A research and education initiative at the MIT
Sloan School of Management
Theoretically-Motivated Long-Term Forecasting with
Limited Data
Paper 240
July 2008
Russel Cooper
Robert Fildes
Gary Madden
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please visit our website at http://digital.mit.edu
or contact the Center directly at digital@mit.edu
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Theoretically-Motivated Long-Term Forecasting
with Limited Data*†
Russel Cooper
Department of Economics
Macquarie University
North Ryde, NSW 2109
Australia
Robert Fildes
Department of Management Science
Lancaster University
Lancaster, LA1 4YX
England
Gary Madden
Department of Economics
Curtin University of Technology
Perth, WA 6845
Australia
16 June 2008
Abstract
This paper forecasts national information and communications technology (ICT)
expenditure shares with limited data. The approach allows for network effects
(through New Economy transition) based on theoretical microeconomic foundations.
In particular, the analysis: develops a model (incorporating network effects and nonhomothetic technology); estimates and tests structural demand parameters; and apply
the results to provide long-term ICT diffusion forecasts. Importantly, the methods
apply to any industry that exhibit externalities and non-homotheticity.
JEL Classification: C61, O14, O33
Keywords: Forecasting Methodology, Limited Data, New Economy, ICT
*
Comments welcome, rcooper@efs.mq.edu.au, r.fildes@lancaster.ac.uk, g.madden@.curtin.edu.au.
† The Australian Research Council funded this paper under Discovery Project grant DP0555882. The
MIT Center for Digital Business and the Columbia Institute for Tele-Information provided support
during the time this paper was revised. Helpful comments were provided by participants in seminars at
Columbia University, Curtin University of Technology and the University of South Africa. The authors
are responsible for all remaining errors.
1. Introduction
The extent to which nations benefit from progress in information industries depends on
their readiness to adopt e-commerce innovations and exploit efficiency gains (Black and
Lynch 2001). In particular, information and communications technology (ICT) provides
an opportunity to reduce costs within firms and across supply and distribution chains, and
improve service quality (Brynjolfsson and Hitt 2000, Smith et al. 2000). Accordingly,
long-term ICT forecasts, and that of enabling network technology more generally, are an
indispensable aid for planning and strategy (Makridakis 1996). That is, reliable forecasts
provide a better basis to understand e-commerce service evolution, and so enable firms to
initiate appropriate investment and production plans, and government to apply public
policy. Clements and Hendry (2003) argue that to ensure the accuracy of econometric
forecasts models should mimic the adaptability of the best forecasting devices while
retaining their foundation in economic analysis. Economic features that must be included
in network industry forecasting models are strong complementary relationships among
network technology, and the presence of consumption and production externalities (Shy
2001). For example, a direct network externality arises when infra-marginal consumers
connect to a (e.g., communications) system (Leibowitz and Margolis 2002). In this
circumstance subscribers’ utility depends on the size of the subscriber base with
compatible access. 1 , 2 Also, network technology complementarity, a feature of New
1
Liebowitz and Margolis (2002: 94) consider efforts to evaluate the empirical importance of network
effects are likely to be worthwhile, given the potential magnitude of these effects and the role that such
claims are playing in public policy.
2
While Artle and Averous (1973) derive a theoretical model that determines the optimal size for a
telephone system, income and prices play no role in the system’s evolution. Rohlfs (1974) formally
Economy transition, is reflected in non-homothetic technology (or preferences), i.e.,
budget shares are non-linear along output expansion paths.
The technology diffusion literature treats adoption following an S-shaped (sigmoid) curve
as a stylized fact, with diffusion rates for successful innovations initially rising then
falling through time, ultimately leading to market satiation (Geroski 2000). Further,
epidemic models typically abstract from agent goal and capability differences, and focus
on the diffusion of information in a tractable and non-strategic setting. However,
estimating standard diffusion models with limited data provides unreliable structural
parameter estimates which are sensitive to small changes in the observation period
(Heeler and Hustad 1980, Islam et al. 2002). Dekimpe et al. (1998) report implausible
estimates for diffusion model parameters in 95% of cases for 57 international
telecommunications data series estimated with 3 to 13 years of data. Additionally, for
practical purposes, forecasts are required reasonably soon after new product introduction,
but sufficient observations for reliable estimation are only realized when an inflection
point is reached (Lenk and Rao 1990, Mahajan et al. 1990, Schroder 2000). In response
to limited data availability (due to recent product launch), Vanstone (2002) obtains US
high-speed Internet forecasts by applying historical analogies to estimate diffusion model
parameters. However, the reliability of this forecast methodology is questionable, with
Schmittlein and Mahajan (1982) reporting substantial variation by product in diffusion
incorporates income and prices to model the equilibrium number of telephone handsets by focusing on
individual constrained choice for telephone subscription. The equilibrium user set (subscriber base) is the
sum of individual utility maximization program outcomes. Another finding is that multiple equilibriums
exist. A small network is relatively unattractive to potential subscribers. However, a large user set can also
result. The greater the user set the more likely a large network is realized.
2
model parameter estimates. An alternative atheoretical approach considers that panel
diffusion processes improve estimation and forecasting accuracy by exploiting both
cross-section and temporal dimensions (Islam and Meade 1990). For instance, Meade and
Islam (2006: 534) argue that when an innovation is released internationally through time,
early adopter nation data can predict late adopter behaviour. Similarly, the segmented
diffusion curves approach assumes that consumers enter a market at different points in
time. This process implicitly implies consumer-specific hazard rates (Robertson et al.
2002). However, this approach relies on heuristic assumptions about model (innovation,
imitation and saturation) parameter stability. Importantly, Goswami and Karmeshu (2004)
consider that estimating fixed-coefficient standard diffusion models is misguided as
parameter values generally exhibit temporal variation, e.g., through demonstration and
non-homotheticity effects. Further, Islam and Fiebig (2001: 254) specify imitation
parameters as random coefficients that vary by country around a grand mean. Finally, in
their study of UK business telephones, Islam and Meade (1996) model the saturation
level as a function of national income.
The approach pursued here is to employ economic theory of networks and pool sample
data in a manner that allows economic agents’ experience to be treated as additional
information resulting from a supra personal (or institutional) process. 3 In the context of
national ICT expenditure allocation, observations are treated as lying on a common
optimal growth path. The approach is intuitively appealing as alternative states of New
Economy transition are accommodated in the modelling. Accordingly, this study
3
Importantly, standard linear (fixed effect, random effect, random coefficient and cross-section varying)
and non-linear models are not motivated by the economic theory of networks (Madden et al. 2002).
3
proposes, as an organizing principle, to model implied rational agent behaviour
embedded within an optimal growth paradigm. In particular, a static form proposed by
Hermalin and Katz (2003) is embedded in a dynamic disequilibrium setting to describe
global ICT network diffusion. Additionally, the model directly links estimating equations
to the economics of networks while making explicit allowance for non-linear technology
and preferences. Also, to allow for potential consumer and producer network effects,
non-constant parameters evolve endogenously through alternative states of New
Economy transition as determined by sample data. The model is estimated on crosscountry panel data. Finally, statistical robustness is evaluated by testing whether
theoretically implied regularity conditions are satisfied. 4
Finally, the methods are applicable to any network industry and many non-network
industries characterized by strong complementary relations. The paper is structured as
follows. Section 2 specifies the share equations. In Section 3 descriptive information
concerning ICT expenditure share data is provided. Econometric estimates are reported in
Section 4. Section 5 discusses alternative scenarios on which model forecasts are based.
Section 6 reports on the outcomes forecasts for both aggregate and component
expenditure shares. A final section suggests some modelling extensions.
4
Meade (1984: 521) emphasizes that estimation of growth curves to forecast market developments should
be subject to significance tests (statistical validity), and that such models should have a demonstrable
forecasting ability and validity (forecasts should be contextually plausible and accompanied by measure of
uncertainty).
4
2. Model Specification
A central feature of national New Economy transition to be recognized in modelling is
the non-homotheticity of ICT expenditure shares. That is, a stylized fact of observed data
is that the ICT input mix changes in concentration from hardware to software with output
growth, even when relative prices are unchanged. Consequently, a general price index
must allow component price weights to vary with ICT input substitution to provide an
accurate measure of economy-wide prices. Accordingly, a GDP price index is
constructed with weights depending on lagged budget shares. 5 Denote PA index as the
true cost of living index for an economy with least (subsistence level) ICT penetration
(Old Economy). By contrast, PB is the relevant price index for a country that critically
depends, economy wide, on ICT inputs (New Economy). PA and PB , respectively, are
specified as a weighted geometric mean of input prices:
ln PA = ∑ i =1α i ln pi and ln PB = ∑ i =1 βi ln pi
n
n
(1)
where ln is the natural logarithm operator. α i and β i satisfy the adding-up conditions
∑
n
i =1
α i = 1 and
∑
n
i =1
βi = 1 , thus ensuring that the indexes are homogeneous of degree
5
Boskin et al. (1998) argue that providing accurate measures of economy-wide prices (e.g., consumer price
index or GDP deflator) is crucial to the analysis of many economic issues. However, they also recognize
that cost of living changes are difficult to measure because of rapidly changing consumption patterns. In
particular, Hausman (2003: 23) argues that the constant basket approach suffers biases as, “It fails to allow
for substitution that occurs when consumers switch away from goods that have become relatively more
expensive and toward goods that have become relatively less expensive. It ignores the introduction of new
goods. It ignores quality changes in existing goods”. Both the new good and quality change aspects result
from any shift from the consumption of Old Economy goods to New Economy goods.
5
one in prices. α i indicate the relative importance of GDP components in constructing the
price index for an Old Economy. 6
In particular, PTC is the true measure of the current GDP deflator as the Old Economy
deflator PA partially adjusts to the economy’s input expansion path. In particular, the true
price index PTC is the mean of the distance-weighted PA and PΩ indexes: 7
⎛ α +ω
i
i
ln PTC = ∑ i =1 ⎜
⎜ 1+ ∑n ω j
j =1
⎝
n
⎞
⎟ ln pi
⎟
⎠
(2)
where ωi update the Old Economy index weights. Equivalently, ln PTC is expressed as:
Ω
⎛ 1 ⎞
ln PTC = ⎜
ln PΩ
⎟ ln PA +
1+ Ω
⎝ 1+ Ω ⎠
(3)
n ⎛ω ⎞
n
where Ω = ∑ i =1 ωi and ln PΩ = ∑ i =1 ⎜ i ⎟ ln pi . That is, (3) is a weighted geometric mean
⎝Ω⎠
In this study GDP is comprised of the expenditure components: Telecommunications ( i = 1 ), Computer
Hardware ( i = 2 ), Computer Software ( i = 3 ), IT Services ( i = 4 ) and Rest of GDP ( i = 5 ). Since PA is
an Old Economy index, α1 to α 4 are relatively small with the α 5 value close to unity, i.e., the price of
non-ICT goods is the principal determinant of the value of the national price index. Conversely, β 5 is
small when compared to α 5 , and the β i contribute relatively more to the value of the New Economy PB
6
price index, i.e., β1 through β 4 are larger than the corresponding
αi
values. The PA and PB values are,
respectively, lower and upper price index bounds for a theoretically correct economy-specific true cost
index PTC taking values lying in the [ PA , PB ] interval. Transition from an Old Economy to New Economy is
modelled via the latent variables φ and η , with the distance φ − η providing a measure of national ICT
permeation relative to best practice (New Economy status).
7
Initially, an externally provided GDP deflator PGDP is employed as a proxy variable for PTC in the
econometric estimation.
6
of the Old Economy price index PA and the PΩ index. Importantly, PΩ relates only to
ICT price movements and ωi weights. The ωi measure national progress toward a New
Economy with:
ωi = δ i ⎡⎣ si ,−1 − π s i ⎤⎦ ,
i = 1,...4
ω5 = 0
(4)
where si is component i share of GDP share, si ,−1 is the previous year share and π s i is
the subsistence component of the reference economy ICT share in the year preceding the
base year. Therefore, si ,−1 − π s i is the extent to which a nation’s ICT expenditure, in the
year preceding the current year, is greater than the base Old Economy (reference country)
subsistence share. Parameters δ i indicate the extent of the influence of recent component
i expenditure on the true price index weights. The index’s path dependence on recent
share movement are reactions to strong complementarity in New Economy transition, e.g.,
hardware expenditures are relatively large compared to software for an Old Economy.
The relationships (2)-(4) are embedded in a model of rational decision-making consistent
with a micro foundations view of the economy. That is, for a given nominal per capita
GDP y% , agent interactions determine efficient per capita quantities q%i for production by
firms and consumption by consumers. 8 A functional form consistent with this view is:
8
MAIDS is a fractional share system derived by Cooper and McLaren (1992). The model modifies the
Almost Ideal Demand system of Deaton and Muellbauer (1980) to restrict the predicted shares to lie within
the unit interval. Further, the MAIDS aggregate price index allows consistent parameter estimates to be
obtained in the absence of expenditure category quality-adjusted price data.
7
y% / PGDP = μ0 + μ1 y% 1−η ln ( y% / PTC )
(1/ PA ) (1/ PB )
φ −η
1+Ω
1−φ
(5)
which is the optimal value function resulting from maximization. 9 The optimisation is in
terms of expenditure shares, i.e., pi q%i / y% (equivalently, pi qi / y ). Roy’s Identity applied
to (5) provides the optimal allocation of expenditure components in share form, viz.
γ i + θi ln ( y% / PTC )
1−η
si =
1 + ln ( y% / PTC )
1−η
(6)
where
γi =
α i + ωi
1+ Ω
and θi =
(φ − η )α i + (1 − φ ) βi
.
1 −η
(7)
Furthermore, the shares depend on the parameters α i , βi , φ and η . 10 With sample data
scaled so that ln( y% / PTC ) = 0 for the lowest GDP value (reference country India) in the
base year (2002) the α i are interpreted as Old Economy (subsistence) expenditure shares.
The βi , which introduce flexibility to allow for non-homotheticity, are New Economy (or
long run) expenditure shares as ln( y% / PTC ) → ∞ . Operationally, φ and η specify the
manner and extent that expenditure shares depend on low income Old Economy α i and
high income New Economy βi parameters, respectively. That is, with φ = 1 the New
9
The optimization program is compatible with constrained choice by a stochastic inter-temporal utility
maximizing agent facing an investment-production-consumption trade-off. In the following analysis, via
time-separability, the agent is assumed to optimize in stages. That is, for given GDP and prices, production
and consumption of categories of GDP are allocated to maximize instantaneous utility. This is equivalent to
specifying an indirect utility function for a benevolent dictator, when abstracting from depreciation and
investment decisions, and international capital flows. The intercept and slope, μo and μ1 in (5), are not
recoverable from maximisation, but identified by forcing (5) to generate the sample value of real GDP for
each economy in the base year.
10
The shares also depend on parameters δ i and π via ωi as specified in (4).
8
Economy parameter βi has no impact on the expenditure share. However, when φ < 1 the
βi influence the share values. Convergence to a New Economy limiting value of the
expenditure share occurs when φ → η , and in the limit the α i only have an impact on the
share via the additive term in the numerator. 11
Clearly, the rate at which a nation converges toward a New Economy depends on the
strength of the network externality. Accordingly, the φ are specified so that the β i have a
greater impact on the expenditure shares when ICT penetration is more pervasive, i.e., the
greater the 1 − φ value relative to φ − η . In particular, φ and η are specified to modify
the influence of PA and PB on expenditure shares as an economy becomes more ICTenabled (i.e., vary by time and national circumstance). Specifically, φ falls from a
maximum of unity for the reference country in the base year to a country-specific value
η : 12
φ j ,t = 1 − (1 − η j ,t )ψ j ,t
(8)
with
ψ j ,t = ζ
sICT , j ,t −1 ( R j ,t − 1) / R j ,t
sICT ,USA,0 ( RUSA,t − 1) / RUSA,t
(9)
where sICT = ∑ i =1 si is the aggregate ICT share and R is normalised real per capita GDP
4
11
si approaches [ (φ − η )α i + (1 − φ ) β i ] / (1 − η ) asymptotically with real GDP defining the limiting value of
the expenditure share for given values of φ and η . Further, as φ → η the limiting value of the share
approaches β i .
As movement in φ and η are country-specific the additional subscript j denotes Country j .
12
9
(unity for India in 2002), viz. 13
R j ,t =
y% j ,t / PGDP ,t
y% India ,1 / PIndia ,1
.
(10)
Furthermore, ζ is a measure of the extent to which 1 − η exerts pressure on φ to fall
below unity, while η varies by country and time according to the rule: 14
η j + ξ log χ j ,t
1 + ξ log χ j ,t
(11)
1 + log( y% j ,t −1 / PTC , j ,t −1 )
.
1 + log( y% j ,0 / PTC , j ,0 )
(12)
η j ,t =
where
χ j ,t =
Equation (8) assumes that φ and η co-evolve in a linked manner, with φ approaching η
from above at a rate dependent on national and USA (base year) ICT share and per capita
normalised real GDP differences. For a ‘near’ Old Economy φ is close to unity 15. As the
New Economy becomes more important η < 1 rises. The rate at which η (productive
capacity of the economy) rises depends on the increase in real per capita GDP from the
13
In base year 2002, t = 1. Prices are normalised to unity for base year 2002, so PGDP , j ,1 = 1 for all
countries and commodity prices calculated in the model.
14
Note η j rises from country-specific η j toward an econometrically estimated common upper bound ξ .
Due to the double log curvature specification (11)-(12), the upper bound is not attainable for reasonable
forecast values of real per capita GDP. Therefore, ξ is not constrained to less than unity in estimation.
Thus the extent that η moves from the base value in forecasting depends on the econometric evidence.
15
This follows for a low income near Old Economy since R j ,t is near unity in this case, forcing ψ j ,t to be
approximately zero.
10
base period, and is measured by the scaled and curved country-specific variable χ . 16
Finally, the curvature and national time variation are chosen to ensure η does not exceed
the upper bound of unity during the forecast period so as to ensure effective global
regularity. 17 The specification of ψ in (8) requires that φ = 1 in the base period for India,
with the specification of curvature, and φ and η variation, guaranteeing φ − η > 0 holds
within sample and through the forecast horizon. Further, imposing the restrictions φ < 1
and η < 1 ensures the conditional asymptotic shares θi are weighted averages of Old
Economy and New Economy parameters α i and β i , respectively. As φ and η are both
country-specific and time varying, so are the conditional asymptotic shares θi , the
implied long-run i th expenditure share. 18
3. Data
An initial set of 70 countries is developed from WITSA Digital Planet 2006 data. These
data are supplemented with national GDP deflators obtained from the International
Monetary Fund International Financial Statistics. Additionally, several African, Asia-
16
The measure is exact for the USA in 2002, viz. ψ USA,1 = ζ .
17
National values are estimated econometrically except for India where the lower bound is set at ½.
If φ approaches η asymptotically the conditional asymptotic shares θ converge to the unconditional
asymptotic shares β i . Closure of the digital divide occurs only if ψ = 1 . In principle closure occurs if a
nation’s real per capita GDP reaches that of the USA, and additionally the nation’s ICT share rises to an
econometrically estimated multiple (viz. 1/ ζ ) of the USA’s base period ICT share . If real per capita GDP
does not catch up, the digital divide can still close if the ICT share exceeds the USA base-period share by
more than the factor 1/ ζ .
18
11
Pacific, Eastern European, Latin American and Middle Eastern country observations are
removed from the sample due to the poor quality of these data. The final sample used for
estimation contains observations for a group of 31 countries. 19 Table 1 and Table 2 list
2002–2004 G8 Member Nation and Asian Tiger (Korea, Taiwan) and Emerging (China,
India) Economy (ATEE) annual expenditure by the components: Telecommunications
(TEL), Computer Hardware (HARD), Computer Software (SOFT) and IT Services
(SERV), respectively. Table 1 and Table 2 also detail G8 and ATEE national GDP
(current US$ million), GDP deflator (PGDP) and normalized real GDP per capita
(RCNOR) data, respectively. RCNOR is set at unity for India in 2002 (the reference
country and year, respectively). That is, a 2003 RCNOR value of 50.1967 for Canada
means that Canadian real GDP per capita is 50.1967 times larger than for India in 2002.
19
The regions (countries) that comprise the sample are: Region 1—North America (Canada, Mexico, USA);
Region 2—Latin America (Brazil, Chile); Region 3—Western Europe (Austria, Belgium, Denmark,
Finland, France, Germany, Ireland, Italy, Netherlands, Norway, Sweden, Switzerland, UK); Region 4—
Eastern Europe (Russia); Region 5—Asia-Pacific (Australia, China, Hong Kong, India, Japan, Malaysia,
New Zealand, Singapore, South Korea, Taiwan, Thailand); and Region 7—South Africa (South Africa). An
extended version of Table 1 for full set of countries employed in estimation is available on request.
12
Table 1. G8 Selected National Statistics, 2002–2004
Country
Year
TEL
HARD
SOFT
Canada
2002
2003
2004
19906
21784
24798
9550
10344
11578
4297
5260
6263
France
2002
2003
2004
35373
44122
50973
15236
17242
18775
Germany
2002
2003
2004
48279
59243
65470
Italy
2002
2003
2004
Japan
SERV
GDP
PGDP
RCNOR
11143
13380
15603
735015
868093
991477
1.0000
1.0316
1.0653
49.7312
50.1967
51.2866
10097
12909
15366
30520
38280
44621
1462372
1794212
2045387
1.0000
1.0148
1.0328
51.1150
51.1092
51.9780
26888
31255
34975
14527
18380
21318
30129
37396
42474
2026750
2450051
2743244
1.0000
1.0111
1.0174
50.9932
50.9320
51.8273
24894
30302
34615
9674
11258
12418
5147
6598
7828
13417
16875
19606
1189183
1472548
1677033
1.0000
1.0295
1.0568
43.8482
43.3266
44.1312
2002
2003
2004
170895
185961
200147
51531
52522
57644
13100
14720
16810
62545
68948
77106
3979661
4297299
4670731
1.0000
0.9857
0.9740
66.0252
66.9280
68.6185
Russia
2002
2003
2004
9134
11566
14798
2345
2881
3900
450.1
570.2
742.2
1158
1537
2099
345471
431492
581384
1.0000
1.1368
1.3486
5.0141
5.3854
5.8326
UK
2002
2003
2004
49450
56650
64191
21442
21965
24120
13472
16467
20226
33804
40513
48724
1576294
1810499
2132806
1.0000
1.0320
1.0543
55.9316
57.3488
58.5783
USA
2002
446636
113537
97204
234747
10469600
1.0000
76.8684
2003
460894
119575
104918
248583
10971250
1.0183
78.4429
2004
499205
132628
115568
268153
11734300
1.0397
80.8439
Note. RCNOR is real GDP per capita normalized to equal unity for India in 2002. RCNOR equal to 50.1967 for
Canada in 2003means that Canadian real GDP per capita is 50.1967 times larger than for India in 2002.
Table 2. ATEE Selected National Statistics, 2002–2004
Country
Year
TEL
HARD
SOFT
SERV
GDP
PGDP
RCNOR
China
2002
2003
2004
37612
41437
47102
20356
27027
39057
2253
3344
5295
2155
5295
7940
1270763
1418260
1653736
1.000
1.0200
1.0846
2.0959
2.2769
2.4693
India
2002
2003
2004
14166
16873
23734
3457
5013
7204
588
948
1350
1787
2859
3876
508033
603363
699975
1.0000
1.0372
1.0783
1.0000
1.0592
1.1162
Korea
2002
2003
2004
22197
25339
29270
9386
9962
10790
1116
1356
1734
3153
3890
4957
549040
608997
681455
1.0000
1.0228
1.0574
24.2655
24.9100
25.8936
Taiwan
2002
11977
3362
739
1073
282243
1.0000
26.5292
2003
12570
3605
860
1226
286409
0.9785
27.3057
2004
13247
4148
1046
1478
306027
0.9597
28.7223
Note. RCNOR is real GDP per capita normalized to equal unity for India in 2002. RCNOR equal to 27.3057 for
Taiwan in 2003means that Taiwan real GDP per capital is 27.3057 times larger than for India in 2002.
13
Table 3. G8 ICT Expenditure Shares, 2002–2004
Country
Year
TELS
HARDS
SOFTS
SERVS
Canada
2002
2003
2004
0.0270
0.0251
0.0250
0.0129
0.0120
0.0117
0.0058
0.0061
0.0063
0.0151
0.0154
0.0157
France
2002
2003
2004
0.0242
0.0246
0.0249
0.0104
0.0096
0.0091
0.0069
0.0072
0.0075
0.0209
0.0213
0.0219
Germany
2002
2003
2004
0.0238
0.0242
0.0239
0.0133
0.0128
0.0128
0.0072
0.0075
0.0078
0.0149
0.0152
0.0155
Italy
2002
2003
2004
0.0209
0.0206
0.0206
0.0081
0.0077
0.0074
0.0043
0.0045
0.0047
0.0113
0.0115
0.0117
Japan
2002
2003
2004
0.0429
0.0433
0.0429
0.0129
0.0122
0.0123
0.0033
0.0034
0.0036
0.0157
0.0160
0.0165
Russia
2002
2003
2004
0.0265
0.0268
0.0255
0.0068
0.0067
0.0067
0.0013
0.0013
0.0013
0.0034
0.0036
0.0036
UK
2002
2003
2004
0.0314
0.0313
0.0301
0.0136
0.0121
0.0113
0.0086
0.0091
0.0095
0.0215
0.0224
0.0229
USA
2002
2003
2004
0.0427
0.0420
0.0425
0.0108
0.0109
0.0113
0.0093
0.0096
0.0098
0.0224
0.0227
0.0229
Table 4. ATEE ICT Expenditure Shares, 2002–2004
Country
Year
TELS
HARDS
SOFTS
SERVS
China
2002
2003
2004
0.0296
0.0293
0.0285
0.0160
0.0191
0.0236
0.0018
0.0024
0.0032
0.0017
0.0037
0.0048
India
2002
2003
2004
0.0279
0.0280
0.0339
0.0069
0.0083
0.0103
0.0012
0.0016
0.0020
0.0035
0.0047
0.0055
Korea
2002
2003
2004
0.0405
0.0416
0.0430
0.0171
0.0164
0.0158
0.0020
0.0022
0.0026
0.0058
0.0064
0.0073
0.0119
0.0126
0.0136
0.0026
0.0030
0.0034
0.0038
0.0043
0.0048
Taiwan
2002
0.0424
2003
0.0439
2004
0.0433
Note. ATEE is Asian Tiger and Emerging Economy.
14
For estimation, expenditure (by category) is expressed as GDP shares. This approach
defines four ICT shares and a ‘Rest of GDP’ category. Table 3 and Table 4 list G8
Member Nation and ATEE nation, respectively, annual ICT expenditure shares. The G8
TEL, HARD, SOFT and SERV shares are positively correlated with RCNOR for France,
Germany, Japan, the UK and the USA (FGJUU, the highest five ranking G8 nations by
RCNOR). A similar result holds for the more affluent ATEE nations (by RCNOR) of
Korea and Taiwan. Further, the absolute levels of TELS and HARDS correspond closely
to that for FGJUU in the G8 however this correspondence does not hold for SOFTS and
SERVS, with the magnitudes for Korea and Japan approximately 25% of the FGJUU
magnitudes. The TEL share for Canada, Italy and Russia is similar in size to that of China
and India. For Computer Software, the share for China, India, Italy and Russia are of
similar values. Conversely, IT Services expenditure share of Canada and Italy are five to
ten times the magnitude of that for China, India and Russia. Clearly, while national
expenditure expansion paths are non-homothetic, allowance is required for national
idiosyncratic behaviour.
4. Estimating Form and Econometric Results
Estimation is based on a panel of annual observations for 31 countries for the period
2002-2004 (with 2001 data providing initial lagged values). Data series analysed include:
nominal GDP (2002 US dollars), population, GDP deflator, GDP expenditure ICT
components and Rest of GDP. For estimation, specifications (4) and (7) are substituted
15
into (6), with four share equations estimated. 20 The pooled cross-country time-series
estimating form is:
α i + δ i ⎡⎣ si , j ,t −1 − π s i , j ⎤⎦
sijt =
+
(φ j ,t − η j ,t )α i + (1 − φ j ,t ) βi
1 − η j ,t
1 + ∑ k =1 δ k ⎡⎣ sk , jt , −1 − π sk , j ⎤⎦
1−η j ,t
1 + ln ( y% j ,t / PGDP , j ,t )
n
ln ( y% j ,t / PGDP , j ,t )
1−η j ,t
.
(13)
For estimation national GDP deflators replace PTC . The ijt index refers to expenditure
share category i (= TELS , HARDS , SOFTS , SERVS ) , country j (= 1,...,31) and time
t (= 2002,..., 2004) . The estimated parameters are: (i) four α i parameters, with the
remaining parameter value inferred by adding-up
∑
n
i =1
α i = 1 ; (ii) four βi parameters,
with the remaining parameter constructed as in (i); (iii) four δ i lagged-share parameters;
(iv) three scaling/curvature parameters π , ζ and ξ ; and (v) thirty national initial
condition η j parameters. Table 5 provides summary fit statistics for the pooled data set.
The reported Durbin-Watson statistics indicate an absence of first-order autocorrelation
among the residuals. Sample R 2 are highest for the SOFTS (0.9796), SERVS (0.9788)
and TELS (0.9493) share equations. R 2 for the HARDS equation is lower at 0.9181.
20
For estimation, the rest of GDP equation is dropped. Non-linear systems maximum likelihood estimation
is invariant to the equation dropped. Post-estimation a pricing module is added for the simulation. This
module provides component price estimates from which a true aggregate price index is constructed. These
data and exogenously supplied forecasts for nominal GDP and population provide a scenario for utilising
(13) in forecasting mode to predict the direction of movement in national ICT shares.
16
Table 5. Econometric Fit Statistics
Share Equation
Telecommunications
IT Hardware
IT Software
IT Services
Note. Log Likelihood = 2146.689
R2
Durbin-Watson Statistic
0.9493
0.9181
0.9796
0.9788
1.9998
2.1543
2.2635
2.1149
Table 6 contains parameter estimates for α HARDS , α SOFTS , α SERVS and α NON-ICT from share
system (13). The parameters are interpreted as predicted shares for an Old Economy, viz.,
an economy with the per capita income level of India (the reference country) in 2002 but
with only subsistence ICT shares (a fraction π of the Indian base period shares s i ). An
estimate for α TELS is determined by adding-up. All coefficients are individually
significant at the 10% level. The magnitude of the estimated parameters are plausible
with the estimated value of αTELS (Telecommunications) largest at 1.03% of GDP,
Computer Hardware (0.33%) is next largest followed by Computer Services (0.18%) and
Computer Software (0.05%). The β i estimates are limiting shares associated with
technology-preferences when real GDP becomes large and new technology permeates the
economy (when φ falls to η ), with the β TELS value determined by adding-up.
Comparison of the estimates for α NON-ICT and β NON-ICT indicate that the aggregate ICT
expenditure share rises from approximately 1.5% of GDP for a low-income Old
Economy (India in 2002) to nearly 10.5% of GDP ultimately for a high-income New
Economy.
21
The δ i estimates indicate the strength of influence of component i
expenditure on the true price index weights. The impact is evident with δ TELS (= 1.4717) ,
21
This interpretation is based on extrapolation using the estimated curvature of the Engel curves.
17
δ HARDS (= 1.3874) , δ SOFTS (= 1.5702) and δ SERVS (= 1.5155) estimates significant at the 1%
level. The strongest effects are for SOFTS and SERVS, followed by TELS and then
HARDS. Additionally, the estimated parameter π (= 0.4813) indicates the relevant
proportion of Indian base period shares that may be identified as subsistence ICT shares
in a low income country with Old Economy technology. Finally, the parameter estimates,
ξ (= 30.8750) and ζ (= 0.3043) , which assist in assuring that the estimated share
equations exhibit non-homotheticity while still satisfying fractional share regularity
conditions, are significant. The η j ,2002 estimates reported in Table 7 represent otherwise
unmeasured differences in technology and domestic conditions. Domestic conditions may
reflect the state of national competition policy and sector-specific regulation. The
estimated parameters are individually significant η = 0 at the 1% level. However, since
η ≥ 1 2 is necessary for regularity of the demand system (at least for low income levels),
the most relevant hypothesis test is whether η = ½ , i.e., whether initial national
conditions are dissimilar to that for the reference country in the initial year (India in
2002). As would be expected, there is a considerable range in values for η , representing
different initial country conditions in most cases. Not surprisingly, the test is unable to
distinguish the initial conditions facing India from those in China, Mexico and Russia.
18
Table 6. Common Cross-Country Parameter Estimates
Parameter
Estimate
Standard Error
t statistic(a)
Constraint / H 0 (b)
α1
0.0103
-
-
1 − ∑ i =2 αi
α2
α3
α4
α5
0.0033
0.0005
0.0018
0.9841
0.0012
0.0003
0.0007
0.0077
2.696
1.861
2.422
127.980
H 0 : α5 = 1
β1
0.0989
-
-
1 − ∑ i =2 βi
β2
β3
β4
β5
0.0038
0.0001
0.0008
0.8963
0.0069
0.0031
0.0073
0.0279
0.554
0.040
0.112
32.172
H 0 : β5 = 1
δ1
δ2
δ3
δ4
1.4717
1.3874
1.5702
1.5155
0.0596
0.0691
0.0687
0.0663
24.699
20.079
22.861
22.875
π
ξ
0.4813
30.8750
0.1601
12.5680
3.006
2.457
ζ
0.3043
-
-
t statistic
5
–34.1
5
ζ =
–2.8
ζ L + ζ U ex
1 + ex
(c)
–1.6657
0.9296
–1.792
x
Note. (a) Column 4 contains t statistics that relate to null hypotheses of the form H 0 : α i = 0 . (b) Column 5
lists theoretically-motivated constraints and null hypotheses not of the form H 0 : α i = 0 . (c) x is estimated
freely, allowing free estimation of ζ within bounds, viz. ζ L < ζ < ζ U where ζ L = 0.22 and ζ U = 0.75
19
Table 7. Country-Specific Initial Condition Parameter Estimates
Country
Australia
Austria
Belgium
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Hong Kong
Ireland
Italy
Japan
η̂
0.9273
0.9387
0.9364
0.9245
0.9060
0.7765
0.6661
0.9323
0.9242
0.9365
0.9310
0.9197
0.8864
0.9155
0.9195
T statistic: H 0
η =0
η =½
Country
52.5
59.2
56.3
23.1
46.1
16.1
4.5
62.9
61.2
65.7
57.7
40.7
37.0
43.9
60.5
Malaysia
Mexico
Netherlands
New Zealand
Norway
Russia
Singapore
South Africa
South Korea
Sweden
Switzerland
Taiwan
Thailand
UK
USA
24.2
27.7
26.2
10.6
20.7
5.7
1.1
29.2
28.1
30.6
26.7
18.6
16.1
19.9
27.6
η̂
0.7931
0.7177
0.9345
0.9347
0.9271
0.5571
0.9323
0.9047
0.8690
0.9355
0.9507
0.9327
0.8860
0.9287
0.9018
t statistic: H 0
η =0
η =½
20.2
3.5
65.0
52.9
56.1
2.2
53.2
25.2
29.0
68.6
75.8
34.7
12.6
66.3
54.0
7.5
1.1
30.2
24.6
25.8
0.2
24.7
11.3
12.3
31.9
35.9
16.1
5.5
30.6
24.0
The country-specific and time-varying parameter estimates are compared with the timevarying estimates of φ for the G8 and ATEE nations in Table 8 and Table 9, respectively.
Convergence to a New Economy limiting value of the expenditure share occurs when
φ → η . Clearly, a narrowing of the gap φ − η provides a measure of the progress toward
a New Economy. The G8 Member Country estimates indicate movement to a New
Economy for Canada, France, Russia, the UK and the US report improvement. However,
there is either a mixed result or virtually no change for Germany, Italy and Japan. Table 9
shows an across the board improvement for all ATEE nations, with substantive gains
being made by China.
20
Table 8. G8 Time-Varying Parameter Estimates
Year
φ j ,t − η j ,t
Difference
Year
Canada
2002
2003
2004
0.9801 – 0.9059
0.9830 – 0.9199
0.9827 – 0.9243
0.9866 – 0.9365
0.9864 – 0.9383
0.9835 – 0.9383
0.0742
0.0631
0.0584
2002
2003
2004
0.9862 – 0.9310
0.9853 – 0.9299
0.9820 – 0.9293
0.0501
0.0481
0.0452
2002
2003
2004
0.9871 – 0.9154
0.9876 – 0.9200
0.9839 – 0.9124
0.0578
0.0619
0.0548
0.9513 – 0.5571
0.9731 – 0.7558
0.9827 – 0.8511
0.3942
0.2173
0.1316
UK
0.0552
0.0554
0.0527
2002
2003
2004
Italy
2002
2003
2004
0.9773 – 0.9195
0.9779 – 0.9160
0.9783 – 0.9235
Russia
Germany
2002
2003
2004
Difference
Japan
France
2002
2003
2004
φ j ,t − η j ,t
0.9814 – 0.9287
0.9835 – 0.9376
0.9845 – 0.9461
0.0527
0.0459
0.0384
USA
0.0717
0.0676
0.0715
2002
2003
2004
0.9701 – 0.9018
0.9722 – 0.9070
0.9754 – 0.9176
0.0683
0.0652
0.0578
Table 9. ATEE Time-Varying Parameter Estimates
Year
φ j ,t − η j ,t
Difference
Year
China
2002
2003
2004
0.9726 – 0.6661
0.9912 – 0.9102
0.9939 – 0.9461
1.0000* – 0.5000*
0.9991 – 0.8910
0.9992 – 0.9543
Difference
Korea
0.3065
0.0810
0.0478
2002
2003
2004
India
2002
2003
2004
φ j ,t − η j ,t
0.9723 – 0.8690
0.9811 – 0.9155
0.9824 – 0.9266
0.1033
0.0656
0.0416
Taiwan
0.5000
0.1081
0.0448
2002
2003
2004
0.9869 – 0.9327
0.9890 – 0.9474
0.9903 – 0.9560
0.0542
0.0416
0.0343
Note. * value is set for the reference country in the base year
5. Pricing Module
Static (within sample) and dynamic (beyond sample) forecasts for G8 and ATEE
21
expenditure shares to 2015 rely on the path of the exogenous variables nominal GDP and
population. Additionally, values for the paths of GDP expenditure component prices are
required to allow construction of the true cost index ln PTC to allow the forecast of GDP
shares. To infer national share component prices, the USA is designated the ICT
innovation leader with their component prices pi0,USA,t forming the basis of a price
simulation module. That is, other country ICT price movements are assumed to react to
changes in the leader’s price pi0,USA,t . In particular, over the period 2001-2004, non-USA
price falls are assumed smaller in magnitude and determined by country-specific
fractional factors ν j , viz.
pi0, j ,t = 1 −ν j ⎡⎣1 − pi0,USA,t ⎤⎦ .
i = 1,..., 4
(14)
Data on ν j are obtained from Szewczyk (2007) in which the GTAP multi-country CGE
model infer relationships between the USA and other country ICT pricing. The implied
Rest of GDP price series p5,0 j ,t are obtained for 2001-2004 by solving implicitly for
p5,0 j ,t in:
⎛ αˆ + δˆ ⎡ s
ˆ ⎤
5
i
i ⎣ i , j ,t −1 − π s i , j ⎦
ln PGDP , j ,t = ∑ i =1 ⎜
4
⎜ 1 + ∑ δˆk ⎡ sk , j ,t −1 − πˆ sk , j ⎤
⎣
⎦
j =1
⎝
⎞
⎟ ln pi0, j ,t .
⎟
⎠
(15)
The pi0, j ,t series are initial values for the national ICT component price series forecasts.
22
To forecast the true cost index ln PTC requires a quantity disequilibrium argument to be
constructed to explain GDP component price trends. In particular, current period
disequilibrium is specified as the difference between last periods predicted quantity and
the immediately preceding actual quantity, viz.
zi , j ,t = sˆi , j ,t −1 y% j ,t −1 / pi0, j ,t −1 − si , j ,t − 2 y% j ,t − 2 / pi0, j ,t − 2 .
(16)
Asymmetric share disequilibrium arguments are obtained from (16) by decomposing
realized values by the rule:
(
)
zi+, j ,t = zi , j ,t + zi , j ,t / 2 and
(
)
zi−, j ,t = zi , j ,t − zi , j ,t / 2 .
(17)
The rationale is that asymmetric price responses are expected to result from negative and
positive quantity disequilibria. Finally, price equations (18) are estimated by pooling
country data and estimating price components. The i th price component equation is
pi0, j ,t = pi0, j ,t −1 ⎡⎣1 + f i , j + gi zi+, j ,t + hi zi−, j ,t ⎤⎦ .
(18)
Equation (18) arguments are the country-specific trend term fi , j , and the positive gi zi+, j ,t
and negative hi zi−, j ,t disequilibrium terms. Econometric estimates of the trend and
disequilibrium parameters are provided in Appendix Table 1 through Appendix Table 5.
Finally, the component i share price is calculated as the prediction:
23
pˆ i , j ,t = pi , j ,t −1 ⎡⎣1 + fˆi , j + gˆ i zi+, j ,t + hˆi zi−, j ,t ⎤⎦ .
(19)
Equation (19) is used to forecast successively one-step-ahead using the previous period’s
predicted price for the lagged price. Further, estimates of the disequilibria are available
recursively for expenditure shares. Specifically, within sample disequilibrium measures
are constructed by obtaining within-sample predictions form the econometrically
estimated share equations, viz.:
αˆ i + δˆi ⎣⎡ si , j ,t −1 − πˆ s i , j ⎦⎤
+
(φˆj ,t − ηˆ j ,t )αˆ i + (1 − φˆj ,t ) βˆi
1−ηˆ j ,t
ln ( y% j ,t / PGDP , j ,t )
1 − ηˆ j ,t
n
1 + ∑ k =1 δˆk ⎡⎣ sk , jt ,−1 − πˆ sk , j ⎤⎦
sˆijt =
1−ηˆ j ,t
1 + ln ( y% j ,t / PGDP , j ,t )
,
(20)
inserting these in (16) and to enable construction of (17). Having estimated (18) over
2002-2004, the disequilibrium measures (17) for 2004 allow (19) to be applied to provide
2005 component price forecasts. Using the component price estimates in a forecasting
version of (15) the true index estimate of the GDP deflator for 2005 is
⎛ αˆ + δˆ ⎡ s
ˆ ⎤
5
i
i ⎣ i , j ,t −1 − π s i , j ⎦
ln PˆTC , j ,t = ∑ i =1 ⎜
4
⎜ 1 + ∑ δˆk ⎡ sk , j ,t −1 − πˆ sk , j ⎤
⎣
⎦
j =1
⎝
⎞
⎟ ln pˆ i , j ,t .
⎟
⎠
(21)
Replacing PGDP with PˆTC in (20), enables 2005 expenditure shares to be forecast. The
process is repeated recursively. Finally, to drive the iterative one-step-ahead forecasts, the
exogenous variables required are nominal GDP and population (or nominal GDP per
capita y% j ,t ). A separate steady growth scenario for nominal GDP and population are
24
generated for sample countries to 2015. 22 These exogenous variable projections are used
in (16)-(21) to forecast national ICT component and rest of GDP shares to 2015.
6. Forecasts
The estimated model provides a base to make forecasts for the 31 countries that comprise
the sample data set. However, attention is focused on the forecast performance of
heterogeneous (in terms of their economic and ICT network development) groups of
nations. A detailed analysis of forecast performance is provided for the Group of Eight
(G8) nations: Canada, France, Germany, Italy, Japan, Russia, the UK and the USA. The
G8 represent 14% of the global population and 60% of economic output. The other group
are the Asian emerging economies of China, India, Korea and Taiwan. At December
2005 China is the largest supplier of ICT goods. Between 1996 and 2004, Chinese ICT
trade rose from $35 billion to $329 billion per annum. By end-2005, India achieved 44%
of the global IT and business process outsourcing market. Further, Indian IT sector
revenues are forecast to reach $56 billion in 2007, with 80% of IT services and software
revenue exported. Further, ICT patent growth for the Tiger Economies (South Korea,
Taiwan) is rising 4 times faster than Japan. The Chinese patent growth performance
(25.6% p.a.) is similar to that for South Korea (26.1% p.a.), while the Indian growth rate
22
Let x represent an exogenous variable – such as nominal GDP or population – which is available for each
country in the dataset over the period 2000-2004. The steady growth scenario is constructed to ensure that
x̂2004 = x2004 by estimating the constant growth regression xt = e g x ( t − 2004) x2004 over the sample period
2000-2004. Given country specific regression estimates gˆ x , steady growth scenarios over the forecast
period for each country are provided, viz. xˆt = e
gˆ x ( t − 2004)
x2004 , t = 2005,..., 2015 .
25
is 12.5% p.a. A more recent trend is the migration of innovation functions from highincome nations to emerging economies. For instance, between 2000 and 2005, the
proportion of Intel’s revenue in the USA fell from $12.4 billion to $5.7 billion, while that
from China rose from $2.1 billion in 2003 to over $5.3 billion in 2005.
The predicted shares are constructed by substituting country-specific parameter estimates
into (11), lagged share parameters δ i and the scale/curvature estimates for π , ζ and ξ
into (4), (9) and (11), and φ and η into (13) with the α i and β i , estimates. The predicted
shares sˆijt , are obtained from (13) within sample using actual GDP deflator data for PGDP .
Table 10 lists the absolute percentage errors for G8 Member Country annual (2002–2004)
within sample forecasts for the expenditure shares: Telecommunications, Computer
Hardware, Computer Software, IT Services and (total) ICT. Absolute percentage errors
greater than 10% are in bold. Corresponding values for the ATEE nations are reported in
Table 11.
The magnitudes of the within-sample forecast errors appear relatively small when
compared to the results provided by Islam et al. (2002: Table 12), with only ten of the
120 (5 shares × 3 years × 8 countries) absolute percentage errors reported in Table 10
exceeding 10% in value. Furthermore, only two of these values (Computer Hardware for
Italy and Japan in 2002 are, respectively, 18.5185% and 19.2308%) are greater than
12.5% in value. By this measure, forecasts are relatively less accurate for the Computer
Hardware and Computer Software categories with four values exceeding 10% for each
26
category. However, there are, at most, only one reported absolute percentage error
exceeding 10% in value per nation per category during the forecast period.
Table 10. G8 Within-Sample Absolute Percentage Errors, 2002–2004
Country
Year
TELS
HARDS
SOFTS
SERVS
ICTS
Canada
2002
2003
2004
5.56
3.92
1.67
2.33
1.50
4.26
8.62
4.41
0.00
8.61
4.65
0.53
4.86
0.35
0.25
France
2002
2003
2004
1.23
11.67
0.91
10.38
5.13
0.82
10.00
6.82
1.00
9.48
7.31
0.00
5.53
8.48
0.44
Germany
2002
2003
2004
1.65
11.19
1.57
9.63
9.68
7.06
5.48
7.69
0.96
5.30
9.14
0.97
3.55
9.93
1.32
Italy
2002
2003
2004
1.44
10.93
2.58
18.52
7.61
3.09
6.98
9.26
3.28
7.08
7.30
2.61
5.16
9.09
2.80
Japan
2002
2003
2004
12.56
1.50
1.21
19.23
4.55
9.09
6.06
5.41
7.14
7.07
5.78
4.71
12.31
3.10
2.04
Russia
2002
2003
2004
4.15
3.26
3.97
0.00
0.00
1.37
0.00
0.00
7.14
0.00
0.00
10.26
2.85
1.81
4.41
UK
2002
2003
2004
4.76
0.88
2.45
10.95
6.02
0.00
6.98
2.00
0.00
5.56
2.04
0.72
6.49
0.65
0.73
USA
2002
2003
2004
3.29
8.83
6.35
4.63
6.42
7.08
3.23
1.06
1.02
3.13
2.21
0.44
3.39
2.79
2.29
Note. Within-sample prediction errors are the absolute percentage deviation from actual values. Absolute
percentage errors (APE) greater than 10% are in bold. ICTS is calculated by applying the APE formula to the
sums of the individual actual and predicted ICT component shares. Thus it is a weighted average of the ICT
component APEs.
Comparison of Table 10 and Table 11, suggests the ATEE within-sample forecast
absolute percentage errors are relatively large. In particular, ten of the 60 (5 shares × 3
years × 4 countries) absolute percentage errors exceed 10% in absolute value. Of these
27
values six relate to China and two are for India. Additionally, four of these values
(Computer Services for China 2002–2004 and Korea in 2002 are, respectively, 17.6%,
20.0%, 15.8% and 13.8%) are greater than 12.5% in value. The most difficult category to
predict accurately is IT services with six of twelve forecast value report an absolute
percentage error exceeding 10%.
Table 11. ATEE Within-Sample Absolute Percentage Errors, 2002–2004
Country
Year
TELS
HARDS
SOFTS
SERVS
ICTS
China
2002
2003
2004
5.74
10.92
12.59
5.00
1.57
0.84
11.11
4.17
0.00
17.65
20.00
15.79
5.94
5.40
4.66
India
2002
2003
2004
4.55
5.56
9.85
5.71
5.49
3.42
8.33
5.88
4.55
11.11
7.69
12.70
5.59
1.66
5.49
Korea
2002
2003
2004
0.25
0.46
2.56
7.60
2.33
4.05
5.00
0.00
3.57
13.79
4.48
3.80
3.28
0.28
1.94
Taiwan
2002
2003
2004
4.47
7.71
10.91
4.20
3.94
0.00
3.85
6.67
5.71
2.63
4.65
4.00
2.49
6.61
7.96
Note. Within-sample prediction errors are the absolute percentage deviation from actual values. Absolute
percentage errors (APE) greater than 10% are in bold. ICTS is calculated by applying the APE formula to the
sums of the individual actual and predicted ICT component shares. Thus it is a weighted average of the ICT
component APEs.
Table 12. Directional Change Prediction Accuracy Summary, 2002–2004
Sample
Observations
TELS
HARDS
SOFTS
SERVS
ICTS
G8
24
19
(79.2)
14
(58.3)
20
(83.3)
20
(83.8)
20
(83.3)
ATEE
12
11
(91.7)
10
(83.3)
12
(100.0)
12
(100.0)
12
(100.0)
Sample
93
76
(81.7)
49
(52.7)
83
(89.3)
82
(88.2)
82
(88.2)
Note. Percentages of observations where actual and predicted share directional changes match in parentheses.
28
Additionally, Table 12 provides a summary of the estimated models ability to forecast the
direction of change of sample data within, viz. the ability of the forecast values to match
rises and falls in the actual series. When the actual and predicted share changed in the
same direction a dummy variable is set to unity, and zero otherwise. 23 The directional
change prediction accuracy of the model is high for all categories, with the exception of
HARDS, with the model predicting the directional change at least 80% of instances.
G8 and ATEE annual dynamic simulation (beyond sample forecast) values by
expenditure shares for 2005–2014 are provided in Table 13 and Table 14, respectively.
The emerging pattern revealed by Table 13 is that the Telecommunications expenditure
share at the end of the forecast horizon is approximately 6.5%-10% of GDP. The
corresponding orders of magnitude for the shares of Computer Hardware, Computer
Software and IT Services components are 1-2% p.a., 1-5 p.a.% and 5-9% p.a.,
respectively. Importantly, the Telecommunications share is forecast to approximately
double within the G8 for the period, whilst the share of Computer Hardware is roughly
constant. For Computer software increases of between two and four fold are suggested,
with IT Services following a similar trend. These patterns are those anticipated in the
transition to a New Economy.
23
G8 and ATEE year and expenditure category accuracy are reported in Appendix Table 6 though
Appendix Table 9 for within and out-of-sample forecasts.
29
Table 13. G8 Beyond-Sample Dynamic Forecasts, 2005–2014
Country
Year
TELS
HARDS
SOFTS
SERVS
ICTS
Canada
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0329
0.0362
0.0399
0.0441
0.0488
0.0539
0.0593
0.0648
0.0705
0.0759
0.0144
0.0151
0.0158
0.0165
0.0172
0.0177
0.0182
0.0185
0.0189
0.0191
0.0086
0.0101
0.0118
0.0139
0.0162
0.0189
0.0220
0.0254
0.0295
0.0340
0.0210
0.0239
0.0272
0.0310
0.0352
0.0397
0.0446
0.0499
0.0560
0.0623
0.0769
0.0853
0.0947
0.1055
0.1174
0.1302
0.1441
0.1586
0.1749
0.1913
France
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0366
0.0404
0.0447
0.0493
0.0542
0.0593
0.0644
0.0695
0.0745
0.0786
0.0127
0.0131
0.0135
0.0138
0.0141
0.0142
0.0143
0.0143
0.0145
0.0145
0.0116
0.0134
0.0155
0.0179
0.0205
0.0235
0.0269
0.0306
0.0358
0.0412
0.0328
0.0369
0.0413
0.0462
0.0514
0.0570
0.0630
0.0694
0.0784
0.0873
0.0937
0.1038
0.1116
0.1241
0.1374
0.1517
0.1672
0.1837
0.2071
0.2303
Germany
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0350
0.0386
0.0425
0.0469
0.0516
0.0566
0.0618
0.0672
0.0726
0.0782
0.0174
0.0177
0.0180
0.0182
0.0183
0.0184
0.0184
0.0183
0.0181
0.0180
0.0118
0.0134
0.0152
0.0172
0.0195
0.0220
0.0247
0.0277
0.0310
0.0351
0.0229
0.0252
0.0278
0.0305
0.0334
0.0364
0.0397
0.0430
0.0466
0.0509
0.0871
0.0949
0.1035
0.1128
0.1228
0.1334
0.1446
0.1562
0.1683
0.1822
Italy
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0292
0.0317
0.0345
0.0378
0.0415
0.0457
0.0503
0.0553
0.0607
0.0664
0.0101
0.0104
0.0107
0.0110
0.0114
0.0117
0.0120
0.0122
0.0124
0.0125
0.0069
0.0079
0.0090
0.0102
0.0117
0.0134
0.0154
0.0177
0.0202
0.0230
0.0871
0.0949
0.1035
0.0964
0.1046
0.1132
0.1225
0.1320
0.1423
0.1549
0.1333
0.1449
0.1577
0.1554
0.1692
0.1840
0.2002
0.2172
0.2356
0.2568
30
Table 13. G8 Beyond-Sample Dynamic Forecasts, 2005–2014
Country
Year
TELS
HARDS
SOFTS
SERVS
ICTS
Japan
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0538
0.0586
0.0638
0.0694
0.0754
0.0816
0.0881
0.0946
0.1010
0.1074
0.0143
0.0143
0.0144
0.0144
0.0144
0.0144
0.0143
0.0141
0.0138
0.0134
0.0045
0.0050
0.0055
0.0061
0.0068
0.0075
0.0083
0.0091
0.0100
0.0109
0.0205
0.0222
0.0242
0.0262
0.0285
0.0308
0.0332
0.0356
0.0380
0.0403
0.0931
0.1001
0.1079
0.0729
0.0782
0.0835
0.0890
0.0944
0.0998
0.1049
Russia
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0294
0.0320
0.0352
0.0393
0.0444
0.0505
0.0575
0.0655
0.0741
0.0831
0.0081
0.0092
0.0106
0.0121
0.0139
0.0158
0.0179
0.0200
0.0220
0.0238
0.0016
0.0020
0.0025
0.0032
0.0040
0.0051
0.0065
0.0082
0.0102
0.0125
0.0047
0.0058
0.0073
0.0091
0.0115
0.0144
0.0178
0.0219
0.0265
0.0315
0.0438
0.0490
0.0556
0.0335
0.0409
0.0497
0.0600
0.0720
0.0852
0.0993
UK
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0407
0.0450
0.0497
0.0548
0.0601
0.0655
0.0707
0.0753
0.0791
0.0825
0.0145
0.0151
0.0156
0.0162
0.0166
0.0170
0.0173
0.0174
0.0166
0.0159
0.0136
0.0159
0.0187
0.0219
0.0256
0.0299
0.0347
0.0399
0.0435
0.0475
0.0318
0.0362
0.0411
0.0467
0.0528
0.0595
0.0668
0.0742
0.0780
0.0824
0.1006
0.1122
0.1251
0.1396
0.1551
0.1719
0.1895
0.2068
0.2172
0.2283
USA
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0462
0.0503
0.0549
0.0599
0.0654
0.0712
0.0774
0.0841
0.0904
0.0955
0.0111
0.0111
0.0111
0.0111
0.0112
0.0112
0.0112
0.0112
0.0111
0.0106
0.0106
0.0117
0.0129
0.0144
0.0161
0.0181
0.0204
0.0232
0.0260
0.0280
0.0240
0.0256
0.0276
0.0299
0.0324
0.0353
0.0384
0.0424
0.0459
0.0478
0.0919
0.0987
0.0792
0.0853
0.0921
0.0999
0.1084
0.1192
0.1289
0.1342
Note. ICTS is the sum of the individual predicted ICT component shares.
31
Table 14 ATEE Beyond-Sample Dynamic Forecasts, 2005–2014
Country
Year
TELS
HARDS
SOFTS
SERVS
ICTS
China
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0312
0.0344
0.0382
0.0424
0.0468
0.0513
0.0556
0.0594
0.0624
0.0647
0.0292
0.0360
0.0438
0.0524
0.0615
0.0705
0.0789
0.0862
0.0920
0.0962
0.0044
0.0060
0.0081
0.0109
0.0144
0.0187
0.0236
0.0292
0.0353
0.0417
0.0048
0.0063
0.0081
0.0103
0.0130
0.0161
0.0195
0.0231
0.0267
0.0303
0.0696
0.0827
0.0982
0.1160
0.1357
0.1566
0.1776
0.1979
0.2164
0.2329
India
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0452
0.0535
0.0634
0.0747
0.0867
0.0989
0.1103
0.1202
0.1283
0.1343
0.0145
0.0179
0.0218
0.0259
0.0301
0.0340
0.0373
0.0398
0.0415
0.0422
0.0030
0.0041
0.0056
0.0075
0.0099
0.0127
0.0158
0.0192
0.0227
0.0263
0.0085
0.0115
0.0154
0.0202
0.0257
0.0320
0.0386
0.0453
0.0519
0.0581
0.0712
0.0870
0.1062
0.1283
0.1524
0.1776
0.2020
0.2245
0.2444
0.2609
Korea
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0519
0.058
0.0651
0.0731
0.0820
0.0915
0.1013
0.1110
0.1205
0.1293
0.0184
0.0198
0.0213
0.0229
0.0245
0.0259
0.0272
0.0281
0.0287
0.0290
0.0032
0.0038
0.0045
0.0054
0.0064
0.0076
0.0090
0.0104
0.0120
0.0137
0.0091
0.0107
0.0125
0.0146
0.0170
0.0196
0.0224
0.0254
0.0283
0.0313
0.0826
0.0923
0.1034
0.1160
0.1299
0.1446
0.1599
0.1749
0.1895
0.2033
Taiwan
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0.0512
0.0587
0.0675
0.0776
0.0885
0.0999
0.1114
0.1224
0.1326
0.1415
0.0160
0.0181
0.0205
0.0229
0.0252
0.0273
0.0292
0.0305
0.0314
0.0318
0.0045
0.0057
0.0072
0.0091
0.0113
0.0139
0.0168
0.0200
0.0234
0.0269
0.0061
0.0075
0.0091
0.0111
0.0133
0.0157
0.0182
0.0209
0.0234
0.0259
0.0778
0.0900
0.1043
0.1207
0.1383
0.1568
0.1756
0.1938
0.2108
0.2261
Note. ICT is determined residually by adding up. Within-sample prediction errors are the absolute
percentage deviation from actual values.
32
The corresponding forecasts for ATEE nations are distinct from those of the G8 Member
Countries. In particular, the 2014 Telecommunications share is forecast between 7%-14%
of GDP (50% larger than for the G8). The Computer Hardware share is 3%-4% of GDP
for India, Korea and Taiwan, with the share for China in excess of 9.5%. Importantly, all
these magnitudes are greater than those for the G8 at the end of the forecast horizon. The
Computer Software (1%-5%) and IT Services (5%-9%) shares are more in line, though
marginally smaller, with the values forecast for G8 Member countries.
Table 15. G8 Out-of-Sample Absolute Percentage Errors, 2005
Country
TELS
HARDS
SOFTS
SERVS
ICTS
Canada
32.30
21.23
25.64
25.52
24.26
France
44.75
32.81
32.96
32.41
31.95
Germany
47.64
23.79
32.48
31.94
30.52
Italy
39.65
33.29
29.78
86.32
66.69
Japan
30.67
15.75
19.04
19.97
21.30
Russia
17.70
20.44
23.38
22.37
17.13
UK
37.14
26.70
28.12
27.27
27.23
USA
7.52
4.30
4.92
4.32
4.69
Table 16. ATEE Out-of-Sample Absolute Percentage Errors, 2005
Country
TELS
HARDS
SOFTS
SERVS
ICTS
China
15.25
14.15
5.62
9.03
11.60
India
26.08
12.56
21.45
23.80
19.43
Korea
26.33
8.85
10.69
11.13
16.71
Taiwan
16.76
16.24
16.70
13.38
14.80
Note. ATEE is Asian Tiger and Emerging Economy.
33
Table 17. Directional Change Prediction Accuracy Summary, 2005
Sample
Observations
TELS
HARDS
SOFTS
SERVS
ICTS
G8
8
4
(50.0)
4
(50.0)
7
(87.5)
6
(75.0)
4
(50.0)
ATEE
4
2
(50.0)
3
(75.0)
4
(100.0)
4
(100.0)
4
(100.0)
Sample
31
22
(71.0)
19
(61.3)
30
(96.8)
27
(87.1)
26
(83.9)
Note. Percentages of observations where actual and predicted share directional changes match in parentheses.
The out-of-sample absolute percentage errors for the G8 and ATEE are reported,
respectively, in Table 15 and Table 16. 24 Are higher for 2005 data than for the within
sample forecasts. The G8 forecasts APEs are twice to three times the magnitude of those
reported for the ATEE nations. Based on the APE the best forecast performance is
obtained fir the US and Russia. In Table 17, the directional accuracy of the accuracy of
the forecasts remains high out-of-sample, with the exception of TELS for both regions,
and HARDS for the G8.
7. Conclusions
The modelling approach employed is based on the premise that network technology
forecasting models should retain their foundation in economic analysis, viz. network
industry features that forecasting models must allow for are strong complementary
relationships among network technology, and the presence of consumption and
24
Importantly, the 2005 ‘actual’ data is that reported in Digital Plant 2006. These data whilst treated as
actual data (given that they were reported in a 2006) publication appear to be forecasts. Comparison of
Digital Planet 2004 with Digital Planet 2006 indicates a substantial revision of the last two observations in
the former document. While, it was expected that Digital Planet 2008 data would provide a substantial
attenuation of the APE magnitudes for 2005 when released, unfortunately the report again underwent
substantial revisions, making direct comparison of actual and forecast values untenable.
34
production externalities. Additionally, the approach explicitly addresses the constraint
that for most new product diffusion applications policy response is required when only
relatively few observations are available for estimation and forecasting. The means
employed to generate reliable and timely forecasts is the pooling of sample data in a
manner that treats ICT expenditure observations as lying on a common optimal growth
path. The corresponding theoretical model of implied rational agent behaviour is
embedded in an optimal growth paradigm. The framework allows for non-linearity of
technology and preferences. Also, to account for potential consumer and producer
network effects non-constant parameter values are allowed to evolve endogenously
through alternative states of network maturation as determined by sample data. The
model’s statistical robustness is evaluated by testing whether theoretically implied
regularity conditions are satisfied.
The model is estimated on data from 31 countries with short time-series. All the
estimated derivative share function values satisfy the implied regularity conditions,
ensuring the estimated share model is effectively globally regular. For the purpose of
examining forecast performance, attention is focused on the G8 Member Nations of
Canada, France, Germany, Italy, Japan, the UK and the USA, and the ATEE nations of
China, India, Korea and Taiwan. G8 and ATEE nations are at different stages of
economic development and New Economy transition. Within-sample simulations suggest
the model tracks ICT expenditure reasonably well when measured by absolute percentage
error magnitude. Ideally, forecast performance should be measured outside the estimation
period. However, due to a paucity of data there is no holdout sample available for direct
35
evaluation. The approach employed is to specify alternative scenarios to assess the
reliability of the estimated dynamic relationship via examination of the theoretically
implied regularity conditions, and so indirectly the forecasts based on the relationship.
The robustness of the model in the forecast period increases the confidence with which
the forecast shares are received. The forecasts clearly reflect the different positions on the
New Economy transition path that G8 and ATEE nations are currently located.
Importantly, the methods are applicable to any network industry and many non-network
industries characterized by strong complementary relations.
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Tigert, D. and Farivar, B. (1981), ‘The Bass New Product Growth Model: A Sensitivity
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39
Appendix Table 1. Telecommunications Price Equation Parameter Estimates
Parameter
Estimate
Standard Error
t statistic
National Trend
–0.0846
0.0081
–0.0968
0.0077
–0.2053
0.0095
–0.0913
0.0078
–0.0818
0.0083
–0.0596
0.0081
–0.0613
0.0084
–0.0623
0.0082
–0.1363
0.0088
–0.0694
0.0078
–0.0517
0.0092
–0.1539
0.0083
–0.0498
0.0082
–0.0667
0.0081
–0.1308
0.0096
–0.0773
0.0081
–0.0793
0.0092
–0.0880
0.0099
–0.0401
0.0075
–0.0914
0.0084
–0.1403
0.0073
–10.4590
–12.6270
–21.5250
–11.6840
–9.8155
–7.3935
–7.3077
–7.5606
–15.5350
–8.8434
–5.6304
–18.5240
–6.0742
–8.1929
–13.6210
–9.5205
–8.6081
–8.9222
–5.3166
–10.9220
–19.1170
f MAYAYSIA
–0.1401
–0.0997
–0.1060
–0.0605
0.0094
0.0088
0.0106
0.0079
–14.9050
–11.3480
–9.9849
–7.6984
f NEW
–0.0711
0.0090
–7.8851
fTHAILAND
–0.1330
–0.0823
–0.1199
–0.0944
0.0115
0.0084
0.0088
0.0077
–11.5950
–9.8360
–13.6990
–12.2500
f SOUTH
–0.0278
0.0080
–3.4869
f CANADA
f MEXICO
fUSA
f BRAZIL
fCHILE
f AUSTRIA
f BELGIUM
f DENMARK
f FINLAND
f FRANCE
f GERMANY
f IRELAND
f ITALY
f NETHERLANDS
f NORWAY
f SWEDEN
f SWITZERLAND
fUK
f RUSSIA
f AUSTRALIA
fCHINA
f HONG KONG
f INDIA
f JAPAN
ZEALAND
f SRI LANKA
f KOREA
fTAIWAN
AFRICA
Positive Disequilibrium
g+
0.1005
0.0114
8.7971
Negative Disequilibrium
g−
–0.0013
0.0286
–0.0469
40
Appendix Table 2. Computer Hardware Price Equation Parameter Estimates
Parameter
f CANADA
f MEXICO
fUSA
f BRAZIL
fCHILE
f AUSTRIA
f BELGIUM
f DENMARK
f FINLAND
f FRANCE
f GERMANY
f IRELAND
f ITALY
f NETHERLANDS
f NORWAY
f SWEDEN
f SWITZERLAND
fUK
f RUSSIA
f AUSTRALIA
fCHINA
f HONG KONG
f INDIA
f JAPAN
f MAYAYSIA
f NEW ZEALAND
f SRI LANKA
f KOREA
fTAIWAN
fTHAILAND
f SOUTH AFRICA
Estimate
Standard Error
t statistic
National Trend
–0.0101
0.5819
–0.0164
0.5407
–0.0297
0.0263
–0.0139
0.0859
–0.0133
0.0259
–0.0042
0.7734
–0.0045
0.7365
–0.0047
0.7465
–0.0150
0.1496
–0.0071
0.7471
–0.0053
0.8126
–0.0190
0.0459
–0.0051
0.2204
–0.0048
0.5901
–0.0150
0.1993
–0.0053
0.6573
–0.0028
0.2356
–0.0071
0.2388
–0.0062
0.0136
–0.0086
0.6608
–0.0238
0.0106
–0.0173
–0.0304
–1.1296
–0.1614
–0.5142
–0.0054
–0.0062
–0.0063
–0.1006
–0.0095
–0.0065
–0.4143
–0.0232
–0.0081
–0.0751
–0.0081
–0.0120
–0.0298
–0.4546
–0.0131
–2.2366
–0.0192
–0.0178
–0.0190
–0.0097
0.0188
0.0438
0.1274
0.1886
–1.0214
–0.4059
–0.1490
–0.0513
–0.0063
0.4002
–0.0158
–0.0138
–0.0103
–0.0154
–0.0161
0.3869
0.3460
0.1171
0.7103
–0.0357
–0.0298
–0.1317
–0.0227
–0.0034
0.0132
–0.2603
Positive Disequilibrium
g+
–0.1022
0.0521
–1.9607
Negative Disequilibrium
g−
–0.0310
0.0163
–1.9038
41
Appendix Table 3. Computer Software Price Equation Parameter Estimates
Parameter
f CANADA
f MEXICO
fUSA
f BRAZIL
fCHILE
f AUSTRIA
f BELGIUM
f DENMARK
f FINLAND
f FRANCE
f GERMANY
f IRELAND
f ITALY
f NETHERLANDS
f NORWAY
f SWEDEN
f SWITZERLAND
fUK
f RUSSIA
f AUSTRALIA
fCHINA
f HONG KONG
f INDIA
f JAPAN
f MAYAYSIA
f NEW ZEALAND
f SRI LANKA
f KOREA
fTAIWAN
fTHAILAND
f SOUTH AFRICA
Estimate
Standard Error
t statistic
National Trend
–0.0075
0.5323
–0.0294
0.7176
–0.0314
0.0550
–0.0247
0.0791
–0.0238
0.5401
0.0086
0.8553
0.0059
0.7195
0.0159
0.8381
–0.0102
0.9486
0.0031
0.7500
0.0059
0.3409
–0.0273
0.0853
–0.0043
0.4258
0.0167
0.8798
–0.0088
0.5848
0.0136
0.7438
0.0298
0.4369
0.0079
0.7338
–0.0113
0.0064
–0.0016
0.9124
–0.0430
0.0066
–0.0141
–0.0410
–0.5703
–0.3128
–0.0441
0.0100
0.0082
0.0190
–0.0107
0.0041
0.0174
–0.3199
–0.0102
0.0190
–0.0151
0.0182
0.0682
0.0107
–1.7535
–0.0018
–6.5506
–0.0310
–0.0309
–0.0259
–0.0164
0.0568
0.2438
0.5741
0.3243
–0.5447
–0.1266
–0.0451
–0.0506
–0.0054
0.5165
–0.0105
–0.0126
–0.0190
–0.0273
–0.0280
0.5781
0.4654
0.1519
0.7124
–0.0219
–0.0409
–0.1797
–0.0393
–0.0036
0.0129
–0.2754
Positive Disequilibrium
g+
–0.3997
0.0687
–5.8183
Negative Disequilibrium
g−
–1.9026
1.6560
–1.1489
42
Appendix Table 4. IT Services Price Equation Parameter Estimates
Parameter
Estimate
Standard Error
t statistic
National Trend
f CANADA
f MEXICO
fUSA
f BRAZIL
fCHILE
f AUSTRIA
f BELGIUM
f DENMARK
f FINLAND
f FRANCE
f GERMANY
f IRELAND
f ITALY
f NETHERLANDS
f NORWAY
f SWEDEN
f SWITZERLAND
fUK
f RUSSIA
f AUSTRALIA
fCHINA
f HONG KONG
f INDIA
f JAPAN
f MAYAYSIA
f NEW ZEALAND
f SRI LANKA
f KOREA
fTAIWAN
fTHAILAND
f SOUTH AFRICA
0.0164
0.0042
0.0152
0.0068
0.0036
0.0173
0.0178
0.0255
0.0163
0.0277
0.0176
0.0099
0.0119
0.0186
0.0219
0.0307
0.0286
0.0303
0.0022
0.0190
0.0057
0.2319
0.6895
0.0379
0.0528
0.6445
0.8163
0.7330
0.5412
0.5234
0.8281
0.8432
0.0522
0.4549
0.7611
0.7331
0.8148
0.5625
0.8620
0.0097
0.7254
0.0095
0.0707
0.0061
0.4004
0.1283
0.0055
0.0211
0.0243
0.0471
0.0311
0.0334
0.0209
0.1906
0.0261
0.0244
0.0299
0.0377
0.0508
0.0352
0.2269
0.0262
0.5958
0.0070
0.0042
0.0078
0.0031
0.0658
0.3625
0.7636
0.1178
0.1060
0.0116
0.0103
0.0261
0.0110
0.5509
0.0199
0.0151
0.0067
0.0073
0.0041
0.6253
0.5655
0.3670
0.6537
0.0241
0.0118
0.0200
0.0063
0.0038
0.0135
0.2829
Positive Disequilibrium
g+
–0.2085
0.0461
–4.5178
Negative Disequilibrium
g−
0.0998
0.1684
0.5926
43
Appendix Table 5. Rest of GDP Price Equation Parameter Estimates
Parameter
Estimate
Standard Error
t statistic
National Trend
f CANADA
f MEXICO
fUSA
f BRAZIL
fCHILE
f AUSTRIA
f BELGIUM
f DENMARK
f FINLAND
f FRANCE
f GERMANY
f IRELAND
f ITALY
f NETHERLANDS
f NORWAY
f SWEDEN
f SWITZERLAND
fUK
f RUSSIA
f AUSTRALIA
fCHINA
f HONG KONG
f INDIA
f JAPAN
f MAYAYSIA
f NEW ZEALAND
f SRI LANKA
f KOREA
fTAIWAN
fTHAILAND
f SOUTH AFRICA
0.0300
0.0744
0.0287
0.1182
0.0657
0.0200
0.0231
0.0199
0.0130
0.0210
0.0116
0.0393
0.0304
0.0240
0.0226
0.0192
0.0112
0.0346
0.1714
0.0380
0.0346
0.0130
0.0087
0.0092
0.0089
0.0148
0.0096
0.0116
0.0141
0.0101
0.0146
0.0117
0.0127
0.0101
0.0118
0.0093
0.0108
0.0128
0.0118
0.0139
0.0113
0.0086
2.3189
8.5985
3.1122
13.3240
4.4496
2.0962
1.9856
1.4132
1.2867
1.4426
0.9863
3.0979
3.0080
2.0289
2.4249
1.7822
0.8804
2.9265
12.3540
3.3789
4.0298
–0.0345
0.0433
–0.0106
0.0525
0.0102
0.0117
0.0124
0.0138
–3.3885
3.7187
–0.8571
3.8099
0.0260
0.0110
2.3637
0.0208
0.0359
–0.0112
0.0285
0.0109
0.0145
0.0101
0.0103
1.9150
2.4785
–1.1033
2.7696
0.0786
0.0101
7.8104
Positive Disequilibrium
g+
–0.0026
0.0075
–0.3490
Negative Disequilibrium
g−
–0.0046
0.0086
–0.5417
44
Appendix Table 6. G8 Directional Change Prediction Accuracy, 2002–2004
Country
Year
TELS
HARDS
SOFTS
SERVS
ICTS
Canada
2002
2003
2004
1
1
1
0
1
1
0
1
1
0
1
1
1
1
1
France
2002
2003
2004
1
1
1
0
0
1
1
1
1
1
1
1
1
1
1
Germany
2002
2003
2004
1
1
1
0
0
1
1
1
1
1
1
1
1
1
1
Italy
2002
2003
2004
1
1
1
0
0
1
1
1
1
1
1
1
1
1
1
Japan
2002
2003
2004
0
0
1
1
0
1
0
0
1
0
0
1
0
0
1
Russia
2002
2003
2004
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
UK
2002
2003
2004
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
0
0
1
USA
2002
0
0
0
2003
0
0
1
2004
1
1
1
Note. = 1, if actual and predicted share directional change matches; = 0, otherwise.
Appendix Table 7. ATEE Directional Change Prediction Accuracy, 2002–2004
Country
Year
TELS
HARDS
SOFTS
SERVS
ICTS
China
2002
2003
2004
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
India
2002
2003
2004
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Korea
2002
2003
2004
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Taiwan
2002
1
0
1
2003
1
1
1
2004
1
1
1
Note. = 1, if actual and predicted share directional change matches; = 0, otherwise.
45
Appendix Table 8. G8 Directional Change Prediction Accuracy, 2005
Country
TELS
HARDS
SOFTS
SERVS
ICTS
Canada
1
0
1
1
0
France
1
0
1
1
1
Germany
0
1
1
1
1
Italy
1
1
1
0
1
Japan
0
0
1
0
0
Russia
0
1
0
1
0
UK
0
0
1
1
0
USA
1
1
1
1
1
Note. = 1, if actual and predicted share directional change matches; = 0, otherwise.
Appendix Table 9. ATEE Directional Change Prediction Accuracy, 2005
Country
TELS
HARDS
SOFTS
SERVS
ICTS
China
0
1
1
1
1
India
1
1
1
1
1
Korea
0
1
1
1
1
Taiwan
1
0
1
1
1
Note. = 1, if actual and predicted share directional change matches; = 0, otherwise.
46