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Telecommunications Forecast Methodologies

How We Generate Our Industry Forecasts

BMI’s industry forecasts are generated using the best-practice techniques of time-series modelling. The precise form of time-series model we use varies from industry to industry, in each case being determined, as per standard practice, by the prevailing features of the industry data being examined. For example, data for some industries may be particularly prone to seasonality, i.e. seasonal trends. In other industries, there may be pronounced non-linearity, whereby large recessions, for example, may occur more frequently than cyclical booms.

Our approach varies from industry to industry. Common to our analysis of every industry, however, is the use of vector autoregressions. Vector autoregressions allow us to forecast a variable using more than the variable’s own history as explanatory information. For example, when forecasting oil prices, we can include information about oil consumption, supply and capacity.

When forecasting for some of our industry sub-component variables, however, using a variable’s own history is often the most desirable method of analysis. Such single-variable analysis is called univariate modelling. We use the most common and versatile form of univariate models: the autoregressive moving average model (ARMA).

In some cases, ARMA techniques are inappropriate because there is insufficient historic data or data quality is poor. In such cases, we use either traditional decomposition methods or smoothing methods as a basis for analysis and forecasting.

It must be remembered that human intervention plays a necessary and desirable part in all our industry forecasting techniques. Intimate knowledge of the data and industry ensures we spot structural breaks, anomalous data, turning points and seasonal features where a purely mechanical forecasting process would not.

Telecommunications Industry

There are a number of principal criteria that drive our forecasts for each telecoms variable.

Subscriber Numbers

Subscriber data are derived in the majority of instances from individual operators and/or national regulators. Subscriber forecasts are then based on a range of variables including:

• Stated network rollout and capex plans;
• Level of maturity of a given market;
• Competition from alternative technologies (i.e. ADSL/wireless vs PSTN);
• Potential regulatory developments (increased liberalisation etc.);
• Cost of technology in relation to GDP per capita;
• Political/economic factors.

Expenditure per capita, percentage of GDP and percentage of fiscal budget are calculated using BMI’s own macroeconomic and demographic forecasts.

Sources

Sources used in Telecoms reports include national ministries and media/telecoms regulatory bodies, officially released company results and figures, national and international industry organisations (such as the CTIA, the GSM Association and the ITU), and international and national news agencies.

Telecoms Business Environment Ratings Methodology

Ratings Overview

Conceptually, BMI’s Telecoms Business Environment Ratings system provides a globally-comparative, numerically-based assessment of the Risk/Return trade-off for the industry in each state covered in BMI Reports. In order to provide clients with a detailed assessment of this trade-off, the overall rating is comprised of two distinct sub-ratings:
Limits of Potential Returns: Evaluates the industry’s current size and growth potential, and also assesses broader industry/state characteristics that may enable/inhibit the industry’s development.
Risks to Realisation of Potential Returns: Evaluates issues within (a) the Telecoms sector, and (b) the broader political/economic/business environment, that indicate the level of uncertainty surrounding the realisation of potential returns.

These ratings are themselves comprised of sub-ratings:

• The ‘Limits’ Rating is comprised of:
• Domestic Market: This evaluates industry growth/size dynamics
• Country Structure: This evaluates the broader economic/socio-demographic environment

• The ‘Risks’ Rating is comprised:
• Market Risks: This is a subjective evaluation of industry regulatory and competitive issues
• Country Risk: This evaluates the industry’s broader Country Risk exposure, which is based on industry-specific vulnerabilities that may be assesses using any combination of the 56 components of BMI's proprietary Political, Economic and Business & Operational Risk Ratings

Weighting

Given the number of indicators/datasets used, it would be inappropriate to give all sub-components equal weight. Consequently, the following weight has been adopted.

Component	Weighting
Limits of Potential Returns	70%
- Telecoms Market	- 65%
- Country Structure	- 35%
Risks to Realisation of Potential Returns	30%
- Market Risks	- 40%
- Country Risk	- 60%

 

Indicators

The following indicators have been used. Overall, the rating uses three subjectively-measured indicators, and around 20 separate indicators/datasets.

Indicator	Rationale
Limits to potential returns
Market structure
ARPU	Indicator denotes depth of telecoms market. High value markets score better than low value ones.
Subscriber numbers	Indicator denotes breadth of telecoms market. Large markets score higher than smaller ones.
Subscriber growth, % y-o-y	Indicator denotes sector dynamism. Scores are based on annual average growth over our 5-yr forecast period and also take into account the penetration rate.
Number of operators	Subjective evaluation against BMI-defined criteria. This indicator evaluates the market openness and competitiveness.
The overall score for Market Structure is also impacted by the taxation rate for the telecoms sector and also, where relevant, broader security issues.
Country structure
Urban-Rural split	A highly urbanised state facilitates network roll out and also implies higher wealth. Thus, predominantly rural states score lower, with the overall score further impacted by country size.
Age range	Proportion of the population under 24 years of age. States with young populations tend to offer more attractive markets.
GDP per capita, US$	A proxy for wealth. High income states receive better scores than low income states.
The overall score for Country structure is also impacted by the coverage of the power transmission network across the state.
Risks to potential returns
Market Risks
Independence of Regulator	Subjective evaluation against BMI-defined criteria. This indicator evaluates predictability of operating environment.
Country Risk
Short-term external risk	Rating from BMI’s Country Risk Ratings (CRR). It denotes the state’s vulnerability to externally-induced economic shock, which tend to be the principal triggers of economic crises.
Policy Continuity	Rating from BMI’s CRR. It evaluates the risk of a sharp change in the broad direction of government policy.
Legal framework	Rating from BMI’s CRR, to denote strength of legal institutions in each state – security of investment can be a key risk in some EM.
Corruption	Rating from BMI’s CRR, to denote risk of additional illegal costs/possibility of opacity in tendering/business operations affecting companies’ ability to compete.

 

 

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