Running Your Own Regression Projectecon1.altervista.org/econ/edu/cup/notes/11_Regression_Project_2018A.pdf · The Growth of the Financial Sector (Spring 2013) Clas Eriksson Regression

Choosing Your TopicCollecting Your Data

Advanced Data SourcesPractical Advice for Your Project

Writing Your Research Report

Running Your Own Regression Project

Clas Eriksson

EST, MDH

February 22, 2018

Clas Eriksson Regression Project




Introduction

These notes build on:Studenmund, A. H.: Using Econometrics, Pearson, chapter 11.

Outline:

1 Choosing Your Topic2 Collecting Your Data3 Advanced Data Sources4 Practical Advice for Your Project5 Writing Your Research Report





Choosing Your Topic I

Three keys to choosing a topic:

(1) Pick something interesting (that you know something about)

The work will be pleasantYou will probably make fewer specification mistakes

(2) Make sure that data are readily available (at least 25observations)

(3) Make sure that there is some substance to your topic

Avoid purely descriptive or tautological topicsLook for topics that address an interesting economic orbehavioral question

Look for topics in your textbooks from previous courses.

Replicate an earlier study with different data(Choose a topic that has already been researched)





Choosing Your Topic II

Economic journals that provide good overviews:

Journal of Economic LiteratureJournal of Economic Perspectives

Some recent ‘Symposia’ in the Journal of EconomicPerspectives:

China (Winter 2017)The Bretton Woods Institutions (Winter 2016)Oil and Gas Markets (Winter 2016)Automation and Labor Markets (Summer 2015)Wealth and Inequality (Winter 2015)Entrepreneurship (Summer 2014)Big Data (Spring 2014)The Euro (Summer 2013)The Growth of the Financial Sector (Spring 2013)





Choosing Your Topic III

‘Top Five’ economics journals:

American Economic ReviewEconometricaJournal of Political EconomyQuarterly Journal of EconomicsReview of Economic Studies

Some specialized economics journals:

Journal of FinanceJournal of Industrial EconomicsJournal of Labor EconomicsJournal of Public EconomicsJournal of Environmental Economics and ManagementRAND Journal of EconomicsReview of Financial Studies





Choosing Your Topic IV

Sometimes the journals use very advanced methods.But they may provide ideas that you can develop (togetherwith your advisor) to something tractable.

Go to the data sources directly.That may generate ideas for topics/hypotheses

Many researchers put out the data sets used in publishedarticles on their websites.You could ‘experiment’ with them and see how the resultschange when you modify the specifications.

When you have your data: do not rush out and run your firstregression. Develop the theory and hypotheses first





What Data to Look ForWhere to Look for Economic DataMissing Data

Collecting Your Data I

Before any quantitative analysis can be done, the data mustbe collected, organized and entered into a computer

Usually time-consuming and frustrating because of:

the difficulty of finding datathe existence of definitional differences between theoreticalvariables and their empirical counterpartsthe high probability of data entry errors or data transmissionerrors

But this time is well spent: if you know the data sources anddefinitions you are much less likely to make mistakes






Collecting Your Data II

What Data to Look For

What specific dependent variable (Y ) and independentvariables (X ) do you want to study? Are they available?

Five issues to consider:

1. Time periods: If Y is measured annually, then the X ’sshould also be measured annually(What is a good length of the time period in differentproblems?)

2. Measuring quantity: A TV today has little in common witha TV 1980. It therefore makes little sense to measure quantityin units.Better to measure the variable in monetary terms (valueencompasses size and quality); more comparable across time






Collecting Your Data III

What Data to Look For (cont’d)

3. Nominal or real terms? Depends on theory. Do we want to‘clean’ for inflation (using e.g. CPI)?TV example again: probably best to use real terms

4. Differences between cross-sectional or time-series dataTV example again: National advertising would be a goodcandidate for an explanatory variable in a time-series model(with national data)But advertising in or near each state (or city) would makesense in a cross-sectional model

5. Be careful when reading (and creating) descriptions of data:

Where did the data originate?Are prices and/or income measured in nominal or real terms?Are prices retail or wholesale?






Collecting Your Data IV

Where to Look for Economic Data

Very common to use publicly available data (instead of datathat you generate yourself)

1. European data sources:

EurostatStatistics Sweden

2. Data sources for growth regressions

Penn World TablesCountry snapshots (Charles I Jones)Data Plotter (David Weil)

3. US Government publications:

Statistical Abstract of the U.S.The annual Economic Report of the PresidentThe Handbook of Labor Statistics






Collecting Your Data V

Where to Look for Economic Data (cont’d)

4. International data sources:

U.N. Statistical YearbookU.N. Yearbook of National Account Statistics

5. Internet resources:

‘Resources for Economists on the Internet’EconomagicWebECEconLit (www.econlit.org)‘Dialog’Links to these sites and other good sources of data are on theWeb site: www.pearsonhighered.com/studenmund






Collecting Your Data VI

Missing Data

What if you can’t find the data for an important variable?

1. If only a few observations are missing:

In a cross-section study you can usually afford to drop theseobservations from the sampleIn a time-series study you may interpolate values (taking themean of adjacent values—be careful)

2. If no data at all is available (for a theoretically relevantvariable), this is likely to cause omitted variables bias

Possible solution: use a proxy variableSuppose that the value of net investment is not available.Then you might use the value of gross investment as a proxy.The assumption must then be that gross investment isdirectly proportional to net investment





SurveysPanel Data

Advanced Data Sources I

In the econometrics course we only have

1 cross-sectional or time-series data2 they have been collected by observing the world around us

(instead of being created)

However:

1 time-series and cross-sectional data can be pooled to formpanel data

2 data can be generated through surveys

It probably doesn’t make sense to use such data on your firstregression project (complicated; time-consuming)(?)





SurveysPanel Data

Advanced Data Sources II

Surveys

Surveys are everywhere:

marketing firms (to learn more about products andcompetition)political candidates (to finetune their campaigns)governments (e.g. keeping track of their citizens)

Not as easy as it might seem. Surveys:

must be carefully thought through (impossible to go back)(How honest will the answers be?)must be worded precisely (and pretested) to avoid confusingthe respondent (or ‘leading’ the respondent to a particularanswer)must have samples that are random and avoid the selection,survivor, and nonresponse biases





SurveysPanel Data

Advanced Data Sources III

Panel Data

Two main reasons for pooling cross-sectional and time-seriesdata into panel data:

To increase the sample sizeTo provide an insight into an analytical question that can’t beobtained by using time-series or cross-sectional data alone(Examples: growth regression; environmental Kuznets curve)

Example: We’re interested in the relationship between budgetdeficits and interest rates

But we only have 10 years’ of annual data—too small a sampleBut if we can find the same type of data for six differentcountries, the sample becomes large enough: 10 · 6 = 60A panel regression can then be run by using country dummies(but read ch. 16 carefully before you try this)





The 10 Commandments of Applied Econometrics (Peter Kennedy)The Ethical Econometrician

Practical Advice for Your Project I

The 10 Commandments of Applied Econometrics (Peter Kennedy)

1. Use common sense and economic theory: Example:

match per capita variables with per capita variables,use real exchange rates to explain real imports or exports, etc

2. Ask the right questions:

Ask many (even silly) questions to ensure that you fullyunderstand the goal of the research

3. Know the context:

Get familiar with the history, institutions, operatingconstraints, measurement peculiarities, cultural customs, etc,underlying the object under study






Practical Advice for Your Project II

The 10 Commandments. . . (cont’d)

4. Inspect the data:

This includes calculating summary statistics, graphs, and datacleaningThe objective is to get to know the data well

5. Keep it sensibly simple:

Begin with a simple model and complicate it only if it failsThis goes for the choices of variables, functional forms andestimation methods

6. Look long and hard at your results:

Check that the results make sense, including signs, magnitudesand significanceApply the ‘laugh test’






Practical Advice for Your Project III


7. Understand the costs and benefits of data mining:

‘Bad’ data mining: deliberately searching for a specificationthat ‘works’ (i.e. ‘torturing’ the data)‘Good’ data mining: experimenting with the data to discoverempirical regularities that can inform economic theory and betested on a second data set

8. Be prepared to compromise:

The Classical Assumptions are only rarely satisfiedApplied econometricians are therefore forced to compromiseand adopt suboptimal solutions, the characteristics andconsequences of which are not always knownEconometrics is necessarily ad hoc: we develop our analysis,including responses to potential problems, as we go along






Practical Advice for Your Project IV


9. Do not confuse statistical significance with meaningfulmagnitude:

If the sample size is large enough (to make the SEs smallenough), any Null hypothesis can be rejectedSubstantive significance—i.e. ‘how large?’—is also important,not just statistical significance

10. Report a sensitivity analysis: are the results sensitive to theassumptions? Dimensions to examine:

sample period, the functional formthe set of explanatory variables, the choice of proxies

If the results are not robust across the examined dimensions,then this casts doubt on the conclusions of the research






Practical Advice for Your Project V

The Ethical Econometrician

Two reasonable goals for econometricians:

Run as few different specifications as possible while stillattempting to avoid the major econometric problems(Exception: sensitivity analysis)Report honestly the number and type of different specificationsestimated so that readers of the research can evaluate howmuch weight to give to your results





Writing Your Research Report I

Most good research reports have a number of elements incommon:

A brief introduction that defines the dependent variable andstates the goals of the research

A short review of relevant previous literature and research

An explanation of the specification of the equation (model):

Independent variablesfunctional formsexpected signs of (or other hypotheses about) the slopecoefficients

A description of the data:

generated variablesdata sourcesdata irregularities (if any)





Writing Your Research Report II

A presentation of each estimated specification, using ourstandard documentation formatIf you estimate more than one specification, be sure to explainwhich one is best (and why!)

A careful analysis of the regression results:

discussion of any econometric problems encounteredcomplete documentation of all:

equations estimatedtests run

A short summary/conclusion that includes any policyrecommendations or suggestions for further research

A bibliography

An appendix that includes all data, all regression runs, and allrelevant computer output


Documents

Running Your Own Regression Projectecon1.altervista.org/econ/edu/cup/notes/11_Regression_Project_2018A.pdf · The Growth of the Financial Sector (Spring 2013) Clas Eriksson Regression