Econometric methods to study markets

NEHA W. QURESHI

FEC-PA3-01

CENTRAL INSTITUTE OF FISHERIES EDUCATION, MUMBAI

ECONOMETRIC METHODS TO STUDY MARKETS

What is Econometrics??*The application of statistical and mathematical

theories to economics for the purpose of testing hypotheses and forecasting future trends. Econometrics takes economic models and tests them through statistical trials. The results are then compared and contrasted against real-life examples.

*Econometrics can be subdivided into two major categories: theoretical and applied.

*Econometrics uses tools such as frequency distributions, probability and probability distributions, statistical inference, simple and multiple regression analysis, simultaneous equations models and time series methods.

*NEED OF ECONOMETRICS*For Estimation

*For Hypothesis Testing

*For Forecasting

METHODOLOGY OF ECONOMETRICS

* 1. Creating a statement of theory or hypothesis.

*2. Collecting data.

*3. Specifying the mathematical model of theory.

*4. Specifying the statistical, or econometric, model of theory.

*5. Estimating the parameters of the chosen econometric model.

*6. Checking for model adequacy: Model specification testing.

*7. Testing the hypothesis derived from the model.

*8. Using the model for prediction or forecasting.

*ECONOMETRIC METHODS

USED IN MARKET

ANALYSIS

CONJOINT ANALYSIS*Conjoint analysis is a statistical technique used

in market research to determine how people value different features that make up an individual product or service.

*The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making.

*A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined.

*These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.

CONJOINT ANALYSIS PROS & CONSADVANTAGES DISADVANTAGES

Estimates psychological tradeoffs that consumers make when evaluating several attributes together

measures preferences at the individual level

uncovers real or hidden drivers which may not be apparent to the respondent themselves

realistic choice or shopping task

able to use physical objects if appropriately designed, the

ability to model interactions between attributes can be used to develop needs based segmentation

designing conjoint studies can be complex

with too many options, respondents resort to simplification strategies

difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features

respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to

poorly designed studies may over-value emotional/preference variables and undervalue concrete variables

does not take into account the number items per purchase so it can give a poor reading of market share

*FACTOR ANALYSIS*Factor analysis is multivariate statistical procedure for

grouping variables into subsets such that the variables with each set are mutually highly correlated, whereas at the same time variables in different subsets are relatively uncorrelated.

*This technique is

Usually considered as data reduction tool

Removes redundancy or duplication from a set of correlated variables

Represents correlated variables with a smaller set of “derived” variables.

Factors are formed that are relatively independent of one another.

DISCRIMINANT ANALYSIS*Discriminant Analysis is a multivariate statistical technique when the dependent variable is categorical and the independent variables are quantitative.

*Discriminant Function Analysis (DA) undertakes the same task as multiple linear regression by predicting an outcome .

*In many cases, the dependent variable consists of two groups or classifications, for example, male versus female, high versus low or good credit risk versus bad credit risk, therefore to classify between them we use Linear Discriminant Analysis (LDA).

OBJECTIVES OF DISCRIMINANT

ANALYSIS*To check whether statistically significant differences exist between the average score profiles on a set of variables for two (or more) defined groups.

*To check which of the independent variables account the most for the differences in the average score profiles of the two or more groups.

*Establishing relationship for classifying units into groups on the basis of their scores on a set of independent variables.

*Knowing the exact composition of the dimensions of discrimination between groups framed from the set of independent variables.

COST AND RETURN ANALYSIS*Cost and return analysis gives an idea about the investment made and their

outcomes. For estimating net profit using costs and return analysis, the basic components needed are capital cost, variable cost, fixed cost, total cost and the revenue.

*Marketing Cost: C= Cf+Cm1+Cm2+………………+Cmi Where, C= Total cost of marketing of the commodity

*Cf = Cost paid by the producer from the time the produce leaves till he sells it

*Cmi= Cost incurred by the ith middlemen in the process of buying and selling the products.

*Marketing Margin:

*Margin refers to the difference between the price paid and received by a specific marketing agency, such as a single retailer, or by any type of marketing agency such as retailers or assemblers or by any combination of marketing agencies such as the marketing system as a whole.

* Absolute margin is expressed in rupees.

* A percentage margin is the absolute difference in price (absolute margin) divided by the selling price.

* Mark-up is the absolute margin divided by the buying price or price paid.

* The three alternative measures which may be used in estimating market margins

* (a) Absolute margin of ith middlemen (Ami) = Pri ( PPi + Cmi)

* (b) Percentage margin of ith middlemen (Pmi)

* PRi - (PPi + Cmi)

* = --------------------- X 100

* PRi

* (c) Mark-up of ith middleman (M2)

*

* PRi - (PPi + Cmi)

* = -------------------- X 100

* Ppi

*Where,

* PRi = Total value of receipts per unit (sale price)

* Ppi = Purchase value of goods per unit (purchase price)

* Cmi = Cost incurred on marketing per unit.

* The margin includes profit to the middlemen and returns to storage, interest on capital, overheads and establishment expenditure.

*Sum of Average Gross margins method :

*The average gross margins of all the intermediaries are added to obtain the total marketing margin as well as the break up of the consumer‟s rupee :

* n Si - Pi

*MT = Σ ---------

* i=1 Oi

*MT = Total marketing margin.

*Si = Sale value of a product for ith firm

*Pi = value paid by the ith firm

*Qi = Quantity of the product handled by its firm

*i = 1, 2, . . . . n (No. of firms involved in the marketing channel).

PRICE SPREADProducer‟s share in consumer‟s rupee

PF

Ps = --- --- x 100

Pr

Where,

*Ps = Producer‟s share

*PF = Price received by the farmer

*Pr = Retail price paid by the consumer

*PRODUCERS’ PRICE INDEX*This is the net price received by the fish farmer at the

time of first sale

*A family of indexes that measures the average change in selling prices received by domestic producers of goods and services over time.

*PPIs measure price change from the perspective of the seller.

*PPI looks at three areas of production: industry-based, commodity-based, and stage-of-processing-based companies. 

*It is one of several price indices.

*Its importance is being undermined by the steady decline in manufactured goods as a share of spending.

*REGRESSION ANALYSIS

*To study the influence of different independent variables on the dependent variable.

*The purpose of multiple regression is to analyze the relationship between metric or dichotomous independent variables and a metric dependent variable.

*Multiple regression determines accuracy of the model and provides predicting values for the dependent variable.

Y = a +b1 x1+ b2 x2….+ bn xn + e* Where,

* Y → is the value of the dependent variable, which is being predicted or explained

* a → is a constant or intercept on Y axis

* b1  → is the regression coefficient for X1 and indicates the change in Y for one unit change in X1, controlling for X2, X3, ---------

* X1  → First independent variable that is explaining the variance in Y

* b2  → is the regression coefficient for X2 and indicates the change in Y for one unit change in X2, controlling for X1, X3, ---------

* X2 → Second independent variable that is explaining the variance in Y and so on.

* FUNCTIONAL ANALYSIS

*The marketing function of business strives to identify, or create, the need for a product, and then to seek out and inform potential customers in hopes that they might be persuaded to buy the product.

*This process requires research and analysis on a variety of elements including: the product, the company, the potential customers, competition and economic circumstances.

*Shepherd’s Formula(1965)*It is used to assess the marketing efficiency of the

various marketing channels.

shepherd’s Formula

ME= V - 1

Where,

V= Consumer price

I= Total marketing Cost

I

Modified Marketing Efficiency

( Acharya and Agarwal 2001) MME = FP

Where, FP= Price received by Producer

MC= Marketing Cost

MM= Marketing margin

MC + MM

CONSTRAINT ANALYSIS1. RANK BASED QUOTIENT * Rank Based Quotient (RBQ) was used to quantify

the data collected by preferential ranking technique by first ranking the parameters and then calculating the Rank Based Quotient (RBQ) given by Sabarathnam (1988), which is as follows:

**Where,

* fi = Number of fish farmers reporting a particular problem under ith rank.

* N = number of fish farmers.

* n = number of problems identified.

2. GARRETT RANKING*To find out the most significant factor which influences the respondent,

Garrett’s ranking technique was used. As per this method, respondents

have been asked to assign the rank for all factors and the outcome of such

ranking have been converted into score value with the help of the

following formula:

* Percentage= 100 (Rij – 0.5)

Where, Rij = Rank given for ith item by jth individual

Nj =No. of items ranked by jth individuals

*With the help of Garrett’s Table, the percent position estimated is

converted into scores. Then for each factor, the scores of each individual

are added and then total value of scores and mean values of score is

calculated. The factors having highest mean value is considered to be

the most important factor.

Nj

*THANK YOU