PRICE INDICES FOR CURRENT COST ACCOUNTING

PRICE INDICES FOR CURRENT COST ACCOUNTING

MICHAEL BOURN, P.J.M. STONEY AND R.F. WYNN”

INTRODUCTION

In April 1976 the Central Statistical Office (CSO) began publication office index numbers for current cost accounting (l), in response to recommendations 93,94, 117 and 118 of the Sandilands Report (2). The index numbers are in three main sections. These are capital expenditure on plant and machinery; capital expenditure on motor vehicles; and expenditure on stocks held either for sale or as materials and fuel.

The indices for capital expenditure on plant and machinery contain separate series for nineteen industry groups, as listed in Table 26 of the Sandilands Report, together with an index for “Total manufacturing industry”. The industry groups closely resemble the 1968 Standard Industrial Classification (3) “order level” breakdown. Data are given quarterly for the period 1956-1971 and monthly for January 1972-January 1976. The base year is 1970. Annual measures are given of a single price index for capital expenditure on plant and machinery by all industries for the period 1948-1956. The indices for capital expenditure on motor vehicles cover four types of vehicle. Data are given quarterly for 1963- 1971 and monthly for January 1972-January 1976.

The indices for stocks contain separate series for seventy-one clusters of industries at the “minimum list heading” level of the 1968 S.I.C. (3). The list is very similar to that given in Table 27 of the Sandilands Report (2). However, the publication of a small number of the indices listed there is delayed temporarily. On the other hand, the wholesale and retail distribution industries are presented in great detail, with eighteen and twenty-one indices, respectively. Separate indices are given where possible for each industry in respect of stocks held for resale and those held as materials or fuel. However, only a single total index is given for each of the many sections of retail and wholesale distribution enumerated; this is also the case for both gas and electricity. This form of publication was recommended in paragraph 600 of the Sandilands Report. The various indices for stocks give monthly data for January 1972-January 1976, except in the cases of gas and electricity for which only quarterly data are given.

The system of current cost accounting (CCA) recommended by the Sandilands Committee can be summarised as a system of value accounting, in which the

*The authors are, respectively, Professor of Business Studies, Lecturer in Business Studies, and Senior Lecturer in Economics, University of Liverpool. (Paper received August 1976)

Journal of Business Finance & Accounting, 3/3(1976) 149

concept of “value to the business” assumes the central role held by the concept of “historic cost” in much accounting practice today. An asset’s value to the business can best be measured by its deprival value, which is the minimum amount that would fully compensate the business for a hypothetical sudden total loss of the asset. In many cases deprival value is best measured by replacement cost. In considering how best to measure the replacement cost of plant and machinery the Sandilands Report notes two particular problems (2, para. 572). These are:

“(a) the quantity and diversity of fured assets held by companies, which make it impracticable in all but a few cases to estimate the current replacement cost (purchase price) of assets on an individual basis; and

(b) the changing technology of industrial assets, in particular, which in many cases makes it difficult to compare existing assets (which may be some years old) with those currently available”.

The Report notes at both paragraphs 22 and 573 that a price index attempts to deal with both of these problems. Therefore, in paragraph 573 it recommends the publication of index number series which “would be designed to provide a ‘standard reference basis’ for making reasonable approximations to the ‘value to the business’ of assets by reference to current replacement costs, and would not be intended to provide an elaborate and detailed basis for estimating the price movements of specific types of assets”.

CCA can be contrasted with the current purchasing power (CCP) approach to inflation accounting previously advocated by the Accounting Standards Steering Committee in their Provisional Statement of Standard Accounting Practice 110.7 (4). In the CPP system historic cost measures are amended by reference to some single index which measures changes in the general purchasing power of money. The index recommended for this purpose was the monthly Retail Price Index (W9.

This paper considers mainly capital expenditure. Its aim is to identify the extent to which movements in each industry-specific price index published by the CSO, are linearly correlated with movements in each of the other indices. Therefore a one-to-one comparison is made between the movements in each index and the movements in each other index within each of the two asset categories, i.e. plant and machinery, and motor vehicles. The movements are also compared with movements in the RPI and the implicit Gross Domestic Fixed Capital Formation deflator for plant and machinery used in the National Income and Expenditure Accounts. The extent of the correlations thus established shows the extent to which the movements compared are in the same direction, have the same order of magnitude, and are subject to the same time-lags.

150 Michael Bourn, P.J.M. Stoney and R.F. Wnn

Further work is being undertaken on stocks, and some preliminary results are presented.

METHOD0 LOGY

The use of rates of price movement, and not price levels, as the subject of the correlation studies is of great importance. In adjusting accounts for inflation one is effectively concerned with short-period comparisons from one accounting period to the next. The adjustments may be calculated by reference to historic costs incurred in an earlier period, but when the system has been in operation for more than one year only a very short-period comparison is in fact being made. The comparison required is from one year to another, rather than from one year to some single reference point year. This is particularly so if, as in the case of the CSO index number series for capital expenditure, neither the reference year (1970) nor the first year of the series (1956) has any particular significance, except that the reference year serves as a common base point for all the series. In essence the problems posed in adjusting accounts for inflation are problems of price change, and of the way in which different indices move relative to one another. Price levels may keep broadly in step over a period of several years, but they may at the same time change at different rates and with different lags from one year to the next.

For these reasons the price comparisons made in this study relate directly to price movements. The data used are therefore basically in the form of the first difference (X, - Xt- In order to allow for differences in the level of X t - 1 between series, the actual transform used is the relative first difference ((X, - xt- 1 YXt- 1).

We have used quarterly data throughout the plant and machinery study from the first quarter of 1956 to the third quarter of 1975, in order to avoid provisional figures thereafter. Similarly, the motor vehicles study uses quarterly data from the first quarter of 1963 to the third quarter of 1975.

The rise of quarterly data, without conversion to an annual basis, permits companies io relate indices more precisely to their asset purchases. In this study it facilitates a more detailed examination of the timing of price changes. Seasonal price movements in the capital expenditure series would offset this advantage to some extent. They are unlikely, in view of the nature of the assets involved. This has been checked by the tests noted below on the randomness of movements in one price index that are not associated with movements in another.

The observation period extends back only as far as 1956, as the detailed series published by CSO start then. They have published a single index with annual measures covering capital expenditure on plant and machinery by all industries

Price indices for current cost accounting 151

for the period 1948-1956 (1, Table 5). This is clearly useless for inter-industry comparisons, and has been ignored.

The RPI is reproduced on a quarterly basis from 1956 in the CSO publication (1, Table 4). Quarterly figures for the GDFCF plant and machinery deflator are available from 1962 in Economic Trends (5).

The study uses regression analysis.

The simple correlation coefficient (R) of two indices can be squared to give the R2 statistic, which estimates the proportion of the variation in one index which is associated with variation in the other via the fitted regression. The R2 statistic can then be adjusted for degrees of freedom to give the statistic R2, which can be compared directly with the results of other models having a different number of explanatory variables Q.

where N is the sample size, ei refers t o the residuals and yi to the dependent variable (the latter expressed in terms of the deviation from the sample mean for the simple regression model

y,=atpxtt ui (2)

estimated by the line

Yt = G t B xt, ei = Yt - i., (3)

so that

The R2 statistic may be negative if the value of R2 is too small in relation to the ratio of the degrees of freedom employed. In such cases the degree of correlation can in effect be taken as zero.

The null hypothesis, that changes in one index are not correlated with changes in another, may be tested by reference to the F-statistic. This can be estimated by equation (5) as a function of R:

152 Michael Bourn, P.3.M. Stoney and R.F. Wynn

Since in this context the statistic is defined as the relation between: (a) the variance of the dependent variable explained by the regression model -

CY2/(K- l ) = ( C y 2 - Ce2)/(K- 1) (6)

and (b) the “unexplained” variance -

Ce2 / (N - K) (7)

Computed values of the F-statistic increase with the validity of the correlations for any given degrees of freedom, as specified by N and K. They can be compared with tabulated critical values to establish their statistical significance.

Since the analysis in this paper is applied to time series data it is important to test for autocorrelation. That is to say, it is important to check the possibility that there is a time-ordered pattern in the dependent variable which is unrelated to the independent variable. Such a pattern would breach the condition of random samplin It might invalidate the interpretation of the coefficient of determination R , and the proposed test of the significance of correlation between variables as measured by F. We have therefore tested for the non-randomness of disturbances by using the first-order test of the Durbin-Watson statistic, d. If this indicates the rejection of the null hypothesis, that changes in one index are not correlated with changes in another (in other words, if autocorrelation is indicated), then the relationship of the variables is re-estimated using the Coch- rane-Orcutt iterative procedure for estimating the parameters of a relationship between first-order transformed variables. In this case the data are further transformed

4

y t - P Y t - l , X t - PXt-1

where p is estimated together with a and p, so that the disturbance of the corresponding transformed model

yt - P Yt- 1 = 4 1 - P ) + P(x t - P X t - I ) + (Ut - put- 1) (8)

is reduced to the disturbance,Et, of the first-order scheme of autocorrelated disturbances

RESULTS Plant and machinery

This paper aims to measure the extent to which movements in each index within


TAB

LE 1

: Rz m

atri

x re

latin

g to

pri

ce in

dice

s for

cap

ital e

xpen

ditu

re o

n pl

ant a

nd m

achi

nery

and

to th

e R

F'I,1

956(

1)/(2

) - 1

975(

2)/(3

), an

d to

the

GD

FCF

(P &

M) d

efla

tor,

1962

(1)/(

2) - 19

75(2

)/(3)

Q

001-

003

101-

103,

211-

240

261-

279

311-

323

331-

370.

380-

385

4114

50 4

6148

9 46

1479

211

499

109

390-

399

4914

99

' M

inim

um L

ist H

eadi

ng

Ep

~~

9 A

gicu

lture

etc

. 1,

000

0512

0.

664

0.02

4*

0.60

1 0.

615

0.52

7 2

Min

inga

ndQ

uany

ing

1.OOO

0.

801

0.10

6*

0.84

3 0.

841

0.75

7 &

Food. d

rink

and

toba

cco

1.00

0 0.

018*

0.

882

0.80

8*

0.75

8 C

hem

ical

s, co

al a

nd p

etro

l 1.O

OO

0.15

5t

0.19

1t

0.22

5t

1.OOO

0.

879

0.74

6 Engineering

and

allie

d in

dust

ries

1.

Ooo

0.

904'

M

anuf

actu

re of

veh

icle

s 1.Ooo

8 T

extil

es. l

eath

er a

nd c

loth

ing

Zp

Pape

r, pr

intin

g and

publ

ishi

ng

?I O

ther

man

ufac

turi

ng

Tot

al m

anuf

actu

ring

3 N

otes

: 1.

The

7 p

er c

ent l

evel

of

sign

ifica

nce f

or th

e F-

stat

istic

(1 -7

6) d

egre

es o

f fr

eedo

m =

6.9

7

2.

The

sym

bol *

deno

tes r

esul

ts re

latin

g to

a fu

st-o

rder

Coc

hran

e4kc

utt t

rans

form

atio

n

3. T

he symbol

t den

otes

resu

lts fo

r whi

ch a

fm

t-or

der

Coc

hran

eOrc

utt

tran

sfor

mat

ion

(K2

5 0

.072

); fo

r (15

2) d

egee

s of

fre

edom

. F =

7.1

5(v

= 0.

104)

.

of th

e da

ta.

of t

he d

ata

prod

uces

littl

e or

no

impr

ovem

ent i

n th

e es

timat

e of

the

Dur

bm-W

atso

n st

atis

tic.

0.48

4 05

31*

0.7

10

0.23

5 0.

683

0.56

6*

0.58

3 1 .O

OO

0.54

8 0.

682'

0.

786

0.27

5 0.

769

0.78

3 0.

673

0.74

3 1 .O

OO

0.6

14

0.81

6 0.

818

0.39

1t

0.82

4 0.

859

0.83

5*

0.66

5 0.

763,

1 .O

Oo

0.62

7 0.

877

0.90

8 0.

272t

0.

922

0.92

0 0.

861

0.74

3 0.

829

0.91

1 1 .O

oo

500

602

702-

704

708

811

810,

812

820,

821

860-

866

871,

873

RPl

G

DFC

F M

inim

um L

ist H

eadi

ng

rp

831-

832

875-

889

(P 8t

M)

3

rh

892,

899

Def

lato

r 3

Q. S

0.64

6 0.

524

0.34

4*

0.06

6*

0.52

6 0.

622

0.28

7*

0.58

2 0.

585

0.54

4 0.

630

Agr

icul

ture

etc

2 0.

823

0.79

9 0.

472*

0.

081*

0.

840

0.78

2 0.

214*

0.

749*

0.

593*

0.

655

0.63

6 Fo

od, d

rink

and

toba

cco

0

0.26

61.

0.24

5 0.

148t

0.

384*

0.

208t

0.

2151

. 0.

064

0.22

6 0.

356*

0.

072

<O*

Che

mic

als,

coal

and

pet

rol

5 0.

828

0.70

6*

0.48

1*

0.01

2*

0.84

4 0.

763

0.305*

0.58

7*

0.77

2 0.

724

0.66

2 M

etal

man

ufac

ture

3

0.81

4 0.

752

0.71

7 0.

016*

0.

744

0.80

9 0.

442t

0.

639*

0.

758

0.41

1*

0.70

6 E

ngin

eeri

ng a

nd a

llied

indu

stri

es

5 0.

786*

0.

585

0.58

8 <0

* 0.

651

0.84

0 0.

385

0.67

6 0.

647

0.49

4 0.

578

Man

ufac

ture

ofve

hicl

es

-.

0.83

0 0.

566*

0.

660

0.04

9*

0.74

1 0.

736

0.27

1*

0.56

7 0.

721

0.55

1 0.

704

Min

inga

nd Q

uarr

ying

0.71

5*

0.63

1 0.

373

0.05

3*

0.63

2 0.

561

0.31

5 0.

580

0.52

4 0.

451

0.40

9t

Tex

tiles

, lea

ther

and

clo

thin

g 0.

814

0.56

4*

0.52

9 0.

071*

0.

706

0.67

7 0.

116*

0.

540*

0.

607

0.56

8 0.

631

Pape

r. pr

intin

g and

pub

lishi

ng

0.83

9 0.

788

0.67

8 0.

161*

0.

768

0.75

8 0.

3841

. 0.

654*

0.

698

0.55

6 0.

680

Oth

er m

anuf

actu

ring

2

0.83

4 0.

716*

0.

553*

0.

046*

0.

836

0.79

4 0.

6447

0.

645*

0.

631*

0.

638

0.69

9 T

otal

man

ufac

turi

ng

E

1.00

0 0.

620*

0.

518*

0.

117*

0.

739

0.75

5 0.

474t

0.

584.

0.

750

0.67

1 0.

797

Con

stru

ctio

n 2.

s 1.

000

0.06

5*

0.44

2 0.

516

0.30

2 0.

500

0.61

6 0.

405

0.67

6 R

oadT

rans

port

,etc

1.

000

0.34

0*

0.10

6*

0.79

2 0.

727

0.30

2*

0.73

6 0.

5117

~ 0.

541

0.36

5.

Ele

ctri

city

3

1.00

0 0.

221*

0.

196*

0.

083*

0.

171*

0.

196*

0.

179

0.02

9* Postal s

ervi

ces,

etc

1.00

0 0.

882

0.40

4*

0.76

1 0.

619

0.59

7 0.

580

Who

lesa

le d

istr

ibut

ion

of p

etro

l 1.

000

0.41

5*

0.80

1 0.

630

0.54

2 0.

659

Oth

er w

hole

sale

dis

trib

utio

n 1.

000

0.39

1 0.

831*

0.

368

0.47

9t

Ret

ail d

istr

ibut

ion

1.00

0 0.

366*

0.

315*

0.

312*

In

sura

nce,

ban

king

etc

1.00

0 0.

630

R.P.I.

1 .0

00

1.00

0 0.

619

0.70

9 Pr

ivat

e Ser

vice

s n.e

.s.

GD

FC (P &

M)

defla

tor

TAB

LE 2

: Dur

bin-

Wat

son s

tatis

tics f

or re

gres

sion

s of p

rice

indi

ces s

peci

fic t

o pl

ant a

nd m

achi

nery

exp

endi

ture

s fo

r the

indu

stri

es

indi

cate

d at

the

head

of

the

colu

mns

, and

the

RPI

and

the

DG

FCF

(P &

M)

defl

ator

Min

imum

Lis

t Heading

0014

03 1

01-1

03 2

11-2

40 2

61-2

79 3

11-3

23 3

31-3

70 3

80-3

85 4

1145

0 46

1489

461

479

21 1

499

109

390-

399

4914

99

Agn

cultu

re e

tc

- Mining a

nd Q

uarr

ying

Food. d

rink

and

toba

cco

Che

mic

als,

coal

and

pet

rol

Met

al m

anuf

actu

re

Engi

neer

ing a

nd allie

d in

dust

ries

M

anuf

actu

re o

f veh

icle

s T

extil

es, l

eath

er a

nd c

loth

ing

Pape

r. pr

intin

g an

d pu

blis

hing

O

ther

man

ufac

turi

ng

Total

man

ufac

turi

ng

Not

es:

2.03

2.1

9 1.6

3. 2.0

0 1.

89

2.01

1.

88

-

1.83

1.45.

2.11

2.

17

2.40

1.67.

- 1.2

2. 1.

78

1.78.

1.94

1.7

7 -

2.44

7 2.

48t

2.49

t 2.0

9 -

1.80

2.

10

1.77

-

2.21.

1-68

. -

1.86

-

Obs

erva

tion

peri

ods:

as

note

d in

Tab

le 1

. T

he lo

wer

lim

it cr

itica

l val

ues of

the

Dur

bin-

Wat

son s

tatis

tic a

t the

5 p

er c

ent l

evel

of

agn

ifh

nce

for

regressions i

nvol

ving

the

CD

FCF (P &

M)

defl

ator

are

1.5

3 <

d <

2.47

; el

sew

here

these l

imits

are

1.61

<d

<2.

39.

The

sym

bol

deno

tes

resu

lts re

latin

g to

a fi

rst-

orde

r Coc

hran

e4hc

utt t

rans

form

atio

n of

the

data.

The

sym

bol t

den

otes

resu

lts fo

r w

hich

a f

irst

ader

Coc

hran

e-O

rcut

t tra

nsfo

rmat

ion

of t

he d

ata provides li

ttle or n

o im

prov

emen

t in

the

estim

ate

of th

e D

urbi

n-W

atso

n st

atis

tic.

2.17

1.8

7. 1.

72

2.29

2.

02

1.89

2.1

6 2.

34

-

2.26

1.

80

2.31

2.

55t

2.07

2.05

1-97

. 2.0

8 1.7

6. -

2.14

1.

89

1.98

2.

46t

2.24

1.

92

2.04

8 2.

11

1.75

2.

00

9

3

f-0

500

602

702-

704

708

811

810,

812

820,

821

860-

866

871,

873

RPI

G

DFC

F M

inim

um L

ist H

eadi

ng

,% 9 5.

2 83

1,83

2 87

5-88

9 (P

&W

89

2-89

9 D

efla

tor

n 5

2.22

1.

91

1.34

* 1.

79*

2.12

2.

14

1.83

* 2.

02

1.83

1.

75

1.96

A

gric

ultu

reet

c ;E:

3

2.12

1.

92'

1.62

1.

83'

1.88

2.

15

1.58.

2.04

1.

76

1.77

1.

82

Min

inga

ndQ

uarr

ying

2.

11

2.01

1.

65*

1.52

* 2.

14

2.07

1.

50*

2.02.

1.93

* 1.

64

1.78

F

ood,

drin

kand

toba

cco

0, 2.

587

2.39

2.

43t

2.00

* 2.4

O-t

2.48

t 2.

21

2.36

2.3

3. 2.

38

1.19

* C

hem

ical

s,co

alan

dpet

rol

2.16

1.88.

1.60

* 1.

33*

2.17

1.

98

1.47

* 1.

90*

1.70

1.

91

1.84

M

etal

man

ufac

ture

2.

01

1.58

1.81

1.

54*

2.08

2.

23

1.42

t 1.

91*

1.59

1.

63*

1.82

E

ngin

eeri

ngan

d al

liedi

ndus

trie

s 8

1.98

* 2.

06

2.12

1.

85*

2.30

2.

19'

1.74

2.

20

2.05

1.

78

1.70

M

anuf

actu

reof

vehi

cles

2.

06*

2.12

1.

89

1.89

' 2.

12

2.13

1.

79

1.85

2.

06

2.28

1.54

7 T

exti

les,

leat

hera

nd c

loth

ing

$. 2.

15

1.79

. 1.

66

1.72

' 1.

72

1.83

1.4

7. 1.

73*

1.73

2.

10

1.90

Pa

per,

prin

tinga

ndpu

blis

hing

W

2.32

1.

62

1.63

1.

73*

2.14

2.

25

1.34

t 1.6

2. 1.

61

1.89

2.

00

Oth

er m

anuf

actu

ring

2.

20

1.86

* 1.

63*

1.58

* 2.

21

2.17

1.

34t

1.89

* 1.8

5. 1.

70

1.86

T

otal

man

ufac

turi

ng

-

1.75.

1.60

* 1.

52*

1.83

1.

87

1.29

t 1.

81'

1.71

1.

89

2.06

C

onst

ruct

ion

-

1.74

* 1.

58*

2.05

2.

06

1.65

* 1.

70

1.94.

1.74

1.

55*

Ele

ctri

city

-

1.87

* 2.

15

2.36

1.

81

1.80

1.

97

1.86

1.

65

Roa

dTra

nspo

rt,e

tc

P

-

1.55

* 1.

55*

1.52

* 1.

51*

1.63

* 1.

87

1.32.

Post

alse

rvic

es,e

tc

-

2.01

1.7

4. 1.

85

2.06

2.

06

1.71

W

hole

sale

dist

ribu

tion

of p

etro

l -

1.69.

2.20

1.

88

1.67

1.

65

Oth

er w

hole

sale

dis

trib

utio

n -

2.13

2.

06*

2.10

* 1.

26t

Ret

aild

istr

ibut

ion

-

1.92.

1.71

1.5

5. In

sura

nce,

ban

king

etc

-

1.83

1.

62

Priv

ate

Serv

ices

n.e

.s.

-

1.76

R

.P.I.

-

GD

FC (P

& M)

defla

tor

5 9

0 3 3

a a

TABL

E 5:

Ran

ked

freq

uenc

y di

strib

utio

n of

E2 e

stim

ates

for i

ndus

tries

cov

ered

und

er m

inim

um li

st h

eadi

ng re

fere

nces

001

to 8

99

and

for t

he R

PI a

nd th

e GDFCF

(P &

M) d

efla

tor

Range of ii

2 e

stim

ates

In

dust

ry.

refe

renc

e or o

ther

0.

1 0.

2 0.

3 0.

4 0.

5 0.

6 0.

7 0.

8 >o

.9

Ran

king

M

LH

4.2

4.3

4.4 45

4.6

4.7

0.8

4.9

num

ber

cate

gory

1 21

1499

2

331-

370

390-

399

3 50

0 4

311-

323

5 21

1-24

0 6

4614

79

4914

99

109

7 10

1-10

3

Total

man

ufac

turin

g in

dust

ries'

1-

1-

-1

63

54

11

--

21

26

62

Engineering

& o

ther

alli

ed in

dust

ries

othe

r than

vehi

cles

C

onst

ruct

ion

-1

1

-1

2

4 5

7 -

Metal

man

ufac

ture

1

1-

11

13

66

1

Food, d

rink

& to

bacc

o 2

-1

-1

13

66

1

Oth

er m

anuf

actu

ring

indu

strie

s' -

1-

2-

16

46

1

Min

ing&

Quv

ryin

g 1

1 1

-

- 5

25

6-

-1

1

-1

34

74

-

8 81

0.81

2 O

ther

who

lesa

le d

istr

ibut

ors a

nd

831.

832

dealers'

9 10

11

12

13

14

15

16

Who

lesale

dis

tribu

tors

of

81

petro

leum

pro

duct

s 38

0-38

5 M

anuf

actu

re of v

ehic

les

4814

89

Paper, printing &

pub

lishi

ng

892-

899

Priva

te se

rvic

es n.

e.s.

602

Elec

trici

ty

41 1

45

0

Text

iles,

leat

her &

clo

thin

g CDFCF (P &

M)

dejla

tw

Insu

ranc

e, b

anki

ng, f

innn

ce a

nd

860-

866

busin

essr

moe

sA

-1

1

-2

3

3 7

4 -

1

-1

11

54

43

1

11

1

--

55

62

-

-2

-1

-4

85

1-

-

11

3

-6

28

-

- 1

-

12

36

4

4-

-

2-

-

22

2 9

4

-

-

-1

1

41

6 4

3

1 -

17

0014

03

Api

cultu

re, f

ores

try &

fishing

2-

1

1 1

8

8 -

- -

18

Ret

oil p

rice

inde

x 1

1-

24

75

1-

-

19

702-

704

Roa

d pa

ssen

ger transport &

road

hau

lage

1

1-

27

45

1-

-

20

820.

821

Ret

ail d

istri

butio

n -

13

85

-1

-

1-

21

26

1-2

79

Che

mica

ls. c

oal &

pet

role

um p

rodu

cts

54

9

3 -

- -

22

70

8 Postal services &

tele

com

mun

icat

ions

1

27

1

1 -

- -

--

-

--

-

'Com

plir

a .L1

min

imum

list

headings c

over

ed b

y nu

mbe

rs 2

1149

9. 2

1ncl

udin

g bui

ldin

g m

ater

ials

, pot

tery

and g

lass

, timber, fu

rnitu

re.

%d

en

in b

uild

ers'

mat

eria

ls an

d ot

her i

ndus

trial

mat

eria

ls an

d m

achi

my.

and

agr

icul

tura

l sup

plie

s. Th

e in

dex

for this

indu

stry

ex

clud

es ca

pita

l exp

endi

ture

on computers.

-. $ TABL

E 4

: R2

matr

ix re

latin

g to

pri

ce in

dice

s for

cap

ital e

xpen

ditu

re o

n m

otor

veh

icle

s, to

the

RPI

and

to

the

GD

FCF

(P &

M)

defl

ator

$

Veh

icle

type

r,

refe

renc

e:

2

GD

FCF

(P &

M)

defl

ator

(i)

(ii

) (ii

i) (iv

) R

PI

i

Veh

icle

type

re

fere

nce

(9

(ii)

(iii)

(N)

RPI

G

DFC

F (P &

M)

defl

ator

'c

OI

0

;+ s.

TABL

E 5:

Dur

bin-

Wat

son s

tatis

tics f

or re

gres

sion

s of

pric

e in

dice

s spe

cifi

c to

capi

tal e

xpen

ditu

re o

n ve

hicl

e ty

pes

indi

cate

d at

the

head

of

the

Col

umns

, and

the

RPI

and

the

GD

FCF (P &

M)

defl

ator

. on

the

sam

e as i

ndic

ated

at t

he e

nds

of th

e rows

Veh

icle

type

re

fere

nce

Veh

icle

type

E

fere

nce:

de

ilato

r (iv

) I

(fi)

(iii)

- 1.

48

2.03

2.

17

-1.6

2 -

2.32

2.

44

1.51

-

2.18

1.

20'

1.60

'

Not

es:

1. V

ehic

le ty

pe re

fere

nces

: see T

able

4.

2. O

bsce

natio

n pe

riods

: see

Tab

le 4

.

-

1.58

1.

61

-

2.14

-

3. T

he lo

wer

lim

it critical v

alue

s of t

he D

urbi

n-W

atso

n sta

tistic

at t

he 5

per c

ent l

evel

of significance

for regressions in

volv

ing t

he in

dex

for v

ehic

le ty

pe (i

) are 1

.35

<d

<2.

65;

else

whe

re th

ese

limits

ar

e l.S

O<d

<2.5

0.

4.

The

sym

bol

deno

tes r

esul

ts re

latin

g to

a fm

st-r

der

Coc

hran

eOrc

utt

tran

sfor

mat

ion

of th

e da

ta,

for w

hich

the

corr

espo

ndin

g lim

its of t

he d

-sta

tistic

are

1.3

4 < d

< 2.6

6 fo

r re

gres

sion

s inv

olvi

ng

the

inde

x fo

r veh

icle

type

(i).

GD

FCF (P &

M)

defl

ator

a category are linearly related with movements in each of the other indices and with general price indices. The results are therefore presented in terms of (a) the R2 statistic, which estimates the proportion of the variation in one index which is associated with variation in another, via the fitted regression, adjusted for degrees of freedom, and (b) the d statistic, which tests for non-random disturbances in the variables.

Detailed results for the plant and machinery (P and M) price indices and for the vehicle price indices are set out in the R2 and d-statistic matrices shown in Tables 1 ,2 ,4 and 5 . For estimates of d outside the range of the lower limit of this statistic at the 5 per cent level of statistical significance, the null hypothesis of random disturbances is considered rejected on the basis of the available evidence.’ This occurs in 37 per cent of the results for the P and M indices. In the case of the vehicle index comparisons, first-order autocorrelated disturbances appear to be a problem for only one of the vehicle categories, in respect of the RPI and the capital deflator.

Those regressions which are unsatisfactory in this respect and yet which are made more useful on adopting a first-order Cochrane-Orcutt transformation of the data are marked by an asterisk. Where this transform produces n3 significant improve- ment, the results for the untransformed data are left as they were, and are marked by a dagger.

Only fifteen d statistics are not significantly improved by the transform. These are concentrated almost exclusively into two industries (i.e. chemicals and allied, coal and petroleum products; and retail distribution) which are also in the bottom three industries ranked by the distribution of R2 in Table 3. This concentration may be attributable to the lag structure of price movements in these industries.

From Table 1 it can be seen that while only fifteen of the P and M index correlation$ are statistically non-significant at the 1 per cent level (R2 < 0.072) there is consideinble variation in the R2 estimates. By far the lowest coefficients are recorded for two industries: postal services and telecommunications, and chemicals and coal and petroleum products. Price movements for retail distribution are only slightly better correlated with those of other industries. These three industries are bad misfits. At the other extreme eighteen of the R2 estimates for the total manufacturing industries index are in excess of 0.6. Between these two extremes results for other industries may be roughly ranked according to the frequency distribution set out in Table 3.

Between the extremes of high and low correlation, the gradation of the different sectors is fairly evenly-stepped. The general impression is of quite considerable disparity in the movements of these industry-specific price indices. This impression is confirmed even if one ignores the three “misfit” industries which have very


low correlations. It is still the case that not one industry has all of its remaining estimates of R2 in the range 0.6 to 1 .O.

The seven highest ranked industries in Table 3 are highly linearly correlated with each other, all the R2 estimates being in excess of 0.8 with acceptable d statistics. The bottom industries show little linear correlation with each other, however. All the correlations within the group of the bottom five industries are below 0.4, with only one unacceptable d statistic.

Both the RF’I and the implicit GDFCF plant and machinery deflator show poor linear correlations with the other indices, ranking eighteenth and fifteenth respectively in Table 3. When they are regressed on each other E2 is estimated as 0.630. Both show much higher linear correlations with the high-ranked industries than with the low-ranked. However, it seems clear that neither is a satisfactory proxy for the full range of indices.

Motor Vehicles

The motor vehicle indices are constructed by type of asset and not by industry The estimates of R2 in Table 4 are for the period 1963-1975 only. They show fairly high linear correlation, especially between types of vehicle which are prima facie similar. Table 5 shows that two Cochrane-Orcutt transforms were necessary, one of which gave an acceptable value of d. From so small a sample, it is not possible to say whether asset indices would generally be more or less correlated with each other than are the published industry indices. In this particular in- stance the RPI again appears to be a poor proxy for the detailed indices, and is noticeably poorer than the GDFCF deflator for this purpost in respect of two of the vehicle categories.

COMPARISON WITH ANOTHER STUDY

The results of this study show that, although the published indices are significantly correlated at the 1% level, they show considerable variation in the level of R2. In general they cannot be substituted effectively, either one for another or by either of the general price level indicators used (i.e. the RPI and the implicit GDFCF plant and machinery deflator). The best proxy found was the index for total manufacturing industries; this is not surprising as it is a weighted average of eight of the other indices, as shown in Table 6.

In another study of the same index numbers, Peasnell and Skerratt (7) (8) reach different conclusions in several respects. In (7) “the hypothesis to be tested con- cern(ed) the extent to which a general index, such as the FU’I, could be used as a proxy for specific indices” (7, p.49) without serious loss of precision. In order to do this, correlations were measured between the levels recorded by the different indices investigated, using annual data. In regressing the 19 Sandilands indices on

162 Michael Bourn, P.J.M. Stoney and R.F. Wynn

the RPI it was found that “the ?i2s are very high, the lowest being 0.96” (7, p.52). However, in four cases the estimated degree of joint movement was outside acceptable limits; these cases were Post Office (sic), retail distribution, insurance etc., and private services n.e.s. It was concluded that in these cases the use of the RPI as a proxy is difficult to defend, but in half the cases it was a passable proxy (7, p.52).

In (8) this analysis was extended in essentially the same terms to compare the nineteen CSO indices of capital expenditure on plant and machinery with (a) a single “best” linear synthesis of them found by principal components analysis, (b) their “average”, (c) the RPI, and (d) an implicit price deflator for the manufacturing-industries-only component of the figures for gross fured capital formation found in the National Income and Expenditure Accounts.

In summary, it was found that, in terms of levels, the principal components index, the simple average, the RPI, and the manufacturing industries capital formation deflator were all highly correlated. Furthermore, most of the individual industry groups were highly correlated with the composite principal components index “which accounts for 98.4% of the information value of the set of 19 indices. This is because the 19 indices move together, and hence it is not really surprising that they be (sic) adequately proxied by a more general index such as the RPI”. (7, p.3). It was noted that the RPI “moves very closely” with the composite Best Linear Index (the principal components index noted above) which “accounts for a substantial proportion of the overall variability of the set of official indices”. (8, p.13).

Furthermore, it was noted that the RPI “hiis the important advantages of being well-tried, frequently revised and promptly published” although “a disadvantage may be its coverage of irrelevant goods and services” (8, p.14)! It was concluded that “the RPI eliminates most of the error in historical cost fured asset ‘valuations’ and that attempts at providing greater precision are not justified”. (8, p.14). If this conclusion were acted upon then in practical terms CCA and CPP accounting would be identical in respect of plant and machinery. However, it seems to con- tradict the earlier conclusion, that the RPI was a passable proxy in only half the cases (7, p.52). The study reported in this paper suggests that even this markedly over-rates the RPI.

There are several reasons why Peasnell and Skerratt’s results differ from those of this study.

First, they have used index number levels in their comparisons, and we have used price movements. Price movements are considered to be the more appropriate measures for the reasons outlined supra.

Rice indices for current cost accounting 163

Second, they have used annual data, and we have used quarterly data.

Third, they have artificially extended their data back to 1948 by using the all- industry index for 1948-55, thus achieving perfect inter-industry correlation over this period.

Fourth, their test of the non-randomness of disturbances by using the Durbin- Watson statistic “showed the presence of autocorrelation. Our attempts to purge the estimated equations of this by the Orcutt-Cochrane transformations was not eompletely successful” (7, p.76). Their results are, in fact, seriously impaired by this problem, their d statistic exceeding unity for only one of their industry comparisons (8, Table 2). Autoconelation is insignificant in our transformed data, presented in Tables 2 and 5.

Fifth, they have used restricted least squares to fit their regression line by sup- pressing the intercept in order to relate all changes to the price level recorded in the initial year. The reason for this is explained (7, p.47) and (8, p.6), although it seems to have little relevance to CCA. We have not so restricted our regressions.

There are other differences of procedure. For example, we have considered it more appropriate to test the implicit plant and machinery deflator than the manufacturing industries capital formation deflator.

For these various reasons the validity of both the results and main conclusions of the Peasnell and Skerratt studies is questioned. The alternative conclusion, that the indices published by CSO are significant and cannot be adequately proxied for CCA purposes by a single published index, seems better founded.

STRUCTURAL BREAK ANALYSIS

It was considered to be potentially interesting to divide the industry-specific indices for plant and machinery into two periods, one characterised by quite severe price inflation and one by less severe inflation. No such analysis was made for motor vehicles. The break made was into the periods (a) from 1956 1st quarter to 1969 4th quarter (less severe inflation) and (b) from 1970 1st quarter to 1975 3rd quarter (more severe inflation).

For reasons of economy of space it is not possible to present the four tables (two each of R2 and 6) produced from the analysis; they can be obtained from the authors on request. The main features of the tables are:

(1) The industry indices were more highly correlated during the period of more severe price inflation than they were before 1970. Specifically, 70% of


the R2’s are significantly greater than zero before 1970, at the 1% level (R2 > 0.093) and 77% in the later period (E2 > 0.238). However, although statistically significant, the E’s account for only a small proportion of the variation in the indices.

(2) The reliability of the results is greater in the later period of more severe price inflation. Only 14.3% of the first-order d statistics lie outside the acceptable range of 1.26 < d < 2.74 in that period. For the earlier period, 23.4% of the d statistics lie outside the acceptable range of 1.53 < d < 2.47. No Coch- rane-Orcutt transformation was undertaken for either period.

(3) Both the RPI and the GDFCF deflator are more highly correlated with the industry-specific series as a whole in the later period of more severe price inflation, when the RPI was significantly correlated with seventeen of the p@t and machinery indices and the deflator with nineteen at the 1% level (R2 > 0.238). In the earlier period each of the two general indices was sipifi- cantly correlated with_only four of the industry indices at the 1% level (R2 > 0.093). However, the R2s showed great variation and none approached the very high levels found by Peasnell and Skerratt.

(4) In virtually all respects the results for the whole period show more significant correlations than for either of the sub-periods. Thus 93% of the R2s were significant at the 1% level (R2 > 0.072) over the whole period; only 7% of the d statistics were outside the acceptable range; and the RPI and deflator were significantly correlated with nineteen and eighteen of the industry indices, respectively. Furthermore, the levels of R2 were generally noticeably higher in the total period than for either sub-period.

We offer no explanation in economic terms for these results. However, they may be influenced by, for example, the operation of a prices and incomes policy for most of the later period, but there are many other possibilities on which to speculate. The choice of sub-periods may have had some effect. But however this may be, what is important within the scope of th is study is that the results for the two sub-periods are not seriously at odds with those obtained for the observation period as a whole.

DATA

It is appropriate to comment on particular features of the data base used. The index numbers for capital expenditure on plant and machinery and motor vehicles are built up from appropriate components of the wholesale price indices. These are compiled “from price quotations for about 1 1,500 closely- defined materials and products representative of goods purchased by, or manu-


TABLE 6 Weights used in constructing sample indices

1. Coal and petroleum products, chemicals and allied industries Weight % Tubes, pipes, valves and fittings 13 Industrial (including process) plant and steelwork 30

35 Electrical plant and machinery 16

6 Instrumentation 100

Weight %

Other mechanical plant and machinery

- -

2. Postal services and telecommunications Electrical machinery, telegraph and telephone apparatus and cables, and office machinery 62

38 Post Office Engineering Union wage rate 100 - -

3. Retail distribution Weight %

ventilating and air conditionhg eqbipment Other mechanical engineering products 7

Commercial refrigeration machinery, and space heating, 12

Domestic television receivers 40 Lamps and light fittings 6 Furniture, floor coverings, and metal office and works

Other products equipment 33

2 100 - -

4. Total manufacturing industries Food, drink and tobacco industries’ expenditure 12

9, 99

99 99

,* 9,

9, 9,

9,

99

9, 9,

Oil refining and chemical 24 Metal manufacturing 8

21 Engineering and allied Vehicles manufacturing 8

Paper, printing and publishing ” 6 11 Other manufacturing

100

Source: Department of 1ndustry:Reproduced by permission of HMSO and CSO. Subsequently published in (1, issue no.2, August 1976, Appendix, pp.61-78).

166 Michael Bourn, P.J.M. Stoney and R.F. W n n

Textiles, leather and clothing ’’ 10

- -

factured for sale on the home market by, manufacturing industry in the U.K.” (1, p.2). Transactions between undertakings in the same sector are excluded.

Details of the weightings used in constructing the indices were obtained from the Department of Industry. Those for the three misfit industries are listed in Table 6 as illustrations. They do seem to be not beyond refinement. However, it is not possible to test whether the misfit status of the industries is in any part due to peculiarities in the weighting structures used. The weightings used in constructing the total manufacturing industries index are also given in Table 6.

A few industries, mainly utilities and transport, are omitted from the CSO index number series. CSO suggest that in these cases the total manufacturing industries index might be used. Our results indicate that it may be an adequate substitute, but this is by no means certain.

The prices reported are those for orders currently placed. Since they are for capital expenditure there may be a significant lead-time before delivery is effected. This lag may affect our correlation results to some unknown extent. I t will also be apparent that companies using the index numbers may on occasion need to exercise care in choosing the most appropriate quarters’ figures.

The indices exclude purchase tax, V.A.T., and the special tax on motor vehicles, but include revenue duties. This may also affect some of our correlation results to an unknown extent. Again, some care may be necessary in utilising the indices.

The Sandilands Report noted that index numbers attempt to deal with the problem of technological change. CSO state that “where an item priced is modified, or ceases to be available and is replaced in the index, an adjustment is made in the calculations to allow for any difference in specification and thus, so far as possible, to maintain comparability” (1, p.2). We understand that there is no standaid treatment, and that adjustments are made ad hoc as each case seems to demand. This is potentially an area of considerable difficulty in series running over twenty years. Furthermore, it is well-known that the life expectancy of much plant and equipment is much more than this. We have been unable to apply any test for the technological change factor, but it is an important issue. Thus, for example, some recent unpublished work by Wills (6), estimating a vintage capital model of the U.S. electricity generating industry, suggests that the standard price index used there, the Handy Whitman Price Index for Electricity Gen- erating Equipment, makes insufficient allowance for quality changes in the plants purchased. Wills’ work indicates that the quality-adjusted price of plant was broadly stable over the period 1947-1969 when the Handy Whitman Index more than doubled. It is concluded from this discussion that:

(a) the CSO indices are fairly reliable at the level of aggregation adopted; Price indices for current cost accounting 167

(b) the main reservation about the indices is over the treatment of technological change;

(c) greater disaggregation to asset-specific indices, or possibly to Minimum List Heading industry-specific indices, would be feasible.

It is understood that CSO expect to publish updated versions of the present tables before the end of 1976. It is further understood that from early in 1977, the indices will be published monthly on a preliminary basis one fortnight after the end of the month in which the data are collected, with revised final figures for most series appearing within the next three months.

We should like to acknowledge the helpful responses made by the officers at the Central Statistical Office and the Department of Industry to our enquiries.

OTHER FORMS OF INDEX NUMBER SERIES

Reliable and verifiable index number series of capital expenditure prices specific to particular companies are the logical requirement of a proper CCA system. The Sandilands Report clearly implies the compromise nature of the industry-group indices, whose publication it recommends, in describing them as “a ‘standard reference basis’ for making reasonable approximations to the ‘value to the business’ of assets” (2 , para.573). It recommends that “where companies operate in more than one industry, more than one index may be used” (2, para.576). The logical conclusion of this approach is clearly indices specific t o particular companies, compiled from detailed officially-published index numbers by plant-type, weighted according to the company’s asset structure.

Peasnell and Skerratt recommend “that consideration be given by the U.K. authorities to the possibility of publishing indices which are disaggregated by type of asset” (7, p.3). We concur with this recommendation, which we believe to be feasible. The indices now published in (1) are compiled from a detailed set of price quotations, as reported above. There is no obvious reason why these quotations cannot be used to publish indices based on asset type. It also seems likely that, alternatively, it would be feasible to publish industry-specific indices disaggregated to the “Minimum List Headings” of the 1968 S.I.C. (3), although this would possibly be both more difficult to do and less useful than the publication of asset-specific indices.

The need for such indices extends to management accounts also, where it may go beyond asset and liability adjustments. Berman (9) outlines a system developed at Philip Morris USA to assist budgeting and planning. He makes a telling point on the need for as close precision as possible in the construction of indices: “Our manufacturing costs alone run about half a billion dollars a year; an error in the


index of 0.1% is worth $500,000!” (9, p.52). Indices specific to the particular company seem to be highly desirable, if not sufficient, elements in enabling this precision to be attained.

CONCLUSIONS AND RECOMMENDATIONS

The main conclusions drawn from this analysis are:

The quarterly movement rates in the industry price indices for plant and machinery published by C.S.O. (l) , corrected for autocorrelation in the disturbance term, are significantly linearly correlated at the 1% level, but the coefficients of determination (corrected for degrees of freedom) have shown wide dispersion both within and between industries over the last twenty years. A substantial part of the movement is not accounted for.

The separate consideration of the data relating to pre-1970, and to 1970- onwards, when the rate of price inflation increased greatly, does not affect the conclusions materially.

Neither the Retail Price Index, nor the most relevant implicit price deflator used in national income accounting, is a satisfactory proxy for the published industry indices.

The area for greatest doubt about the published indices is the treatment of technological change.

For the purposes of CCA it is desirable that official indices based on types of asset be published, and this is thought to be perfectly feasible from the data base already being maintained.

It is therefore recommended that in order to implement CCA in relation to expenditure on plant and machinery

(1) the new price indices based on industries should continue to be published;

( 2 )

(3)

(4)

the RPI should not be accepted as a substitute for more specific indices;

price indices based on types of asset should be published by CSO;

companies should be encouraged to compile CCA accounts using the relevant asset-specific indices, suitably weighted for their own situation;

(5) further consideration should be given to the means of allowing for technological change in the indices.

Pnce indices for current cost accounting 169

PRELIMINARY RESULTS ON STOCKS

The CSO also published 95 price indices for stocks (1, Table 3), giving monthly readings from January 1972 to January 1976, as noted in the introduction to this paper. A similar, although shortened, analysis was applied to them. Two matrices, one each of 8' and d statistics, of order 96 x 96, and using monthly data from January 1972 to June 1975 were produced. Later figures were excluded because of their provisional nature. The matrices are too large to be presented here. However, they have the following distinctive features, inter alia.

(1) High estimates of R2 tend to be associated with low estimates of d. Each matrix has 4,608 elements. There are only 732 estimates of RZ which both are significant at the 1% level (Rz > 0.136) and have corresponding d values which are insignificant at the 5% level (1.44 < d < 2.56). Rather few of these 732 estimates of Rz exceed 0.5.

(2) The high estimates for 8' tend to be heavily concentrated in the Whole- sale Distribution and Retail trades. These values are probably unreliable. The associated d statistics indicate very highly positively autocorrelated disturbances in these categories, in general, although no Cochrane-Orcutt transformation has been undertaken other than as in (5) below.

(3) Low estimates of RZ tend to be associated with acceptable values of d , indicating the absence of autocorrelated disturbances.

(4) The RF'I is correlated very highly with almost all of the stocks indices, as Table 7 shows. There are only a few exceptions, the most notable being coal and oil merchants. In such cases there are likely to be special features (e.g. structural breaks) in each time-series. Thus, there are several breaks in the coal and oil merchants series, as in April 1972, October 1972, May 1973, October 1973 and November 1974.

(5) Many of the d statistics associated with the estimates of RZ for the RPI were unacceptable. The Cochrane-Orcutt transformation for first-order autocorrelated disturbances was tried for the RPI. It was found to improve d to acceptable values at the 5% level of significakce in almost all cases, whilst causing scarcely any change in the values of RZ.

(6) There is no noticeable overall difference in the results for the indices representing stocks held for resale and those representing stocks of materials and fuel, particularly when correlations were made with the RPI.

Since the commodities covered by the retail distribution group of indices make


TABLE 7

Frequency distribution of estimates of R2 for RPI regressed on stock indices January 1972 - June 1975

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 >o.g <O" -0.2 4 . 3 -0.4 -0.5 -0.6 -0.7 -0.8 4.9

1 - 2 - 3 1 6 43 56 Construction and Manufacturing

Wholesale 1 - 1 3 - 1 2 - 1 9 18

Retail - 1 - - 1 - 5 2 9 3 21

- -

TOTAL 1 1 2 3 2 1 1 0 3 1 6 55 95

up much of the RPI it is surprising that the estimates of R2 are on the whole somewhat lower for that group than those for wholesale distribution which are in turn lower than those for manufacturing industry. However, the overall picture is one of high correlation between the RPI and the stocks indices in total for the period covered. The RPI might thus be a reasonable proxy for the ninety-five indices published if a very high degree of precision is not sought. However, in this context the direct impact on operating profit of variations in stock values and the cost of sales has to be remembered. It may also be material that, as shown by the structural break analysis earlier in this paper, the plant and machinery indices tended to be more significantly correlated with the RPI for the sub-period from 1970 than for the earlier period from 1956.

Further investigation might include consideration of at least the following points.

(1) Is the sample period, of only just over three years, (i.e. 42 observations) long enough for the results to be judged as reliable in the long-run?

(2) Does it matter that the sample period was one of unusually high price inflation throughout?

(3) Would appropriate disaggregates of the RPI provide better proxies for certain categories of stocks than the RPI itself? (4) Cochrane-Orcutt transformations can be undertaken.

NOTE

As noted in Tables 2 and 4 these limits are 1.6 1 < dY 2.39 in the case of the '


78-observations series for the P and M indices, 1 S O < d< 2.50 for the 50- observations vehicle index series other than that for passenger cars, and 1.35 < d < 2.65 for the latter for which there are 30 observations available up to the third quarter 1975.

Critical values of the d-statistic have an upper and a lower limit between which the test is inconclusive. Strictly speaking, the upper limit, which lies closer to 2, is a more sure test of the first-order autocorrelated disturbances but the limits chosen here lie between the upper limits for the 1 and 2.5 per cent levels of significance and therefore represent something of a compromise between the level of significance and how far to encroach into the inconclusive range of the test. REFERENCES

Central Statistical Office “Price Index numbers for current cost accounting, No.1” (HMSO, April 1976). Issue 110.2 was published in August 1976.

Sandilands Report “Report of the inflation accounting commit tee” (HMSO, Cmnd. 6225, September 1975).

Central Statistical Office “Standard Industrial Classification: Revised 1968” (HMSO, 3rd ed. 1968).

PSSAP No.7 “Accounting for changes in the purchasing power of money” (A.S.S.C., May 1974).

“Economic Trends: Annual Statistical Supplement” (HMSO, September 1975).

Wills, H. “Estimation of a vintage capital model for electricity generating”. Paper given to the conference on Capital at the University of Southampton, July 1976.

Peasnell, K.V. and Skerratt, L.C.L. “Current cost accounting: the index number problem” ICRA Occasional Paper 110.8 (ICRA, 1976).

Peasnell, K.V. and Skerratt, L.C.L. “The empirical evaluation of the Sandi- lands plant and machinery indices: a principal components approach”. Paper given to the Annual Conference of the Association of University Teachers of Accounting at Huddersfield, April 1976.

Berman, George R. “Constructing and using a company cost index” THE BUSINESS QUARTERLY, Summer 1976, pp.50-53.

Michael Bourn, P.J.M. Stoney and R.F. W n n

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PRICE INDICES FOR CURRENT COST ACCOUNTING