Examples Of Opinion Poll Methods

The Opinion Poll Methods are used to collect opinions of experts with sufficient knowledge about the market, such as sales executives, professional marketing experts, sales representatives and market consultants.

• Expert-Opinion Method: Companies with enough resources in terms of human and financial capital, can capitalize on them while assessing the demand for a product in a region or locality. Since sales representatives are in touch with the customer directly, they are supposed to know the future purchase plans of their customers, their preference, their reaction to the introduction of new products, their reactions to the market changes and the demand for competitive alternatives. Thus, sales representatives are likely to provide an approximate,

The most common examples of leading indicators are - net business investment index, a new order for durable goods, change in the value of inventories, corporate profits after tax, etc.

• Coincidental Series: The coincidental series include indicators which move up and down simultaneously with general economic activities. The examples of coincidental series – the rate of unemployment, the number of employees in the non-agricultural sector, sales recorded by manufacturing, retail, and trading sectors, gross national product at constant prices.

• Lagging Series: A series consisting of those indicators, which after some time-lag follows the change. Some of the lagging series are- outstanding loan, labor cost per unit production, lending rate for short-term loans, etc.

The following are the criteria on which the indicators are chosen:

─ The economic significance of the indicator; such as greater the significance the greater is the score of the

ARIMA

b. ARIMAX

A time series model can predict trends based only on the original dataset that is used to create the model. Any new data added to the model when making a prediction is automatically incorporated into the trend analysis.

• Regression techniques: Regression analysis can be used to assess the relationship between one or more independent or predictor variables and the dependent or response variable – which is the variable we want to predict. Therefore, it can not only provide a forecast, but also explain the relationship between dependent variables and independent factors.

Multiple regression analysis involves two or more predictor variables, and is a slightly more advanced forecasting method, but believed to be the most accurate when used correctly. It can be used also for time-series analysis by incorporating trend, seasonality components into the analysis.

• Random Forest: Decision trees can select important “Features” and ignore irrelevant ones. It also gives a relationship between the features and predictions thus easing interpretation of models. However, in case of Random forest it does not produce an explicit model to attain a relationship between the features and prediction, instead as an ensemble of trees inherits the property of ability to select important features. This is a black box modeling