econometrics part 1

ECONOMETRICS

PART 1

ECONOMICS AND FINANCE

M-EF_6

SUMMER TERM 2015

1. INTRODUCTION STRUCTURE

1.1 What is Econometrics?

1.2 Financial vs. Economic Econometrics

1.3 Types of Data

1.4 Formulating an Econometric Model

1.5 Articles in Empirical Finance

1.6 Econometric Software Packages

1.7 Learning Outcome

18/03/2015 FACULTY OF SOCIETY & ECONOMICS 2

1. INTRODUCTION PRELIMINARY REMARKS

Econometrics on an introductory level (no prior knowledge of econometrics required)

but sophisticated background in maths and (inferential) stats

Some theory, but

emphasis will be on using and applying techniques (case studies from literature, exercises)

Examples and terminology predominantly from finance (and partly from economics)

Finance compared to pure economics has gained importance

Consult additional finance literature (CAPM, APT, etc.)

Repeat maths and stats if relevant


1.INTRODUCTION 1.1 WHAT IS ECONOMETRICS?

Meaning: measurement in economics

Not only applied in economics, main techniques are of equal importance in finance; here: financial econometrics application of statistical methods to problems in finance

Examples Testing theories in finance (e.g. CAPM, APT, Black-Scholes, etc.)

Determining asset (often stock) prices/returns

Testing hypotheses concerning relationships of variables (spot and future markets, information efficiency, exchange rates, volatility)

Effects of changes in economic conditions or of announcements on financial markets; forecasting values (level and volatility of returns)

Decision-making (trading rules, hedging)


1. INTRODUCTION 1.2 FINANCIAL VS. ECONOMIC ECONOMETRICS

Toolkit same

but problems (or emphasis) and data sets different

Data: differences in frequency, accuracy, seasonality,

Economics

Often lack of data

Sometimes measured or estimated with error

Subject to many revisions


1. INTRODUCTION 1.2 FINANCIAL VS. ECONOMIC ECONOMETRICS

Normally not the case in finance (one problem: stock market index rebalancing or rebasing)

Available: different types of financial data, long history, many sources, higher frequency

In finance problems with handling and processing a large amount of data real trend/pattern or random features?


1. INTRODUCTION 1.3 TYPES OF DATA

Time series data Collected over a long period of time on one/more variable(s)

of one object

Particular frequency of observation or collection of data points (depends on collection/recording): continuously, per minute/hour/day,

weekly, monthly, quarterly, annually,

All data must be of same frequency of observation

Data can be quantitative (prices, quantities shares) or qualitative (day, rating, characteristics of persons/products)

Examples: stock index vs. business cycle; companys stock price vs. dividends; trade deficit vs. exchange rate

Data: different variables x1, , xN for many periods t = 1, , T



Cross-sectional data Data on one/more variable/s collected at a single point in time

(for one day, month, year, )

For different objects (countries, companies, persons, products/services)

Examples: stock prices, returns, ratings of companies; GDP and inflation of countries

Data: different variables x1, , xN for j = 1, , M objects at t = t*

Panel data Have both dimensions (time series = over period of time +

cross-sections = for different countries/firms/individuals)

Example: daily prices of stocks of all index companies over three years

Data: different variables x1, , xN for some points in time and objects



Continuous vs. discrete data Continuous data: can take any value (but values can be limited by

precision), determined by measurement; example: return, price, weight

Discrete data: can only take certain values (integers/whole numbers), determined by counting; examples: persons, companies, shares

Cardinal vs. ordinal vs. nominal data Cardinal: numerical value has meaning, equal distance between

numerical values (prices, returns, temperature)

Ordinal: provide a position or ordering (position in sports, mark)

Nominal: value without meaning, no ordering, values are arbitrarily assigned to show a difference (coding assigned to qualitative data, such

as male/female, places/countries, )

Different treatment and modelling


1. INTRODUCTION 1.4 FORMULATING AN ECONOMETRIC MODEL

1. Statement of problem/s and objective/s

Based on formulation of a theoretical model

Or intuition from financial theory (relation of some variables)

Should be good approximation of reality, useful for purpose

2. Collection of relevant data Primary data (survey, questionnaire)

Secondary data (information provider, internet, official publications)

3. Choice of estimation method

Univariate or multivariate approach, single or multiple approach

Functional form of equation


1. INTRODUCTION 1.4 FORMULATING AN ECONOMETRIC MODEL

4. Statistical evaluation

Assumptions required to estimate model parameters

Assumptions satisfied by data and model?

Does model describe data adequately?

Yes 5.; No 1.-3. again (new model, data, estimation techniques)

5. Interpretation of model

Estimation of sizes/signs fit to theory/intuition

Yes 6.; No 1.-3. again

6. Use of model

Test the theory, forecasts, suggest action,


1. INTRODUCTION 1.5 ARTICLES IN EMPIRICAL FINANCE

Clear and specific application of techniques and development of theory

Published in widely available, peer-reviewed journals

Methodology: techniques validly applied?, tests conducted for violations of assumptions?

Interpretation of results?, Assessment of strength of results? Are results related to questions? Replication by other

researchers possible?

Appropriate conclusions (given the results)? Overstatement of importance of results?


1. INTRODUCTION 1.6 ECONOMETRIC SOFTWARE PACKAGES

Many available: EVIEWS, GAUSS, LIMDEP, MATLAB, R (optional tutorial!), RATS, SAS, SPLUS, SPSS (optional

tutorial!), TSP,

Decision about software depends on many criteria Compatible with types of data

Suitable for models, familiar with dynamic problems

User-friendly and fast

Inexpensive

Applied in journals/text books

Provide manuals, offer technical support, etc.


1. INTRODUCTION 1.7 LEARNING OUTCOME

Meaning of time series, cross-sectional and panel data

Difference between qualitative and quantitative, and continuous and discrete data

Definition of cardinal, ordinal and nominal data

Different steps of formulating an econometric model (in-depth understanding)


econometrics part 1

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