Lecture 9 pik a mix macro finance project management
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Lecture 9: Pik -a-mix macro, finance; Project management. Referencing workshop materials Critical use of sources.docx (Filename) referencing.pptx What type of reference.docx (Filename) We are scheduling another bespoke Skills Centre workshop for next term. Writing Economics

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Lecture 9: Pik -a-mix macro, finance; Project management

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Lecture 9 pik a mix macro finance project management

Lecture 9: Pik-a-mixmacro, finance; Project management

By the way have you seen

  • Referencing workshop materials

    • Critical use of sources.docx(Filename)

    • referencing.pptx

    • What type of reference.docx(Filename)

      We are scheduling another bespoke Skills Centre workshop for next term.

      Writing Economics


      Writing Economics. Robert Neugeboren with Mireille Jacobson. @2001 The President and Fellows of Harvard University (minor revisions in Jan. 2005) ...

By the way, have you seen

Some resources

ECONOMICS 452 TIME SERIESWITHSTATA -- econ.queensu.ca/faculty/gregory/econ452/manual.pdf‎

2 Working with economic and financial data in Stata(Chris Baum)

Kennedy, A Guide to Econometrics, Chapter 18, “Time Series Econometrics”

Wooldridge, IntroductoryEconometrics

Some resources

Macro theory

  • Discussion of macro theory: You can use an extremely simple model.

  • E.g., “Export demand in China” … Start with a textbook e.g., Obstfeld and Krugman. 

  • “the following variables are predicted to have the following signs”

Macro Theory

Lit review

  • Critical evaluation, find broad themes.

  • If your project is a literature survey, try to make an argument of your own, or a ‘spin’ on the literature. Do not parrot jargon you don’t understand.

  • Try to relate it to policy … Things like quantitative easing.

  • Look at the Bank of England quarterly bulletin.

LIt review

Some key estimation issues for macroeconomic and financial data time series

Some key Estimation issues for macroeconomic and financial data (time series)

  • Trends

    • Time series variables will tend to have long-term trends. Do not infer causality from a common trend

  • Seasonality

    • Some variables (e.g., consumption) will differ by season. Regressing one variable with seasonality on another variable with seasonality will suggest a spurious relationship.

    • You may want to control for a trend term or ‘de-trend’

    • You may want to control for a seasonal term or ‘de-season’

    • Using panel data can help

Lecture 9 pik a mix macro finance project management

Autocorrelation: “Today’s error term is correlated to yesterday’s error term”

An ‘efficiency and standard errors problem’ (see previous lecture

May indicate a ‘mis-specified model’; you may need lag dependent and/or independent variable terms

Recall: ‘Moving average’ and ‘autoregressive’ terms

You may have a ‘non-stationary’ process

If you fit a model of lags, be careful to understand and explain what you are doing!

You can acknowledge the depth of your understanding and the limits of your analysis

Non stationarity

  • Stationarity: A regression is only meaningful on a stationary data series or one where there is cointegrationbetween two variables.

    • This basically means a stable long-term relationship

  • A ‘random walk’ is a non-stationary process.

    • Today’s value is yesterday’s value plus a mean-zero error term.

    • This ‘Gaussian’ process drifts wherever it wants; it has no population mean.

(non) Stationarity


  • When running a time series regression, try to consider, and test for stationarityor a co-integrating relationship.

  • When are not stationary, the same variables in ‘first-differences’ are often stationary.

    Rem: ‘first differenced’: Yt=yt-yt-1

  • You can test for, e.g., trend-stationarity of a first-differenced variable

  • Try not to get too fancy. Try to understand every technique you use. Go back to textbooks, lecture notes, and first-principles.


Lecture 9 pik a mix macro finance project management


  • 85. What evidence, if any, is there that government budget deficits affect national income (i.e., aggregate output) and employment.

    For a time series analysis:

  • Compare to “David Hendry” paper on UK Debt.

  • Regressing GDP growth on debt,

  • Consider: a change in flow regressed on a stock  transform this?

    Time series properties of these

  • Come up with dynamic specification

  • Residual autocorrelation

     Wooldridge text as a good reference.

  • Effect of debt on growth:

    • Consider single variables regression models.

    • Understand lags etc.

    • Standard time series econometrics.

The stata support classes

The stata support classes

Timetable of project

Timetable of project

  • All project supervision should be completed no later than the last day of the Spring Term -- 21 March 2014.

    • No further supervision is available during the Easter holidays!

  • I am on study leave next term; supervision of EC831 will pass to another lecturer

  • Complete the final version of your project at least one full week before the deadline to allow for last minute delays in printing and checking.

  • Online submission only of your project (also lit review/project plan) --‐‑ no later than 12.00hrs (midday) on the specified deadlines!

    Deadline for final submission: 25 April 2014, mid-day

How to budget your time

How to budget your time

Strike a balance, don’t put all your effort into one component to the exclusion of others.

Keep the big picture in mind. Keep asking ‘how does this relate to my outline, how am I working towards my planned goals’

  • Literature review/survey, discussing previous techniques and evidence

  • Economic theory

  • Methodology and econometric technique: considering, understanding, explaining and justifying

  • Working with and analysing of data

  • Presenting and discussing your results

  • Outlining, re-outlining; writing, re-writing

    • Focus especially on the abstract/introduction

      These are not isolated; one part may feed back into another.

The process of researching while writing

The process of “researching while writing”

  • Slogging through: Keep a positive attitude, but don’t rest on your laurels

  • Do not wait till the last moment … not in doing your writing, or your data work or your presentation of results

  • Don't be a perfectionist who ends up writing half a paper – write an OK paper, and then make a good paper, and then make it great paper.

     “The perfect is the enemy of the good”

Lecture 9 pik a mix macro finance project management

“[Writing] begins with mere fluency, getting the stuff down on paper. And it ends with revising again and again, until you've removed all the traps and ugliness. …[Sitting] down to write can be a problem, for it is then that your subconscious, which is dismayed by the anxiety of filling up blank pieces of paper, suggests [goofing off or doing any other type of chore]. [Fight] doubt -- the conviction that everything you've done so far is rubbish-will wash over you from time to time. … The only help is a cheerful faith that more work will raise even this rubbish up to your newly acquired standards. Irrational cheerfulness is hard to teach but good to have for any work.”


Learn from the feedback you have received

Learn from the feedback you have received

  • On your literature review and project plan

  • On previous previous term papers too

  • Remember to learn from your dissertation for your later life

Adapt improve



What are you missing? How can you adapt? How can you improve?

Typical comments on literature reviews surveys

Typical comments on literature reviews/surveys

Reviewing (suggestions for improvement); overall assessment and recommendations

What is your focal question?

Read related literature

Look at related articles in peer-reviewed journals to see how to format or express this.

See what other authors have written on this

Consider other opinions and arguments on this point

Needs to better explain the cited literature

Needs to better relate the cited literature to this paper

Needs to better consider the empirical literature that answers this question and related questions, and explain the techniques used, and how these may or may not be applied to the current paper.

-- Another empirical study made by Smith et al.(2010) also found that the stringent labour standards for agricultural produce imposed by the European Union lead to significant losses for the African producers.

How did these authors do their analysis? What data and techniques did they use? Explain the precise nature and strength of their findings.

Needs to explain theoretical models more clearly and formally

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