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ECON2300 Final: ECON2300 Study Guide Final Exams

139 Pages
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Spring 2018

Department
Economics
Course Code
ECON2300
Professor
All
Study Guide
Final

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Econometrics: ] Lecture 1
The Econometric Model:
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.
In economics we express our ideas about relationships between economics variables using the
mathematical concept of a function. An example of this is when expressing the price of a
house in terms of its size.
Price = f(size)
Hedonic Model: A model that decomposes the item being researched into its
constituent characteristics, and obtains estimates of the contributory value of
each characteristic An example of a hedonic model for house price might be
expressed as:
Price f (size,bedrooms,bathrooms, stories, age, pool, airconditioning)
Economic theory does not claim to be able to predict the specific behaviour of any individual
or firm, but rather is describes the average or systematic behaviour of many individuals for
firms.
Economic models = Generalisation
In fact we realise that there will be a random and unpredictable component e that we will call
random error. Hence the econometric model for price would be
Price f (size,bedrooms,bathrooms, stories,age, pool,airconditioning) e
The random error e, accounts for the many factors that affect sales that we have omitted from
this simplistic model, and it also reflects the intrinsic uncertainty in economic activity.
Take for example the demand relation:
qd f (p, ps, pc,i) 1 2p 3ps 4pc 5i
The corresponding econometric model is:
qd f (p, ps, pc, i) 1 2p 3ps 4pc 5i e
Econometric Models include the error term, e
In every model there are two parts:
1. A systematic portion part we obtain from economic theory, includes assumptions
about the functional form.
2. An unobservable random component “noise” component which obscures our
understanding of the relationship among variables: e.
How Do we Obtain Data?
In an ideal world:
1. We would design an experiment to obtain economic observations or sample
information
2. Repeating the experiment N times would create a sample of N sample observations
In the real world:
Economists work in a complex world in which data on variables are “observed” and rarely
obtained from a controlled experiment. It is just not feasible to conduct an experiment to
obtain data. Thus we use non-experimental data generated by an uncontrolled experiment.
Experimental data: Variables can be fixed at specific values in repeated trials of the
experiment
Non-experimental data: Values are neither fixed nor repeatable
Most economic, financial or accounting data are collected for administrative rather than
research purposes, often by government agencies or industry. The data may be:
Time-series form data collected over discrete intervals of time (stock market index,
CPI,
GDP, interest rates, the annual price of wheat in Australia from 1880 to 2009)
Cross-sectional form data collected over sample units in a particular time period
(income in suburbs in Brisbane during 2009, or household census)
Panel data form data that follow individual microunits over time (data for 30
countries for the period 1980-2005, monthly value of 3 stock market indices over the
last 5 years)
Data may be collected at various level of aggregation:
Micro data collected on individual economic decision-making units units such as
individuals, households, or firms
Macro data resulting from a pooling or aggregating over individuals, households, or
firms at the local, state, or national levels
Data collected may also represent flow or a stock:
Flow outcome measures over a period of time, such as the consumption of petrol
during the last quarter of 2005
Stock outcome measured at a particular point in time, such as the quantity of crude
oil held by BHP in its Australian storage tanks on April 1, 2002, or the asset value of
Macquarie Bank on 5th July 2009.
Data collected may be quantitative or qualitative:
Quantitative numerical data, data that can be expressed as numbers or some
transformation of them such as real prices or per capital income
Qualitative outcomes that of an “either-or” situation that is whether an attribute is
present or not. Eg. Colour, or whether a consumer purchased a certain good or not
(Dummy variables)
Statistical Inference:
The aim of statistics is to “infer” or learn something about the real world by analysing a
sample of data. The ways which statistical inference are carried out include:
Estimating economic parameters, such as elasticities
Predicting economic outcomes, such as the enrolments in bachelor degree programs
in Australia for the next 5 years. Testing economic hypotheses, such as: Ii
newspaper advertising better than “email” advertising for increasing sales?
Econometrics includes all of these aspects of statistical inference. There are two types of
inference:
1. Deductive: go from a general case to a specific case: this is used in
mathematical proofs
2. Inferential: go from a specific case to a general case: this is used in statistics
Review of Statistic Concepts:
Random variables: Discrete and Continuous
Random variable: A random variable is a variable whose value is unknown until it is
observed, it is not perfectly predictable. The value of the random variable results from an
experiment (controlled or uncontrolled). Uppercase letters (e.g. X) are usually used to denote
random variables. Lower case letters (e.g. x) are usually used to denote values of random
variables.
Discrete random variable:
A discrete random variable can take only a finite number of values that can be counted by
using the positive integers
E.g. The number of cars you own, your age in whole years, etc.
1 If person is female
Dummy variables: D

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Description
Econometrics: ] Lecture 1 The Econometric Model: 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. In economics we express our ideas about relationships between economics variables using the mathematical concept of a function. An example of this is when expressing the price of a house in terms of its size. Price = f(size) Hedonic Model: A model that decomposes the item being researched into its constituent characteristics, and obtains estimates of the contributory value of each characteristic An example of a hedonic model for house price might be expressed as: Price f (size,bedrooms,bathrooms, stories, age, pool, airconditioning) Economic theory does not claim to be able to predict the specific behaviour of any individual or firm, but rather is describes the average or systematic behaviour of many individuals for firms. Economic models = Generalisation In fact we realise that there will be a random and unpredictable component e that we will call random error. Hence the econometric model for price would be Price f (size,bedrooms,bathrooms, stories,age, pool,airconditioning) e The random error e, accounts for the many factors that affect sales that we have omitted from this simplistic model, and it also reflects the intrinsic uncertainty in economic activity. Take for example the demand relation: d s c s c q f (p, p , p ,i) 1 2p 3p 4p 5i The corresponding econometric model is: d s c s c q f (p, p , p , i) 1 2p 3 4p 5i e Econometric Models include the error term, e In every model there are two parts: 1. A systematic portion part we obtain from economic theory, includes assumptions about the functional form.
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