There is an ongoing debate about the roles of quantitative and qualitative inputs in demand esti- mation and forecasting. Those in the qualitative camp argue that statistical analysis can only go so far. Demand estimates can be further improved by incorporating purely qualitative factors. Quantitative advocates insist that qualitative, intuitive, holistic approaches only serve to introduce errors, biases, and extraneous factors into the estimation task.

Suppose the head of a theater chain is convinced that any number of bits of qualitative information (the identity of the director, the film's terrific script and rock-music sound track, the Hollywood "buzz" about the film during production, even the easing of his ulcer) influence the film's ultimate box-office revenue.

How might one test which approach?purely qualitative or statistical? provides better demand or revenue estimates? Are there ways to combine the two approaches? Provide concrete suggestions.

Support response with analysis.

Wilkinson, N. (2005).Managerial Economics: A problem-solving approach.Cambridge:Cambridge.

In the previous chapter it was generally assumed that the demand function for a firm or market was known; in practice it has to be estimated from empirical data, and that is the subject of this chapter. When we speak of estimation there are a number of stages involved in this process. Some of these stages may be omitted in the simpler methods of estimation, like the first two described in the next section. However, with a statistical study, or econometric analysis as it is often called, there are essentially the following seven stages: 1 Statement of a theory or hypothesis. This usually comes from a mixture of economic theory and previous empirical studies. An example of such a theory might be that the quantity people buy Start a capture r product might depend more on the past price than on the current price. This obviously has implications regarding perfect knowledge, information costs, habit formation and 'irrational' behaviour. 2 Model specification. This means determining what variables should be included in the demand model and what mathematical form or forms such a relationship should take. These issues are again determined on the basis of economic theory and prior empirical studies. Various alternative models may be specified at this stage, since economic theory is often not robust enough to be definitive regarding the details of the form of model. This aspect is discussed in section. 3 Data collection. This stage can only be performed after the demand model has been specified, otherwise it is not known for which variables we have to collect data. However, there may be some interaction between the two stages, particularly as far as the mathematical form is concerned; as stated above, economic theory alone may be unable to specify this and we may have to refer to graphs of the data or even statistical calculations, as explained in sections and, in order to do this. Therefore the presentation of data will also be considered in this stage. We have to discuss both the type of data to be collected and the sources of data. These issues are considered in section. 4 Estimation of parameters. This means computing the values of the coefficients of the variables in the model, which as we have seen in the previous chapter correspond to the effects of an independent variable on the dependent variable. These effects can be measured in different ways, for example in terms of the marginal effects and elasticities already discussed. We have to have a technique to estimate these values, and the method of ordinary least squares (OLS) regression will be used in this context. This stage is examined in sections, and : the justification of the method is explained in appendix A. 5 Checking goodness of fit. Once a model, or maybe several alternative models, have been estimated, it is necessary to examine how well the models fit the data and to determine which model fits best. If the fit is not good it may be necessary to return to step 2 and respecify the model before moving on to the next stage. This aspect is considered in section.Ming goodness oft. Once a model, or maybe several alternative models, have been estimated, it is necessary to examine how well the models t the data and to determine which model ts best. If the t is not good it may be necessary to return to step 2 and respecify the model before moving on to the next stage. This aspect is considered in section. M resting. Having determined the best model, we want to test the hypothesis stated in the rst step; in the example quoted we want to test whether current price or past price has a greater effect on sales. This stage is discussed in appendix, since it involves inferential statistics of a more advanced nature than the rest of the chapter. Mg. This is the ultimate focus of most econometric analysis. In this context we are trying to forecast sales, and maybe producing many forecasts in the light of various possible scenarios. Some aspects of this can be considered without covering the previous stage, and these are discussed in section 4.3. It should be clear from the above process that, as far as managerial decision-making is concerned, the last two stages are the most important. However, it is not possible to test hypotheses or make forecasts reliably without a good understanding of the prior stages. 4.2 Methods There are a variety of ways that can be used to estimate demand, each of which has certain advantages and disadvantages. 4.2.1 Consumer sunreyS Firms can obtain information regarding their demand functions by using interviews and questionnaires, asking questions about buying habits, motives and intentions. These can be quick 4.2 Methods There are a variety of ways that can be used to estimate demand, each of which has certain advantages and disadvantages. 4 4.2.1 Consumer surveys Firms can obtain information regarding their demand functions by using interviews and questionnaires, asking questions about buying habits, motives and intentions. These can be quick on-the-street interviews, or in-depth ones. They might ask, for example, how much more petrol respondents would buy if its price were reduced by 15 pence per litre, or which brand of several possibilities they prefer. These methods have certain drawbacks: 1 Validity. Consumers often find it difficult to answer hypothetical questions, and sometimes they will deliberately mislead the interviewer to give the answer they think the interviewer wants. 2 Reliability. It is difficult to collect precise quantitative data by such means. 3 Sample bias. Those responding to questions may not be typical consumers. In spite of these problems, there are advantages of surveys: 1 They give up-to-date information reflecting the current business environment. 2 Much useful information can be obtained that would be difficult to uncover in other ways; for example, if consumers are ignorant of the relative prices of different brands, it may be concluded that they are not sensitive to price changes. Firms can also establish product characteristics that are important to the buyer, and priorities. Methods such as multidimensional scaling can be used to give rating scores on product characteristics. 4.2.2 Market experiments As with consumer surveys these can be performed in many ways. Laboratory experiments or consumer clinics seek to test consumer reactions to changes in variables in the demand function in a controlled environment. Consumers are normally given small amounts of money and allowed to choose how to spend this on different goods at prices that are varied by the investigator. However, such experiments have to be set up very carefully to obtain valid and reliable results; the knowledge of being in an artificial environment can affect consumer behaviour. Other types of market study involve using real markets in different geographic locations and varying the controllable factors affecting demand. This kind of test marketing has the advantage4.2.3 Statistical methods While the above methods are useful, they often do not provide management with the kind of detailed information necessary to estimate a useful demand function, and thereby test the relevant hypotheses and make forecasts. Statistical techniques, especially regression analysis, provide the most powerful means of estimating demand functions. Regression techniques do have various limitations: 1 They require a lot of data in order to be performed. 2 They necessitate a large amount of computation. 3 They suffer from a number of technical problems, which are discussed in appendix. In spite of these limitations, regression techniques have become the most popular method of demand estimation, since the widespread availability of powerful desktop PCs and software packages have made at least the first two problems easy to overcome. They are therefore the main subject of this chapter. 4.3 Model specification As stated in the introduction, there are two aspects to this stage. In order to understand this we must first distinguish a statistical relationship from a deterministic relationship. The latter are relationships known with certainty, for example the relationship among revenue, price and quantity: R =P x Q; if P and Q are known R can be determined exactly. Statistical relationships are much more common in economics and involve an element of uncertainty. The deterministic relationship is considered first. 4.3.1 Mathematical models It is assumed to begin with that the relationship is deterministic. With a simple demand curve the relationship would therefore be: Q = f(P) (4.1) If we are also interested in how sales are affected by the past price, the model might in general become: 2: = f(P , PL) (4.2)