View
213
Download
0
Category
Preview:
Citation preview
Some Background: Aim and implementation
• Aim: explore relationship between energy consumption, energy prices, environmental taxation and energy pollution
• Three workstreams– Can cross-sectoral policies like an ETR be
justified on the basis of the dynamic properties of the data?
– Are price-based policies like an ETR likely to have a considerable effect on consumption?
– What is the shape of the relationship between pollution, economic activity and resources prices
done
done
To do
Paper I
• Can cross-sectoral policies like an ETR be justified on the basis of the dynamic properties of the data?
• Take energy intensity of each sector • Subtract the average energy intensity in the industrial
sector
i.e. time invariant differential; linear trend; structural breaks; transitory components
Run unit root tests (panel, breaks, panel + breaks)Implications: nature of the difference and ability to foresee- No rejecting difference among sectors is stochastic and
persistent sectoral policies needed to accommodate deviations and persistent shocks
- Rejecting difference among sectors is deterministic cross-sectoral policies are fine
Results: 1) Reject when allowing for breaks both at the panel and single time series level; 2) price out of the equation;
tiiiidti vmtvxy )( 0 ,
Paper II - Sectors
Identifier Sectors ISIC Taxonomy
1 MIN Mining and Quarrying 13-14
2 FT Food and Tobacco 15-16
3 TXT Textile and Leather 17-19
4 PPP Pulp, Paper and Printing 21-22
5 CHE Chemicals 24
6 NMM Non-Metallic Minerals 26
7 MAC Machinery 28-32
8 TRA Transport Equipment 34-35
9 CON Construction 45
10 MET Metals 27
Samples & Variables
Time span: UK 1978 -2004 and 1991-2004
Germany: 1991-2004
Sources: ONS, IEA, DeStatis
Economic activity: index of GVA
Energy consumption: sum of Coal, Electricity, Natural Gas and Petroleum Products
Energy price sector-based weighted
average of fuel consumption
i sit
i sittist
cons
consfpep
s= sector; i = fuel
Energy Consumption (UK)
2000
3000
4000
5000
6000
7000
8000
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
CHE MET FT MAC
0
500
1000
1500
2000
2500
3000
3500
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
PPP NMM MIN TXT TRA CON
Energy Price UK (index)
80
90
100
110
120
130
140
150
160
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
MIN FT TXT PPP CHE NMM MAC
TRA CON MET
Economic Activity UK (index)
60
70
80
90
100
110
120
130
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
MIN FT TXT PPP CHE NMM
MAC TRA CON MET
Time Series Estimators
• ARDL(1,1,1), ARDL(1,0,1), ARDL(1,1,0), ARDL(1,0,0) w & w/o time trend
• Static model ARDL(0,0,0) • SC-based selection• Rather dubious coefficients
ttttttt ppgvaβgvaβyy 1101101
Time Series Estimators (UK 78-04)
FT TXT PPP CHE NMM MAC CON MET TOT
et-10.51
(3.41)0.69
(4.75)0.78
(9.82)0.81
(7.40)-0.14
(-0.76)0.48
(3.82)0.68
(3.46)-0.29
(-1.50)0.78
(7.38)
yt1.21
(2.31)-0.29
(-0.95)0.69
(2.81)-0.35
(-2.38)0.44
(2.71)0.17
(0.73)-0.53
(-3.45)0.54
(2.34)-0.01
(-0.03)
y t-1 -0.76
(-3.07) 0.62
(2.32)0.45
(1.79) -0.42
(-1.74)
pt0.05
(0.31)-0.91
(-2.13)-0.48
(-2.48)-0.67
(-2.70)-0.34
(-3.18)-0.23
(-1.77)-0.77
(-3.82)-0.83
(-3.27)-0.03
(-0.15)
pt-1 -0.26
(-1.62) -0.58
(-2.00)
trend-0.01
(-2.68)-0.03
(-1.81) -0.03
(-6.47)-0.01
(-3.72) -0.07
(-5.91)
LRY2.46
(1.87)
-0.92(-1.22)
-0.34(-0.46)
-1.89(-1.54)
0.93(8.63)
1.20(3.19)
-1.65(-2.22)
0.09(0.65)
-0.02(-0.03)
LR P
-0.42(-1.63)
-2.92(-1.76)
-2.12(-1.87)
-3.62(-1.39)
-0.30(-2.92)
-0.44(-1.90)
-2.41(-1.90)
-1.09(-5.22)
-0.12(-0.15)
Odd Dynamics Economic Theory and Size ??
Issues in a panel time series estimation (N, T)
Static vs. Dynamic: speed of adjustment to equilibrium,
Static = within periodDynamic = allowing for adjustment period
Cross Sectional Dependency: common shocksCommon latent factors in the errors (not modelled explicitly by the xs)Common factors in the regressorsExamples: Common institutional factors; common technological change;
common world/national price
Homogenous vs. Heterogeneous: similarities across sectors
Homo: Imposing same coefficients on all subsectorsHetero: Allowing for sector-specific parameters
Panel Homogenous - static
Static Fixed and Random effect itiitity βx '
+ : consistent if parameters are heterogeneous
-: assuming within period adjustment ititity βx 'First Differences Estimators
Different approach to get rid of unobserved individual effects
Panel Homogenous - Dynamic
Dynamic FE and RE
- : Nickell bias (removed asymptotically as T goes to inf)
-: heterogeneity bias
+: allowing dynamic adjustmentAnderson-HsiaoTake FD; instrument for lagged FD
itititiit yy ε ρα '1 βx
itititit yy ερ βx '1
GMM yWAWyyWAWy NN
''
1
1
1''
1 ̂
Gain in efficiency compared to AH
Additional instruments (W) + weighting matrix (A)
One-two steps
Panel Heterogeneous – static and dynamic
Model
How to allow for heterogeneity?
1) Mean-Group estimator
2) Random Coefficient estimator
…..
using OLS coefficients N and T big enough???
ii 1 ii 2ββ
itititiit yy ε ρα '1 βx
N
iMG N
1i
1 β̂ β̂
11
1
1
i
N
j ji PPD ΣWWP 1'2
iiii
N
j jjD1ˆˆ
Cross Sectional Dependency - I
iti uf tiitit , 1 2i , ,0~ iidu it
itxix ufxx titiitit , 1 2i , ,0~ xitx iidu
Modelititiity εα ' βx
ittitiit exy zc 'βαFE/MG - PCGet 1-2 principal components from
residuals from OLS time series modelsRun FE / MG
N and T big enough …..
Cross Sectional Dependency
Demeaned Mean Group
Removing X-section error dependency & latent common factors by demeaningCould affect negatively the variance; Error if heterogeneity is present
ititiiit ey xβ ~~ titit yyy ~
ittitiitiiit eycy xdxβCommon correlated Mean Group
Rather general in terms of settings – MG-related issues
Results – UK & D 1991-2004UK 1991-2004 CMG only
GVA: 0.60 (0.05-1.15); Price: -0.47 (-0.79 - -0.15)
GERMANY 1991-2004: GVA onlyFD: 0.37 (0.17 – 0.58) Dynamic FE : 0.49 (0.11 – 0.87)AH : 0.88 (0.18 – 1.59)GMM : 0.57 (0.05–1.09) DMG : 0.55 (0.26 – 0.85)
Results UK - 1978-2004 – Price - I
-2
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
Static FE or RE FD Dynamic FE orRE
GMM Static MG Static RCM FE-PC MG-PC CMG
Dynamics: increasing size of the coefficient but also stand. err.
Nickell effect vs. “dynamic” bias vs. heterogeneity bias
Static models: Other models:
Results UK - 1978-2004 – Price - I
-2
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
Static FE or RE FD Dynamic FE orRE
GMM Static MG Static RCM FE-PC MG-PC CMG
RCM similar to MG: s you would expect
Dynamic heterogeneous: too much to cope with it
Static models: Other models:
Results UK - 1978-2004 – Price - I
-2
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
Static FE or RE FD Dynamic FE orRE
GMM Static MG Static RCM FE-PC MG-PC CMG
Common (tech, institutional) factors not big effect in this dataset
Bimodal distribution of estimator small overlap:
Static model: Other models:
Results UK - 1978-2004 – Price - I
-2
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
Static FE or RE FD Dynamic FE orRE
GMM Static MG Static RCM FE-PC MG-PC CMG
OVERALL: Bimodal distribution of estimator small overlap:
Static models: Other models:
Results UK - 1978-2004 - Economic Activity
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Stat Pool FD Dyn Pool GMM FE PC DMG
Conclusions - I
Inability to estimate time –series models at the sectoral level
Value added:
1) Increasing confidence through comparison
2) Hetero models (rarely applied in the energy literature 3 examples)
3) Allowing for common factors (never applied in the energy literature)
Heterogeneity and Dynamics: increasing the response to price changes
Value: Conservative (static): -0.40; Common-ground (overlap): -0.58; Average (all estimators): -0.74
Being conservative neglecting heterogeneity of production functions; assuming within period adjustment to equilibrium
Conclusions - II
Other recent sources in the literature:
Agnolucci (2007) and Hunt et al (2003): both implementing STSMs on industry in UK
- GVA: 0.39 vs. 0.72 vs. 0.55 (here)
- Price: -0.74 vs. -0.20 : high vs. low again (reconciling different results)
Some support for high because of the restrictions when adopting estimators indicating low in this study
However, even when being conservative (static) : -0.40 is a decent size elasticity for price-based policies
Most likely it is an underestimate
Recommended