Introduction to Econometrics

Introduction

Decision making in business and economics is often supported by the use of quantitative information.

Econometrics is concerned with summarizing relevant data information by means of a model. Such econometric models help to understand the relation between economic and business variables and to analyse the possible effects of decisions.

Econometrics was founded as a scientific discipline around 1930. In the early years, most applications dealt with macroeconomic questions to help governments and large firms in making their long-term decisions.

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Introduction

Nowadays econometrics forms an indispensable tool to model empirical reality in almost all economic and business disciplines.

There are three major reasons for this increasing attention for factual data and econometric models. Economic theory often does not give the quantitative information that

is needed in practical decision making. Relevant quantitative data are available in many economic and

business disciplines. Realistic models can easily be solved by modern econometric

techniques to support everyday decisions of economists and business managers.

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Introduction

In areas such as finance and marketing, quantitative data (on price movements, sales patterns, and so on) are collected on a regular basis, weekly, daily, or even every split second. Much information is also available in microeconomics (for instance, on the spending behaviour of households).

Econometric techniques have been developed to deal with all such kinds of information.

Econometrics is an interdisciplinary field. It uses insights from economics and business in selecting the relevant variables and models, it uses computer science methods to collect the data and to solve econometric models, and it uses statistics and mathematics to develop econometric methods that are appropriate for the data and the problem at hand.

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Econometrics as an interdisciplinary field

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Economics & Business

Econometrics

Computer Science

Statistics Mathematics

Purpose of this course

To obtains a solid understanding of econometric methods and an active training in econometrics as it is applied in practice. This involves the following steps.

1. Question- Formulate the economic and business questions of central interest.

2. Information - Collect and analyse relevant statistical data.

3. Model -Formulate and estimate an appropriate econometric model.

4. Analysis - Analyse the empirical validity of the model.

5. Application - Apply the model to answer the questions and to support decisions.

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Econometric modelling7

Economic or Business problem of

interest

Data Economic Model

Statistical Method Software

Econometric model

Ok?

No

Yes

Revise

Use for forecasting and decision making

Econometrics as separate from statistics

Econometrics is based upon the development of statistical methods for estimating economic relationships, testing economic theories and evaluating and implementing economic and business policy.

Econ0metrics has evolved as a separate discipline from mathematical statistics because the former focuses on problems inherent in collecting and analyzing non experimental economic data.

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Steps in empirical Economic Analysis

Econometric methods are relevant in virtually every branch of applied economics. They come into play either when we have an economic theory to test or when we have a relationship in mind that has some importance for business decisions or policy analysis. An empirical analysis uses data to test a theory or to estimate a relationship.

The first step in any empirical analysis is obviously the careful formulation of the question of interest. The question might deal with testing a certain aspect of an economic theory or it might pertain to testing the effect of a governmental policy

In some cases, especially those that involve testing of economic theories, a formal economic model is constructed to begin with. But it is more common to use economic theory less formally, or even to rely entirely on intuition.

After the economic model (formal or developed less formally or based on intuition) is specified, it needs to be turned into an econometric model.

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Example of economic models

A formal economic model: Becker’s (1968) economic model of criminal behaviour: In a seminal article, Noble Prize winner Gary Becker postulated a utility maximizing framework to describe an individuals participation in crime. From Becker’s perspective, decision to undertake illegal activity is one of resource allocation, with the benefits and costs of competing activities taken into account

Y = f(x1, x2, x3, x4, x5, x6,x7)Where, y=hours spent in criminal activities; x1 = wage for

an hour spent in criminal activity; x2= hourly wage in legal employment; x3 = income other than from crime or employment; x4 = probability of getting caught; x5 = probability of getting convicted if caught, x6 = expected sentence if convicted and x7 = age

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Example of economic models

Less formal economic model: A labour economist would like to examine the effects of workers productivity on wage. In this case there is little need for formal economic theory. Basic economic understanding is sufficient for realizing that factors workers are paid commensurate with their productivity which in turn depends on factors such as education, experience and training. Thus, one can think of a model

Wage = f (education, experience, training)

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Economic to Econometric model

First, in each of the last two models f(.) needs to specified Second, the variables in each of these models which cannot be

reasonably observed needs to be dealt with These ambiguities inherent in an economic model are

resolved by specifying a particular econometric model An econometric model for economic model 1: Crime =b0+b1wagem+b2othinc+b3freqarr+b4freqconv +b5avgsen+b6age+e An econometric model for economic model 2: Wage = b0+b1edu + b2exper +b3training +e Where the term ‘e’ contains factors such as ‘innate ability,

quality of education, family background etc

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Data

Often in empirical analysis we straight away start with the econometric model and then various hypotheses of interest can be stated in terms of unknown parameters and go on refining the model

Once the econometric model is specified, the requisite data is collected

The econometric methods are used to estimate the parameters in the econometric model and to formally test the hypotheses of interest

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Structure of Economic Data

Cross-section data set – consists of a sample of individuals, households, firms, cities etc taken at a given point of time. Sometimes data on all units may not correspond to precisely the same time period. An important feature of cross-sectional data is that they can often be assumed to be obtained from random sampling from an underlying population. But this assumption may not always be true.

Time series data set- consists of observations on a variable or several variables over time. A key feature of this data that makes it difficult to analyze is that economic observations are rarely independent across time. Second, issue is the frequency of data.

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Structure of Economic Data

Pooled Cross Sections data- some data have both cross-sectional and time series features. For example, two cross sectional household surveys are taken in the USA, one in1985 and one in 1990 using the same survey. To increase our sample size, we can form a pooled cross section by combining the two years. Here the econometric model normally has year as a separate variable. This data is most often analyzed as a standard cross section , except that we often need to account for the secular differences in variables across time.

Panel Data set – consists of a time series for each cross-sectional member in the data set. Key feature of this data set which distinguishes it from pooled cross section is the same cross sectional units (households, firms etc) are followed over a given time period

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Causality and Notion of Ceteris Paribus in Econometric Analysis

In most tests of economic theory, and certainly for evaluating policies, the economists’ goal is to infer that one variable has a causal effect on another variable. Simply finding an association between two or more variables might be suggestive, but unless causality can be established, it is rarely compelling.

Second, the notion of ceteris paribus- which means ‘other (relevant) factors being equal’- plays an important role in causal analysis in econometric modeling.

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Discussion ahead

Against the backdrop, we start with cross section data set and focus on explaining causal relationship between the variables under study →Regression analysis

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