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Autos 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.

Automobile Industry

There are a number of principal criteria that drive our extrapolations and forecasts for each auto variable.

Production And Sales

At a general level, we approach our forecasting from both a micro angle and a macro perspective, assessing the expansion plans of relevant multinationals and indigenous firms, while also taking account of the prevailing economic outlook. In this latter respect, BMI projections for macro variables such as industrial output, private consumption, government investment, monetary policy and GDP growth play a key role.

Figures for production are derived from a generic source (thereby ensuring maximum comparability between country data-sets), and include all vehicles with four wheels or more. For sales, we rely on data from government agencies and national automobile associations. Unless otherwise stated, sales numbers include domestically produced and imported vehicles, but not exports.

The sector’s contribution to GDP is projected by taking the US dollar production value as a proportion of nominal GDP, using BMI’s own macroeconomic and demographic forecasts.

Auto Imports And Exports

These variables are predominately calculated at the micro level, using individual company reports. Changes in government policy – particularly with regard to tariffs and quotas – also have a significant bearing.

Sources

Aside from government departments and official company reports, we rely on the International Organization of Motor Vehicle Manufacturers (OICA), other established think tanks, institutes, and international and national news agencies.

Automotives Business Environment Ratings Methodology

Ratings Overview

Conceptually, BMI’s Automotives 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: This 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: This evaluates issues within (a) the Automotives 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%
- Domestic Market	- 65%
- Country Structure	- 35%
Risks to Realisation of Potential Returns	30%
- Market Risks	- 50%
- Country Risk	- 50%

 

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
Domestic Market
Vehicle ownership, % of popn	Indicators provides numerically-based context of existing autos endowment within state
Total vehicle stock, mn
Total vehicle production, mn	Indicators denotes size and strength of domestic autos sector
Vehicle production growth (to end-forecast period)
Total vehicle sales, mn	Indicators denote both total level of demand and future growth trajectory. States with high sales volume and rapid growth score higher
Vehicle sales growth (to end-forecast period)
Country Structure
Urban/rural split, %	A highly urbanised state suggests a better, more concentrated transport network and also higher wealth. Thus, predominantly rural states score lower
Rigidity of employment index	Rating from BMI’s Country Risk Ratings (CRR) to denote ease of hiring/firing workers. Lower labour regulations score higher
Labour costs index	Rating from BMI’s CRR to denote country comparative labour costs. Low cost countries score higher
GDP per capita, US$	A proxy for wealth. High income states receive better scores than low income states
Risks to Realisation of Potential Returns
Market Risks
Competitive landscape	Subjective rating based on the number of existing operators. A large number of existing players suggests a highly competitive market
Regulatory Environment	Subjective rating based on the industry-specific regulatory environment and the presence of potentially restrictive legislation.
Country Risk
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
Bureaucracy	Rating from BMI’s CRR to denote ease of conducting business in the state
Market Orientation	Subjective rating from BMI’s CRR to denote predictability of openness to foreign investment and trade
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 Emerging Markets
Long Term Monetary Risk	Rating from BMI’s CRR. Automotive purchases tend to be funded by loans; consequently high real interest rate environment will retard industry demand, as well as constrain new plant investment
Long Term External Risk	Rating from BMI’s CRR. It denotes the state’s vulnerability to externally-induced economic shocks, which tend to be the principal triggers of economic crises.
Long Term Financial Risk	Rating from BMI’s CRR. It denotes risk of currency crisis and stability of banking sector. The former would hit revenues in hard currency, while the latter would curtail investment funding.

 

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