Econometrics for Management, Lecture notes of Econometrics and Mathematical Economics

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Chapter 1

Introduction

Introduction:

The Nature and Purpose of Econometrics

• What is Econometrics?
• Literal meaning is “measurement in economics”.
• “Econometrics is about how we can use theory and data from economics, business and the social sciences, along with tools from statistics, to answer “how much” type questions.” (Hill, Griffiths, and Judge, Introduction to Econometrics, 2 nd edition, John Wiley & Sons, Inc., 2001).

Continuous and Discrete Data

• Continuous data can take on any value and are not confined

to take specific numbers. (Eg. Profit, )

• On the other hand, discrete data can only take on certain

values, which are usually integers (Eg. Number of

employees, quantity sold for countable)

Cardinal, Interval, Ordinal and Nominal Numbers

• Another way in which we could classify numbers is according to whether they are cardinal, ordinal, or nominal.
• Cardinal numbers are those where the actual numerical values that a particular variable takes have meaning, and where there is an equal distance between the numerical values with absolute zero.
• Interval numbers are those where the order has meaning, interval between any two values has meaning but there is no absolute zero.
• Ordinal numbers can only be interpreted as providing a position or an ordering.

Steps involved in the formulation of econometric models Already Existing Theory or Empirical Evidence Formulation of an Estimable Theoretical Model Collection of Data Model Estimation Is the Model Statistically Adequate? No Yes Reformulate Model Interpret Model

Bayesian versus Classical Statistics

• The philosophical approach to model-building used here throughout is based on ‘classical statistics’
• This involves postulating a theory and then setting up a model and collecting data to test that theory
• Based on the results from the model, the theory is supported or refuted
• There is, however, an entirely different approach known as Bayesian statistics
• Here, the theory and model are developed together

Bayesian versus Classical Statistics (Cont’d)

• Some classical researchers are uncomfortable with the Bayesian use of prior probabilities based on judgement
• If the priors are very strong, a great deal of evidence from the data would be required to overturn them
• So the researcher would end up with the conclusions that he/she wanted in the first place!
• In the classical case by contrast, judgement is not supposed to enter the process and thus it is argued to be more objective.

Some Points to Consider When Reading papers in the academic literature

1. Does the paper involve the development of a theoretical model or is it merely a technique looking for an application, or an exercise in data mining?
2. Is the data of “good quality”? Is it from a reliable source? Is the size of the sample sufficiently large for asymptotic theory to be invoked?
3. Have the techniques been validly applied? Have diagnostic tests for violations of been conducted for any assumptions made in the estimation of the model?

Additional points to consider in applied econometrics

Use common sense and management theory

• The role of theory extends beyond the development of

the specification; it is crucial to the interpretation of the

results and to identification of predictions from the

empirical results that should be test.

Know the context

• Do not try to model without understanding the non-

statistical aspects of the real-life system you are trying to

subject to statistical analysis. (Belsley and Welch, 1988).

• History, institutions, operating constraints, measurement

peculiarities, cultural customs.

• How were the data gathered? Additional points to consider in applied econometrics

Test the estimation

• To check that the results make sense.
• The signs of coefficients as expected? Important variables

statistically significant? Are coefficient magnitudes

reasonable? Are the results consistent with theory?

Additional points to consider in applied econometrics

Report a sensitivity analysis

• Are the results sensitive to the sample period, the

functional form, the set of explanatory variables, or

measurement of proxies for the variables?

• Are robust estimation results markedly different? Additional points to consider in applied econometrics