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INDUSTRIAL BENEFITS AND COSTS OFGREENHOUSE GAS ABATEMENT

STRATEGIES: APPLICATIONS OF E3ME

Working Paper or Ta!k ":Ro#$!%ne!! o In&$!%ria' Pro(e)%ion! an&

Si*$'a%ion Proper%ie!

Nei' +enning! an& Ben Gar&inerCa*#ri&ge E)ono*e%ri)!

De)e*#er ,---

These working papers are prepared for private circulation among the participants of theE3ME project. Please do not quote without permission of the author. Comments,corrections and additions are gratefull received. The views represented in this paper

are those of the author!s" and are not necessaril those of the European Commission.The E3ME project is supported # the Commission of the European Communities,$irectorate%&eneral '(( for )cience, *esearch and $evelopment, under the +ork

programme on% uclear Energ !- /0E%T1E*M(E" 2 4%2 5, Project *eference- )3%CT 6%72 .

Participating organisations8

Co%ordinator8 Cam#ridge Econometrics !CE", Cam#ridge, /9 Contractor8 Cham#re de Commerce et d:(ndustrie de Paris !CC(P", ;rance

Contractor8 E*EC !c

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>n Energ %Environment%Econom Model for Europe

Con%en%!

Page

Contents A

EBecutive )ummar 3

2 (ntroduction 4

2.2 Purpose of the Paper 42.A *emaining )ections of the Paper

A Methodolog D

A.2 )hrinkage Methodolog DA.A >*$0 Methodolog 5

3 *esults 22

3.2 Equation parameters 2A3.A Comparison of forecasts A2

4 Conclusions and *ecommendations A4

4.2 Main ;indings A44.A *ecommendations for further research A

*eferences AD

A

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E.e)$%i/e S$**ar0

The main features, conclusions and recommendations of the working paper are asfollows8

;eatures8

• > summar of the pro#lem in hand is given i.e. that either model mis%specificationand poor data qualit often results in implausi#le parameter estimates.

• The use of shrinkage estimators !)mith, 2 D" in dealing with implausi#le estimatesis suggested as an alternative to simple parameter restrictions.

• > #rief outline of the theor #ehind the shrinkage procedure is given along withvarious suggestions for alternative wa s of shrinking the parameters, the choice ofwhich ma depend on the cause of the poor data qualit .

• The >*$0 !>utoregressive $istri#uted 0ag" approach to error%correctionmodelling is investigated as an alternative to the Engle%&ranger two%step method.The possi#ilit of appl ing the shrinkage procedure to >*$0 parameter estimates isalso considered.

*esults8

• ;our sets of parameter estimates are provided *estricted Engle%&ranger !as presentl used in E3ME", shrinkage Engle%&ranger, >*$0 and shrinkage >*$0.

• The shrinkage procedure manages to generate parameters of more plausi#lemagnitude for the majorit of E3ME sectorsF man incorrectl %signed estimates aresuccessfull shrunk towards a correctl %signed mean%group estimate.

• The >*$0 approach to cointegration has theoretical advantages to the Engle%&ranger approach and when applied to the E3ME emplo ment equationsconsistentl produces an error%correction coefficient of a sensi#le magnitude.

• The large num#er of regressors involved in the >*$0 specification made it difficultto deal with incorrectl signed estimates using the shrinkage procedure withoutlosing the heterogeneit of the estimates across regions.

• > forecast using E3ME was simulated using the shrunk parametersF a comparisonof the E/ and /9 emplo ment forecasts are made against the #aseline E3ME andM$M !Multi%sectoral $ namic Model of the /9 econom " forecasts.

*ecommendations8

• )hrinkage is a useful tool for dealing with implausi#le parameter estimates from theEngle%&ranger procedure.

• )hrinkage sometimes produces parameter estimates that cause emplo ment to reactimplausi#l to changes in other varia#les, particularl to the wage rate. (t istherefore necessar to var the shrinkage factor across industries in order to ensurethat parameter estimates do not suffer from GeBcessiveH shrinkage.

• The implementation of the >*$0 approach to cointegration should #e seriouslconsidered as an alternative to Engle%&ranger estimation. The application ofshrinkage to >*$0 coefficients is not recommended however unless a wa can #efound to maintain parameter heterogeneit more effectivel .

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, In%ro&$)%ion

,1, P$rpo!e o %2e Paper(n large%scale sectoral modelling, the a#ilit to produce sensi#le parameters !correctlsigned and of plausi#le magnitude" is important for forecast performance and for making sure that the general character of results from scenario anal sis are consistentwith a priori #eliefs. This requirement has #een of a particular interest during theconstruction of the E3ME model, a large%scale multi%sectoral model of Europe. ThesiIe of the model !currentl some , 77 stochastic equations", reflecting its sectoral andcountr disaggregation, mean that a general approach has #een taken when specif ingand estimating the modelHs equations. Essentiall , a given specification is adopted for a

particular equation, eg emplo ment%hours, and this specification is then independentlestimated for each sector in each countr covered # the model. (n the current version!A.A" of the model, this means the estimation of 47 equations !25 countries # 37

sectors" for each stochastic relationship, eg emplo ment.> common pro#lem faced when estimating such a large num#er of equations is thatsome parameters ma #e wrongl signed or of inappropriate magnitude, eg a negativeoutput coefficient in an emplo ment equation. The pragmatic solution to this pro#lemis to restrict the offending coefficient to a more accepta#le value, i.e. suggested # therelevant economic literature. This is clearl unsatisfactor , as the wrongl signedcoefficient could well reflect a model mis%specification. (n this case, further investigation is warranted to check for an sector or countr %specific factors at work inthe relationship, or the eBistence of structural #reaks. 1owever, another possi#le causeof poor parameter estimates is data qualit . T picall the greater the degree of sectoral

disaggregation, the less relia#le are the data % this is especiall the case when attemptingto model smaller European countries such as ?elgium and 0uBem#ourg, or countrieswhere the relia#ilit of the sources is more questiona#le, such as in )pain and Portugal.(n either case the same s mptom arises, however8 implausi#le parameter estimatesleading to unsta#le or implausi#le forecasts and scenarios.

The question underl ing the method applied in this paper is as follows8 can evidence for parameter estimates in other countries or sectors #e used in a formal procedure toimprove parameter estimates which are implausi#le !i.e. which are distinct outliers"without requiring the parameters to #e identical. Previousl , some anal sis of the

potential for using averaged parameter results !which one might assume to #e morerelia#le" has taken place. 1owever, this was limited to eBploration of fiBed%effect andrandom%effect pooling methodologies, and proved unsatisfactor #ecause inter%groupdifferences #eing too great for adjustment # a constant%dumm term or residual effect.The prospect of using a third option !com#ining the results of independent estimationwith the greater relia#ilit of average estimates" has #een proposed # )mith !2 D",who concluded, on the #asis of a variet of disaggregated data sets, that theintroduction of shrinkage estimators improved forecast performance !measured as meansquare errors" relative to the original !individual" estimates.

This paper applies the shrinkage methodolog to the E3ME model, and so eBtends theempirical work alread undertaken # including them in a multi%equation s stem. Thevehicle used for this purpose will #e the set of emplo ment equations, and comparisonsare made with #oth the static equation performance and the d namic simulation

properties over a within%sample data set. The paper discusses the practical modelling

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advantages and disadvantages from the use of this technique. The anal sis is # nomeans eBhaustive, as there are a variet of methods involved in undertaking the anal sisand choices have to #e made. (n such circumstances, the decision made is defended onits merits, and the alternative options are discussed.

,1" Re*aining Se)%ion! o %2e PaperThe remaining sections of this paper are as follows. )ection A outlines the theoreticalaspects of the E3ME model !and in particular the emplo ment%hours equations" and theshrinkage methodolog . )ection 3 presents the empirical results !equation estimationand model simulation" and provides interpretation and anal sis of the findings. ur conclusions are given in )ection 4, together with recommendations for further work or eBtending the anal sis.

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" Me%2o&o'og0

"1, S2rinkage Me%2o&o'og0

"1,1, Genera' in%ro&$)%ion

)uppose one has a panel of data, i.e. a time series tJ2,...,T , for a num#er of groups,iJ2,..., , and wishes to estimate a first%order autoregressive distri#uted lag model of theform8

J K B K Kit 2 hi,t%2 i i,t%2 itβ β λ ε 7

+here there are 1 eBogenous regressors, p lags !in this case, pJ2" and T is sufficientllarge that the model can #e estimated for each group. +rite this as8

J : I Ki,t i i,t itγ ε

+here γ i is a kB2 vector of parameters and I i is a com#ined kB2 vector of regressors,with kJ!1K2"!pK2". ote that in the paper, the Engle &ranger methodolog usedmeans that the shrinkage is performed separatel on the long%run and d namicspecifications, although the are connected through the error correction coefficient.

The groups could #e countries, regions or sectors and are heterogeneous in the sensethat there is no reason to eBpect the parameters to #e identical across groupsF thus

pooling is not appropriate. 1owever, one might eBpect them to #e similar in the sensethat the same general form of model is eBpected to hold and the parameters are unlikelto #e ver different.

(n these circumstances a sensi#le procedure would seem to #e to calculate independent

parameter estimates ! γ Li" for each group using the 0) or (= procedure !whichever ismore appropriate" and then estimate what Pesaran and )mith !2 " call the mean

group estimator as8

γ γ M J + L i i∑

+here + i is a weighting matriB. ;inall , the individual shrinkage estimators will #egiven # 8

γ γ γ i i i iM J N M K !( % N " L

+here N i is another weighting matriB.

The advantages of such a procedure are that the similarit #etween groups can #e usedto improve the precision of the estimates for each group, without having to make theoverl strong assumption that each group is identical.

"1,1" Rea!on! or !2rinking %2e para*e%er! in E3ME

There is a trade%off #etween the eBplanator power over historical data of a model andhow sensi#le the parameters are !i.e., how useful the model can #e for simulationeBercises". Parameters that do #etter in forecasting ma not #e the right ones accordingto the traditional criteria of un#iasedness and consistenc .

)mith !2 D" showed that shrinking the original 0) !or (=, as in E3ME" estimators

towards a common mean performed #etter in forecasting than independent ! 0)"estimators. (n fact, pooled fiBed%effect estimators can perform #etter than #oth simple

D

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0) and shrinkage estimators in forecasting, even if the look totall insensi#le whenthe constrain the coefficient to #e the same across all regions.

(n E3ME, the adoption of fiBed%effect pooled estimators would constrain the differenteconomies to react identicall to an given shock, so that genuine cross%countrdifferences would #e ignored, greatl reducing the modelHs usefulness for policanal sis. 1owever, one of the main pro#lems with the model came in the past frominsta#ilit due to large elasticities. The shrinkage estimation is an intermediate

procedure that maintains some information a#out differences in the #ehaviouralcharacterisation of the regions without permitting eBtreme values that pro#a#l reflectdata weaknesses.

To summarise, the three #asic criteria on which we value a model are8

• in%sample fit• usefulness for polic anal sis• forecasting accurac

There is no straightforward wa of trading off #etween these criteria. ;or instance, therestricted independent estimates ma provide a good in%sample fit while a pooledestimator ma give the #est forecasts, et neither ma #e much use at polic anal sisdue to the magnitude of certain coefficients. To #e useful for polic anal sis, somedegree of heterogeneit is required, #ut pro#a#l not that which comes from completelindependent estimation.

"1,13 Dea'ing i%2 o$%'ier!: &i eren% po!!i#'e !e%! o eig2%!

(t is possi#le to attach different meanings to :similar: and this gives rise to quite differentinterpretations for the estimators of γ i∗. >s Maddala !2 2" discusses, the can #e

derived as &eneralised 0east )quares estimators of *andom Coefficient Models, as?a esian estimators ,and as )tein -ames like shrinkage estimators. Each of thesederivations will lead to somewhat different definitions of similarit and somewhatdifferent choices for the weighting matrices. The )wam !2 62" version of the weightshas #een chosen as the #asic starting point, since this seems to #e the estimator mostoften cited in the literature !1siao, 2 5D and &reene, 2 3".

"1,14 Weig2%ing a))or&ing %o $n)er%ain%0

(n the #asic )wam procedure individual parameters are weighted according touncertaint . The less the :precision: of the estimation of the parameter !i.e., the higher the variance associated with the parameter", the less weight it is given in the calculationof the common mean and the more it will shrink to the common mean. The idea is thatif the elasticit of emplo ment%hours to output in the equation for ?elgium has a lower standard error than in the equation for 0uBem#ourg, the common mean should #e closer to the :?elgian: elasticit . (f the estimate of this parameter varies more across thedifferent regions than the elasticit of emplo ment%hours to wages, then the first will #eshrunk # more than the latter.

The inverse of the variance%covariance matrices of the residuals in each equation and of the variance%covariance matriB of the coefficient across the equations !the :precision:matrices" are used to construct the weighting matrices + and N.

W G V G V i i i= + +∑ − − −O ! " P ! "2 2 2

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Q V G Gi i= +− − − −O P2 2 2 2

where & represents the variance%covariance matriB of coefficients across the equationsand = i the variance%covariance matriB of the coefficients in each regression. (t shouldalso #e noted that the standard errors from the first%stage Engle%&ranger estimationma #e #iased, which ma in turn influence the estimator.

"1,15 Weig2%ing a))or&ing %o i*por%an)e

1owever, in practice the criterion of uncertaint ma not #e sufficient and other factorsmight #e taken into account. The qualit of the data is likel to #e different acrossdifferent countries and therefore one could prefer to give a larger weight to theestimators for countries with superior data. ?ecause these are usuall the larger countries, in this case the #igger is the econom , the more weight it will have in thecalculation of the common mean and the less it will shrink towards it.

W w G V w G V i i i i i= + +∑ − − −O ! " P ! "2 2 2

Q k V hG hGi i i= +− − − −O P2 2 2 2

where each Gprecision matriB has a weight w i proportional to the siIe of the econom!taken as a proB for the accurac of the data and the importance of the econom in themodel". Choosing hQ2 will give more weight to the common estimate for all groups andone could choose k iR2 to give less weight to a particularl #adl estimated group. ?oththese adjustments increase Ni. >lthough in deriving the results #elow ki was set equal toone, a set of parameters for the emplo ment equation was derived for several values of h. Each countr was, however, given an equal weighting in the procedure initiall .

"1" ARDL Me%2o&o'og0

"1"1, Genera' in%ro&$)%ion>t the moment the two%stage Engle%&ranger procedure is used in E3ME. 1owever, thestandard errors in the two%step procedure have #een demonstrated to #e inconsistent!see CharemIa and $eadman, 2 A". Therefore the procedure to shrink the parameters,which relies eBtensivel on the standard errors, ma give misleading results. Therecommendation is therefore to re%parameterise the modelF the main alternative

parameterisation #eing considered is the >*$0 representation.

> long%run model of the following t pe8

MBf Kf JM t27t

can have the following >*$0!2,2" specification8

222277t J −− +++ t t t x y x β α β α

where the long run elasticit is given # the ratio

"%"

and the error%correction model analogous to the d namic equation from the Engle%

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&ranger procedure is given #

227t "2!J −−−∆∆ t t ECM x α β

ote that the d namic equation will not contain a constant unless the long%run equation

contains a linear trend. >lso note that the sum of the coefficients on the lags of thedependent varia#le should lie #etween Iero and one for the equation to #e sta#leFcointegration #etween the varia#les will ensure that this is the case.

"1"1" ARDL !pe)i i)a%ion o %2e e*p'o0*en% e6$a%ion

The estimated emplo ment equation follows an >*$0!A,2,2,2,2,2" specification toensure a direct analog with the Engle%&ranger specification currentl used in E3ME8

The long%run cointegrating relation can #e calculated # removing the time su#scriptand then collecting terms8

>nd the error%correction mechanism # re%arranging the original equation8

The advantages of the >*$0 method are that the long%run parameter estimates are nolonger inefficient and the estimated standard errors are no longer #iased. (t is also truethat more of the >*$0 parameters can #e freel estimated e.g. to ensure a positiverelationship #etween output and emplo ment ?7 must #e positive #ut ?2 ma #e freelestimated providing ?2K?A is greater than Iero.

The o#vious disadvantage of appl ing the >*$0 method on a short time series is thelow degree of freedom involved in estimating an equation with twelve regressors andonl A or so o#servations.

"1"13 App'0ing !2rinkage %o ARDL e!%i*a%e!The >*$0 specification is perfectl equivalent to the standard error correction modelfrom a statistical point of view. 1owever, the standard errors the produce are likel to

t t t t t t t

t t t t t t t

YRKE YRKE PQRM PQRM YRH YRH

YRWC YRWC YRYRYRE YRE YRE

ε γ γ ϕ ϕ φ φ

δ δ β β α α α +++++++

++++++=

−−−

−−−−

227227227

227227AA227

M2

M2

M2

M2

M22

M

A2

27

A2

27

A2

27

A2

27

A2

27

A2

7

t t t

t t t

YRKE PQRM YRH

YRWC YRYRE

α α

γ γ

α α

ϕ ϕ

α α

φ φ α α

δ δ α α

β β α α

α

−−

++

−−

++

−−

++

−−

++

−−

++

−−=

t t t t

t t t t t

ECM YRE YRKE

PQRM YRH YRWC YRYRE

ε α α α γ ϕ φ δ β

+−−−∆−∆+

∆+∆+∆+∆=∆

−− 2A22A7

7777

"2!

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#e different i.e. >*$0 estimates of the standard errors are un#iased. Therefore theshrinkage estimators are likel to differ as are the resulting forecasting properties.

;or the >*$0 case two different methods of shrinking are possi#le8!i" shrink all the parameters and then derive the long%run and d namic parameters eB%

post.!ii" derive the long%run parameters, calculate their standard errors and then shrink onl

these and the coefficient on the lagged dependent varia#le.

(n the results section, parameter estimates from the >*$0 specification are provided.)hrinkage estimates along the lines of !i" are also provided.

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3 Re!$'%!

This section reports the results from the application of the shrinkage procedures to the parameters of E3ME and compares these results with those o#tained following thecurrent estimation procedure prior to the eBogenous assumptions on the sign and siIe of the coefficients which remove insignificant or wrongl signed parameters. ote that allta#les show the values of parameters averaged across the countries covered # themodel. >lso provided are the parameter estimates using the >*$0 approach with #othsimple restrictions and shrinkage applied.

The averaged results show a reasona#l well%#ehaved set of parameter estimates. Evenafter the imposition of restrictions there is no limit to the siIe of parameters if correctlsigned, however, and this can lead to outliers causing model insta#ilit whenforecasting. )ome large coefficients are clearl present, so it is an o#jective of theshrinkage procedure to provide a more mechanical wa of restricting the estimates thanis currentl used.

>s eBpected, a much tighter fit of parameters is o#tained with shrinkage, although thefact that the coefficients are now unrestricted means that some incorrectl %signedestimates pass through. This is particularl true for >griculture where a negativeoutput elasticit is reported even when averaged across all regions. $ealing with this

pro#lem ultimatel requires replacing such sets of estimates with their respectiverestricted equivalents.

)tructural #reaks that have not #een taken into account or #ad data can #e at the originof these pro#lems. (n some cases, the pro#lem ma #e relevant onl for a limited

num#er of countries, so that the mean is correct and the shrinkage gives naturallsensi#le results. (n other cases, the unrestricted (= estimate is particularl wrong andthe shrinkage towards the common mean is not enough to correct for the wrong sign.The eBtent to which a parameter is shrunk towards the mean depends on the variance of the parameter, #ut also on the covariance with the other parameters. The structure of the variance%covariance matriB of coefficients has meant that in some cases the shrunk

parameter is further from the mean than the unrestricted parameter estimate.

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31, E6$a%ion para*e%er! >ll sets of parameters derived were sensi#le on the whole. ?ecause parameter estimatesdeemed implausi#le even after shrinkage were replaced with their restricted equivalentsand onl parameter averages are presented in the ta#les, it is difficult to distinguish

#etween them on first inspection. 1owever, upon a closer inspection of the parameter estimates for individual equations the following points are worth noting.

31,1, S2rinkage app'ie& %o Eng'e7Granger para*e%er!

• The estimates are generall as plausi#le as those derived from appl ing restrictions.• The estimates do not suffer from the homogeneit encountered when appl ing

restrictions.• There is a clear trade%off #etween minimising the num#er of incorrectl %signed

estimates and maBimising the degree of parameter heterogeneit . (t is thereforerecommended that the degree of shrinkage #e varied across industries such that aminimum level of parameter heterogeneit ma #e maintained.

31,1" ARDL approa)2 %o )oin%egra%ion

• The error%correction coefficient appears to require restriction far less frequentlthan is the case with Engle%&ranger estimation, although the coefficient is oftenrather large in magnitude.

• The estimates are more efficient, appear to #e just as plausi#le and are often moreheterogeneous than those from the Engle%&ranger estimation.

• ?ecause the equation involves twelve regressors, the associated degree of freedomis alwa s lower than is the case with each of the two equations estimated # Engle%

&ranger, which involve five and seven regressors respectivel .

31,13 S2rinkage app'ie& %o ARDL para*e%er!

• The estimated standard errors of the parameter estimates, which are used in theshrinkage procedure, are un#iased.

• The trade%off #etween ielding correctl %signed estimates and maintaining parameter heterogeneit #ecomes almost impossi#le to deal with when there are tooman regressors. Consequentl , 7S of the estimates have to #e replaced # theirrestricted equivalents while those remaining appear rather too homogenous.

2A

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Ta#le 3.28 0ong%run restricted parameters averaged across regions

LONG-RUN PARAMETERS

OUT WAGE HRS OILP TECH Agriculture etc 1.05 -0.9 -0.92 -0.13 -0.09

Coal & Coke 0.94 -1.1 -1 -0.3 -0.16

Oil & Gas Extraction 0.19 -0.16 -0.39 0.64 0.3

Gas Distri ution 0.43 -0.51 -2.71 -0.29 0.13

Re!ine" Oil 0.32 -0.29 -1.17 -0.16 0.28

Electricit# etc 0.56 -0.52 -0.54 -0.09 -0.01

Water Su$$l# 1.05 -0.61 -1 -0.19 -0.16

%err & on'% (etal 0.63 -0.71 -1.25 -0.08 0.2on')etallic (in*Pr* 0.55 -0.7 -1.02 -0.08 0.09

C+e)icals 0.64 -0.72 -1.92 -0.07 0.01

(etal Pro"ucts 0.59 -0.69 -0.51 -0.03 0.05

Agri* & In"ust* ( ac+ 0.89 -0.76 -0.75 -0.1 -0.03

O!!ice (ac+ines 0.59 -0.6 -2.1 -0.15 0.01

Electrical Goo"s 0.83 -0.6 -0.47 -0.07 -0.22

Trans$ort E,ui$)ent 0.77 -0.94 -1.51 -0.06 -0.14

%oo"- Drink & To acc 0.75 -0.71 -0.22 -0.04 0.03Tex*- Clot+* & %oot. 0.66 -0.72 -0.55 -0.11 -0.24

Pa$er & Printing Pr* 0.34 -0.47 -1.68 -0.08 0.08

Ru er & Plastic Pr* 0.68 -0.71 -0.62 -0.06 -0.03

Rec#cling/E)iss A at 0 0 0 0 0

Ot+er (anu!actures 0.62 -0.7 -0.58 -0.12 -0.08

Construction 0.98 -0.7 -1.12 -0.1 -0.17

Distri ution etc 0.5 -0.49 -0.88 -0.05 0

Lo"ging & Catering 1.06 -0.48 -0.46 -0.03 -0.07Inlan" Trans$ort 0.26 -0.41 -0.41 -0.03 0.14

Sea & Air Trans$ort 0.35 -0.29 -1.96 -0.09 -0.07

Ot+er Trans$ort 0.52 -0.4 -0.21 -0.01 -0.16

Co))unications 0.2 -0.46 -0.89 -0.02 0.17

0ank* %inance & Ins* 0.41 -0.47 -2.16 -0.08 0.04

Ot+er (arket Ser1* 0.74 -0.4 -1.29 -0.1 0.09

on')arket Ser1ices 0.86 -1.05 -0.31 -0.06 -0.07

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Ta#le 3.A8 $ namic restricted parameters averaged across regions

DYNAMIC PARAMETERS

DOUT DWAGE DHRS DOILP DTECH DLE(P LEC( Agriculture etc 0.04 -0.11 -0.21 -0.02 -0.02 0.12 -0.28

Coal & Coke 0.56 -0.57 -2.61 -0.1 0.08 0.07 -0.21

Oil & Gas Extraction 0.46 -0.06 -0.83 -0.06 1.6 0.29 -0.62

Gas Distri ution 0.13 -0.14 -1.93 -0.05 0 0.05 -0.25

Re!ine" Oil 0.27 -0.2 -0.31 -0.12 0.03 0.14 -0.37

Electricit# etc 0.24 -0.25 -1.12 -0.08 0.04 0.25 -0.44

Water Su$$l# 0.37 -0.69 -1.14 -0.24 -0.34 0.09 -0.48

%err & on'% (etal 0.29 -0.2 -1.24 -0.07 0.09 0.2 -0.28

on')etallic (in*Pr* 0.08 -0.4 -0.49 -0.02 0.08 0.26 -0.39

C+e)icals 0.28 -0.21 -1.91 -0.02 0 0.14 -0.43

(etal Pro"ucts 0.33 -0.29 -0.63 -0.04 0 0.27 -0.32

Agri* & In"ust* ( ac+ 0.34 -0.26 -0.37 -0.02 0.08 0.4 -0.24

O!!ice (ac+ines 0.28 -0.35 -1.37 -0.02 0.06 0.21 -0.32

Electrical Goo"s 0.72 -0.54 -0.91 -0.03 0.08 0.11 -0.39

Trans$ort E,ui$)ent 0.22 -0.32 -1.28 -0.01 0.18 0.03 -0.28

%oo"- Drink & To acc 0.16 -0.1 -0.12 -0.01 0.01 0 -0.26

Tex*- Clot+* & %oot. 0.39 -0.02 -0.49 -0.01 0.04 0.05 -0.22

Pa$er & Printing Pr* 0.52 -0.49 -1 -0.09 0.03 0.09 -0.34

Ru er & Plastic Pr* 0.27 -0.24 -0.45 -0.04 0.06 0.26 -0.22

Rec#cling/E)iss A at 0 0 0 0 0 0 0

Ot+er (anu!actures 0.38 -0.21 -0.17 -0.06 0.01 0.31 -0.34

Construction 0.39 -0.55 -1.14 -0.06 0.03 0.11 -0.33

Distri ution etc 0.14 -0.2 -2.11 -0.04 0.03 0.19 -0.27

Lo"ging & Catering 0.17 -0.26 -0.15 -0.02 -0.12 0.15 -0.29

Inlan" Trans$ort 0.18 -0.05 -0.28 -0.03 0.01 0.05 -0.28

Sea & Air Trans$ort 0.13 -0.14 -0.24 -0.04 -0.02 0.43 -0.31

Ot+er Trans$ort 0.36 -0.32 -1.44 -0.01 -0.01 0.23 -0.36

Co))unications 0.05 -0.14 -0.64 -0.01 0.09 0.24 -0.27

0ank* %inance & Ins* 0.05 -0.05 -0.38 -0.01 0.02 0.37 -0.22

Ot+er (arket Ser1* 0.52 -0.23 -0.97 -0.03 0.01 0.12 -0.39

on')arket Ser1ices 0.47 -0.36 -0.18 -0.01 0 0.22 -0.27

24

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Ta#le 3.38 0ong%run shrinkage parameters averaged across regions

LONG-RUN PARAMETERS

OUT WAGE HRS OILP TECH

Agriculture etc 0.32 -0.75 -0.12 -0.06 -0.04

Coal & Coke 0.71 -0.43 -1.27 -0.11 0.17

Oil & Gas Extraction 0.33 -0.22 -0.12 -0.02 0.31

Gas Distri ution 0.24 -0.24 -2.38 -0.11 0.2

Re!ine" Oil 0.37 -0.22 -0.18 -0.11 0.07

Electricit# etc 0.38 -0.24 -0.42 -0.03 0.02

Water Su$$l# 0.72 -0.46 -0.33 -0.1 0.03

%err & on'% (etal 0.44 -0.62 -1.26 -0.12 0.13

on')etallic (in*Pr* 0.38 -0.53 -0.45 -0.11 0.07

C+e)icals 0.45 -0.53 -0.66 -0.07 0.02

(etal Pro"ucts 0.47 -0.48 -1.23 -0.04 0.09

Agri* & In"ust* ( ac+ 0.64 -0.63 -0.67 -0.05 0.03

O!!ice (ac+ines 0.33 -0.41 -1.68 -0.07 0.05

Electrical Goo"s 0.63 -0.53 -0.09 -0.02 -0.03

Trans$ort E,ui$)ent 0.51 -0.71 -0.88 -0.04 -0.01

%oo"- Drink & To acc 0.32 -0.44 -0.05 -0.04 -0.02

Tex*- Clot+* & %oot. 0.43 -0.34 -0.16 -0.02 0.06

Pa$er & Printing Pr* 0.38 -0.36 -0.08 -0.04 0

Ru er & Plastic Pr* 0.39 -0.39 -0.18 -0.08 -0.02


Ot+er (anu!actures 0.53 -0.64 -0.29 -0.08 -0.09

Construction 0.7 -0.72 -0.36 -0.12 0.06

Distri ution etc 0.28 -0.29 -0.52 -0.01 0.05

Lo"ging & Catering 0.7 -0.38 -0.3 -0.02 0.04

Inlan" Trans$ort 0.3 -0.31 -0.17 -0.02 0.06

Sea & Air Trans$ort 0.3 -0.23 -0.45 -0.05 -0.11

Ot+er Trans$ort 0.51 -0.32 -0.1 -0.02 0.03

Co))unications 0.22 -0.37 -1.23 0 0.03

0ank* %inance & Ins* 0.39 -0.31 -1.36 -0.02 0.14

Ot+er (arket Ser1* 0.88 -0.42 -0.34 -0.02 0.06

on')arket Ser1ices 0.8 -0.59 -0.5 -0.01 -0.01

2

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Ta#le 3.48 $ namic shrinkage parameters averaged across regions

DYNAMIC PARAMETERS

DOUT DWAGE DHRS DOILP DTECH DLE(P LEC(

Agriculture etc 0.08 -0.16 -0.42 0 -0.02 0.01 -0.2

Coal & Coke 0.47 -0.28 -0.98 -0.04 0.08 0.14 -0.26

Oil & Gas Extraction 0.22 -0.08 0 -0.67 -0.05 0.2 -0.63

Gas Distri ution 0.02 -0.07 -0.81 -0.07 -0.01 0.2 -0.25

Re!ine" Oil 0.24 -0.07 -0.09 -0.02 0.1 0.01 -0.3

Electricit# etc 0.2 -0.1 -0.38 -0.03 0.03 0.16 -0.21

Water Su$$l# 0.42 -0.42 -1.26 -0.08 -0.01 0.1 -0.24

%err & on'% (etal 0.21 -0.27 -1.32 -0.01 0 0.17 -0.18

on')etallic (in*Pr* 0.24 -0.4 -0.16 -0.01 0.04 0.21 -0.19

C+e)icals 0.15 -0.13 -0.24 -0.04 0.04 0.11 -0.27

(etal Pro"ucts 0.33 -0.24 -0.68 -0.02 0.04 0.27 -0.21

Agri* & In"ust* ( ac+ 0.28 -0.17 -0.52 -0.01 0.04 0.25 -0.18

O!!ice (ac+ines 0.36 -0.25 -0.37 -0.05 0.08 0.33 -0.28

Electrical Goo"s 0.17 -0.29 -0.02 0 0.07 0.19 -0.21

Trans$ort E,ui$)ent 0.25 -0.17 -1.09 -0.01 0.01 0.27 -0.21

%oo"- Drink & To acc 0.36 -0.25 -0.63 0 0.04 0.17 -0.22

Tex*- Clot+* & %oot. 0.24 -0.17 -0.24 -0.01 0.04 0.18 -0.18

Pa$er & Printing Pr* 0.31 -0.32 -0.59 -0.01 0.02 0.04 -0.26

Ru er & Plastic Pr* 0.27 -0.23 -0.59 -0.02 0.03 0.11 -0.26


Ot+er (anu!actures 0.22 -0.15 -0.22 -0.01 0.08 0.22 -0.18

Construction 0.53 -0.47 -0.17 -0.05 0.01 -0.04 -0.19

Distri ution etc 0.21 -0.11 -0.44 -0.02 0.03 0.26 -0.22

Lo"ging & Catering 0.35 -0.19 -0.24 -0.01 0.03 0.15 -0.26

Inlan" Trans$ort 0.24 -0.15 -0.34 -0.01 0.05 0.09 -0.24

Sea & Air Trans$ort 0.19 -0.12 -0.83 -0.03 0.03 0.13 -0.23

Ot+er Trans$ort 0.23 -0.19 -0.01 0 -0.01 0.15 -0.18

Co))unications 0.14 -0.15 -0.15 0 0.1 0.28 -0.2

0ank* %inance & Ins* 0.07 -0.03 -1.07 -0.01 0.03 0.41 -0.2

Ot+er (arket Ser1* 0.35 -0.16 -0.54 -0.01 0.02 0.18 -0.22

on')arket Ser1ices 0.36 -0.29 -0.31 -0.02 0.01 0.12 -0.17

2D

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Ta#le 3. 8 0ong%run >*$0 parameters averaged across regions

LONG-RUN PARAMETERS

OUT WAGE HRS OILP TECH Agriculture etc 0.49 -0.76 -1.26 -0.07 -0.04

Coal & Coke 0.61 -0.71 -1.32 -0.21 0.08


Gas Distri ution 0.4 -0.6 -0.98 -0.2 0.17

Re!ine" Oil 0.51 -0.42 -0.22 -0.13 -0.07

Electricit# etc 0.24 -0.25 -0.45 -0.06 0.07

Water Su$$l# 0.78 -0.33 -1 -0.13 0.09

%err & on'% (etal 1.16 -1.11 -1.41 -0.24 0.16

on')etallic (in*Pr* 0.6 -0.49 -0.57 -0.1 0.04

C+e)icals 0.54 -0.68 -0.9 -0.05 0.11

(etal Pro"ucts 0.67 -0.56 -0.45 -0.04 0.03

Agri* & In"ust* ( ac+ 0.71 -0.54 -1.14 -0.12 0.1

O!!ice ( ac+ines 0.38 -0.37 -1.61 -0.13 -0.1

Electrical Goo"s 0.41 -0.34 -1.06 -0.1 -0.05

Trans$ort E,ui$)ent 0.58 -1.09 -0.79 -0.05 0.01

%oo"- Drink & To acc 0.79 -0.8 -0.56 -0.07 0.1

Tex*- Clot+* & %oot. 0.63 -0.44 -0.51 -0.09 0.04Pa$er & Printing Pr* 0.3 -0.36 -0.33 -0.14 0.01

Ru er & Plastic Pr* 0.75 -0.84 -0.3 -0.06 -0.01


Ot+er (anu!actures 0.45 -0.5 -0.4 -0.08 -0.06

Construction 0.43 -0.39 -0.41 -0.12 0.31

Distri ution etc 0.38 -0.25 -0.92 -0.04 0.04

Lo"ging & Catering 0.69 -0.33 -1.29 -0.03 0.13

Inlan" Trans$ort 0.29 -0.15 -0.59 -0.04 0.11

Sea & Air Trans$ort 0.35 -0.42 -2 -0.1 0.08

Ot+er Trans$ort 0.43 -0.19 -0.05 -0.05 0

Co))unications 0.13 -0.2 -0.32 -0.01 0.08

0ank* %inance & Ins* 0.3 -0.24 -1.74 -0.05 0.07

Ot+er (arket Ser1* 0.78 -0.31 -0.99 -0.07 0.05

on')arket Ser1ices 0.68 -0.24 -0.93 -0.03 0.01

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Ta#le 3.D8 $ namic >*$0 parameters averaged across regions

DYNAMIC PARAMETERS

DOUT DWAGE DHRS DOILP DTECH DLE(P LEC( Agriculture etc 0.26 -0.25 -0.17 -0.01 -0.01 0.07 -0.38

Coal & Coke 0.22 -0.22 -0.33 -0.07 0.03 0.1 -0.41


Gas Distri ution 0.3 -0.11 -0.18 -0.06 0.09 -0.11 -0.38

Re!ine" Oil 0.26 -0.11 -0.17 -0.11 -0.04 0.06 -0.47

Electricit# etc 0.14 -0.04 -0.15 -0.04 0.03 -0.07 -0.4

Water Su$$l# 0.32 -0.27 -0.62 -0.05 0.05 0.09 -0.34

%err & on'% (etal 0.16 -0.29 -0.27 -0.02 0.03 0 -0.26

on')etallic (in*Pr* 0.27 -0.34 -0.11 -0.03 0.01 0.1 -0.33

C+e)icals 0.25 -0.24 -0.24 -0.04 0.04 0.14 -0.45

(etal Pro"ucts 0.42 -0.4 -0.31 -0.01 0.01 0.17 -0.39

Agri* & In"ust* ( ac+ 0.35 -0.29 -0.25 -0.02 0.06 0.13 -0.33

O!!ice (ac+ines 0.43 -0.31 -0.12 -0.04 -0.02 0.37 -0.35

Electrical Goo"s 0.42 -0.37 -0.12 -0.04 0 0.25 -0.3

Trans$ort E,ui$)ent 0.27 -0.38 -0.15 -0.02 0.01 0.18 -0.37

%oo"- Drink & To acc 0.29 -0.21 -0.14 -0.01 0.02 0.07 -0.28

Tex*- Clot+* & %oot. 0.35 -0.25 -0.12 -0.02 0.01 0.02 -0.27

Pa$er & Printing Pr* 0.29 -0.33 -0.43 -0.04 0 0.19 -0.46

Ru er & Plastic Pr* 0.49 -0.4 -0.07 -0.03 -0.01 0.04 -0.5


Ot+er (anu!actures 0.25 -0.24 -0.29 -0.01 -0.01 0.24 -0.47

Construction 0.38 -0.24 -0.27 -0.05 0.09 0.11 -0.42

Distri ution etc 0.21 -0.16 -0.08 -0.02 0.01 0.15 -0.4

Lo"ging & Catering 0.23 -0.24 -0.45 -0.01 0.05 0.16 -0.43

Inlan" Trans$ort 0.15 -0.09 -0.34 -0.02 0.06 0.11 -0.51

Sea & Air Trans$ort 0.16 -0.2 -0.27 -0.04 0.01 0.1 -0.31

Ot+er Trans$ort 0.23 -0.23 -0.13 -0.02 0 0.15 -0.33

Co))unications 0.04 -0.14 -0.15 -0.01 0.02 0.18 -0.36

0ank* %inance & Ins* 0.11 -0.09 -0.14 -0.01 0.02 0.32 -0.46

Ot+er (arket Ser1* 0.33 -0.27 -0.15 -0.02 0.03 0.01 -0.53

on')arket Ser1ices 0.39 -0.29 -0.11 -0.01 0 0.13 -0.44

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Ta#le 3.68 0ong%run >*$0 shrinkage parameters averaged across regions

LONG-RUN PARAMETERS

OUT WAGE HRS OILP TECH

Agriculture etc 0.44 -0.63 -0.78 -0.11 -0.03

Coal & Coke 0 0 0 0 0


Gas Distri ution 0.16 -0.16 -1.24 -0.07 0.12

Re!ine" Oil 0.21 -0.2 -0.22 -0.09 0.02

Electricit# etc 0.3 -0.2 -0.52 -0.03 0.06

Water Su$$l# 0.49 -0.67 -0.72 -0.07 0.17

%err & on'% (etal 0.55 -0.82 -0.83 -0.18 0.11

on')etallic (in*Pr* 0.61 -0.59 -0.06 -0.11 0.08

C+e)icals 0.45 -0.53 -0.51 -0.05 0.03

(etal Pro"ucts 0.4 -0.54 -0.36 -0.04 0.09

Agri* & In"ust* ( ac+ 0.66 -0.62 -0.2 -0.15 0.11

O!!ice ( ac+ines 0.26 -0.33 -0.55 -0.07 -0.01

Electrical Goo"s 0.44 -0.44 -0.45 -0.13 -0.01

Trans$ort E,ui$)ent 0.44 -0.72 -0.41 -0.08 -0.06

%oo"- Drink & To acc 0.5 -0.54 -0.27 -0.08 0.07

Tex*- Clot+* & %oot. 0.83 -0.74 -0.04 -0.12 0.04

Pa$er & Printing Pr* 0.4 -0.52 -0.07 -0.11 0.06

Ru er & Plastic Pr* 0.52 -0.57 -0.33 -0.11 0.06


Ot+er (anu!actures 0.39 -0.49 -0.44 -0.07 -0.01

Construction 0.45 -0.49 -0.06 -0.15 0.13

Distri ution etc 0.43 -0.34 -0.5 -0.02 0.05

Lo"ging & Catering 0.59 -0.36 -0.65 -0.02 0.07

Inlan" Trans$ort 0.3 -0.16 -0.43 -0.04 0.1

Sea & Air Trans$ort 0.2 -0.22 -0.44 -0.04 -0.02

Ot+er Trans$ort 0.39 -0.14 -0.25 -0.07 0.01

Co))unications 0.07 -0.25 -0.73 -0.01 0.1

0ank* %inance & Ins* 0.31 -0.23 -1.85 -0.03 0.14

Ot+er (arket Ser1* 0.74 -0.29 -0.96 -0.05 0.06

on')arket Ser1ices 0.69 -0.41 -0.79 -0.01 0.04

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Ta#le 3.58 $ namic >*$0 shrinkage parameters averaged across regions

DYNAMIC PARAMETERS

DOUT DWAGE DHRS DOILP DTECH DLE(P LEC(

Agriculture etc 0.18 -0.15 -0.13 0 0 0.03 -0.29

Coal & Coke 0 0 0 0 0 0 0


Gas Distri ution 0.12 -0.05 -0.08 -0.05 0.05 0.05 -0.46

Re!ine" Oil 0.12 -0.06 -0.21 -0.05 0.01 0.14 -0.5

Electricit# etc 0.11 -0.07 -0.31 -0.03 0.02 0.03 -0.44

Water Su$$l# 0.46 -0.35 -0.35 -0.03 0.05 0.08 -0.36

%err & on'% (etal 0.12 -0.22 -0.28 -0.02 0.02 -0.04 -0.3

on')etallic (in*Pr* 0.28 -0.33 -0.02 -0.03 0.02 0.02 -0.34

C+e)icals 0.22 -0.19 -0.19 -0.05 0.01 0.1 -0.47

(etal Pro"ucts 0.27 -0.26 -0.29 0 0.03 0.1 -0.36

Agri* & In"ust* ( ac+ 0.31 -0.28 -0.04 -0.03 0.04 0.04 -0.31

O!!ice (ac+ines 0.27 -0.19 -0.21 -0.02 0.01 0.09 -0.45

Electrical Goo"s 0.32 -0.28 -0.08 -0.06 -0.01 0.12 -0.46

Trans$ort E,ui$)ent 0.26 -0.27 -0.37 -0.01 -0.01 0.13 -0.33

%oo"- Drink & To acc 0.3 -0.21 -0.12 -0.01 0.02 0.07 -0.35

Tex*- Clot+* & %oot. 0.39 -0.34 -0.09 -0.04 0.02 0 -0.4

Pa$er & Printing Pr* 0.22 -0.19 -0.16 -0.03 0.02 0.11 -0.42

Ru er & Plastic Pr* 0.34 -0.29 -0.17 -0.05 0 -0.02 -0.44


Ot+er (anu!actures 0.22 -0.19 -0.24 0 -0.01 0.14 -0.41

Construction 0.39 -0.37 -0.09 -0.03 0.04 0.1 -0.39

Distri ution etc 0.23 -0.16 -0.14 -0.01 0.02 0.15 -0.4

Lo"ging & Catering 0.37 -0.33 -0.34 -0.02 0.03 0.07 -0.45Inlan" Trans$ort 0.17 -0.16 -0.25 -0.03 0.04 0.06 -0.43

Sea & Air Trans$ort 0.09 -0.17 -0.23 -0.02 -0.01 0.11 -0.43

Ot+er Trans$ort 0.22 -0.26 -0.05 -0.04 0 0.1 -0.49

Co))unications 0.09 -0.13 -0.14 -0.01 0.03 0.08 -0.31

0ank* %inance & Ins* 0.07 -0.08 -0.26 -0.01 0.02 0.11 -0.36

Ot+er (arket Ser1* 0.25 -0.17 -0.06 -0.01 0.02 0.11 -0.41

on')arket Ser1ices 0.46 -0.29 -0.07 0 0.01 0.11 -0.33

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31" Co*pari!on o ore)a!%!The E3ME emplo ment forecast for the /9 is more consistent with the M$M forecastwhen the shrinkage parameters are used !;igures 3.2%3.A". ?oth predict a fall inemplo ment growth in A772 followed # a rise over the su#sequent two ears. (ncontrast the unshrunk E3ME parameters predict a sharp decline in emplo ment growthall the wa to A773 followed # a sustained rise over the su#sequent four ears.

The E/ forecast !;igures 3.3%3.4" using the shrinkage estimates places emplo ment ona lower path than the E3ME #aseline forecast. >part from an initial fall, emplo mentgrowth is generall less erratic although whether this is due to improved sta#ilit or reduced sensitivit !or pro#a#l #oth" it is hard to tell.

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Figure 3.1: Comp ri!o" o# Mo$e% Fore& !'! o#U( emp%o)me"'

*+,,,* ,,,* ,,,*/,,,3,,,,31,,,3*,,,

1 / / .

* , , ,

* , , *

* , , 0

* , , +

* , , .

* , 1 ,

Ye r

E m p

% o ) m e "

' 1 ' 2 o u ! "

$ ! 3

Re!'ri&'e$S2ru"4MDM/*

Figure 3.*: Comp ri!o" o# Mo$e% Fore& !'! o#U( emp%o)me"' gro5'2

81889

,1889

"1889

31889

, - - -

" 8 8 ,

" 8 8 3

" 8 8 5

" 8 8 :

" 8 8 -

Ye r

E m p

% o ) m e "

'

1 6

& 2 " g e

3

Re!'ri&'e$S2ru"4MDM/*

AA

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Figure 3.3: Comp ri!o" o# Mo$e% Fore& !'! o#EU emp%o)me"'

17,,,,

177,,,

1+,,,,

1+7,,,

1 ,,,,

1 / / .

* , , ,

* , , *

* , , 0

* , , +

* , , .

* , 1 ,

Ye r

E m p

% o ) m e "

' 1 ' 2 o u ! "

$ ! 3

Re!'ri&'e$S2ru"4

Figure 3.0: Comp ri!o" o# Mo$e% Fore& !'! o#EU emp%o)me"' gro5'2

81889

81"89

81489

81;89

81

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4 Con)'$!ion! an& Re)o**en&a%ion!

41, Main Fin&ing!

The use of the shrinkage estimators in place of the restricted (= estimators is justified interms of two criteria. )hrinkage estimation should remove too eBtreme coefficients thatare causing insta#ilit of the model. >nd, as * )mith !2 D" showed, forecasting

performances should also #e improved.

41,1, E e)% on para*e%er !%a#i'i%0=!en!i#i'i%0

The use of shrinkage estimators has improved the sensi#ilit of the parametersF largeroutliers have #een removed and a greater degree of heterogeneit has #een added. >s aresult, the sta#ilit of the model has in some aspects improved. Moreover, if eBogenous

restrictions are not imposed, the peculiar characteristics of regions and sectors are preserved. 1owever, the sensitivit of emplo ment to the wage parameter meant thatthe forecast for emplo ment growth for some sectors was eBcessive in some sectors. (tis therefore important to take care not to shrink the wage parameters too far towards themean%group estimator.

41,1" ARDL e!%i*a%ion

>*$0 estimation, with restrictions applied along the lines of those alread used inE3ME, tended to provide parameter estimates which were, on average, moreheterogeneous than those estimated using the Engle and &ranger two%step method.?ecause the >*$0 approach to cointegration involved a single equation consisting of2A dependent varia#les, the degrees of freedom were naturall lower than whenestimating two separate equations with fewer dependent varia#les. This is a cause ofsome concern in a model where short time%series are used #ut the improved efficienc of the long%run estimates compensates for this when implemented in the E3ME modelsolution.

41,13 S2rinkage app'ie& %o ARDL e!%i*a%e!

The large num#er of regressors involved in >*$0 estimation has meant that it is

eBtremel difficult to preserve parameter heterogeneit without replacing nearl all of the shrinkage estimates with their restricted equivalents. (t is therefore therecommendation of this paper that while #oth shrinkage and >*$0 estimation arevia#le alternatives to Engle%&ranger with restrictions, the two techniques should not #eused in com#ination without further research.

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41" Re)o**en&a%ion! or $r%2er re!ear)2

41"1, S2rinking a)ro!! region! or !e)%or!

(n this eBercise the parameters have #een shrunk across regions #ut not across sectors,on the h pothesis that for eBample, the chemicals industr in the /9 #ears moresimilarit to chemicals in &erman than agriculture in the /9. (n fact, it is possi#lethat the eBistence of a common wage setting in the /9 for all the sectors makes theopposite true so that shrinking across sectors might #e more appropriate. )hrinkingacross countries and across sectors would make use of all the information contained inthe data. Thus, some of the pro#lems encountered like the eBistence of wrongl signedcoefficients in certain sectors could #e removed without having to use such a largeshrinkage factor.

41"1" E.%en!ion %o o%2er area! o %2e *o&e'1aving anal sed the properties of the shrinkage estimators for the emplo mentequations the neBt logical step is to eBtend the coverage to other areas of the model.)hrinkage estimation has #een applied to the energ demand equations on aneBperimental #asis. The research ma then #e eBtended to the modelHs other la#ourmarket equations !hours%worked, la#our costs and la#our force participation" #efore wemove on to other #locks, e.g. trade, household consumption etc.

41"13 Re7!pe)i i)a%ion o E6$a%ion!

? re%specif ing the 0* and )* equations so that fewer parameters need to #eestimated, one is a#le to use a more parsimonious specification and the time taken toestimate the parameters ma #e reduced. The implication is that shrinkage ma #eapplied far more easil and the trade%off #etween parameter plausi#ilit andheterogeneit more easil monitored. (nitial estimation has #een carried out on a set of emplo ment%hours equations although shrinkage has et to #e applied. There alsoeBists potential for the re%specification of other equations on more parsimonious linesalthough this is an eBercise for the future.

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5 Re eren)e!

?riscoe, & and * +ilson !2 2", GEBplanations of the demand for la#our in the /nited9ingdom engineering sectorH, pp!ied E"onomi"s , A3, pp 23% AD.

CharemIa and $eadman !2 A", #ew $ire"tions in E"onometri"s Pra"ti"e ,Edward Elgar.

Coacle -, ; 9ulasi and * P )mith !2 ", Current ""ount %o!&en"y Pro'!emand the (e!dstein)Horioka Pu**!e , ?irk#eck $iscussion Paper 5< , Economic-ournal Ma 2 D.

$*( !2 A" G(mpact of a Package of EC Measures to Control C A Emissionson European (ndustr H, (ina! Report prepared +or the CEC , -anuar 2 A.

Engle, * ; and C + - &ranger !2 56" GCointegration and error correction8representation, estimation and testingH, E"onometri"a , , ppA 2%6D.

&reene + 1 !2 3", E"onometri" na!ysis , And Edition, Macmillan.1endr , $ ;, > Pagan and - $ )argan !2 54" G$ namic )pecificationH, in

Hand'ook o+ E"onometri"s , =ol ((, &riliches, and M $ (ntrigilator !eds",>msterdam, orth 1olland.

1siao C !2 5D", na!ysis o+ Pane! $ata , Cam#ridge /niversit Press,Cam#ridge.

0ee, 9 C, M 1 Pesaran and * & Pierse !2 7" G>ggregation ?ias in 0a#our $emand Equations for the /9 Econom H, Chapter D in ?arker, T and M 1Pesaran !eds" $isaggregation in E"onometri" Mode!!ing , *outledge.

0ee, 9 C and M 1 Pesaran !2 3" GThe *ole of )ectoral (nteractions in +age$etermination in the /9 Ecnom H, ,he E"onomi" -ourna! , -anuar 2 3.

Maddala !2 66", E"onometri"s , Mc&raw 1ill.

Maddala !2 2", ,o Poo! or not to Poo!. ,hat is the Question , -ournal of Nuantitative Economics, 6,A, A %AD4.

Pesaran M 1, and * P )mith !2 a", Estimating /ong)Run Re!ationships +rom $ynami" Heterogeneous Pane!s , -ournal of Econometrics, D5,2, 6 %223.

Pesaran and * P )mith !2 #", >lternative >pproaches to Estimating 0ong%*un Energ $emand Elasticities, an >pplication to >sian $eveloping Countries,

in T ?arker, P Ekins and -ohnstone !eds", G!o'a! Warming and Energy $emand , *outledge, 2 %4D.

Pesaran M 1 and * P )mith !2 ", $ynami" /inear Mode!s +or Heterogeneous Pane!s , $>E +orking Paper 73, forthcoming in Mat as and)evestre, The Econometrics of Panel $ata, And edition.

)mith, * !2 D", ,he +ore"asting per+orman"e o+ shrinkage estimators inheterogeneous pane!s , unpu#lished manuscript, ?irk#eck College, 0ondon.

)wam , P.>.=.?. !2 62", %tatisti"a! 0n+eren"e in Random Coe++i"ient Regression Mode!s , 0ecture otes in perations *esearch and Mathematical

) stem, , )pringer%=erlag ?erlin.

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task02 (1)