is above potential output only when
inflation is above expected inflation.
• SRAS: πEt Expected Inflation
• Expectations Augmented Philips curve
πt = πEt + θ∙[yt – yP]
• Adaptive Expectations
πt = πt-1 + θ∙[yt – yP]
Short-run Inflation/Output
Tradeoff
• But workers will demand wages that will
support their real wages, which will
require wage growth to keep up with
expected inflation.
• Friedman says workers will base inflation
expectations on inflation that has been
observed in the past. π =πt-1
Adaptive Expectations
• Adaptive expectations generate some dynamics
to inflation-output trade-off.
• In the short-run, an expansion in money growth
will lead to an increase in inflation and output.
• But after a period, inflation expectations will
increase, leading to more inflation. Eventually
output will return to its long run level.
– Only accelerating inflation can lead to long run output
increases.
– Once the government increases money supply
growth, they cannot reduce inflation without incurring
a recession.
SRASt+∞
SRASt+2
SRASt+1
Y
π
πt+∞
SRASt
πt+2
πt+1
πE
ADt
ADt’
μ↑
Y
Breakdown in Inflation and
Unemployment Relationship
Unemployement Rate
12
10
8
6
4
2
0
0
2
4
Inflation
6
8
10
Final Exam
Material including taxes, money demand,
inflation, Keynesian model, rational
expectations model
• 14/12/2011 12:30-15:00 LTF
• Semi-open book: 1 L4 paper w/
handwritten notes both sides, calculator,
writing materials.