Using mopos in the classroom
1 / 18

Using MoPoS in the Classroom - PowerPoint PPT Presentation

  • Uploaded on

Using MoPoS in the Classroom. Yvan Lengwiler WWZ, Economic Theory University of Basel, Switzerland. 2½ learning aims. 1) MoPoS aims at providing a complement to the usual curve shifting exercises.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Using MoPoS in the Classroom' - wesley-adams

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Using mopos in the classroom

Using MoPoS in the Classroom

Yvan Lengwiler

WWZ, Economic Theory

University of Basel, Switzerland

2 learning aims
2½ learning aims

1) MoPoS aims at providing a complement to the usual curve shifting exercises.

Students should understand the key macro-economic relationships as correlations, not as abstract curves.

And they should recognize the most important stylized facts in the simulation.

2) MoPoS tries to convey a realistic experience of the job of a central banker.

Lots of information is missing, but decisions have to be taken, and they have strong effects, sometimes with long lags.

2½) Students should have fun doing this.

Curve shifting
Curve shifting…

When teaching introductory macro we usually use a more or less involved combination of coordinate systems in which we shift several more or less abstract curves around.

A third semester student once told me:

"Essentially, economics is curve shifting, right?"

Of course, nothing could be more wrong, but this is the impression we give.

Vs stochastic simulation
…vs. stochastic simulation

MoPoS is a stochastic simulation of a small reduced-form mainstream macro model, which also allows the user to interact with it.

Unlike in comparative statics (i.e. curve shifting) exercises, the user of MoPoS is confronted with a constant flow of shocks. He experiences the dynamics of his policy decisions through time.

Interaction with a stochastic simulation provides a different view of the most important macroeconomic relationships, such as the IS or the Phillips curve.

It focuses on correlations and lags rather than comparative statics and abstract curves.

Monetary policy making
Monetary policy making

MoPoS allows the user to play the role of a central bank governor.

It provides a realistic simulation of the decision problem of the monetary authority, because…

  • The economy is subject to a constant stream of unobservable shocks. The player constantly experiences surprises.

  • Decisions have to be made based on very limited and not perfectly reliable information.

  • Decisions interact with the (virtual) economy in complex ways. Effects of these decisions are often visible only with a considerable lag.

  • Strategy of the player affects expectations, and these influence the available trade-offs.

  • Neither a very cautious nor a stop-go policy are successful.

The model
The model

  • Standard IS-LM-PC model with stochastic potential growth. Calibrated to quarterly data.

  • All shocks are AR(1). Innovations are normally distributed (or in some instances, can be chosen to be leptokurtic).

  • Variance and autoregressive coefficient of shocks can be set for each equation.

  • Inflation expectations are convex combination of static expectation and OLS-expectation.

  • OLS-expectation is an inflation forecast [regression of current inflation on lagged inflation, real growth, and money growth, with 1 to 4 lags].

Core of the model
Core of the model

Stochastic potential growth, Phillips-Curve, IS curve, quasi-rational ("OLS-learning") expectation.

pols is forecast of regression of current inflation on lagged inflation, real growth, money growth, with 1 to 4 lags.  is inflation stickiness parameter.

Monetary block and control block
Monetary block and control block

  • There is also a monetary block (an LM-curve with money demand shocks).

  • But money is passive in the model (except that it enters inflation forecast).

  • Control is either given to the user or to the Taylor rule ("autopilot").

  • Output gap is estimated as residual of log-linear regression of real output on time trend.

Observation errors
Observation errors

Macroeconomic statistics are typically subject to a great deal of imprecision. They are often revised, replacing the "initial estimate" with the "final estimate."

Yet, this is what the monetary authority has, and it has to decide upon the policy stance based on such imprecise information.

The model1



The model

(indirectly through money demand)

nominal interest rate

inflation expectation

real interest rate

business cycle



  • The literature on the role of credibility in many kinds of strategic situations — but especially in monetary policy — is large.

  • In the simulation, a player who follows some pattern (rule?), will earn a significant parameter of his decision variable on inflation forecast.

  • Through this simple mechanism, he has to build up this reputation by demonstrating that he does not tolerate inflation.

  • Such a reputation makes the player's job easier because his acts affect not only the interest rate, but inflation expectations as well.

Liquidity trap
Liquidity trap

It can happen that the virtual economy falls into a deflationary spiral and ends up in a liquidity trap, because the nominal interest rate is not allowed to become negative.

In reality, a true liquidity trap is a very rare phenomenon. It requires the nominal return rates on all assets to vanish.

In the simulation, however, there is only one interest rate, and when this goes to zero and deflation is strong enough — bang! — you cannot escape the trap anymore.

So, the simulation is much more likely to experience the trap than a real economy.

Possible exercises
Possible exercises

  • Several scenarios come with the download, such as "stability," "recession," or "new economy," but students can also generate new, random scenarios.

  • Students should make log of their decisions and observations. Leads to more reflection.

  • Usually I ask the students first to try to make "smiley" happy as long as possible.

    • …won't last, due to long run vertical Phillips curve, but takes out the obvious temptation to see a happy face.

Possible exercises1
Possible exercises

  • Next I ask the students to turn on the autopilot (the Taylor rule).

    • The students can try to recognize the "stylized facts" they know from the lecture.

    • They can also observe how the Taylor rule behaves. Why does it do what it does? Does it make miskates?

  • Finally, the students are asked to keep the economy stable (starting from any of the standard scenarios).

    • This is very difficult. Hopefully, students learn what lags really mean, and why sometimes a "preemptive strike" constitutes good policy.

Possible exercises2
Possible exercises

  • In order to share it, or for discussion in class, students can…

    • save a simulation (with Simulator >Save Simulation)

    • or print it (with File > Print).

  • So far, I have used the game with an intermediate macro class, with a course for continuing education, and with an Executive MBA class.

    • But I think that one could also use it with some benefit in some more advanced classes.

More advanced classes
More advanced classes

  • "Advanced mode" allows much more diverse exercises.

    • Compare true output gap with estimated gap (maybe using different methods of estimation).

  • Make additional graphs (for instance, graph the empirical Phillips curve is "real" time).

  • Explore the effect of the para-meters of the Taylor rule on the performance of this particular feedback rule.

  • One can also implement a whole new feedback rule.

  • Experiment with the parameters of the simultation.

  • One can even make the parameters themselves stochastic, giving rise to a "robust control problem."


Check out

and open the «software» link to download software and for additional information.

Email me at

[email protected]

for comments or questions.