theoretically-motivated long-term forecasting with limited...
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Theoretically-Motivated Long-Term Forecasting with Limited Data
Paper 240 Russel Cooper Robert Fildes Gary Madden
July 2008
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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 non-homothetic 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, [email protected], [email protected], [email protected]. † 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
2
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.
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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).
4
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 cross-
country 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).
5
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 AP index as the
true cost of living index for an economy with least (subsistence level) ICT penetration
(Old Economy). By contrast, BP is the relevant price index for a country that critically
depends, economy wide, on ICT inputs (New Economy). AP and BP , respectively, are
specified as a weighted geometric mean of input prices:
1 1ln ln and ln lnn n
A i i B i ii iP p P pα β
= == =∑ ∑ (1)
where ln is the natural logarithm operator. iα and iβ satisfy the adding-up conditions
1 11 and 1n n
i ii iα β
= == =∑ ∑ , 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.
6
one in prices. iα indicate the relative importance of GDP components in constructing the
price index for an Old Economy.6
In particular, TCP is the true measure of the current GDP deflator as the Old Economy
deflator AP partially adjusts to the economy’s input expansion path. In particular, the true
price index TCP is the mean of the distance-weighted AP and PΩ indexes:7
11
ln ln1
n i iTC ini
jj
P pα ωω=
=
⎛ ⎞+⎜ ⎟=⎜ ⎟+⎝ ⎠
∑∑
(2)
where iω update the Old Economy index weights. Equivalently, ln TCP is expressed as:
1ln ln ln1 1TC AP P PΩ
Ω⎛ ⎞= +⎜ ⎟+Ω +Ω⎝ ⎠ (3)
where 1
nii
ω=
Ω =∑ and 1
ln lnn iii
P pωΩ =
⎛ ⎞= ⎜ ⎟Ω⎝ ⎠∑ . That is, (3) is a weighted geometric mean
6 In this study GDP is comprised of the expenditure components: Telecommunications ( 1i = ), Computer Hardware ( 2i = ), Computer Software ( 3i = ), IT Services ( 4i = ) and Rest of GDP ( 5i = ). Since AP is an Old Economy index, 1α to 4α are relatively small with the 5α 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, 5β is small when compared to 5α , and the iβ contribute relatively more to the value of the New Economy BP
price index, i.e., 1β through 4β are larger than the corresponding iα values. The AP and BP values are, respectively, lower and upper price index bounds for a theoretically correct economy-specific true cost index TCP taking values lying in the [ , ]A BP P 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 GDPP is employed as a proxy variable for TCP in the econometric estimation.
7
of the Old Economy price index AP and the PΩ index. Importantly, PΩ relates only to
ICT price movements and iω weights. The iω measure national progress toward a New
Economy with:
, 1 5, 1,...4 0i i i is s iω δ π ω−⎡ ⎤= − = =⎣ ⎦ (4)
where is is component i share of GDP share, , 1is − is the previous year share and isπ is
the subsistence component of the reference economy ICT share in the year preceding the
base year. Therefore, , 1i is sπ− − 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 iδ 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 iq% 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.
8
( ) ( ) ( )1 110 1/ ln / 1/ 1/GDP TC A By P y y P P Pφ η φημ μ +Ω − −−= +% % % (5)
which is the optimal value function resulting from maximization.9 The optimisation is in
terms of expenditure shares, i.e., /i ip q y% % (equivalently, /i ip q y ). Roy’s Identity applied
to (5) provides the optimal allocation of expenditure components in share form, viz.
( )( )
1
1
ln /1 ln /i i TC
iTC
y Ps
y P
η
η
γ θ −
−
+=
+
%
% (6)
where
( ) (1 ) and1 1
i i i ii i
α ω φ η α φ βγ θη
+ − + −= =
+Ω −. (7)
Furthermore, the shares depend on the parameters iα , iβ , φ and η .10 With sample data
scaled so that ln( / ) 0TCy P =% for the lowest GDP value (reference country India) in the
base year (2002) the iα are interpreted as Old Economy (subsistence) expenditure shares.
The iβ , which introduce flexibility to allow for non-homotheticity, are New Economy (or
long run) expenditure shares as ln( / )TCy P →∞% . Operationally, φ and η specify the
manner and extent that expenditure shares depend on low income Old Economy iα and
high income New Economy iβ parameters, respectively. That is, with 1φ = 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, oμ and 1μ 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 iδ and π via iω as specified in (4).
9
Economy parameter iβ has no impact on the expenditure share. However, when 1φ < the
iβ influence the share values. Convergence to a New Economy limiting value of the
expenditure share occurs when φ η→ , and in the limit the iα 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 iβ 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 AP and BP on expenditure shares as an economy becomes more ICT-
enabled (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
, , ,1 (1 )j t j t j tφ η ψ= − − (8)with
, , 1 , ,,
, ,0 , ,
( 1) /( 1) /
ICT j t j t j tj t
ICT USA USA t USA t
s R Rs R R
ψ ζ − −=
− (9)
where 4
1ICT iis s
== ∑ is the aggregate ICT share and R is normalised real per capita GDP
11 is approaches [ ]( ) (1 ) / (1 )i iφ η α φ β 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 iβ . 12 As movement in φ and η are country-specific the additional subscript j denotes Country j .
10
(unity for India in 2002), viz.13
, ,,
,1 ,1
//
j t GDP tj t
India India
y PR
y P=
%
%. (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
,,
,
log1 log
j j tj t
j t
η ξ χη
ξ χ+
=+
(11)
where
, 1 , , 1,
,0 , ,0
1 log( / )1 log( / )
j t TC j tj t
j TC j
y Py P
χ − −+=
+
%
%. (12)
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 unity15. As the
New Economy becomes more important 1η < 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, 1.t = Prices are normalised to unity for base year 2002, so , ,1 1GDP jP = for all countries and commodity prices calculated in the model. 14 Note jη rises from country-specific jη 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 thatη moves from the base value in forecasting depends on the econometric evidence. 15 This follows for a low income near Old Economy since ,j tR is near unity in this case, forcing ,j tψ to be approximately zero.
11
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 1φ = in the base period for India,
with the specification of curvature, and φ and η variation, guaranteeing 0φ η− > holds
within sample and through the forecast horizon. Further, imposing the restrictions 1φ <
and 1η < ensures the conditional asymptotic shares iθ are weighted averages of Old
Economy and New Economy parameters iα and iβ , respectively. As φ and η are both
country-specific and time varying, so are the conditional asymptotic shares iθ , the
implied long-run thi 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. ,1USAψ ζ= . 17 National values are estimated econometrically except for India where the lower bound is set at ½. 18 If φ approaches η asymptotically the conditional asymptotic shares θ converge to the unconditional asymptotic shares iβ . Closure of the digital divide occurs only if 1ψ = . 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 .
12
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.
13
Table 1. G8 Selected National Statistics, 2002–2004 Country Year TEL HARD SOFT SERV GDP PGDP RCNOR
Canada 2002 19906 9550 4297 11143 735015 1.0000 49.7312 2003 21784 10344 5260 13380 868093 1.0316 50.1967 2004 24798 11578 6263 15603 991477 1.0653 51.2866 France 2002 35373 15236 10097 30520 1462372 1.0000 51.1150 2003 44122 17242 12909 38280 1794212 1.0148 51.1092 2004 50973 18775 15366 44621 2045387 1.0328 51.9780 Germany 2002 48279 26888 14527 30129 2026750 1.0000 50.9932 2003 59243 31255 18380 37396 2450051 1.0111 50.9320 2004 65470 34975 21318 42474 2743244 1.0174 51.8273 Italy 2002 24894 9674 5147 13417 1189183 1.0000 43.8482 2003 30302 11258 6598 16875 1472548 1.0295 43.3266 2004 34615 12418 7828 19606 1677033 1.0568 44.1312 Japan 2002 170895 51531 13100 62545 3979661 1.0000 66.0252 2003 185961 52522 14720 68948 4297299 0.9857 66.9280 2004 200147 57644 16810 77106 4670731 0.9740 68.6185 Russia 2002 9134 2345 450.1 1158 345471 1.0000 5.0141 2003 11566 2881 570.2 1537 431492 1.1368 5.3854 2004 14798 3900 742.2 2099 581384 1.3486 5.8326 UK 2002 49450 21442 13472 33804 1576294 1.0000 55.9316 2003 56650 21965 16467 40513 1810499 1.0320 57.3488 2004 64191 24120 20226 48724 2132806 1.0543 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 37612 20356 2253 2155 1270763 1.000 2.0959 2003 41437 27027 3344 5295 1418260 1.0200 2.2769 2004 47102 39057 5295 7940 1653736 1.0846 2.4693 India 2002 14166 3457 588 1787 508033 1.0000 1.0000 2003 16873 5013 948 2859 603363 1.0372 1.0592 2004 23734 7204 1350 3876 699975 1.0783 1.1162 Korea 2002 22197 9386 1116 3153 549040 1.0000 24.2655 2003 25339 9962 1356 3890 608997 1.0228 24.9100 2004 29270 10790 1734 4957 681455 1.0574 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.
14
Table 3. G8 ICT Expenditure Shares, 2002–2004
Country Year TELS HARDS SOFTS SERVS Canada 2002 0.0270 0.0129 0.0058 0.0151 2003 0.0251 0.0120 0.0061 0.0154 2004 0.0250 0.0117 0.0063 0.0157 France 2002 0.0242 0.0104 0.0069 0.0209 2003 0.0246 0.0096 0.0072 0.0213 2004 0.0249 0.0091 0.0075 0.0219 Germany 2002 0.0238 0.0133 0.0072 0.0149 2003 0.0242 0.0128 0.0075 0.0152 2004 0.0239 0.0128 0.0078 0.0155 Italy 2002 0.0209 0.0081 0.0043 0.0113 2003 0.0206 0.0077 0.0045 0.0115 2004 0.0206 0.0074 0.0047 0.0117 Japan 2002 0.0429 0.0129 0.0033 0.0157 2003 0.0433 0.0122 0.0034 0.0160 2004 0.0429 0.0123 0.0036 0.0165 Russia 2002 0.0265 0.0068 0.0013 0.0034 2003 0.0268 0.0067 0.0013 0.0036 2004 0.0255 0.0067 0.0013 0.0036 UK 2002 0.0314 0.0136 0.0086 0.0215 2003 0.0313 0.0121 0.0091 0.0224 2004 0.0301 0.0113 0.0095 0.0229 USA 2002 0.0427 0.0108 0.0093 0.0224 2003 0.0420 0.0109 0.0096 0.0227 2004 0.0425 0.0113 0.0098 0.0229
Table 4. ATEE ICT Expenditure Shares, 2002–2004 Country Year TELS HARDS SOFTS SERVS
China 2002 0.0296 0.0160 0.0018 0.0017 2003 0.0293 0.0191 0.0024 0.0037 2004 0.0285 0.0236 0.0032 0.0048 India 2002 0.0279 0.0069 0.0012 0.0035 2003 0.0280 0.0083 0.0016 0.0047 2004 0.0339 0.0103 0.0020 0.0055 Korea 2002 0.0405 0.0171 0.0020 0.0058 2003 0.0416 0.0164 0.0022 0.0064 2004 0.0430 0.0158 0.0026 0.0073 Taiwan 2002 0.0424 0.0119 0.0026 0.0038 2003 0.0439 0.0126 0.0030 0.0043 2004 0.0433 0.0136 0.0034 0.0048
Note. ATEE is Asian Tiger and Emerging Economy.
15
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
16
into (6), with four share equations estimated. 20 The pooled cross-country time-series
estimating form is:
( )
( )
,
,
1, , 1 , , , ,, , ,
,, , 1 ,11
, , ,
( ) (1 )ln /
11
1 ln /
j t
j t
i i i j t i j j t j t i j t ij t GDP j tn
j tk k jt k jkijt
j t GDP j t
s sy P
s ss
y P
η
η
α δ π φ η α φ βηδ π
−−
−=−
⎡ ⎤+ − − + −⎣ ⎦ +−⎡ ⎤+ −⎣ ⎦=
+
∑%
%.
(13)
For estimation national GDP deflators replace TCP . The ijt index refers to expenditure
share category ( , , , )i TELS HARDS SOFTS SERVS= , country ( 1,...,31)j = and time
( 2002,..., 2004)t = . The estimated parameters are: (i) four iα parameters, with the
remaining parameter value inferred by adding-up 1
1nii
α=
=∑ ; (ii) four iβ parameters,
with the remaining parameter constructed as in (i); (iii) four iδ lagged-share parameters;
(iv) three scaling/curvature parameters π , ζ and ξ ; and (v) thirty national initial
condition jη 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 2R are highest for the SOFTS (0.9796), SERVS (0.9788)
and TELS (0.9493) share equations. 2R 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.
17
Table 5. Econometric Fit Statistics Share Equation 2R Durbin-Watson Statistic Telecommunications 0.9493 1.9998 IT Hardware 0.9181 2.1543 IT Software 0.9796 2.2635 IT Services 0.9788 2.1149 Note. Log Likelihood = 2146.689
Table 6 contains parameter estimates for HARDSα , SOFTSα , SERVSα and NON-ICTα 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 is ). An
estimate for TELSα 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 TELSα (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 iβ 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 TELSβ value determined by adding-up.
Comparison of the estimates for NON-ICTα and NON-ICTβ 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 iδ estimates indicate the strength of influence of component i
expenditure on the true price index weights. The impact is evident with TELS( 1.4717)δ = ,
21 This interpretation is based on extrapolation using the estimated curvature of the Engel curves.
18
HARDS ( 1.3874)δ = , SOFTS ( 1.5702)δ = and SERVS ( 1.5155)δ = estimates significant at the 1%
level. The strongest effects are for SOFTS and SERVS, followed by TELS and then
HARDS. Additionally, the estimated parameter ( 0.4813)π = 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,
( 30.8750)ξ = and ( 0.3043)ζ = , which assist in assuring that the estimated share
equations exhibit non-homotheticity while still satisfying fractional share regularity
conditions, are significant. The ,2002jη 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 0η = at the 1% level. However, since
12η ≥ is necessary for regularity of the demand system (at least for low income levels),
the most relevant hypothesis test is whether ½η = , 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.
19
Table 6. Common Cross-Country Parameter Estimates Parameter Estimate Standard Error t statistic(a) Constraint / 0H (b)
t statistic 1α 0.0103 - - 5
21 ii
α=
−∑ 2α 0.0033 0.0012 2.696 3α 0.0005 0.0003 1.861 4α 0.0018 0.0007 2.422 5α 0.9841 0.0077 127.980 0 5: 1H α = –34.1 1β 0.0989 - - 5
21 ii
β=
−∑ 2β 0.0038 0.0069 0.554 3β 0.0001 0.0031 0.040 4β 0.0008 0.0073 0.112 5β 0.8963 0.0279 32.172 0 5: 1H β = –2.8 1δ 1.4717 0.0596 24.699 2δ 1.3874 0.0691 20.079 3δ 1.5702 0.0687 22.861 4δ 1.5155 0.0663 22.875 π 0.4813 0.1601 3.006 ξ 30.8750 12.5680 2.457
ζ 0.3043 - - 1
xL U
x
ee
ζ ζζ
+=
+
x (c) –1.6657 0.9296 –1.792 Note. (a) Column 4 contains t statistics that relate to null hypotheses of the form 0 : 0iH α = . (b) Column 5 lists theoretically-motivated constraints and null hypotheses not of the form 0 : 0iH α = . (c) x is estimated freely, allowing free estimation of ζ within bounds, viz. L Uζ ζ ζ< < where 0.22Lζ = and 0.75Uζ =
20
Table 7. Country-Specific Initial Condition Parameter Estimates T statistic: 0H t statistic: 0H
Country η 0η = ½η =
Country η 0η = ½η = Australia 0.9273 52.5 24.2 Malaysia 0.7931 20.2 7.5 Austria 0.9387 59.2 27.7 Mexico 0.7177 3.5 1.1 Belgium 0.9364 56.3 26.2 Netherlands 0.9345 65.0 30.2 Brazil 0.9245 23.1 10.6 New Zealand 0.9347 52.9 24.6 Canada 0.9060 46.1 20.7 Norway 0.9271 56.1 25.8 Chile 0.7765 16.1 5.7 Russia 0.5571 2.2 0.2 China 0.6661 4.5 1.1 Singapore 0.9323 53.2 24.7 Denmark 0.9323 62.9 29.2 South Africa 0.9047 25.2 11.3 Finland 0.9242 61.2 28.1 South Korea 0.8690 29.0 12.3 France 0.9365 65.7 30.6 Sweden 0.9355 68.6 31.9 Germany 0.9310 57.7 26.7 Switzerland 0.9507 75.8 35.9 Hong Kong 0.9197 40.7 18.6 Taiwan 0.9327 34.7 16.1 Ireland 0.8864 37.0 16.1 Thailand 0.8860 12.6 5.5 Italy 0.9155 43.9 19.9 UK 0.9287 66.3 30.6 Japan 0.9195 60.5 27.6
USA 0.9018 54.0 24.0
The country-specific and time-varying parameter estimates are compared with the time-
varying 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.
21
Table 8. G8 Time-Varying Parameter Estimates Year , ,j t j tφ η− Difference Year , ,j t j tφ η− Difference
Canada Japan
2002 0.9801 – 0.9059 0.0742 2002 0.9773 – 0.9195 0.0578 2003 0.9830 – 0.9199 0.0631 2003 0.9779 – 0.9160 0.0619 2004 0.9827 – 0.9243 0.0584 2004 0.9783 – 0.9235 0.0548
France Russia
2002 0.9866 – 0.9365 0.0501 2002 0.9513 – 0.5571 0.3942 2003 0.9864 – 0.9383 0.0481 2003 0.9731 – 0.7558 0.2173 2004 0.9835 – 0.9383 0.0452 2004 0.9827 – 0.8511 0.1316
Germany UK
2002 0.9862 – 0.9310 0.0552 2002 0.9814 – 0.9287 0.0527 2003 0.9853 – 0.9299 0.0554 2003 0.9835 – 0.9376 0.0459 2004 0.9820 – 0.9293 0.0527 2004 0.9845 – 0.9461 0.0384
Italy USA
2002 0.9871 – 0.9154 0.0717 2002 0.9701 – 0.9018 0.0683 2003 0.9876 – 0.9200 0.0676 2003 0.9722 – 0.9070 0.0652 2004 0.9839 – 0.9124 0.0715 2004 0.9754 – 0.9176 0.0578
Table 9. ATEE Time-Varying Parameter Estimates
Year , ,j t j tφ η− Difference Year , ,j t j tφ η− Difference
China Korea
2002 0.9726 – 0.6661 0.3065 2002 0.9723 – 0.8690 0.1033 2003 0.9912 – 0.9102 0.0810 2003 0.9811 – 0.9155 0.0656 2004 0.9939 – 0.9461 0.0478 2004 0.9824 – 0.9266 0.0416
India Taiwan
2002 1.0000* – 0.5000* 0.5000 2002 0.9869 – 0.9327 0.0542 2003 0.9991 – 0.8910 0.1081 2003 0.9890 – 0.9474 0.0416 2004 0.9992 – 0.9543 0.0448 2004 0.9903 – 0.9560 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
22
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 TCP 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 0, ,i USA tp 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 0, ,i USA tp . In particular, over the period 2001-2004, non-USA
price falls are assumed smaller in magnitude and determined by country-specific
fractional factors jν , viz.
0 0, , , ,1 1 . 1,..., 4i j t j i USA tp p iν ⎡ ⎤= − − =⎣ ⎦ (14)
Data on jν 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 05, ,j tp are obtained for 2001-2004 by solving implicitly for
05, ,j tp in:
5 , , 1 , 0, , , ,41
, , 1 ,1
ˆˆ ˆln ln
ˆ ˆ1i i i j t i j
GDP j t i j tik k j t k jj
s sP p
s s
α δ π
δ π−
=−=
⎛ ⎞⎡ ⎤+ −⎣ ⎦⎜ ⎟=⎜ ⎟⎡ ⎤+ −⎣ ⎦⎝ ⎠
∑∑
. (15)
The 0, ,i j tp series are initial values for the national ICT component price series forecasts.
23
To forecast the true cost index ln TCP 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.
0 0, , , , 1 , 1 , , 1 , , 2 , 2 , , 2ˆ / /i j t i j t j t i j t i j t j t i j tz s y p s y p− − − − − −= −% % . (16)
Asymmetric share disequilibrium arguments are obtained from (16) by decomposing
realized values by the rule:
( ), , , , , , / 2i j t i j t i j tz z z+ = + and ( ), , , , , , / 2i j t i j t i j tz z z− = − . (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 thi price component equation is
0 0, , , , 1 , , , , ,1i j t i j t i j i i j t i i j tp p f g z h z+ −
− ⎡ ⎤= + + +⎣ ⎦ . (18)
Equation (18) arguments are the country-specific trend term ,i jf , and the positive , ,i i j tg z+
and negative , ,i i j th z− 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:
24
, , , , 1 , , , , ,ˆ ˆˆ ˆ1i j t i j t i j i i j t i i j tp p f g z h z+ −
−⎡ ⎤= + + +⎣ ⎦ . (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.:
( )
( )
,
,
ˆ1, , 1 , , , ,, , ,
,, , 1 ,1ˆ1
, , ,
ˆ ˆ ˆ ˆˆ ˆ ˆ ˆ( ) (1 )ln /ˆ ˆ1ˆ1
ˆ1 ln /
j t
j t
i i i j t i j j t j t i j t ij t GDP j tn
j tk k jt k jkijt
j t GDP j t
s sy P
s ss
y P
η
η
α δ π φ η α φ βηδ π
−−
−=−
⎡ ⎤+ − − + −⎣ ⎦ +−⎡ ⎤+ −⎣ ⎦=
+
∑%
%,
(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
5 , , 1 ,, , , ,41
, , 1 ,1
ˆˆ ˆˆ ˆln lnˆ ˆ1
i i i j t i jTC j t i j ti
k k j t k jj
s sP p
s s
α δ π
δ π−
=−=
⎛ ⎞⎡ ⎤+ −⎣ ⎦⎜ ⎟=⎜ ⎟⎡ ⎤+ −⎣ ⎦⎝ ⎠
∑∑
. (21)
Replacing GDPP with TCP 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 ,j ty% ). A separate steady growth scenario for nominal GDP and population are
25
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
2004 2004x x= by estimating the constant growth regression ( 2004)2004
xg ttx e x−= over the sample period
2000-2004. Given country specific regression estimates ˆ xg , steady growth scenarios over the forecast
period for each country are provided, viz. ˆ ( 2004)2004ˆ xg t
tx e x−= , 2005,..., 2015t = .
26
is 12.5% p.a. A more recent trend is the migration of innovation functions from high-
income 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 iδ and the scale/curvature estimates for π , ζ and ξ
into (4), (9) and (11), and φ and η into (13) with the iα and iβ , estimates. The predicted
shares ijts , are obtained from (13) within sample using actual GDP deflator data for GDPP .
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
27
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 5.56 2.33 8.62 8.61 4.86 2003 3.92 1.50 4.41 4.65 0.35 2004 1.67 4.26 0.00 0.53 0.25 France 2002 1.23 10.38 10.00 9.48 5.53 2003 11.67 5.13 6.82 7.31 8.48 2004 0.91 0.82 1.00 0.00 0.44 Germany 2002 1.65 9.63 5.48 5.30 3.55 2003 11.19 9.68 7.69 9.14 9.93 2004 1.57 7.06 0.96 0.97 1.32 Italy 2002 1.44 18.52 6.98 7.08 5.16 2003 10.93 7.61 9.26 7.30 9.09 2004 2.58 3.09 3.28 2.61 2.80 Japan 2002 12.56 19.23 6.06 7.07 12.31 2003 1.50 4.55 5.41 5.78 3.10 2004 1.21 9.09 7.14 4.71 2.04 Russia 2002 4.15 0.00 0.00 0.00 2.85 2003 3.26 0.00 0.00 0.00 1.81 2004 3.97 1.37 7.14 10.26 4.41 UK 2002 4.76 10.95 6.98 5.56 6.49 2003 0.88 6.02 2.00 2.04 0.65 2004 2.45 0.00 0.00 0.72 0.73 USA 2002 3.29 4.63 3.23 3.13 3.39 2003 8.83 6.42 1.06 2.21 2.79 2004 6.35 7.08 1.02 0.44 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 ICTcomponent 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
28
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 5.74 5.00 11.11 17.65 5.94 2003 10.92 1.57 4.17 20.00 5.40 2004 12.59 0.84 0.00 15.79 4.66 India 2002 4.55 5.71 8.33 11.11 5.59 2003 5.56 5.49 5.88 7.69 1.66 2004 9.85 3.42 4.55 12.70 5.49 Korea 2002 0.25 7.60 5.00 13.79 3.28 2003 0.46 2.33 0.00 4.48 0.28 2004 2.56 4.05 3.57 3.80 1.94 Taiwan 2002 4.47 4.20 3.85 2.63 2.49 2003 7.71 3.94 6.67 4.65 6.61 2004 10.91 0.00 5.71 4.00 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 ICTcomponent APEs.
Table 12. Directional Change Prediction Accuracy Summary, 2002–2004 Sample Observations TELS HARDS SOFTS SERVS ICTS G8 24 19 14 20 20 20 (79.2) (58.3) (83.3) (83.8) (83.3) ATEE 12 11 10 12 12 12 (91.7) (83.3) (100.0) (100.0) (100.0) Sample 93 76 49 83 82 82 (81.7) (52.7) (89.3) (88.2) (88.2) Note. Percentages of observations where actual and predicted share directional changes match in parentheses.
29
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.
30
Table 13. G8 Beyond-Sample Dynamic Forecasts, 2005–2014 Country Year TELS HARDS SOFTS SERVS ICTS Canada 2005 0.0329 0.0144 0.0086 0.0210 0.0769 2006 0.0362 0.0151 0.0101 0.0239 0.0853 2007 0.0399 0.0158 0.0118 0.0272 0.0947 2008 0.0441 0.0165 0.0139 0.0310 0.1055 2009 0.0488 0.0172 0.0162 0.0352 0.1174 2010 0.0539 0.0177 0.0189 0.0397 0.1302 2011 0.0593 0.0182 0.0220 0.0446 0.1441 2012 0.0648 0.0185 0.0254 0.0499 0.1586 2013 0.0705 0.0189 0.0295 0.0560 0.1749 2014 0.0759 0.0191 0.0340 0.0623 0.1913
France 2005 0.0366 0.0127 0.0116 0.0328 0.0937 2006 0.0404 0.0131 0.0134 0.0369 0.1038 2007 0.0447 0.0135 0.0155 0.0413 0.1116 2008 0.0493 0.0138 0.0179 0.0462 0.1241 2009 0.0542 0.0141 0.0205 0.0514 0.1374 2010 0.0593 0.0142 0.0235 0.0570 0.1517 2011 0.0644 0.0143 0.0269 0.0630 0.1672 2012 0.0695 0.0143 0.0306 0.0694 0.1837 2013 0.0745 0.0145 0.0358 0.0784 0.2071 2014 0.0786 0.0145 0.0412 0.0873 0.2303 Germany 2005 0.0350 0.0174 0.0118 0.0229 0.0871 2006 0.0386 0.0177 0.0134 0.0252 0.0949 2007 0.0425 0.0180 0.0152 0.0278 0.1035 2008 0.0469 0.0182 0.0172 0.0305 0.1128 2009 0.0516 0.0183 0.0195 0.0334 0.1228 2010 0.0566 0.0184 0.0220 0.0364 0.1334 2011 0.0618 0.0184 0.0247 0.0397 0.1446 2012 0.0672 0.0183 0.0277 0.0430 0.1562 2013 0.0726 0.0181 0.0310 0.0466 0.1683 2014 0.0782 0.0180 0.0351 0.0509 0.1822
Italy 2005 0.0292 0.0101 0.0069 0.0871 0.1333 2006 0.0317 0.0104 0.0079 0.0949 0.1449 2007 0.0345 0.0107 0.0090 0.1035 0.1577 2008 0.0378 0.0110 0.0102 0.0964 0.1554 2009 0.0415 0.0114 0.0117 0.1046 0.1692 2010 0.0457 0.0117 0.0134 0.1132 0.1840 2011 0.0503 0.0120 0.0154 0.1225 0.2002 2012 0.0553 0.0122 0.0177 0.1320 0.2172 2013 0.0607 0.0124 0.0202 0.1423 0.2356 2014 0.0664 0.0125 0.0230 0.1549 0.2568
31
Table 13. G8 Beyond-Sample Dynamic Forecasts, 2005–2014 Country Year TELS HARDS SOFTS SERVS ICTS Japan 2005 0.0538 0.0143 0.0045 0.0205 0.0931 2006 0.0586 0.0143 0.0050 0.0222 0.1001 2007 0.0638 0.0144 0.0055 0.0242 0.1079 2008 0.0694 0.0144 0.0061 0.0262 0.0729 2009 0.0754 0.0144 0.0068 0.0285 0.0782 2010 0.0816 0.0144 0.0075 0.0308 0.0835 2011 0.0881 0.0143 0.0083 0.0332 0.0890 2012 0.0946 0.0141 0.0091 0.0356 0.0944 2013 0.1010 0.0138 0.0100 0.0380 0.0998 2014 0.1074 0.0134 0.0109 0.0403 0.1049
Russia 2005 0.0294 0.0081 0.0016 0.0047 0.0438 2006 0.0320 0.0092 0.0020 0.0058 0.0490 2007 0.0352 0.0106 0.0025 0.0073 0.0556 2008 0.0393 0.0121 0.0032 0.0091 0.0335 2009 0.0444 0.0139 0.0040 0.0115 0.0409 2010 0.0505 0.0158 0.0051 0.0144 0.0497 2011 0.0575 0.0179 0.0065 0.0178 0.0600 2012 0.0655 0.0200 0.0082 0.0219 0.0720 2013 0.0741 0.0220 0.0102 0.0265 0.0852 2014 0.0831 0.0238 0.0125 0.0315 0.0993
UK 2005 0.0407 0.0145 0.0136 0.0318 0.1006 2006 0.0450 0.0151 0.0159 0.0362 0.1122 2007 0.0497 0.0156 0.0187 0.0411 0.1251 2008 0.0548 0.0162 0.0219 0.0467 0.1396 2009 0.0601 0.0166 0.0256 0.0528 0.1551 2010 0.0655 0.0170 0.0299 0.0595 0.1719 2011 0.0707 0.0173 0.0347 0.0668 0.1895 2012 0.0753 0.0174 0.0399 0.0742 0.2068 2013 0.0791 0.0166 0.0435 0.0780 0.2172 2014 0.0825 0.0159 0.0475 0.0824 0.2283
USA 2005 0.0462 0.0111 0.0106 0.0240 0.0919 2006 0.0503 0.0111 0.0117 0.0256 0.0987 2007 0.0549 0.0111 0.0129 0.0276 0.0792 2008 0.0599 0.0111 0.0144 0.0299 0.0853 2009 0.0654 0.0112 0.0161 0.0324 0.0921 2010 0.0712 0.0112 0.0181 0.0353 0.0999 2011 0.0774 0.0112 0.0204 0.0384 0.1084 2012 0.0841 0.0112 0.0232 0.0424 0.1192 2013 0.0904 0.0111 0.0260 0.0459 0.1289 2014 0.0955 0.0106 0.0280 0.0478 0.1342
Note. ICTS is the sum of the individual predicted ICT component shares.
32
Table 14 ATEE Beyond-Sample Dynamic Forecasts, 2005–2014 Country Year TELS HARDS SOFTS SERVS ICTS
China 2005 0.0312 0.0292 0.0044 0.0048 0.0696 2006 0.0344 0.0360 0.0060 0.0063 0.0827 2007 0.0382 0.0438 0.0081 0.0081 0.0982 2008 0.0424 0.0524 0.0109 0.0103 0.1160 2009 0.0468 0.0615 0.0144 0.0130 0.1357 2010 0.0513 0.0705 0.0187 0.0161 0.1566 2011 0.0556 0.0789 0.0236 0.0195 0.1776 2012 0.0594 0.0862 0.0292 0.0231 0.1979 2013 0.0624 0.0920 0.0353 0.0267 0.2164 2014 0.0647 0.0962 0.0417 0.0303 0.2329
India 2005 0.0452 0.0145 0.0030 0.0085 0.0712 2006 0.0535 0.0179 0.0041 0.0115 0.0870 2007 0.0634 0.0218 0.0056 0.0154 0.1062 2008 0.0747 0.0259 0.0075 0.0202 0.1283 2009 0.0867 0.0301 0.0099 0.0257 0.1524 2010 0.0989 0.0340 0.0127 0.0320 0.1776 2011 0.1103 0.0373 0.0158 0.0386 0.2020 2012 0.1202 0.0398 0.0192 0.0453 0.2245 2013 0.1283 0.0415 0.0227 0.0519 0.2444 2014 0.1343 0.0422 0.0263 0.0581 0.2609
Korea 2005 0.0519 0.0184 0.0032 0.0091 0.0826 2006 0.058 0.0198 0.0038 0.0107 0.0923 2007 0.0651 0.0213 0.0045 0.0125 0.1034 2008 0.0731 0.0229 0.0054 0.0146 0.1160 2009 0.0820 0.0245 0.0064 0.0170 0.1299 2010 0.0915 0.0259 0.0076 0.0196 0.1446 2011 0.1013 0.0272 0.0090 0.0224 0.1599 2012 0.1110 0.0281 0.0104 0.0254 0.1749 2013 0.1205 0.0287 0.0120 0.0283 0.1895 2014 0.1293 0.0290 0.0137 0.0313 0.2033
Taiwan 2005 0.0512 0.0160 0.0045 0.0061 0.0778 2006 0.0587 0.0181 0.0057 0.0075 0.0900 2007 0.0675 0.0205 0.0072 0.0091 0.1043 2008 0.0776 0.0229 0.0091 0.0111 0.1207 2009 0.0885 0.0252 0.0113 0.0133 0.1383 2010 0.0999 0.0273 0.0139 0.0157 0.1568 2011 0.1114 0.0292 0.0168 0.0182 0.1756 2012 0.1224 0.0305 0.0200 0.0209 0.1938 2013 0.1326 0.0314 0.0234 0.0234 0.2108 2014 0.1415 0.0318 0.0269 0.0259 0.2261
Note. ICT is determined residually by adding up. Within-sample prediction errors are the absolute percentage deviation from actual values.
33
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.
34
Table 17. Directional Change Prediction Accuracy Summary, 2005 Sample Observations TELS HARDS SOFTS SERVS ICTS G8 8 4 4 7 6 4 (50.0) (50.0) (87.5) (75.0) (50.0) ATEE 4 2 3 4 4 4 (50.0) (75.0) (100.0) (100.0) (100.0) Sample 31 22 19 30 27 26 (71.0) (61.3) (96.8) (87.1) (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.
35
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
36
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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Appendix Table 1. Telecommunications Price Equation Parameter Estimates Parameter Estimate Standard Error t statistic
National Trend
CANADAf –0.0846 0.0081 –10.4590 MEXICOf –0.0968 0.0077 –12.6270 USAf –0.2053 0.0095 –21.5250 BRAZILf –0.0913 0.0078 –11.6840 CHILEf –0.0818 0.0083 –9.8155 AUSTRIAf –0.0596 0.0081 –7.3935 BELGIUMf –0.0613 0.0084 –7.3077 DENMARKf –0.0623 0.0082 –7.5606 FINLANDf –0.1363 0.0088 –15.5350 FRANCEf –0.0694 0.0078 –8.8434 GERMANYf –0.0517 0.0092 –5.6304 IRELANDf –0.1539 0.0083 –18.5240 ITALYf –0.0498 0.0082 –6.0742 NETHERLANDSf –0.0667 0.0081 –8.1929 NORWAYf –0.1308 0.0096 –13.6210 SWEDENf –0.0773 0.0081 –9.5205 SWITZERLANDf –0.0793 0.0092 –8.6081 UKf –0.0880 0.0099 –8.9222 RUSSIAf –0.0401 0.0075 –5.3166 AUSTRALIAf –0.0914 0.0084 –10.9220 CHINAf –0.1403 0.0073 –19.1170 HONG KONGf –0.1401 0.0094 –14.9050 INDIAf –0.0997 0.0088 –11.3480 JAPANf –0.1060 0.0106 –9.9849 MAYAYSIAf –0.0605 0.0079 –7.6984 NEW ZEALANDf –0.0711 0.0090 –7.8851 SRI LANKAf –0.1330 0.0115 –11.5950 KOREAf –0.0823 0.0084 –9.8360 TAIWANf –0.1199 0.0088 –13.6990 THAILANDf –0.0944 0.0077 –12.2500 SOUTH AFRICAf –0.0278 0.0080 –3.4869
Positive Disequilibrium
g + 0.1005 0.0114 8.7971
Negative Disequilibrium g − –0.0013 0.0286 –0.0469
41
Appendix Table 2. Computer Hardware Price Equation Parameter Estimates Parameter Estimate Standard Error t statistic
National Trend
CANADAf –0.0101 0.5819 –0.0173 MEXICOf –0.0164 0.5407 –0.0304 USAf –0.0297 0.0263 –1.1296 BRAZILf –0.0139 0.0859 –0.1614 CHILEf –0.0133 0.0259 –0.5142 AUSTRIAf –0.0042 0.7734 –0.0054 BELGIUMf –0.0045 0.7365 –0.0062 DENMARKf –0.0047 0.7465 –0.0063 FINLANDf –0.0150 0.1496 –0.1006 FRANCEf –0.0071 0.7471 –0.0095 GERMANYf –0.0053 0.8126 –0.0065 IRELANDf –0.0190 0.0459 –0.4143 ITALYf –0.0051 0.2204 –0.0232 NETHERLANDSf –0.0048 0.5901 –0.0081 NORWAYf –0.0150 0.1993 –0.0751 SWEDENf –0.0053 0.6573 –0.0081 SWITZERLANDf –0.0028 0.2356 –0.0120 UKf –0.0071 0.2388 –0.0298 RUSSIAf –0.0062 0.0136 –0.4546 AUSTRALIAf –0.0086 0.6608 –0.0131 CHINAf –0.0238 0.0106 –2.2366 HONG KONGf –0.0192 0.0188 –1.0214 INDIAf –0.0178 0.0438 –0.4059 JAPANf –0.0190 0.1274 –0.1490 MAYAYSIAf –0.0097 0.1886 –0.0513 NEW ZEALANDf –0.0063 0.4002 –0.0158 SRI LANKAf –0.0138 0.3869 –0.0357 KOREAf –0.0103 0.3460 –0.0298 TAIWANf –0.0154 0.1171 –0.1317 THAILANDf –0.0161 0.7103 –0.0227 SOUTH AFRICAf –0.0034 0.0132 –0.2603
Positive Disequilibrium
g + –0.1022 0.0521 –1.9607
Negative Disequilibrium g − –0.0310 0.0163 –1.9038
42
Appendix Table 3. Computer Software Price Equation Parameter Estimates Parameter Estimate Standard Error t statistic
National Trend
CANADAf –0.0075 0.5323 –0.0141 MEXICOf –0.0294 0.7176 –0.0410 USAf –0.0314 0.0550 –0.5703 BRAZILf –0.0247 0.0791 –0.3128 CHILEf –0.0238 0.5401 –0.0441 AUSTRIAf 0.0086 0.8553 0.0100 BELGIUMf 0.0059 0.7195 0.0082 DENMARKf 0.0159 0.8381 0.0190 FINLANDf –0.0102 0.9486 –0.0107 FRANCEf 0.0031 0.7500 0.0041 GERMANYf 0.0059 0.3409 0.0174 IRELANDf –0.0273 0.0853 –0.3199 ITALYf –0.0043 0.4258 –0.0102 NETHERLANDSf 0.0167 0.8798 0.0190 NORWAYf –0.0088 0.5848 –0.0151 SWEDENf 0.0136 0.7438 0.0182 SWITZERLANDf 0.0298 0.4369 0.0682 UKf 0.0079 0.7338 0.0107 RUSSIAf –0.0113 0.0064 –1.7535 AUSTRALIAf –0.0016 0.9124 –0.0018 CHINAf –0.0430 0.0066 –6.5506 HONG KONGf –0.0310 0.0568 –0.5447 INDIAf –0.0309 0.2438 –0.1266 JAPANf –0.0259 0.5741 –0.0451 MAYAYSIAf –0.0164 0.3243 –0.0506 NEW ZEALANDf –0.0054 0.5165 –0.0105 SRI LANKAf –0.0126 0.5781 –0.0219 KOREAf –0.0190 0.4654 –0.0409 TAIWANf –0.0273 0.1519 –0.1797 THAILANDf –0.0280 0.7124 –0.0393 SOUTH AFRICAf –0.0036 0.0129 –0.2754
Positive Disequilibrium
g + –0.3997 0.0687 –5.8183
Negative Disequilibrium g − –1.9026 1.6560 –1.1489
43
Appendix Table 4. IT Services Price Equation Parameter Estimates Parameter Estimate Standard Error t statistic
National Trend
CANADAf 0.0164 0.2319 0.0707 MEXICOf 0.0042 0.6895 0.0061 USAf 0.0152 0.0379 0.4004 BRAZILf 0.0068 0.0528 0.1283 CHILEf 0.0036 0.6445 0.0055 AUSTRIAf 0.0173 0.8163 0.0211 BELGIUMf 0.0178 0.7330 0.0243 DENMARKf 0.0255 0.5412 0.0471 FINLANDf 0.0163 0.5234 0.0311 FRANCEf 0.0277 0.8281 0.0334 GERMANYf 0.0176 0.8432 0.0209 IRELANDf 0.0099 0.0522 0.1906 ITALYf 0.0119 0.4549 0.0261 NETHERLANDSf 0.0186 0.7611 0.0244 NORWAYf 0.0219 0.7331 0.0299 SWEDENf 0.0307 0.8148 0.0377 SWITZERLANDf 0.0286 0.5625 0.0508 UKf 0.0303 0.8620 0.0352 RUSSIAf 0.0022 0.0097 0.2269 AUSTRALIAf 0.0190 0.7254 0.0262 CHINAf 0.0057 0.0095 0.5958 HONG KONGf 0.0070 0.0658 0.1060 INDIAf 0.0042 0.3625 0.0116 JAPANf 0.0078 0.7636 0.0103 MAYAYSIAf 0.0031 0.1178 0.0261 NEW ZEALANDf 0.0110 0.5509 0.0199 SRI LANKAf 0.0151 0.6253 0.0241 KOREAf 0.0067 0.5655 0.0118 TAIWANf 0.0073 0.3670 0.0200 THAILANDf 0.0041 0.6537 0.0063 SOUTH AFRICAf 0.0038 0.0135 0.2829
Positive Disequilibrium
g + –0.2085 0.0461 –4.5178
Negative Disequilibrium g − 0.0998 0.1684 0.5926
44
Appendix Table 5. Rest of GDP Price Equation Parameter Estimates Parameter Estimate Standard Error t statistic
National Trend
CANADAf 0.0300 0.0130 2.3189 MEXICOf 0.0744 0.0087 8.5985 USAf 0.0287 0.0092 3.1122 BRAZILf 0.1182 0.0089 13.3240 CHILEf 0.0657 0.0148 4.4496 AUSTRIAf 0.0200 0.0096 2.0962 BELGIUMf 0.0231 0.0116 1.9856 DENMARKf 0.0199 0.0141 1.4132 FINLANDf 0.0130 0.0101 1.2867 FRANCEf 0.0210 0.0146 1.4426 GERMANYf 0.0116 0.0117 0.9863 IRELANDf 0.0393 0.0127 3.0979 ITALYf 0.0304 0.0101 3.0080 NETHERLANDSf 0.0240 0.0118 2.0289 NORWAYf 0.0226 0.0093 2.4249 SWEDENf 0.0192 0.0108 1.7822 SWITZERLANDf 0.0112 0.0128 0.8804 UKf 0.0346 0.0118 2.9265 RUSSIAf 0.1714 0.0139 12.3540 AUSTRALIAf 0.0380 0.0113 3.3789 CHINAf 0.0346 0.0086 4.0298 HONG KONGf –0.0345 0.0102 –3.3885 INDIAf 0.0433 0.0117 3.7187 JAPANf –0.0106 0.0124 –0.8571 MAYAYSIAf 0.0525 0.0138 3.8099 NEW ZEALANDf 0.0260 0.0110 2.3637 SRI LANKAf 0.0208 0.0109 1.9150 KOREAf 0.0359 0.0145 2.4785 TAIWANf –0.0112 0.0101 –1.1033 THAILANDf 0.0285 0.0103 2.7696 SOUTH AFRICAf 0.0786 0.0101 7.8104
Positive Disequilibrium
g + –0.0026 0.0075 –0.3490
Negative Disequilibrium g − –0.0046 0.0086 –0.5417
45
Appendix Table 6. G8 Directional Change Prediction Accuracy, 2002–2004 Country Year TELS HARDS SOFTS SERVS ICTS
Canada 2002 1 0 0 0 1 2003 1 1 1 1 1 2004 1 1 1 1 1 France 2002 1 0 1 1 1 2003 1 0 1 1 1 2004 1 1 1 1 1 Germany 2002 1 0 1 1 1 2003 1 0 1 1 1 2004 1 1 1 1 1 Italy 2002 1 0 1 1 1 2003 1 0 1 1 1 2004 1 1 1 1 1 Japan 2002 0 1 0 0 0 2003 0 0 0 0 0 2004 1 1 1 1 1 Russia 2002 0 1 1 1 1 2003 1 1 1 1 1 2004 1 1 1 1 1 UK 2002 1 1 1 1 1 2003 1 1 1 1 1 2004 1 1 1 1 1 USA 2002 0 0 0 0 0 2003 0 0 1 1 0 2004 1 1 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 1 1 1 1 1 2003 0 1 1 1 1 2004 1 1 1 1 1 India 2002 1 1 1 1 1 2003 1 1 1 1 1 2004 1 1 1 1 1 Korea 2002 1 0 1 1 1 2003 1 1 1 1 1 2004 1 1 1 1 1 Taiwan 2002 1 0 1 1 1 2003 1 1 1 1 1 2004 1 1 1 1 1 Note. = 1, if actual and predicted share directional change matches; = 0, otherwise.
46
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.