WPS7974
Policy Research Working Paper 7974
Assessing the Accuracy of Electricity
Demand Forecasts in Developing Countries
Jevgenijs Steinbuks
Development Research Group
Environment and Energy Team
February 2017
Policy Research Working Paper 7974
Abstract
This study assesses the accuracy of time-series econometric electricity demand grows at an exogenous rate or is propor-
methods in forecasting electricity demand in developing tional to real gross domestic product growth. The quality
countries. The analysis of historical time series for 106 of the forecasts, however, diminishes for the countries and
developing countries over 1960–2012 demonstrates that regions, where rapid economic and structural transforma-
econometric forecasts are highly accurate for the majority tion or exposure to conflicts and environmental disasters
of these countries. These forecasts significantly outperform makes it difficult to establish stable historical demand trends.
predictions of simple heuristic models, which assume that
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contacted at jsteinbuks@worldbank.org.
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Assessing the Accuracy of Electricity Demand
Forecasts in Developing Countries∗
Jevgenijs Steinbuks
Development Research Group, The World Bank
JEL: Q47
Keywords: electricity demand forecasting, time-series econometric mod-
els
∗ Acknowledgments: The author thanks Deb Chattopadhyay, Vivien Foster, Arthur
Kochnakyan, Herman Stekler, Mike Toman, Joeri de Wit and the seminar participants at
the Oxford Institute for Energy Studies and the World Bank for their helpful suggestions and
comments. Responsibility for the content of the paper is the author’s alone and does not
necessarily reﬂect the views of his institution or member countries of the World Bank.
1 Introduction
Forecasting the future demand of electricity is an important issue for the utility
companies, policy-makers, and private investors in developing countries. Reli-
able electricity demand forecasts are essential for long-term planning of future
generation facilities and transmission augmentation.1 As excess power is not
easily storable, underestimating electricity demand results in supply shortages
and forced power outages, which have detrimental eﬀects on productivity and
economic growth (Calderón and Servén 2004; Fisher-Vanden, et al., 2015; All-
cott et al., 2016). However, overestimating demand may result in overinvestment
in generation capacity and ultimately even higher electricity prices because, at
least for traditional utilities, investment costs need to be recovered to maintain
ﬁnancial viability.
Forecasting long-term electricity demand is a diﬃcult problem as it is subject
to a range of uncertainties, which include, among other factors, underlying
population growth, changing technology, economic conditions, and prevailing
weather conditions (and the timing of those conditions). This problem can be
particularly challenging in developing countries, where data are often elusive,
political inﬂuences are often brought to bear, and historical electricity demand
itself is more volatile owing to macroeconomic or political instability.
Despite the vast signiﬁcance of having accurate and reliable electricity de-
mand forecasts for utilities, investors and policy makers, the electricity demand
forecasting literature comprises of a handful of studies. Table 1 summarizes
this limited research on electricity production and consumption econometric
forecasts.2 Most of the studies focus on developed economies. Only ﬁve studies
(Abdel-Aal and Al-Garni 1997, Sadownik and Barbosa 1999, Saab et al. 2001,
Inglesi 2010, and El-Shazly 2013) forecast electricity demand for developing
countries (Saudi Arabia, Brazil, Lebanon, South Africa, and the Arab Republic
of Egypt, respectively). As regards data frequency, these studies are almost
evenly split between short-term forecasts based on monthly data and long-term
1 Though not the focus of this study it is worth noting that reliable electricity demand
forecasts are also inportant for short-term load allocation because they help the utilities to
optimize the amount of generated power, i.e., maximize their revenue and minimize operational
(including environmental) costs.
2 This summary focuses on medium- to long-term econometric projections and does not
include high-frequency forecast studies of day-ahead electricity demand. It also omits non-
econometric forecast studies based on soft computing techniques such as fuzzy logic, genetic
algorithm, and neural networks, and bottom-up computational models such as MARKAL
and LEAP. For a comprehensive review of these methods and their applications to energy
forecasting, please refer to Suganthi and Samuel (2012).
2
Table 1: Summary of Previous Studies of Electricity Consumption Forecasts
Author Country Frequency Sample Forecast Method
Abdel-Aal and Al-Garni (1997) Saudi Arabia Monthly 1987-1993 12 months ARIMA
Bianco et al. (2009) Italy Yearly 1970-2007 5 years ARDL
Baltagi et al. (2002) United States Yearly 1970-1990 1-5 years ARDL
Harris and Liu (1993) United States Monthly 1969-1990 3 years ARIMA
Dilaver and Hunt (2011) Turkey Yearly 1960-2008 12 years UCM
El-Shazly (2013) Egypt, Arab Rep. Yearly 1982-2010 2 years ARDL / ECM
Inglesi (2010) South Africa Yearly 1980-2005 15 years ECM
Joutz et al. (1995) United States Monthly 1977-1991 10 months VAR / VECM
3
Mohamed and Bodger (2005) New Zealand Yearly 1965-1999 15 years Linear Regression
Narayan and Smyth (2005) Australia Yearly 1966-1999 10 years ARDL / ECM
Pao (2009) Taiwan, China Yearly 1980-2007 1-6 years State Space Models
Saab et al. (2001) Lebanon Monthly 1970-1999 10 years ARIMA
Sadownik and Barbosa (1999) Brazil Monthly 1990-1994 1 month UCM
Tserkezos (1992) Greece Monthly 1975-1989 24 months ARIMA
Zachariadis (2010) Cyprus Yearly 1960-2007 43 years ARDL
Notes. ARIMA: Autoregressive integrated moving average model. ARDL: Autoregressive distributed
lag model. VAR: Vector autoregressive model. (V)ECM: (Vector) error correction model.
UCM: Unobserved components model.
forecasts based on yearly data. The largest part of these studies employs uni-
variate time series methods with exogenous regressors. Few other studies use
multivariate time series methods or state space econometric models. With the
exception of Baltagi et al. (2002), none of these studies attempt to compare the
forecast accuracy of diﬀerent forecasting models.3 Given signiﬁcant variation in
country coverage, time frame, forecast horizons, and econometric methods, the
results of these studies are diﬃcult, if not impossible, to reconcile.
The purpose of this study is to assess the accuracy of diﬀerent econometric
methods in forecasting electricity demand in developing countries. Based on the
time series econometrics literature we ﬁrst develop an econometric framework for
forecasting electricity demand. We then obtain a number of electricity demand
forecasts based on historical time series of 106 developing countries over the
period 1960-2012. Finally, we evaluate the accuracy of the electricity demand
forecasts resulting from diﬀerent econometric methods and model speciﬁcations.
Our results demonstrate that time-series econometric forecasts yield highly
accurate predictions for the evolution of electricity demand in the majority of
developing countries. The forecasts based on the best performing method do
signiﬁcantly improve over the predictions of two heuristical models, commonly
used by development practitioners, which assume that electricity demand grows
at an exogenous rate or is proportional to real GDP growth. The quality of the
forecasts, however, diminishes for the countries and regions, where rapid eco-
nomic and structural transformation or exposure to conﬂicts and environmental
disasters makes it diﬃcult to establish stable historical demand trends.
2 Forecasting Methods and Accuracy Tests
This section brieﬂy documents the econometric framework for forecasting elec-
tricity demand and evaluating its forecast accuracy. It ﬁrst discusses implica-
tions of the stationarity property on forecastability of electricity demand time
series. It then summarizes econometric methods employed for forecasting elec-
tricity demand. Finally, it describes measures of forecast errors for assessing
forecast accuracy and comparing the quality of diﬀerent forecasting methods.
3 Baltagi et al. (2002) only focus on a small set of estimators within Autoregressive dis-
tributed lag (ARDL) model.
4
2.1 Testing for Data Stationarity
As electricity generation and consumption data series are typically nonstation-
ary (i.e., their mean and/or variance are varying with time), an important aspect
of forecasting model selection concerns the appropriate treatment of nonstation-
ary data. The diﬀerence-stationary processes contain stochastic trends that are
integrated of order k, so that diﬀerencing k times yields a stationary series.
The diﬀerence stationary processes have poor forecastability as forecast error
variances grow linearly in the forecast horizon for these processes (Clements
and Hendry 2001). Establishing whether the data generating process is the
diﬀerence stationary one is therefore of particular concern.
To test whether the data are the diﬀerence stationary we perform the modi-
ﬁed Dickey–Fuller test (also known as the DF-GLS test) proposed by Elliott et
al (1996).4 The test involves ﬁtting a regression of the form
k
X
y t = ↵ + yt 1+ k yt k + "t (1)
i=1
where yt are the electricity production series, "t is the error term, ↵,
and are the parameters to be estimated, k is the lag order of time t, and
is the diﬀerence operator. The DF-GLS test is performed on detrended data
by Generalized Least Squares (GLS) and involves testing the null hypothesis
H0 : = 0. If the test cannot reject the null hypothesis, this implies that yt is
a random walk, possibly with drift and the data are diﬀerence stationary. Our
choice of lag order in regression (1) is based on the modiﬁed Akaike information
criterion developed by Ng and Perron (2000).
2.2 Forecasting Methods
Table 2 summarizes econometric methods employed for forecasting electricity
demand. A brief formal representation of these methods is documented in Ap-
pendix A.1. For advanced textbook treatment of these methods, please refer to
Harvey (1989), Hamilton (1994), Lütkepohl (2005), and Enders (2010).
4 For robustness purposes we have also performed other tests for data stationarity, such as
Augmented Dickey–Fuller test and Phillips and Perron (1988) unit root test. The results were
little changed.
5
Table 2: Methods for Assessing Electricity Production Forecasts
Method Description
VAR3/VECM3 Trivariate vector autoregressive model /
Vector error correction model
VAR2/VECM2 Bivariate vector autoregressive model /
Vector error correction model
ARIMA Autoregressive integrated moving average model
GARCH Generalized autoregressive conditional
heteroskedasticity model
Holt-Winters Holt–Winter’s linear smoothing model
UCM-RWD Unobserved components model:
Random walk with a drift
UCM-LLTM Unobserved components model:
Local level with deterministic trend
UCM-RWSC Unobserved components model:
Random walk with a stochastic cycle
These methods can be broadly grouped into three categories. Vector autore-
gressive model (VAR) and Vector error correction model (VECM) are the mul-
tivariate time series forecasting methods that are most appropriate when elec-
tricity demand is closely related to other macroeconomic fundamentals. Over
the long term, electricity demand is inﬂuenced by economic and demographic
growth, changes in energy intensity, and shifting input prices. Among these
drivers, gross domestic product (GDP) is often the strongest correlate of elec-
tricity demand (Steinbuks et al., 2017). And the data for input prices and
structural fundamentals aﬀecting energy intensity are scarce for most of the de-
veloping countries. In light of the above, we employ trivariate methods, which
assume that a country’s electricity demand is co-determined by GDP and popu-
lation growth and bivariate methods, which assume that the country’s electricity
demand is co-determined by its GDP growth only.
Autoregressive integrated moving average (ARIMA) and generalized autore-
gressive conditional heteroskedasticity (GARCH) models are univariate time se-
ries forecasting methods that work best when other drivers of electricity demand
are exogenous and have a small eﬀect on electricity demand. These models as-
sume that the best predictors of electricity demand are its past realizations.
Additionally, the GARCH model is particularly helpful for forecasting electric-
ity demand in countries, where electricity supply is highly volatile.
Finally, Holt-Winters and unobserved components methods are the most
suitable for forecasting electricity demand that evolves around a linear trend,
6
which can be either deterministic or stochastic. Additionally, the random walk
with a stochastic cycle model (RWSC) may further improve forecasting accuracy
in countries, where electricity demand exhibits cyclical behavior.
Autoregressive time series models (both multivariate and univariate) and the
Holt-Winters method are applied to forecast both stationary and non-stationary
electricity demand time series. Unobserved components models are only applied
to forecast non-stationary electricity demand series. For all autoregressive time
series models, we also estimate diﬀerent speciﬁcations, assuming diﬀerent lag
structures (for details, please refer to Appendix). Altogether we estimate 33
model speciﬁcations for stationary electricity demand series and 36 model spec-
iﬁcations for non-stationary series.
2.3 Measures of Forecast Accuracy of Individual Methods
We employ two popular measures of forecast errors for assessing forecast accu-
racy of an individual method: symmetric mean absolute percent error (sMAPE)
and root mean squared error (RMSE). sMAPE is deﬁned as the average absolute
percent error of electricity consumption forecasts, y F , minus actuals divided by
the average of absolute values of forecasts and actuals across all forecasts made
for a given horizon:
T
" #
1X F
yt yt
sM AP E = F
(2)
T t=1 yt + |yt | /2
By using the symmetric MAPE, we avoid the problem of large errors when
the actual values are close to zero, and the problem of the large diﬀerence
between the absolute percentage errors when actuals are greater than forecasts
and vice versa (Makridakis and Hibon, 2000).
The RMSE is a quadratic scoring rule which measures the average mag-
nitude of the error. RMSE is deﬁned as the diﬀerence between forecast and
corresponding observed values that are each squared and then averaged over
the sample:
s
PT F 2
t=1 yt yt
RM SE = (3)
T
As forecast errors are squared before they are averaged, the RMSE gives a
relatively high weight to larger errors. The RMSE is, therefore, most useful
when large errors are particularly undesirable.
7
2.4 Measures of Forecast Accuracy of Competing Meth-
ods
An important question that occurs in assessing the accuracy of electricity de-
mand forecasts is how to formally compare the quality of diﬀerent forecasting
methods. Makridakis and Hibon (2000, p. 457) argue that “the absolute ac-
curacy of the various methods is not as important as how well these methods
perform relative to some benchmark.” We choose two benchmarks, the random
walk model (Näıve), and the ﬁxed GDP multiplier model (Näıve2). The for-
mer is a standard benchmark in the forecasting literature, which sets predicted
electricity demand to the last available data value of stationary series. The
latter benchmark assumes that electricity demand grows at the exogenous rate,
which is the same rate as country’s GDP growth.5 The choice of this bench-
mark is motivated by common practices by development professionals. Given
the paucity of data and the methodological challenges, they frequently derive
electricity demand forecasts from GDP-based demand growth forecasts as prox-
ies for the growth in demand for electricity (Bhattacharyya and Timilsina 2010,
Steinbuks et al. 2017).
To assess the accuracy of electricity demand forecasts, we calculate the me-
dian relative absolute error (MdRAE), which is the absolute error for the pro-
posed model relative to the absolute error for a random walk model. It is deﬁned
as
8 9
F,i
< yt yt =
M dRAE = p50 (4)
: y F,N a¨
ıve
yt ;
t
It ranges from 0 (a perfect forecast) to 1.0 (equal to the random walk), to
greater than 1 (worse than the random walk). The RAE is similar to Theil’s
U2, except that it is a linear rather than a quadratic measure. It is designed
to be easy to interpret, and it lends itself easily to summarizing across horizons
and series as it controls for scale and the diﬃculty of forecasting. The median
RAE is recommended for comparing the accuracy of alternative models as it
also controls for outliers (for information on the performance of this measure,
see Armstrong and Collopy, 1992). We also compute the median percentage
better measure, which reports the median of the percentage diﬀerence between
sMAPE forecasting error of proposed model and one of the two benchmark
5 For a more detailed description of these models, please refer to Appendix A.2.
8
models. Finally, we perform the Diebold and Mariano (1995) test to assess
whether diﬀerences between competing forecasts are statistically signiﬁcant or
simply due to sampling variability.6
3 Electricity Demand Measurement, Data and
the Forecast Horizon
The ultimate goal of this study is to forecast electricity demand, i.e., the to-
tal ﬁnal consumption.7 However, in many developing countries, particularly in
South Asia and Sub-saharan Africa regions, these data are either not available or
available for a relatively short time frame due to diﬃculties with an accurate ac-
counting of electricity at the end use level.8 In light of these limitations, we have
to rely on the more accurate electricity production (output) data for forecasting
purposes. As electricity is a nonstorable and poorly tradable commodity, the
output is a reasonable proxy for the total ﬁnal consumption. However, we have
to acknowledge that using electricity output data may lead to biased forecasts
in a handful of developing countries with high exposure to electricity trade.
As regards data sources, the electricity generation (output) data come from
the OECD/IEA Extended World Energy Balances database (IEA, 2016). The
data on population and real GDP come from Penn World Tables, version 8
(Feenstra et al., 2013). The resulting dataset covers 106 developing countries
over the period between 1960 and 2012.
Finally, we have to specify the within sample forecast horizons for assessing
the accuracy of the forecasting methods. These are set to ﬁve and ten years,
conditional on at least ten observations in the forecast validation sample. Addi-
tionally, we report out of sample forecasts over the period 2013-2022. For each
country in the dataset, the out of sample forecasts are chosen based on the fore-
6 Fora more detailed description of the Diebold and Mariano (1995) test please refer to
appendix section A.3.
7 Bhattacharyya and Timilsina (2010) point out that the reliance on consumption data for
the demand forecasting implies that only the satisﬁed demand is captured the suppressed
demand is not taken into consideration. This problem can be potentially important in the
presence of electricity market distortions and, correspondingly, unrealized demand (e.g., load
shedding). As estimating unrealized demand typically requires high-quality micro-level panel
data of enterprises and households, which are typically not available, addressing this problem
is beyond the scope of this paper.
8 These diﬃculties include the inaccurate recording of electricity consumption due to the
poor technical capacity of electric utilities (Jamasb 2006), the absence of reliable electricity
meters (Victor and Heller 2007), and large unaccounted losses from electricity theft (Smith
2004, Joseph 2010).
9
casting method corresponding to lowest within sample 5 year forecast horizon
sMAPE. Appendix Table A3.1 shows the historical and forecasted electricity
demand growth rates for each country. Country-speciﬁc forecast plots are also
shown in the appendix.
4 Evaluating Accuracy of Diﬀerent Methods
This section describes the evaluation of diﬀerent forecasting methods’ accuracy.
In subsection 4.1 we compare diﬀerent forecasting methods based on the chosen
measures of predictive accuracy (for a description of these measures see subsec-
tion 2.3). In subsection 4.2 we examine the eﬀectiveness of the best performing
method across diﬀerent categories of developing countries.
4.1 Comparisons across error measures
Tables 3 and 4 report frequencies of best-performing methods according to
sMAPE and RFSE criteria, respectively.9 For both measures of forecasts accu-
racy, the GARCH model has the highest incidence of delivering best predictions
over both 5- and 10-year forecast horizons, followed by the bivariate VAR / VEC
model over the 5-year forecast horizon and the trivariate VAR / VEC model over
the 10-year forecast horizon. None of the chosen forecasting methods appears
clearly superior to other methods. However, VAR/VEC and ARIMA/GARCH
models cumulatively account for a dominant share of best performing models.
Other methods (Holt-Winters and Unobserved Components models) tend to
perform better in a relatively small number of cases.
9 For VAR/VEC and ARIMA/GARCH models, the best performing method is a speciﬁca-
tion with the number of lagged terms that minimizes sMAPE and RFSE forecast errors.
10
Table 3: Frequency Tabulation of Best Performing Methods: sMAPE criterion
5 year forecast horizon 10 year forecast horizon
Model
Count Frequency Count Frequency
VAR3 / VEC3 15 14.15% 30 28.57%
VAR2 / VEC2 21 19.81% 20 19.03%
GARCH 39 36.79% 34 32.35%
ARIMA 13 12.25% 9 8.55%
HOLT-WINTERS 6 5.66% 8 7.62%
UCM-RWD 3 2.83% 2 1.90%
UCM-RWC 9 8.49% 2 1.90%
Total 106 100% 105 100%
Table 4: Frequency Tabulation of Best Performing Methods: RMSE criterion
5 year forecast horizon 10 year forecast horizon
Model
Count Frequency Count Frequency
VAR3 / VEC3 15 14.15% 33 31.41%
VAR2 / VEC2 23 21.69% 19 18.09%
GARCH 29 27.35% 33 31.41%
ARIMA 17 16.02% 7 6.65%
HOLT-WINTERS 7 6.60% 10 9.52%
UCM-RWD 5 4.72% 2 1.90%
UCM-LLTM 1 0.94% 0 0.00%
UCM-RWC 9 8.49% 1 0.95%
Total 106 100% 105 100%
Tables 5 and 6 show how well the forecasting methods perform compared to
benchmark models, Näıve and Näıve2. For each forecast horizon, these tables
report the median percentage better measure (see subsection 2.4) as well as
the percentage of times the diﬀerence between the forecast errors is statistically
signiﬁcant based on the Diebold and Mariano (1995) forecast accuracy test.
Table 5 compares the accuracy of forecasting methods relative to the Näıve
model, which assumes that electricity demand is a random walk. We see that
the best performing model based on sMAPE criterion yields considerable im-
provement over Näıve model. The median sMAPE forecast error of the Näıve
model is 77 percent higher than forecast error of the best performing model
over the 5-year forecast horizon and 74 percent higher over the 10 year forecast
11
Table 5: Comparison of various methods with Näıve as the benchmark
5 year forecast horizon 10 year forecast horizon
Model
Median % signiﬁcant Median % signiﬁcant
% Better (p = 0.05) % Better (p = 0.05)
Lowest sMAPE 77% 85.0% 74% 67.5%
VAR3 / VEC3 19% 83.3% 16% 68.3%
VAR2 / VEC2 7% 96.5% 11% 72.5%
GARCH 37% 84.6% 10% 69.3%
ARIMA 13% 83.9% -8% 70.1%
HOLT-WINTERS -2% 86.0% -11% 78.1%
UCM-RWD -9% 86.8% -22% 82.5%
UCM-LLTM -10% 88.4% -23% 82.5%
UCM-RWC -40% 94.8% -58% 87.9%
horizon. And the diﬀerence between forecast errors is statistically signiﬁcant
(assuming 5 percent level) for 85 percent of countries over the 5 year forecast
horizon and for 67.5 percent of countries over the 10-year forecast horizon. As
regards speciﬁc forecasting methods, VAR/VEC and GARCH methods yield
more accurate forecasts than the Näıve model over both 5- and 10-year forecast
horizons, with median accuracy improvement ranging between 7 and 37 percent.
To the contrary, the Holt-Winters method and Unobserved Components Models
yield less accurate forecasts over both 5- and 10-year forecast horizons, with me-
dian accuracy decline ranging between 2 and 58 percent. Finally, the ARIMA
model produces more accurate forecasts than the Näıve model over the 5-year
forecast horizon, with median accuracy improvement of 13 percent. However,
the ARIMA model yields less accurate forecasts than the Näıve model over 10-
year forecast horizon, with median accuracy decline of 8 percent. Regardless of
the direction of forecast error diﬀerences, they are mostly statistically signiﬁcant
across all methods, ranging between 83.3 to 96.5 percent of countries over the
5-year forecast horizon, and between 68.3 and 87.9 percent of countries over the
10-year forecast horizon.
Table 6 compares the accuracy of forecasting methods relative to Näıve2
model, which assumes that electricity demand grows at the same rate as GDP.
The results are qualitatively similar to those reported in Table 5, and the quan-
titative improvements over forecasts of Näıve2 model are even more pronounced.
The median sMAPE forecast error of the Näıve2 model is 184% percent higher
than forecast error of the best performing model over the 5-year forecast hori-
12
Table 6: Comparison of various methods with Näıve2 as the benchmark
5 year forecast horizon 10 year forecast horizon
Model
Median % signiﬁcant Median % signiﬁcant
% Better (p = 0.05) % Better (p = 0.05)
Lowest sMAPE 184% 88.0% 124% 73.0%
VAR3 / VEC3 68% 91.2% 52% 73.5%
VAR2 / VEC2 57% 95.2% 43% 73.7%
GARCH 121% 85.1% 54% 74.5%
ARIMA 69% 91.8% 23% 76.3%
HOLT-WINTERS 45% 90.2% 17% 81.7%
UCM-RWD 35% 90.5% 1% 79.5%
UCM-LLTM 31% 90.8% 0% 77.1%
UCM-RWC -16% 94.2% -40% 91.2%
zon and 124 percent higher over the 10-year forecast horizon. The performance
of speciﬁc forecasting methods over the Näıve2 forecasting model is also im-
proved. Speciﬁcally, VAR/VEC, GARCH, ARIMA, and Holt-Winters methods
all yield more accurate forecasts than the Näıve2 model over both 5- and 10-
year forecast horizons, with median accuracy improvement ranging between 17
and 121 percent. As regards Unobserved Components models, both RWD and
LLTM methods deliver more accurate forecasts over the 5-year forecast hori-
zon, whereas their forecast accuracy over the 5 year forecast horizon is of the
same magnitude as that of the Näıve2 model. Finally, the RWC model yields
less accurate forecasts than the Näıve2 model over both 5- and 10-year forecast
horizons, with median accuracy decline between 16 and 40 percent. Similar to
results reported in Table 5, the diﬀerences in predicted forecasts between fore-
casting methods and the Näıve2 model are mostly statistically signiﬁcant across
all methods, ranging between 85.1 to 95.2 percent of countries over the 5-year
forecast horizon, and between 73 and 91.2 percent of countries over the 10-year
forecast horizon.
4.2 Comparisons across developing country groups
Tables 7 - 10 compare eﬀectiveness of the best performing method (based on
sMAPE criterion) across developing countries based on their regional, income,
generation capacity and energy intensity characteristics. Table 7 reports the
average sMAPE and MdRAE measures of forecast accuracy across regions over
13
Table 7: Comparison of Forecast Errors across Regions
5 year forecast horizon 10 year forecast horizon
Region
sMAPE MdRAE sMAPE MdRAE
AFR 0.09 0.44 0.11 0.63
EAP 0.05 0.54 0.05 0.53
ECA 0.06 0.45 0.07 0.49
LAC 0.05 0.52 0.08 0.62
MENA 0.05 0.42 0.08 0.52
SAR 0.02 0.25 0.05 0.41
the 5- and 10-year forecast horizons. All in all, the best performing method
is highly accurate with average sMAPE varying between 2 and 9 percent over
the 5-year forecasting horizon and between 5 and 11 percent over the 10-year
forecasting horizon, respectively. Consistent with the results from the previous
section, the best performing method is also more accurate than the Näıve model,
with average MdRAE varying between 0.25 and 0.54 over the 5-year forecast-
ing horizon, and between 0.41 and 0.63 over the 10-year forecasting horizon,
respectively.
It follows from Table 7 that the forecast accuracy is the highest for the coun-
tries of the South Asia region over both 5- and 10-year forecasting horizons and
across both types of error accuracy measures. The forecast accuracy is the low-
est for the Sub-Saharan Africa region based on the sMAPE criterion over both
5- and 10- year forecasting horizons, with other regions having broadly com-
parable forecast errors. The forecast accuracy based on the MdRAE criterion
is the lowest for the East Asia and Paciﬁc and the Latin America regions over
the 5-year forecasting horizon, and for the Sub-Saharan Africa and the Latin
America regions over the 10-year forecasting horizon.
To frther elucidate the observed diﬀerences in the forecast accuracy across
regions, this study also reports the average sMAPE and MdRAE measures of
forecast accuracy for individual countries, grouped across regions over the 5- and
10-year forecast horizons (see Appendix Table A3.2). For most countries, both
sMAPE and MdRAE errors are small, which indicates that the best performing
method is both highly accurate and yields considerable improvements over the
Näıve model. However, the forecasting accuracy is greatly diminished for coun-
tries that have recently undertaken major investments (Ethiopia, Cameroon,
Myanmar) or disinvestments (Lithuania) in electricity generation assets; coun-
14
tries that have volatile electricity demand and / or rely heavily on electricity
imports (Albania, Benin, Botswana); or countries aﬀected by major conﬂicts
(Iraq, Libya, Syrian Arab Republic) or environmental disasters (Haiti).
Table 8 shows the average sMAPE and MdRAE measures of forecast accu-
racy across country income groups over the 5- and 10-year forecast horizons.
It follows from Table 8 that electricity demand forecasts are less accurate for
the lower income countries. For low income countries, the average sMAPE is
9 and 12 percent over the 5- and 10-year forecast horizons, respectively. These
errors are twice as high compared to high-income countries. The accuracy of
forecasting methods relative to the Näıve model is also considerably diminished
for lower income countries. For low-income countries, the value of MdRAE is
0.79 over the 10-year forecast horizon, which indicates that the best performing
method is just 21 percent more accurate than the Näıve model.
Table 8: Comparison of Forecast Errors across Income Groups
5 year forecast horizon 10 year forecast horizon
Income
sMAPE MdRAE sMAPE MdRAE
Low 0.09 0.49 0.12 0.79
Low-Middle 0.05 0.39 0.07 0.44
Upper-Middle 0.06 0.50 0.08 0.59
High 0.05 0.46 0.06 0.49
Table 9 shows the average sMAPE and MdRAE measures of forecast accu-
racy across installed capacity categories over the 5 and 10 year forecast horizons.
Forecast accuracy is the highest for the countries with large installed capacity
and diminishes signiﬁcantly as the size of the installed capacity falls. For coun-
tries with the largest installed capacity (over 100GW), the average sMAPE is
3 and 4 percent over the 5 and 10 year forecast horizons, respectively. These
errors are twice as low as compared to countries with medium installed capac-
ity size (1 to 10 GW). For countries with the smallest installed capacity (less
than 1GW), electricity generation is particularly diﬃcult to forecast, with the
average sMAPE of 9 and 10 percent over the 5- and 10-year forecast horizons,
respectively. The countries with large installed capacity also have higher accu-
racy of forecasting methods relative to the Näıve model. The value of MdRAE
for countries with the largest installed capacity (over 100GW) is 0.34 and 0.23
over the 10-year forecast horizon, which is 1.5-2 times smaller as compared to
15
countries with the smallest installed capacity size (less than 1GW).
Table 9: Comparison of Forecast Errors across Installed Capacity Categories
5 year forecast horizon 10 year forecast horizon
Installed Capacity
sMAPE MdRAE sMAPE MdRAE
less than 1GW 0.09 0.50 0.10 0.57
1GW-10GW 0.06 0.47 0.08 0.59
10GW-100GW 0.02 0.41 0.04 0.47
more than 100GW 0.03 0.34 0.04 0.23
Table 10 shows the average sMAPE and MdRAE measures of forecast accu-
racy across energy intensity categories over the 5 and 10 year forecast horizons.
Forecast accuracy is the highest for the most energy intensive countries (more
than 12$/kgoe) with average sMAPE of 2 and 4 percent over the 5 and 10 year
forecast horizons, respectively. Compared to other countries these errors are 2
to 3 times smaller over the 5 year forecast horizon and 1.5 to 2 times smaller
over the 10 year forecast horizon. However, the more energy intensive countries
also have the lower accuracy of forecasting methods relative to the Näıve model,
at least for the shorter term forecast horizon. The value of MdRAE for the most
energy intensive countries is 0.75 over the 5 year forecast horizon, which is twice
as high as compared to the most energy eﬃcient countries. To the contrary, the
most energy eﬃcient countries have the lowest forecast accuracy relative to the
Näıve model over the 10 year forecast horizon, with MdRAE of 0.68.
Table 10: Comparison of Forecast Errors across Energy Intensity Categories
5 year forecast horizon 10 year forecast horizon
Energy Intensity
sMAPE MdRAE sMAPE MdRAE
<3$/kgoe 0.04 0.34 0.09 0.68
3$/kg-6$/kgoe 0.06 0.41 0.08 0.47
6$/kg-9$/kgoe 0.07 0.43 0.09 0.56
9$/kg-12$/kgoe 0.06 0.50 0.06 0.46
>12$/kgoe 0.02 0.75 0.04 0.52
16
5 Conclusions
Accurate projections of electricity demand are essential for planning power sys-
tems and appraising investment projects in developing countries. Nonetheless,
demand forecasting issues are not rigorously studied and are not always given
adequate attention among development practitioners. This study demonstrates
that time-series econometric methods yield highly accurate forecast predictions
for the majority of developing countries. Econometric forecasts signiﬁcantly out-
perform simple heuristical rules used by practitioners, who frequently assume
that electricity demand grows at some exogenous rate or is proportional to
real GDP growth. These improvements notwithstanding, relying on time-series
econometric methods alone may produce inaccurate forecasts in some developing
countries. We show that econometric forecasts of electricity demand are chal-
lenging for developing countries that are in the process of rapid economic and
structural transformation or are prone to conﬂicts and environmental disasters.
These include, among other, the countries in Sub-Saharan Africa region, the
low-income countries, and the countries with small electricity generation sys-
tems. For those countries, in particular, a more rigorous forecasting approach,
using a combination of micro-econometric and computational modeling methods
would be preferred.
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Appendix
A.1 Description of Forecasting Methods
A.1.1 VAR / VEC Model
Vector autoregressive model (VAR) is a commonly used tool for forecasting
multivariate stationary time series that are simultaneously determined, e.g.,
electricity demand, and its drivers such as GDP, population, etc. The structure
of VAR model is that each variable is a linear function of past lags of itself
and past lags of the other variables. The VAR model with lag order p with k
endogenous and m exogenous variables can be written as
yt = AYt−1 + B0 xt + "t , (A1.1)
where yt is the K ⇥ 1 vector of endogenous variables, A is a K ⇥ Kp matrix
of coeﬃcients, Yt is the Kp ⇥ 1 matrix of endogenous variables of lag order
p, B0 is a K ⇥ M matrix of coeﬃcients, xt is the M ⇥ 1 vector of exogenous
variables, and "t is the K ⇥ 1 vector of white noise innovations.
Vector error correction model (VECM) provides a framework for estimation,
inference, and forecasting of diﬀerence stationary multivariate time series, when
these variables are simultaneously determined. VECM representation of VAR
model of lag order p deﬁned by equation (A1.1) is given by
p
X 1
yt = Pyt−1 + Gi yt−i + B0 xt + "t (A1.2)
i =1
Pj =p Pj =p
where ⇧ = j =1 Aj Ik , i = j =i+1 Aj , and other terms are same as
in equation (A1.1). If the variables yt are diﬀerence stationary, the matrix P
in A1.2 has rank 0 r < K , where r is the number of linearly independent
cointegrating vectors. As matrix P has reduced rank the cointegrating vectors
are not identiﬁed without further restrictions. We apply standard normalization
restrictions suggested by Johansen (1995). For both VAR and VECM models
we set the maximum number of lagged terms, p, equal to four.
A.1.2 ARIMA Model
AutoRegressive Integrated Moving Average (ARIMA) models are appropriate
if there is a reason to believe that other drivers of electricity consumption are
22
exogenous or have little eﬀect on electricity demand forecasts. They provide a
parsimonious description of a weakly stationary stochastic processes in terms
of two polynomials, one for the auto-regression and the second for the moving
average. Pure ARMA models can be written as autoregressions in the dependent
variable. An ARIMA(p,d,q) model can be written as
p
X q
X
d d
yt = ↵ + ⇢i yt i+ ✓ i "t i + "t , (A1.3)
i=1 i=1
where yt is the dependent variable, ↵ is a constant term, ⇢ and ✓ are the
coeﬃcients of autoregressive and moving average processes of lag orders p and q,
d is the order of time-series integration (zero for stationary series), and "t is the
error term that is assumed to be a white noise. We set the maximum number
of lagged autoregressive terms, p, equal to four and the maximum number of
lagged moving average terms, q, equal to two.
A.1.3 GARCH Model
Generalized autoregressive conditional heteroskedasticity (GARCH) models are
frequently used for forecasting univariate time series when there is reason to
believe that the error terms have a characteristic size or variance. This model
is particularly relevant for developing countries with highly volatile electricity
demand. The variance equation in the GARCH(p,q) model can be written as
p
X q
X
Var ("t ) = + µ i "2
t i+
2
i t i, (A1.4)
i=1 j =1
where p is the length of squared innovations (ARCH terms) lags and p is the
length of variances (GARCH terms) lags. The GARCH model simultaneously
combines equations (A1.3) and (A1.4).
A.1.4 Holt-Winters Method
Holt-Winters method is used for forecasting time series that can be modeled as
a linear trend in which the intercept and the coeﬃcient on time vary over time.
The method was shown to produce optimal forecasts for the ARIMA(0,2,2)
model and some local linear models (Gardner, 1985). The Holt-Winters method
forecasts series of the form
23
bt+1 = at + bt t
y (A1.5)
bt is the forecast of the original series yt , and at and bt are coeﬃcients
where y
that drift over time. Given starting values, a0 and b0 , the updating equations
are recursively formulated as
at = ↵yt + (1 ↵)(at 1 + bt 1) (A1.6)
and
bt = ( a t at 1) + (1 ) bt 1 (A1.7)
where smoothing parameters ↵ and are chosen by an iterative process to
minimize the in-sample sum-of-squared prediction errors.
A.1.5 Unobserved Components Models
The Random Walk with a Drift (RWD) and the Local Level with Deterministic
Trend (LLTD) models are most appropriate for forecasting diﬀerence-stationary
time series that evolve around a linear appearing trend. Mathematical repre-
sentation of the RWD and LLDT models is given by equations
yt = µ t
µt = µt−1 + ↵ + "t , (A1.8)
(RWD) and
yt = µ t + u t
µt = µt−1 + ↵ + "t , (A1.9)
(LLDT), where µt is the conditional expectation of electricity demand series,
yt , ↵ is a drift parameter, and "t and ut are the white noise error terms.
The Random Walk with a Stochastic Cycle Model (RWSC) is most ap-
propriate for forecasting diﬀerence-stationary time series that exhibit cyclical
behavior. Mathematical representation of the RWSC model is given by
24
yt = µ t + t
µt = µt−1 + "t ,
t = t−1 ⇢cos + et−1 ⇢sin + !t ,
et = t−1 ⇢sin + et−1 ⇢cos + !
et , (A1.10)
where is a frequency of the cyclical component, ⇢ is a unit less scaling (or
dampening) factor, et is auxiliary variable, and "t , !t , and !
et are the white
noise error terms.
A.2 Description of Benchmark Models
A.2.1 Naïve Model
The forecasts of the Näıve model for covariance stationary data are simply the
last available data value. It is deﬁned as follows:
yt+i = yt , (A1.11)
where i = 1, 2, ..., m, and m = 5 for 5-year ahead forecasts and m = 10 for
10-year ahead forecasts. In statistical terms the Näıve model is a random walk
model, which assumes that the trend in the data cannot be predicted, and that
the best forecast for the future is their own most recent value.
The forecasts of the Näıve model for diﬀerence stationary data are the dif-
ference of the last available data value summed over the forecast period, and
added to the last available data value. It is deﬁned as follows:
t
X +m
yt+i = yt + ( yt yt 1) , (A1.12)
i=t+1
where m = 5 for 5-year ahead forecasts and m = 10 for 10-year ahead
forecasts. In statistical terms the Näıve model holds the same interpretation as
25
a random walk model.
A.2.2 Naïve2 Model
The Näıve2 model assumes that electricity demand grows at exogenous rate,
which is the same rate as country’s GDP growth. It is deﬁned as follows:
i
yt+i = (1 + k ) yt , (A1.13)
where k is the expected growth in GDP. In this study we assume it is equal
to the historical GDP growth average over last 5 years in the sample.
A.3 Diebold-Mariano (1995) Test
The Diebold and Mariano (1995) (DM) parametric test is a well-known pro-
cedure for testing the null hypothesis of no diﬀerence in the accuracy of two
competing forecasts.
T
Let {(e1t , e2t )}t=1 be a bivariate time series vector of competing forecast
errors. The quality of the forecasts is to be evaluated according to a speciﬁed
loss function, g (·). Let us assume that the loss function depends only on the
forecast errors, and let dt = g (e1t ) g (e2t ) be the loss diﬀerential. Then, the
null hypothesis of unconditional equal forecast accuracy is
H0 : E [ d t ] = 0 , (A1.14)
i.e., the errors associated with the two forecasts are, on average, of equal
magnitude. If the null is rejected, the forecasting method that yields the smallest
T
loss will be chosen. Given a series of loss diﬀerentials, {dt }t=1 , a test of (A1.14)
is based on their sample mean:
XT
¯= 1
d dt . (A1.15)
T t=1
The DM test it is given by
d¯
DM = q (A1.16)
Vˆ ( d)
ˆ (d) is an estimate of the asymptotic variance of d. Whenever an
where V
26
optimal forecast is produced from a proper information set, the resulting h-step
forecast errors will follow a moving-average (MA) process of order (h 1) of the
form et = ✓0 "t + ✓1 "t 1 + ... + ✓h 1 "t h+1 . Diebold and Mariano (1995) propose
estimating the variance using the truncated kernel with a bandwidth of (h 1)
for h-step forecasts:
" h 1
#
X
ˆ ( d) = 1
V ˆ0 + 2 ˆk , (A1.17)
T
k=1
where ˆk is an estimate of the kth auto covariance of dt , given by
T
1 X ¯)(dt ¯) :
ˆk = ( dt d k d
T
t=k+1
Luger (2004, p. 2) argues that “if the loss-diﬀerential series satisﬁes some
regularity assumptions such as covariance stationarity, short memory, and the
existence of moments that ensure the applicability of a central limit theorem,
then the DM test statistic has an asymptotic standard normal distribution under
the null hypothesis.”
27
Tables
Table A3.1: Historical and Forecast Rates of Electricity Demand Growth
Historical growth Forecast growth, 2015-2020
country
2000-2004 2005-2009 2010-2014⇤ 5% CI Mean 95% CI
Sub-Saharan Africa
Angola 18.6% 19.1% 3.5% -2.1% 3.2% 7.0%
Benin 5.5% 8.0% 1.2% -7.5% 0.0% 2.3%
Botswana -1.7% -7.7% -2.0% -20.0% -10.0% -4.9%
Cameroon 3.0% 9.5% 2.1% 1.3% 1.5% 1.7%
Congo 9.3% 16.3% 14.2% -20.0% 4.3% 8.7%
Congo, Dem. Rep. 4.7% 1.3% 0.1% -2.1% 0.1% 1.5%
Côte d’Ivoire 3.7% 1.0% 2.1% 2.0% 1.9% 1.8%
Eritrea 7.4% 1.6% 5.2% 4.3% 4.1% 3.8%
Ethiopia 14.0% 15.0% 22.3% 23.2% 24.4% 25.4%
Gabon 3.8% 5.3% 2.8% 1.6% 2.1% 2.6%
Ghana -1.2% 10.0% 3.3% 1.6% 1.4% 1.2%
Kenya 8.6% 5.0% 4.8% 4.1% 4.4% 4.6%
Mauritius 5.6% 3.7% 2.2% 1.4% 2.2% 2.8%
Mozambique 7.4% 5.1% 1.1% -5.1% -1.1% 1.1%
Namibia 3.6% -4.3% 1.5% 0.3% 0.4% 0.5%
Nigeria 12.0% 2.2% 2.3% 3.3% 3.0% 2.8%
Senegal 11.7% 4.2% 3.9% 3.6% 3.5% 3.4%
South Africa 3.3% 1.2% 0.1% -1.0% 0.2% 1.1%
Sudan 9.8% 19.2% 22.0% 26.6% 26.3% 26.0%
Tanzania 8.8% 9.2% 0.5% -20.0% 0.5% 71.1%
Togo 1.6% -1.1% -4.2% n/a -2.8% 5.7%
Zambia 2.9% 5.3% 5.3% 6.6% 8.5% 9.7%
Zimbabwe 6.8% -1.6% 0.7% -9.6% -3.3% 0.1%
East Asia and Paciﬁc
Brunei 5.7% 3.2% 1.6% 1.4% 2.4% 3.2%
Cambodia 23.0% 0.6% -0.5% -11.3% -1.6% 1.2%
China 16.9% 13.5% 17.1% 12.1% 12.4% 12.7%
Indonesia 7.4% 6.6% 7.8% 6.5% 6.9% 7.3%
Korea, Dem. People’s Rep. 3.6% -1.1% 0.8% n/a 4.0% 212.8%
Malaysia 3.9% 10.2% 5.8% -2.7% -1.4% -0.2%
Mongolia 3.2% 5.2% 6.4% 3.9% 5.8% 7.4%
Myanmar 3.5% 5.1% 21.4% n/a 3.4% 203.3%
Philippines 5.0% 4.0% 2.7% 2.3% 2.7% 3.0%
Singapore 4.1% 3.7% 2.2% 1.6% 2.2% 2.7%
Thailand 7.5% 4.1% 1.3% 2.1% 2.0% 2.0%
Vietnam 20.4% 15.4% 13.9% 12.8% 12.9% 13.0%
Notes. * - includes mean forecasts for 2013-2014. CI: Conﬁdence Interval
28
Table A3.1 Historical and Forecast Rates of Electricity demand Growth
(continued)
Historical growth Forecast growth, 2015-2020
country
2000-2004 2005-2009 2010-2014⇤ 5% CI Mean 95% CI
Europe and Central Asia
Albania 3.0% 7.9% -4.5% -4.1% 0.7% 2.0%
Armenia 1.2% 0.6% 7.9% n/a 6.8% 8.6%
Azerbaijan 4.5% -3.6% 5.0% -20.0% -1.3% -0.7%
Belarus 3.7% 2.5% -2.3% -0.5% -1.1% -1.5%
Bosnia and Herzegovina 4.2% 7.2% -1.6% 1.6% 3.3% 4.4%
Bulgaria 1.6% 0.9% 2.8% 1.8%
Croatia 3.1% 2.7% 0.8% 1.0% 1.6% 2.0%
Cyprus 6.0% 4.3% -1.5% -1.5% 0.0% 1.2%
Georgia -0.4% 7.9% 9.2% 13.6% 12.6% 11.8%
Hungary 0.3% 0.9% -0.7% -1.4% 0.2% 1.5%
Kazakhstan 6.4% 4.4% 4.3% -3.5% 3.3% 7.6%
Kyrgyz Republic -0.1% -3.7% 0.8% -0.7% 0.1% 0.7%
Latvia 3.7% 7.0% -6.1% -0.2%
Lithuania 5.9% -13.1% 8.1% 37.3% 15.1% 14.6%
Macedonia 0.4% 0.9% 0.5% 1.4% 1.2% 1.1%
Malta 3.4% -1.1% 0.8% -1.9% -0.2% 1.1%
Moldova 1.4% 0.4% 0.4% 0.4% 0.4% 0.4%
Poland 1.7% 0.2% 1.2% -1.2% 0.5% 1.8%
Romania 2.9% 0.4% 1.7% 1.0%
Russian Federation 1.7% 1.8% 1.3% 1.4% 1.9% 2.4%
Serbia 1.4% 0.5% 0.4% 0.9% 0.5% 0.3%
Tajikistan 4.0% -0.8% -0.4% -0.3% -0.3% -0.2%
Turkey 5.9% 6.1% 7.0% -20.0% 2.8% 53.4%
Turkmenistan 6.0% 6.0% 2.6% 1.4% 1.4% 1.3%
Ukraine 1.7% 0.3% 0.2% -2.8% -2.5% -2.2%
Uzbekistan 1.0% 1.0% 2.9% 3.6% 3.4% 3.3%
Latin America and Caribbean
Argentina 3.7% 3.7% 4.2% 3.4% 3.8% 4.1%
Bolivia 5.2% 8.4% 6.4% 6.3% 7.1% 7.8%
Brazil 3.1% 5.6% 4.1% 3.8% 4.0% 4.2%
Chile 6.2% 3.0% 3.9% -1.0% 1.6% 3.8%
Colombia 3.3% 3.6% 2.4% 2.2% 2.1% 2.0%
Costa Rica 3.9% 3.2% 2.3% n/a 2.0% n/a
Cuba 0.4% 2.7% 0.0% -0.8% 0.5% 1.4%
Dominican Republic 9.7% 4.2% 2.6% 2.3% 2.1% 1.9%
Ecuador 4.0% 10.7% 3.7% -0.6% 2.9% 6.0%
El Salvador 8.6% 4.8% -0.6% -2.0% -0.2% 1.2%
Guatemala 6.6% 2.1% 2.2% 1.8% 1.8% 1.7%
Haiti 0.3% 1.1% 13.6% 3.3% 2.7% 2.3%
Honduras 10.7% 4.2% 3.5% 2.2% 2.1% 2.1%
Jamaica 2.5% -8.4% 1.7% 2.3% 2.0% 1.8%
29
Table A3.1 Historical and Forecast Rates of Electricity Demand Growth
(continued)
Historical growth Forecast growth, 2015-2020
country
2000-2004 2005-2009 2010-2014⇤ 5% CI Mean 95% CI
Latin America and Caribbean (continued)
Mexico 3.9% 2.2% 3.6% 2.1% 2.0% 2.0%
Nicaragua 6.0% 4.0% 3.6% 3.1% 2.9% 2.8%
Panama 3.8% 5.5% 5.0% 3.0% 2.9% 2.8%
Paraguay -0.9% 1.1% 3.5% -0.4% 2.2% 2.8%
Peru 5.6% 8.2% 9.1% 7.0% 8.8% 10.4%
Trinidad & Tobago 5.9% 4.0% 5.2% n/a 6.5% n/a
Uruguay 0.2% 8.6% 3.7% 2.3% 3.1% 3.7%
Venezuela, RB 4.7% 2.4% 2.3% 1.7% 2.2% 2.7%
Middle East and North Africa
Algeria 6.7% 7.0% 6.3% 5.1% 5.8% 6.3%
Bahrain 8.0% 4.2% 2.3% 0.6% 2.2% 3.5%
Egypt, Arab Rep. 7.8% 7.0% 6.3% 4.7% 4.7% 4.6%
Iran, Islamic Rep. 9.3% 6.2% 2.9% 2.2% 2.2% 2.3%
Iraq -0.9% 13.0% 3.9% 2.1%
Israel 2.8% 4.1% 2.1% 2.1% 2.0% 1.9%
Jordan 6.2% 10.6% 0.7% -20.0% 1.3% 20.7%
Kuwait 7.1% 6.1% 3.2% 3.4% 3.3% 3.3%
Lebanon 5.5% 5.3% 0.7% 1.6% 2.5% 3.1%
Libya 9.3% 8.9% -6.9% n/a 17.6% 86.7%
Morocco 10.0% 4.5% 6.0% 2.2% 3.8% 5.1%
Oman 7.8% 11.3% 13.6% 13.6% 14.3% 15.1%
Qatar 11.5% 19.1% 13.5% 14.1% 13.9% 13.7%
Saudi Arabia 7.9% 7.3% 6.3% 5.1% 6.0% 6.8%
Syrian Arab Republic 7.7% 6.6% -14.6% -20.0% -20.0% -20.0%
Tunisia 3.9% 6.4% 3.1% 3.2% 3.7% 4.1%
United Arab Emirates 10.4% 12.2% 1.8% 2.1% 2.1% 2.0%
Yemen 7.9% 12.5% -1.1% 2.5% 2.3% 2.1%
South Asia
Bangladesh 13.5% 11.6% 10.0% 10.3% 10.4% 10.5%
India 5.1% 7.4% 9.2% 9.7% 9.2% 8.6%
Nepal 10.5% 5.3% 7.3% 6.1% 6.0% 5.9%
Pakistan 7.5% 0.2% 0.2% -1.6% -0.1% 1.0%
Sri Lanka 6.6% 3.2% 3.8% 2.2% 2.1% 2.0%
30
Table A3.2: Comparison of Forecast Errors across Countries
5 year forecast horizon 10 year forecast horizon
Country
sMAPE MdRAE sMAPE MdRAE
Sub-Saharan Africa
Angola 0.09 0.34 0.08 0.26
Benin 0.29 0.7 0.2 0.44
Botswana 0.34 0.66 0.15 0.51
Cameroon 0.01 0.08 0.22 0.88
Congo 0.38 0.89 0.24 0.39
Congo, Dem. Rep. 0.02 0.17 0.08 0.58
Côte d’Ivoire 0.03 0.29 0.03 0.38
Eritrea 0.02 0.23 0.07 0.68
Ethiopia 0.06 0.26 0.38 1.38
Gabon 0.04 0.86 0.03 0.98
Ghana 0.19 0.71 0.13 0.97
Kenya 0.03 0.45 0.15 1.08
Mauritius 0.01 1.02 0.01 0.07
Mozambique 0.06 1.14 0.07 0.78
Namibia 0.07 0.26 0.07 0.99
Nigeria 0.08 0.14 0.08 0.31
Senegal 0.02 0.37 0.03 0.26
South Africa 0.02 0.23 0.03 0.46
Sudan 0.04 0.18 0.13 0.6
Tanzania 0.02 0.16 0.06 0.31
Togo 0.19 0.36 0.15 0.61
Zambia 0.02 0.18 0.05 1.09
Zimbabwe 0.04 0.51 0.08 0.58
East Asia and Paciﬁc
Brunei 0.01 0.44 0.04 0.3
Cambodia 0.17 0.84 n/a n/a
China 0.04 0.38 0.06 0.3
Indonesia 0.02 0.13 0.02 0.26
Korea, Dem. People’s Rep. 0.06 0.62 0.08 0.71
Malaysia 0.03 0.23 0.05 0.36
Mongolia 0.05 0.47 0.04 0.43
Myanmar 0.11 0.84 0.14 2.29
Philippines 0.02 0.49 0.02 0.22
Singapore 0.02 1.03 0.01 0.61
Thailand 0.02 0.96 0.02 0.2
Vietnam 0.02 0.07 0.02 0.09
31
Table A3.3: Comparison of Forecast Errors across Countries (continued)
5 year forecast horizon 10 year forecast horizon
Country
sMAPE MdRAE sMAPE MdRAE
Europe and Central Asia
Albania 0.17 0.3 0.26 0.85
Armenia 0.05 0.29 0.07 0.35
Azerbaijan 0.06 0.57 0.09 0.59
Belarus 0.06 0.74 0.08 0.33
Bosnia and Herzegovina 0.08 0.16 0.07 0.21
Bulgaria 0.03 0.73 0.03 0.56
Croatia 0.1 0.66 0.09 1.08
Cyprus 0.05 0.2 0.05 0.31
Georgia 0.04 0.33 0.09 0.35
Hungary 0.08 0.64 0.04 0.34
Kazakhstan 0.02 0.23 0.02 0.08
Kyrgyz Republic 0.09 0.6 0.11 0.57
Latvia 0.07 0.29 0.11 0.39
Lithuania 0.28 0.43 0.23 0.79
Macedonia 0.05 0.66 0.06 0.64
Malta 0.04 0.4 0.06 1.02
Moldova 0.02 0.1 0.02 0.05
Poland 0.03 0.42 0.03 0.96
Romania 0.04 0.49 0.03 0.48
Russian Federation 0.02 0.3 0.02 0.17
Serbia 0.01 0.6 0.02 0.33
Tajikistan 0.04 0.46 0.07 0.71
Turkey 0.05 1.06 0.09 0.63
Turkmenistan 0.01 0.06 0.02 0.05
Ukraine 0.05 0.8 0.04 0.22
Uzbekistan 0.01 0.12 0.02 0.68
Latin America and Caribbean
Argentina 0.03 0.29 0.09 0.66
Bolivia 0.02 0.12 0.11 0.83
Brazil 0.01 0.08 0.11 1.22
Chile 0.03 0.52 0.04 0.16
Colombia 0.01 0.58 0.02 0.46
Costa Rica 0.01 0.65 0.02 0.44
Cuba 0.03 0.64 0.03 0.9
Dominican Republic 0.02 0.94 0.08 0.53
Ecuador 0.04 0.31 0.17 1.25
El Salvador 0.03 0.41 0.07 0.53
Guatemala 0.02 0.58 0.07 0.39
Haiti 0.39 0.92 0.2 1.14
Honduras 0.02 0.95 0.12 0.63
Jamaica 0.2 0.4 0.27 0.86
32
Table A3.3: Comparison of Forecast Errors across Countries (continued)
5 year forecast horizon 10 year forecast horizon
Country
sMAPE MdRAE sMAPE MdRAE
Latin America and Caribbean (continued)
Mexico 0.02 0.65 0.02 0.31
Nicaragua 0.04 0.68 0.02 0.3
Panama 0.04 0.54 0.03 0.39
Paraguay 0.02 0.47 0.03 0.52
Peru 0.02 0.19 0.02 0.13
Trinidad & Tobago 0.01 0.22 0.07 0.56
Uruguay 0.06 0.94 0.15 0.51
Venezuela, RB 0.01 0.42 0.05 0.82
Middle East and North Africa
Algeria 0.08 0.58 0.14 1.24
Bahrain 0.01 0.99 0.07 0.47
Egypt, Arab Rep. 0.01 0.22 0.02 0.12
Iran, Islamic Rep. 0.01 0.15 0.02 0.21
Iraq 0.26 0.95 0.16 0.66
Israel 0.02 0.61 0.02 0.55
Jordan 0.02 0.51 0.15 0.82
Kuwait 0.01 0.22 0.04 0.32
Lebanon 0.05 0.31 0.06 0.99
Libya 0.07 0.3 0.22 1.15
Morocco 0.05 0.76 0.05 0.35
Oman 0.05 0.24 0.06 0.38
Qatar 0.02 0.11 0.03 0.17
Saudi Arabia 0.06 0.49 0.04 0.36
Syrian Arab Republic 0.1 0.07 0.07 0.06
Tunisia 0.02 0.29 0.05 1.13
United Arab Emirates 0.05 0.21 0.05 0.17
Yemen 0.08 0.46 0.09 0.21
South Asia
Bangladesh 0.01 0.05 0.07 0.27
India 0.02 0.18 0.06 0.52
Nepal 0.02 0.18 0.03 0.3
Pakistan 0.01 0.06 0.04 0.47
Sri Lanka 0.03 0.8 0.07 0.51
33