Applied econometrics project 1

| October 22, 2018

EC 3064,
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EC3064, Applied
Econometric Project

Two lectures (2
hours each, first two weeks of term):

1st lecture: Technicalities and advice on
doing the project and presentation

2nd lecture: A (very) short course in applied
econometrics: how to apply econometric methods to selected empirical problems

Drop-by questions and answers session, 2 May
(Thursday), 5:00- 7:00pm, FJ SW PW 485

MediaCom session: Econometrics outside academia:
7 March, (Thursday), 5:00-7:00, room TBA

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The
aim of the module

·
To develop specific skills in applied
econometric research by means of supervised independent computer-based project
work.

·
To
develop the general transferable skills of:
§report
writing

§oral
presentation

Intended
learning outcomes

Not
much learning here (in terms of methodology): this is a form of an exam by
assessment.

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Intended learning
outcomes

You will learn
(mainly by yourself):

1.
How
to formulate an economic problem worth researching

2.
How
to make economic theory operational

3.
How
to find data and prepare them for econometric modelling

4.
How to put your economic and econometric
(theoretical) knowledge into practical use in terms of estimation, testing,
forecasting, policy analysis, interpretation, concluding and generalising

5.
How
to write an economic report

6.
How
to write executive summary

7.
How
to do a short oral presentation

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The general presumptions:

Imagine that….

·
You are working in the research division
of a central bank (or a ministry of finance, or large corporation) and you have
been given a by the Governor of the Bank.

·
Your report is to be read by the
Governor, of the Minister or the Chairman. Therefore,
it has to be:

1.Very clear and
easy to read

2.Grounded within
appropriate literature

3.Methods used
must be clearly explained

4.
Technical jargon should be avoided and
all technical terms explained or referenced to.

5.
Conclusions
and executive summary must be clear, precise and easy to read.

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Every empirical
report in economics has to be replicable

Replicability

Three
principles of replicability: Shallow replicability
principle

A reader of your project should be able
to obtain identical results using the data you provide.

Medium replicability
principle

A reader of your project should be able
to obtain identical results collecting data from the sources you quote and
conducting operations on data exactly as described in the project.

Deep replicability
principle

A reader of your project should be able
to reach identical conclusions using different data conforming your
generalisations.

The project must fulfil the shallow and medium
replicability principles.

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Distribution of
marks

Project (90% weight)

1.

Executive summary, giving the
main features and conclusions of the study in not

more than 300 words

[10%]

2

Clear account of what problems
the project addresses

[10%]

3.

Explanation of the economic
theory and literature

[10%]

4

Description of data and their
properties

[20%]

5

Building, estimation and
application of the model

[30%]

6

Conclusions

[20%]

Oral
presentation (10% weight)

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1.
How to formulate an economic problem worth researching

Looks trivial (consumers’ demand, money
demand, purchasing power parity, relative prices, commodity demand, terms
structure, Phillips curve, inflation persistence)

But:

1.
Make
sure you understand how this is grounded within economics

2.
Make sure you have sufficient knowledge
about the countries (commodities) chosen

3.
Make sure you know how to conclude
possible outcome (especially in terms of policy conclusions)

4.
Make
sure you know how to get data

5.
Make
sure you understand the data

6.
Make sure you know what type of
econometrics has to be used and that you are confident with the methods

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Generally:

·
Only use the methods you understand.
Better to use simple methods competently than mess up with complicated ones

·
It is advisable to decide on comparison
of two countries (or two commodities), at it will be easier to compare and draw
conclusions

·
Avoid data with possible seasonality.
For macroeconomic analysis use annual data

EC 3064,
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All the make sure’s one by
one

Ad
1: Make sure you understand how this is grounded within economics

No
need to learn anything extra. Refer to your knowledge of macroeconomics from
earlier courses.

Ad 2: Make sure you have knowledge about the countries
(commodities) chosene.g.: modelling money demand

The basic model is:mt -pt =b1ytr +b2rt +b3pt +et

·
Level
of development (comparability) of both countries

·
Types of monetary policy (affectingmt
,
pt
,
rt
): was there financial repression, periods of systematic overvaluation, periods
of hyperinflation

·
Any
differences in short-run dynamics; if so, how they could be interpreted?

·
Any
structural breaks during the last 20-30 years (affectinget )?

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Ad 3: Make
sure you know how to conclude possible outcome (especially in terms of policy
conclusions)

·
What
are the expected signs of the parameters?

·
What it would mean if in one countryb2 is insignificant
and in the other it is not?

·
What it would mean that in one country
there is neutrality of money and in the other it is not?

·
If you conclude that one country is
doing (or will do) worse than the other, what policy prescription would you
suggest?

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Ad 4: Make sure you
know how to get data

1. University
library:

Go to University webpage or University
of Leicester Digital Library and find the page Statistics, company and
financial data. You will see a list of useful links to start browsing.

2.USDA Economic Research Service

http://www.ers.usda.gov/data-products/international-macroeconomic-data-set.aspx

3.National Institute of Economic Research http://www.nber.org/data/

4.Economic Network (links to national statistics and
other sources of free data) http://www.economicsnetwork.ac.uk/links/data_free
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5.Global Finance site

http://www.gfmag.com/tools/global-database/economic-data/12069-global-economic-indicators-key-sources-and-links.html#axzz2HqjMfhL4

6.Collection of statistical links of the University
of Auckland library (also available from the University of Leicester library
site)

http://www.offstats.auckland.ac.nz/

7.For
stock market and company data: Yahoo finance http://uk.finance.yahoo.com/

Example: searching for data through
Economic Network

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To save data in
Excel (old) format
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General
comments:

·
Some data are is reverse order (from the
newest to oldest), e.g. Yahoo Finance data. Use Excel to reverse the order
·
Read carefully data descriptions (see
later Make sure that you understand the data)

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Another way, by
starting from University Library
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scroll down…
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Can be used for quick comparison of
countries

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Ad 5: Make sure you
understand the data

Read carefully descriptions of data and understand
the definitions (e.g. M1, CPI, etc.) Common mistakes with data:

1.
Confusing CPI with inflation
a)
Annual data:

CPI: Consumers’ Price Index, (base):
CPIt
=
Pt´100percentage price
increase over a chosen year
.gif”>P0

Annual CPI
inflation rate:pt

æ

Pt

-1

ö

´100=

CPIt

,

÷

Pt-1

CPIt-1

è

ø

Rate of growth
of prices:

rt=pt

-100 =

Pt

-1=

Pt

-Pt-1

» log Pt

– log Pt-1

Pt-1

Pt-1

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University Challenge question: in
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Pt

45%

t

1

is the rate of
growth of prices: a) constant, b) falling, c) increasing?

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b)
Quarterly data:
(i)
Annual
inflation measured quarterly (or q/q inflation):

p

a

æ

Pt

-1

ö

´100

=

CPIt

,

t

÷

Pt-4

CPIt-4

è

ø

Average price increases
in relation to the corresponding quarter on the previous year

(ii)
Quarterly
inflation:

p

q

æ

Pt

-1

ö

´100

=

CPIt

t

÷

Pt-1

CPIt-1

è

ø

Note that annual (q/q) inflation might not be
subject to seasonality, while quarterly inflation is seasonal.

Check that in
the series of q/q inflation data there must be autocorrelation.

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2.
Change of base of indices
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Recomputing of
all data points for the base 1990=100
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3. Confusing real
and nominal variables

Real
variable: where the effects of inflation have been factored in.

Nominal
variable: where the effects of inflation have not been accounted for.

1. Nominal Interest Rates vs. Real
Interest Rates

rt=
it-pt

2. Nominal GDP vs. real GDP
(lowercase letters mean logarithms)

real

Yt nominal

or

real

nominal

Yt

=

yt

= yt

-pt /100

Pt

Ad 6: Make sure you know what type of
econometrics has to be used and that you are confident with the methods

See Lecture 2. Generally, be modest: it is better to
use simple method competently than a sophisticated method wrongly.

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How to
register topic of your project

Topic
registration:soft
deadline 12 February

Softmeans that it can
be breached, but those who submit the title after the deadlinemight be
asked to choose the topic if it has already been taken.

If two or more identical or very similar topics have
been submitted on time, this one will be chosen which is best motivated. The
others will be asked to change topic.

email
to wch@le.ec.uk, subject line: EC3065 Contents of this mail:

1.Proposed title
2.Data source
(e.g. OECD database, etc.)

3.
Proposed methodology (e.g. static OLS
regression, general-to-specific modelling, cointegration analysis, etc.)

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Structure and
contents of the project

1.
Executive summary(1 page)

·
Aim,

·
Theory,

·
Data,

·
Methods,

·
Conclusions regarding the quality of the
results (comparative and policy analysis and/or forecasts)

Avoid technical jargon. It is addressed
to a central bank Governor, or a senior executive of a large corporation, who
wants to spend 2 minutes on it and to find out the answer to the research
problem and its practical consequences.

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Two
examples of executive summaries Example 1:

The
projects presents estimated models of money demand in China and South Korea. It
uses annual data from the period 1995-2012. The model contains data on M1, real
income and base interest rates. The short-run models have been estimated by the
ordinary least squares method and the long-run models by the Johansen method.
For each country the Johansen method indicates the existence of one
cointegrating vector, at the significance level of 0.05. Fit of both short-run
models is good: for China the coefficient of determination is 0.65 and for
South Korea it is 0.72. The regression coefficients are significant, with the
exception of that for the interest rate in China, which is not significant.
However, the distributions of the residuals are not normal, neither for China
nor South Korea, Forecasting properties of the model for South Korea are
admissible (in the ex-post forecasting exercise both forecast chi-square and
Chow forecasting statistics are insignificant at the 5% significance level for
forecasting for up to 5 years), while for China statistics are insignificant
for only 2 years forecasts. The overall conclusion is that the model for South
Korea is better than that for China.

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Example 2

The
projects concentrates on the problem of explaining relationships between
monetary and real variables in China and South Korea and their relevance for
monetary policy decisions. Using annual data for 1995-2012, linear short-run
and long-run relations have been modelled. It was found out that for South
Korea forecasts are admissible for up to 5 years ahead, while the China they
are admissible for up to two years. Results indicate that the monetary policy
based on manipulation of the base interest rate might be less effective in
China in comparison to Korea. Also, the uncertainty related to effects of such
policy is greater for China. Is can be concluded that, in order to improve on
the efficiency of monetary policy, China should either use different monetary
instruments (like level of compulsory reserves, or relaxing on the exchange
rate control), or to strengthen the institutional base of the domestic banking.
The results might be weakened by possible structural breaks not taken into
account (for China in 2000 and in South Korea in 1998) and changes of
definitions of some relevant variables in both countries.

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2
Basic theory and empirical results
achieved so far(2-3
pages),

Do
not call this section ‘literature review’.

·
Definitions

·
Theoretical
assumptions

·
Theoretical
results

·
Possible
drawbacks and limitations

·
Possible
alternative theories (maybe worth testing)

Good
use of references:to the theory (lecture reading) and
(ideally) to other empiricalwork. Do not overdue it! Do not discuss (or
refer to) works you do not understand.

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Readings and
referencing

Readings:No need to read additional theoretical
literature. It makes sense to find, readand
quote empirical literature on the subject chosen (list of exemplary references
for some subjects is provided)

Referencing:One-to-one correspondence principle:
there should not be any items in thelist of
references which are actually not referenced and vice versa.

References
in text as: Charemza and Deadman (1997), Hildreth and Pudney (1999), Lee,
Pesaran and Smith (1997), Soper (1997) and Office for National Statistics
(1999), respectively. If text is already in brackets, use commas, e.g.

(this topic has
been discussed by Hildreth and Pudney, 1999.)

If number of
authors exceeds 3, use the first name and then et al..

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Composition of list
of references(alphabetically)

Book:

Charemza, W.W. and
Deadman, D.F. (1997). New directions in econometric practice, 2nd
edition. Cheltenham: Edward Elgar.
Chapter
in a book:

Hildreth, A. and Pudney,
S.E. (1999). Linked cross section employer-worker surveys. In Biffignandi, S.
(ed.) Micro- and Macrodata of Firms, Heidelberg: Physica Verlag, pp.
509-540.

Journal articles:

Lee, K.C., Pesaran, M.H.
and Smith, R. (1997). Growth and convergence in a multi-country empirical
stochastic Solow model, Journal of Applied Econometrics, vol 12, pp.
357-392.

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Internet article:

Soper, J.B. (1997).
‘Integrating interactive media in courses: the WinEcon software with workbook
approach’, Journal of Interactive Media in Education http://www.jime.open.ac.uk/97/2

Data publication:

Office for National
Statistics (1999). Family Spending. Report of the 1998 Family Expenditure
Survey.London: HMSO

Internet source:

Million, N. (2001),
‘Monetary policies, the oil crisis and the Fisher effect hypothesis, University
Paris I’ – Eurequa, paper presented at the European Meeting of the Econometric
Society, Venice, 2002, http://www.eea-esem.com/papers/eea-esem/esem2002/1596/million.pdf

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3 The development of estimable
models(e.g.
Patterson, ch. 10): (2-3 pages)

·
How to replace in the theoretical model
the macroeconomic notions by statistical data.

·
Problem
of unobservable variables:
§How
to solve out unobservable variables (e.g. permanent income)

§How
(and why) to simplify

§How
to deal with expectations

3
Description of data quality and sources(2-3 pages)

·
Clear
definitions of particular variables

·
Choice
of data period (brief justification)

·
Pre-sample,
sample and forecast periods

Remember
the replicability principles

·
Recalculation
tricks:
§Related
to changes in definition and measurements of variables

§Related
to changes in definition of the base for index variables

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§Approximation
of gaps (missing observations) in the series Recalculation of the nominal into
real variables
§Your
assessment of the data quality

Important:
write down as much as possible about data deficiencies. No need to use perfect
data!

5 Static and dynamic properties of the
data(9
pages)

· Visual,
descriptive analysis of time series of data. Graphs:

1.
Time series of data: original and
transformed for the modelling (e.g. income and money in logs)

2.
Histograms of potentially stationary
series (e.g. interest rates, first differences of logs of money and income
variables)

3.
Visual
analysis of stationarity, and normality of data

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4.
Are there any outliers (structural
breaks) in the series. If so, why did they happen? (look back to the
literature, check the relevant issues of The Economist, etc.)

·
Stationarity
properties (Dickey-Fuller test, Perron test, etc.)

Good, thorough
analysis
(relevant
output should go to the Appendix)

·
Further statistical analysis of stationary
transformations of data (autocorrelation function, normality, outliers)

6.
Estimation and economic interpretation of the
long-run relationship(4-5 pages)

·
Formulation and economic rationale for
the long-run relationship (is the cointegrating vector known?). What are the
expected signs (and values) of the long-run parameters?

·
Cointegration
analysis, graphical and analytical.

7.
Construction of the short-run error-correction model
(3-4
pages)

·
Directly

·
Through
ADL modelling

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Are the results interpretable? (expected
signs of estimates, autocorrelation, Student-t statistics).

8 Forecasting and policy analysis(2-3 pages)

Ex-postforecasting
analysis: Chow and Forecast2tests. Are there
forecastingproperties of the model adequate? What are the
policy-related consequences of your empirical findings?

9.
Conclusions:1page

What
has been achieved; what is good and what is bad about the model?

a)
Are
assumptions realistic?

b)
Are
data adequate (long enough, of a good quality, etc)?

c)
Is
any important series missing or approximated?

d)
Are
estimates interpretable?

e)
Is
model good enough for forecasting/policy analysis?

f)
How
it can be further developed in the future?

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In order to achieve
high marks

v
Do a comparative analysis: do the
computations for two different countries/markets/commodities and compare the
results

v
Mistakes in interpretation an conclusions are more
costly than the mistakes in computations

v
Remember of replicability
principles; describe all computations in such way that it is possible to
replicate your results.

In order to achieve any
mark greater than 0:

The assignment must be accompanied by an
appendix containing data and all computations

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Presentation

ØEach
student will give a maximum 8 minutes presentation in front of two staff
assessors and the other (five or six) students during the one-hour slot.

ØAll
the students must be present for the whole hour and act as the audience.

ØAddress
you presentation to an intelligent, but not necessarily expert, audience (avoid
unnecessary technical jargon)

Ø
Make your slides readable (not too much text on the
slides, use reasonably large font)

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Some advice:

Do not prepare too many slides. During 8 minutes you
won’t be able to go clearly through more than 5-10 slides.

Example of
slides structure

1:
Title
and main hypothesis

2:
Some
further hypothesis

3:
Outline
of the theory (very brief)

4:
Few
information about the countries (markets) chosen

5:
Some
information about time series

6:
A
plot of the series

7:
A
slide on econometric methods

8:
Main
results achieved/expected

9:
Policy
conclusions/prescriptions/advice

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Possible
additional/alternative slides:

Ø
Why did I choose this particular model?

ØControversies
in the literature

ØWhat
puzzles me here?

ØBrief
history of market (countries), only if relevant to the method (theory)

Time your
presentation accurately!
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EC3064, Applied
Econometric Project

Two lectures (2
hours each, first two weeks of term):

1st lecture: Technicalities and advice on
doing the project and presentation

2nd lecture: A (very) short course in applied
econometrics: how to apply econometric methods to selected empirical problems

Drop-by questions and answers session, 2 May
(Thursday), 5:00- 7:00pm, FJ SW PW 485

MediaCom session: Econometrics outside academia:
7 March, (Thursday), 5:00-7:00, room TBA

EC 3064, PAGE2
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The
aim of the module

·
To develop specific skills in applied
econometric research by means of supervised independent computer-based project
work.

·
To
develop the general transferable skills of:
§report
writing

§oral
presentation

Intended
learning outcomes

Not
much learning here (in terms of methodology): this is a form of an exam by
assessment.

EC 3064,
PAGE3
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Intended learning
outcomes

You will learn
(mainly by yourself):

1.
How
to formulate an economic problem worth researching

2.
How
to make economic theory operational

3.
How
to find data and prepare them for econometric modelling

4.
How to put your economic and econometric
(theoretical) knowledge into practical use in terms of estimation, testing,
forecasting, policy analysis, interpretation, concluding and generalising

5.
How
to write an economic report

6.
How
to write executive summary

7.
How
to do a short oral presentation

EC 3064,
PAGE4
.jpg”>
The general presumptions:

Imagine that….

·
You are working in the research division
of a central bank (or a ministry of finance, or large corporation) and you have
been given a by the Governor of the Bank.

·
Your report is to be read by the
Governor, of the Minister or the Chairman. Therefore,
it has to be:

1.Very clear and
easy to read

2.Grounded within
appropriate literature

3.Methods used
must be clearly explained

4.
Technical jargon should be avoided and
all technical terms explained or referenced to.

5.
Conclusions
and executive summary must be clear, precise and easy to read.

EC 3064,
PAGE5
.jpg”>
Every empirical
report in economics has to be replicable

Replicability

Three
principles of replicability: Shallow replicability
principle

A reader of your project should be able
to obtain identical results using the data you provide.

Medium replicability
principle

A reader of your project should be able
to obtain identical results collecting data from the sources you quote and
conducting operations on data exactly as described in the project.

Deep replicability
principle

A reader of your project should be able
to reach identical conclusions using different data conforming your
generalisations.

The project must fulfil the shallow and medium
replicability principles.

EC 3064,
PAGE6
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Distribution of
marks

Project (90% weight)

1.

Executive summary, giving the
main features and conclusions of the study in not

more than 300 words

[10%]

2

Clear account of what problems
the project addresses

[10%]

3.

Explanation of the economic
theory and literature

[10%]

4

Description of data and their
properties

[20%]

5

Building, estimation and
application of the model

[30%]

6

Conclusions

[20%]

Oral
presentation (10% weight)

EC 3064,
PAGE7
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1.
How to formulate an economic problem worth researching

Looks trivial (consumers’ demand, money
demand, purchasing power parity, relative prices, commodity demand, terms
structure, Phillips curve, inflation persistence)

But:

1.
Make
sure you understand how this is grounded within economics

2.
Make sure you have sufficient knowledge
about the countries (commodities) chosen

3.
Make sure you know how to conclude
possible outcome (especially in terms of policy conclusions)

4.
Make
sure you know how to get data

5.
Make
sure you understand the data

6.
Make sure you know what type of
econometrics has to be used and that you are confident with the methods

EC 3064, PAGE8
.jpg”>

Generally:

·
Only use the methods you understand.
Better to use simple methods competently than mess up with complicated ones

·
It is advisable to decide on comparison
of two countries (or two commodities), at it will be easier to compare and draw
conclusions

·
Avoid data with possible seasonality.
For macroeconomic analysis use annual data

EC 3064,
PAGE9
.jpg”>
All the make sure’s one by
one

Ad
1: Make sure you understand how this is grounded within economics

No
need to learn anything extra. Refer to your knowledge of macroeconomics from
earlier courses.

Ad 2: Make sure you have knowledge about the countries
(commodities) chosene.g.: modelling money demand

The basic model is:mt -pt =b1ytr +b2rt +b3pt +et

·
Level
of development (comparability) of both countries

·
Types of monetary policy (affectingmt
,
pt
,
rt
): was there financial repression, periods of systematic overvaluation, periods
of hyperinflation

·
Any
differences in short-run dynamics; if so, how they could be interpreted?

·
Any
structural breaks during the last 20-30 years (affectinget )?

EC 3064,
PAGE10
.jpg”>

Ad 3: Make
sure you know how to conclude possible outcome (especially in terms of policy
conclusions)

·
What
are the expected signs of the parameters?

·
What it would mean if in one countryb2 is insignificant
and in the other it is not?

·
What it would mean that in one country
there is neutrality of money and in the other it is not?

·
If you conclude that one country is
doing (or will do) worse than the other, what policy prescription would you
suggest?

EC 3064,
PAGE11
.jpg”>
Ad 4: Make sure you
know how to get data

1. University
library:

Go to University webpage or University
of Leicester Digital Library and find the page Statistics, company and
financial data. You will see a list of useful links to start browsing.

2.USDA Economic Research Service

http://www.ers.usda.gov/data-products/international-macroeconomic-data-set.aspx

3.National Institute of Economic Research http://www.nber.org/data/

4.Economic Network (links to national statistics and
other sources of free data) http://www.economicsnetwork.ac.uk/links/data_free
.jpg”>

EC 3064,
PAGE12
.jpg”>
5.Global Finance site

http://www.gfmag.com/tools/global-database/economic-data/12069-global-economic-indicators-key-sources-and-links.html#axzz2HqjMfhL4

6.Collection of statistical links of the University
of Auckland library (also available from the University of Leicester library
site)

http://www.offstats.auckland.ac.nz/

7.For
stock market and company data: Yahoo finance http://uk.finance.yahoo.com/

Example: searching for data through
Economic Network

EC 3064, PAGE13
.jpg”>

EC 3064, PAGE14
.jpg”>

EC 3064, PAGE15
.jpg”>

EC 3064, PAGE16
.jpg”>

EC 3064, PAGE17
.jpg”>

EC 3064, PAGE18
.jpg”>

EC 3064, PAGE19
.jpg”>

EC 3064, PAGE20
.jpg”>

EC 3064, PAGE21
.jpg”>

EC 3064,
PAGE22
.jpg”>
To save data in
Excel (old) format
.jpg”>

EC 3064, PAGE23
.jpg”>

General
comments:

·
Some data are is reverse order (from the
newest to oldest), e.g. Yahoo Finance data. Use Excel to reverse the order
·
Read carefully data descriptions (see
later Make sure that you understand the data)

EC 3064,
PAGE24
.jpg”>
Another way, by
starting from University Library
.jpg”>

EC 3064, PAGE25
.jpg”>

EC 3064, PAGE26
.jpg”>

EC 3064,
PAGE27
.jpg”>
scroll down…
.jpg”>

Can be used for quick comparison of
countries

EC 3064, PAGE28
.jpg”>

Ad 5: Make sure you
understand the data

Read carefully descriptions of data and understand
the definitions (e.g. M1, CPI, etc.) Common mistakes with data:

1.
Confusing CPI with inflation
a)
Annual data:

CPI: Consumers’ Price Index, (base):
CPIt
=
Pt´100percentage price
increase over a chosen year
.gif”>P0

Annual CPI
inflation rate:pt

æ

Pt

-1

ö

´100=

CPIt

,

÷

Pt-1

CPIt-1

è

ø

Rate of growth
of prices:

rt=pt

-100 =

Pt

-1=

Pt

-Pt-1

» log Pt

– log Pt-1

Pt-1

Pt-1

EC 3064, PAGE29
.jpg”>

University Challenge question: in
.jpg”>

Pt

45%

t

1

is the rate of
growth of prices: a) constant, b) falling, c) increasing?

EC 3064,
PAGE30
.jpg”>
b)
Quarterly data:
(i)
Annual
inflation measured quarterly (or q/q inflation):

p

a

æ

Pt

-1

ö

´100

=

CPIt

,

t

÷

Pt-4

CPIt-4

è

ø

Average price increases
in relation to the corresponding quarter on the previous year

(ii)
Quarterly
inflation:

p

q

æ

Pt

-1

ö

´100

=

CPIt

t

÷

Pt-1

CPIt-1

è

ø

Note that annual (q/q) inflation might not be
subject to seasonality, while quarterly inflation is seasonal.

Check that in
the series of q/q inflation data there must be autocorrelation.

EC 3064,
PAGE31
.jpg”>
2.
Change of base of indices
.jpg”>

EC 3064,
PAGE32
.jpg”>
Recomputing of
all data points for the base 1990=100
.jpg”>

EC 3064, PAGE33
.jpg”>

3. Confusing real
and nominal variables

Real
variable: where the effects of inflation have been factored in.

Nominal
variable: where the effects of inflation have not been accounted for.

1. Nominal Interest Rates vs. Real
Interest Rates

rt=
it-pt

2. Nominal GDP vs. real GDP
(lowercase letters mean logarithms)

real

Yt nominal

or

real

nominal

Yt

=

yt

= yt

-pt /100

Pt

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…the xxxxxxxxx of xxx Minister or xxx Chairman Therefore, xx has xx xxx 1 xxxx clear and xxxx to read x Grounded xxxxxx xxxxxxxxxxx literature x Methods used xxxx be clearly xxxxxxxxx 4 xxxxxxxxx xxxxxx should xx avoided and xxx technical terms xxxxxxxxx or xxxxxxxxxx xx 5 xxxxxxxxxxx and executive xxxxxxx must be xxxxxx precise xxx xxxx to xxxx EC 3064, xxxxx Every empirical xxxxxx in xxxxxxxxx xxx to xx replicable Replicability xxxxx principles of xxxxxxxxxxxxxx Shallow xxxxxxxxxxxxx xxxxxxxxx A xxxxxx of your xxxxxxx should be xxxx to xxxxxx xxxxxxxxx results xxxxx the data xxx provide Medium xxxxxxxxxxxxx principle x xxxxxx of xxxx project should xx able to xxxxxx identical xxxxxxx xxxxxxxxxx data xxxx the sources xxx quote and xxxxxxxxxx operations xx xxxx exactly xx described in xxx project Deep xxxxxxxxxxxxx principle x xxxxxx of xxxx project should xx able to xxxxx identical xxxxxxxxxxx xxxxx different xxxx conforming your xxxxxxxxxxxxxxx The project xxxx fulfil xxx xxxxxxx and xxxxxx replicability principles xx 3064, PAGE6 xxxxxxxxxxxx of xxxxx xxxxxxx (90% xxxxxxx 1 Executive xxxxxxxx giving the xxxx features xxx xxxxxxxxxxx of xxx study in xxx more than xxx words xxxxx x Clear xxxxxxx of what xxxxxxxx the project xxxxxxxxx [10%] x xxxxxxxxxxx of xxx economic theory xxx literature [10%] x Description xx xxxx and xxxxx properties [20%] x Building, estimation xxx application xx xxx model xxxxx 6 Conclusions xxxxx Oral presentation xxxx weight) xx xxxxx PAGE7 x How to xxxxxxxxx an economic xxxxxxx worth xxxxxxxxxxx xxxxx trivial xxxxxxxxxxxxx demand, money xxxxxxx purchasing power xxxxxxx relative xxxxxxx xxxxxxxxx demand, xxxxx structure, Phillips xxxxxx inflation persistence) xxxx 1 xxxx xxxx you xxxxxxxxxx how this xx grounded within xxxxxxxxx 2 xxxx xxxx you xxxx sufficient knowledge xxxxx the countries xxxxxxxxxxxxx chosen x xxxx sure xxx know how xx conclude possible xxxxxxx (especially xx xxxxx of xxxxxx conclusions) 4 xxxx sure you xxxx how xx xxx data x Make sure xxx understand the xxxx 6 xxxx xxxx you xxxx what type xx econometrics has xx be xxxx xxx that xxx are confident xxxx the methods xx 3064, xxxxx xxxxxxxxxx · xxxx use the xxxxxxx you understand xxxxxx to xxx xxxxxx methods xxxxxxxxxxx than mess xx with complicated xxxx · xx xx advisable xx decide on xxxxxxxxxx of two xxxxxxxxx (or xxx xxxxxxxxxxxxx at xx will be xxxxxx to compare xxx draw xxxxxxxxxxx xx Avoid xxxx with possible xxxxxxxxxxx For macroeconomic xxxxxxxx use xxxxxx xxxx EC xxxxx PAGE9 All xxx make sure’s xxx by xxx xx 1: xxxx sure you xxxxxxxxxx how this xx grounded xxxxxx xxxxxxxxx No xxxx to learn xxxxxxxx extra Refer xx your xxxxxxxxx xx macroeconomics xxxx earlier courses xx 2: Make xxxx you xxxx xxxxxxxxx about xxx countries (commodities) xxxxxxx g : xxxxxxxxx money xxxxxx xxx basic xxxxx is:mt -pt xxxxxx +b2rt +b3pt xxx · xxxxx xx development xxxxxxxxxxxxxxx of both xxxxxxxxx · Types xx monetary xxxxxx xxxxxxxxxxxx , xx , rt xx was there xxxxxxxxx repression, xxxxxxx xx systematic xxxxxxxxxxxxxx periods of xxxxxxxxxxxxxx · Any xxxxxxxxxxx in xxxxxxxxx xxxxxxxxx if xxx how they xxxxx be interpreted? xx Any xxxxxxxxxx xxxxxx during xxx last 20-30 xxxxx (affectinget )? xx 3064, xxxxxx xx 3: xxxx sure you xxxx how to xxxxxxxx possible xxxxxxx xxxxxxxxxxx in xxxxx of policy xxxxxxxxxxxx · What xxx the xxxxxxxx xxxxx of xxx parameters? · xxxx it would xxxx if xx xxx countryb2 xx insignificant and xx the other xx is xxxx xx What xx would mean xxxx in one xxxxxxx there xx xxxxxxxxxx of xxxxx and in xxx other it xx not? xx xx you xxxxxxxx that one xxxxxxx is doing xxx will xxx xxxxx than xxx other, what xxxxxx prescription would xxx suggest? xx xxxxx PAGE11 xx 4: Make xxxx you know…

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11111122.doc (253.5 KB)Preview: J-B xxxxxxxxx is xxxxxxxxxxx as EMBED xxxxxxxx 3 with x degrees xx xxxxxxx I xxxx that except xxx Bangladesh and xxx Lanka xx xxxxxx reject xxx null hypothesis xx normal distribution xxx correlation xxxxxxxxxxxx xx the xxx real exchange xxxx series are xxxxxxxxx in xxxxx x Most xx the correlations xxx very high xxxxx 2 xxxxxxxxxxx xxxxxxxxxxxx for xxx Log Real xxxxxxxx Rate of xxx SAARC xxxxxxxxx xxxxxxxxxxx India xxxxxxxx Bangladesh Sri xxxxx Nepal Bhutan xxxxx 1 xxxx xxxxxxxx 0 xxxx 1 0000 xxxxxxxxxx 0 8173 x 8041 x xxxx Sri xxxxx 0 6751 x 6968 0 xxxx 1 xxxx xxxxx 0 xxxx 0 9234 x 8571 0 xxxx 1 xxxx xxxxxx 0 xxxx 0 8793 x 6632 0 xxxx 0 xxxx x 0000 xxxxxxx 1 and x plot the xxxx exchange xxxx xxxx exchange xxxx and the xxxxxxxxx of the xxxx exchange xxxxx xxxxxxxxxxxx of xxx SAARC countries xxxx 1970-2012 Figure x Real xxxxxxxx xxxx (1970-2012) xxxxxx 2 Log xxxx Exchange Rate xxxxxxxxxxx The xxxxxxxxxxxxxxx xxxxxxxxx are xxxxx in Figures x to 8 xxxxxx employing xxx xxxxxx unit xxxx and other xxxx the autocorrelation xxxxx of xxx xxxxxxxxx of xxxx exchange rates xxx each country xxx derived xxx xxxxxxxxxxxxxxxx of xxx real exchange xxxx up to xx lags xxxxxxx xx Figure x to Figure x show that xxxxxxxxxxxxxxx in xxx xxxxxx dies xxx very slowly xxxx simple analysis xxxxxxxxx that xxx xxxxx to xxx real exchange xxxx does not xxxxxxxxx quickly xxxxxxxxxxx xx shock xxxx a long xxxxxx suggests the xxxxxxxxxxx of xxxxxxxxxxxxxx xxxxxx Figure x Autocorrelation function xxx Log Real xxxxxxxx Rate xxxxx xxxxxx 4 xxxxxxxxxxxxxxx Function for xxx Real Exchange xxxx Pakistan xxxxxx x Autocorrelation xxxxxxxx for Log xxxx Exchange Rate xxxxxxxxxx Figure x xxxxxxxxxxxxxxx function xxx Log Real xxxxxxxx Rate Sri xxxxx Figure x xxxxxxxxxxxxxxx function xxx Log Real xxxxxxxx Rate Nepal xxxxxx 8 xxxxxxxxxxxxxxx xxxxxxxx for xxx Real Exchange xxxx Bhutan 4 xxxxxxxxx Methodology xx xxxx section, xx provide a xxxxx explanation of xxx five xxxxxxxxxx xxxx root xxxxx used in xxx empirical analysis xx this xxxxx xxxxx tests xxx the conventional xxxxxxxxx Dickey and xxxxxx (1979, xxxxx xxxxx the xxxxxxxx and Perron xxxx 1988) test, xxx Kwiatkowski-Phillip-Schmidt-Shin xxxxxx xxxxxx the xxxxxxxx Dickey-Fuller test xxxxx on generalized xxxxx squares xxxxxxx xxx the xxxxxxxxxxxxx (1992) unit xxxx test with xxxxxxxxxx break x x Augmented xxxxxx and Fuller xxxx The ADF xxxx is xxxxx xx the xxxxxxxxx regression EMBED xxxxxxxx 3 EMBED xxxxxxxx 3 xxx xxxxx Equation x EMBED Equation x (4) The xxx auxiliary xxxxxxxxxx xxxxx for x unit root xx EMBED Equation x EMBED xxxxxxxx x denotes xxx deterministic time xxxxx EMBED Equation x is xxx xxxxxx first xxxxxxxxxxx to accommodate xxxxxx correlation in xxx errors, xxxxx xxxxxxxx 3 xxx EMBED Equation x , EMBED xxxxxxxx 3 x xxxxx Equation x and EMBED xxxxxxxx 3 are xxx parameters xx xx estimated xxxxxxxx (3) tests xxx the null xxxxxxxxxx of x xxxx root xxxxxxx a mean xxxxxxxxxx alternative in xxxxx Equation x xxxxxxxx (4), xx contrast, tests xxx null hypothesis xx a xxxx xxxx against x trend stationary xxxxxxxxxxx The null xxx the xxxxxxxxx xxxxxxxxxx for x unit root xx EMBED Equation x are xxxxx xxxxxxxx 3 xxxxx Equation 3 xxxxx Equation 3 xxxxx Equation x xxxxx relevant xxxxxxxx values are xxxxxxxxx from various xxxxxxxx we xxx xxx approximate xxxxxxxx values compiled xx MacKinnon (1991) xxx any xxxxx xxxxxx size, xx the estimate xx EMBED Equation x is xxx xxxxxxxxxxxxx different xxxx zero then xxx null hypothesis xx a xxxx xxxx cannot xx rejected On xxx other hand xx EMBED xxxxxxxx x , xxxx the alternative xxxxxxxxxx of a xxxx stationary xx xxxxx stationary xxxxxxxxxx holds 4 x The PP xxxx The xx xxxx is xxxx based on xxxxxxxxx (3) and xxxx but xxxxxxx xxx lagged xxxxxxxxxxx While the xxx test corrects xxx higher xxxxx xxxxxx correlation xx adding lagged xxxxxxxxxx terms to xxx right-hand xxxxx xxx PP xxxx makes a xxxxxxxxxxxxxx correction to xxxxxxx for xxxxxxxx xxxxxx correlation xxxxx Carlo studies xxxxxxx that the xx test xxxxxxxxx xxx greater xxxxx than the xxx test (see xxxxxxxx et xx x 1993) x 3 DF-GLS xxxx Elliot, Rothenberg xxx Stock xxxxxx xxxxxxxx two xxxxxxxx versions of xxx Dickey-Fuller t xxxx the xxxxx xxx Point xxxxxxx tests which xxxx substantially improved xxxxx over xxx xxx test xxxx an unknown xxxxx is present xxx efficient xxxxxxxxx xxxx of xxxxxx et al xxxxxx is based xx the xxxxx xxxxxxx test xx the alternative xxxxxxxxxx EMBED Equation x , xxxxx xxxxx Equation x , EMBED xxxxxxxx 3 and xxxxx Equation x xx the xxxxxx size The xxxxx test is xxxxx on xxx xxxxxxxxx equation xxxxx Equation 3 xxx where EMBED xxxxxxxx 3 xxxxxxxxxx xxx locally xxxxxxxx process obtained xxxx EMBED Equation x In xxxx xxxx EMBED xxxxxxxx 3 for x locally detrended xxxxxx with x xxxxxxxx and x linear trend, xxx EMBED Equation x for x xxxxxx without x linear trend xxxxx Equation 3 xx the xxxxx xxxxxxxxxxx from xxx least squares xxxxxxxxxx of EMBED xxxxxxxx 3 xx xxxxx Equation x , where xxxxx Equation 3 xxx EMBED xxxxxxxx x , xxx EMBED Equation x Following Elliot xx al xxxxxxx xxxxx Equation x is set xxxxx to -13 x The xxxx xxxxxxxxxx of xxxxx Equation 3 xx tested against xxx alternative xxxxx xxxxxxxx 3 x 4 The xxxx Test The xxxx (1992) xxxx xxx unit xxxx differs from xxx ADF and xxx PP xxxx xx that xxx series EMBED xxxxxxxx 3 is xxxxxxx to xx xxxxxxxx stationary xxxxx the null xxx differently, the xxxx test xxxxxxxx xxx null xxx the alternative xxxxxxxxxx The KPSS xxxxxxxxx is xxxxx xx the xxxxxxxxx from the xxxxxxxx least squares xxxxxxxxxx which xxxxx xxx following xxxx EMBED Equation x EMBED Equation x where x xx a xxxxxx deterministic trend, xxxxx Equation 3 xx a xxxxxxxxxx xxxxxx and xxxxx Equation 3 xx a random xxxx EMBED xxxxxxxx x , xxxxx EMBED Equation x are i x d xxxxx xxxxxxxx 3 xxx initial value xx EMBED Equation x is xxxxxxx xx fixed xxx is interpreted xx an intercept xxx test xx xxxxxxxxx by xxxxx regressing EMBED xxxxxxxx 3 on x constant xxx x trend xxxx allowing one xx obtain the xxxxxxxxx The xxxx xxxxxxxxx is xxxxxxx as EMBED xxxxxxxx 3 EMBED xxxxxxxx 3 xxxxx xxxxx Equation x , EMBED xxxxxxxx 3 is xxx partial xxx xx the xxxxxxxxxx EMBED Equation x is a xxxxxxxxxx non-parametric xxxxxxxx xx the xxxxxxxxxxx variance and xxxxx Equation 3 xx the xxxxxx xxxx Kwiatkowski xx al (1992) xxxx that the xxxxxxxxx EMBED xxxxxxxx x has x nonstandard distribution, xxx critical values xxx provided xxxxxxx xx the xxxxxxxxxx value of xxxxx Equation 3 xx large, xxxx xxx null xx stationarity for xxx KPSS test xx rejected xx xxxx study x test for xxx unit root xxxxxxxxxx as xxxx xx the xxxxxxxxxx hypothesis It xxxxxx me in xxxxxxxxxxxxxx among xxxxxx xxxx appear xx be stationary, xxxxxx that appear xx have xxxx xxxxx and xxxxxx for which xxx data (or xxx tests) xxx xxx sufficiently xxxxxxxxxxx to be xxxx whether they xxx stationary xx xxxxxxxxxx According xx Henricsson and xxxxxxx (1995), comparing xxx outcomes xx xxxxxxx both xxx null of xxxxxxxxxxxx and the xxxx of xxxxxxxxxxxxxxxx xxxxxxxx more xxxxxxxxxxx than any xxxxxx testing Simultaneous xxxxxxxx of xxxx xxxxx strengthens xxx inferences about xxx stationarity or xxxxxxxxxxxxxxxx of x xxxx series, xxxxxxxxxx when the xxxxxxxx of the xxx nulls xxxxxxxxxxx xxxx other xxxx joint testing xxx been known xx confirmatory xxxxxxxx xxx example, xx the null xx stationarity is xxxxxxxx (rejected) xxx xxx null xx non-stationarity is xxxxxxxx (accepted), we xxxx confirmation xxxx xxx series xx stationary (non-stationary) xxxxxxxxxxx we can xxx have xxxxxxxxxxxx xx both xxxxx are accepted xx both are xxxxxxxx 4 x xxxxx and xxxxxxx Test Following xxxxxxx characterization of xxx form xx xxxxxxxxxx break, xxxxx and Andrews xxxxxxx with three xxxxxx to xxxx xxx a xxxx root (1) xxxxx A, which xxxxxxx a xxxxxxxx xxxxxx in xxx level of xxx series (2) xxxxx B, xxxxx xxxxxx for x one-time change xx the slope xx the xxxxx xxxxxxxxx and
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