Information Technology 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 autogressions 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.
IT Industry
There are a number of criteria that drive our forecasts for each IT variable.
IT forecasting is complicated due to the fragmented nature of the market, with little transparency of vendor data and low apparent agreement between many sets of figures in terms of market definition, base and methodology. In addition, forecasts are naturally affected by consideration of a variety of internal and external political and economic factors.
Within best-practice techniques of time-series modelling, BMI’s quarterly updated forecasts are improved substantially by intimate knowledge of the prevailing features of each local market.
Individual variables taken into account in creating each forecast include:
- Overall economic context, and GDP and demographic trends;
- Underlying ‘information society’ trends;
- Projected GDP share of industry;
- Maturity of market structure;
- Regulatory developments and government policies;
- Developments in key client sectors, such as telecommunications, banking and e-government;
- Technological developments, and diffusion rates;
- Exogenous events.
Estimates are calculated using BMI’s own macroeconomic and demographic forecasts.
Sources
Sources used in IT reports include national ministries and ICT regulatory bodies, national industry associations, and international industry organisations such as the International Telecommunication Union (ITU), officially released company results and figures, and international and national industry news agencies.
IT Business Environment Ratings Methodology
Ratings Overview
Conceptually, BMI’s IT 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 IT 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 wholly inappropriate to give all sub-components equal weight. Consequently, the following weight has been adopted.
Component |
Weighting |
Limits of potential returns |
70% |
– IT market |
– 65% |
– Country Structure |
– 35% |
Risks to realisation of potential returns |
30% |
– Industry Risks |
– 40% |
– Country Risk |
– 60% |
Source: BMI |
|
Indicators
The following indicators have been used. Overall, the rating uses two subjectively-measured indicators, and over 20 separate indicators/datasets.
Indicator |
Rationale |
Limits to potential returns |
|
IT Market |
|
IT market value, US$bn |
Denotes breadth of IT market. Large markets score higher than smaller ones. |
Sector value growth, % y-o-y |
Denotes sector dynamism. Scores based on annual average growth over 5-year forecast period. |
Government initiatives and spending |
Denotes spending boost provided by public sector; which can be a crucial determinant of sector development. |
Hardware, % of total sales |
Denotes maturity of market. A high proportion of hardware sales - compared to services/software - indicates that the overall IT market is immature. |
Country Structure |
|
Urban-rural split |
Urbanisation is used as a proxy for development of medical facilities. Predominantly rural therefore states score lower. |
GDP per capita, US$ |
As superior goods, a high GDP per capita supports long term industry prospects. |
Overall score for country structure is also affected by the coverage of the power transmission network across the state |
|
Risks to potential returns |
|
Market Risks |
|
Intellectual property (IP) laws |
Markets with fair and enforced IP regulations score higher than those with endemic counterfeiting. |
ICT policy |
Subjective evaluation of official policy towards IT development, as enshrined in statute and tax code. |
Country Risk |
|
Short term external risk |
Rating from Country Risk Ratings (CRR) evaluates the vulnerability to external shock – which is the principal cause of economic crises. Such a crisis will cut investment. |
Short term financial risk |
Rating from BMI’s CRR, to denote risk of currency crisis and stability of banking sector. The former would hit revenues in hard currency, while the latter would curtail investment funding. |
Trade bureaucracy |
Rating from BMI’s CRR to denote ease of trading with the state. |
Legal framework |
Rating from CRR denotes the strength of legal institutions in each state – security of investment can be a key risk in some emerging markets |
Bureaucracy |
Rating from CRR denotes ease of conducting business in the state |
Corruption |
Rating from CRR denotes the risk of additional illegal costs/possibility of opacity in tendering/business operations affecting companies’ ability to compete |
Source: BMI |
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