MSc Time Series Econometrics

Spring 2015,Taught by Tony Yates

What is time series econometrics?

• Using data series on variables like, eg, inflation, unemployment, or growth to:– Forecast [important for central banks, or finance

houses trying to price bonds, or any organisation trying to plan for the future]

– Test the implications of, eg, macroeconomic models, to sort out good theories from bad ones: for example, is there a long run trade-off between inflation and unemployment?

– TSE also has many applications in meteorology, biology, physics, chemistry…

What is TSE: example from my old job at the BoE

Every quarter the Bank of England’s Monetary Policy Committee meets to produce one of these charts.

It’s their Inflation Forecast and a vital input to their decisions about interest rates and quantitative easing.

The forecast is based on several kinds of time series model.

These models encode a view about how the economy propagates shocks out into the future.

And they are estimated.

BoE and time series modelling

• Necessity for one time series modelling task – foreasting

• ..Born out of the reality of another time series fact: that there are ‘long and variable’ lags between policy changes and effects on inflation and output

• Have to know what future inflation will be for a given policy in order to assess whether to change it

Time series econometrics/economics

• In general, time series econometrics essential and useful because of ‘time series economics’.

• Economic events have consequences not just for today, but for the future.

• Individual firms and consumers: Capital, durable goods, asset purchases, setting a rigid price, irreversible investment.

• Policymaking agents: taxes and interest rates.

Rep vs Het agent time series economics

• Direct link between representative agent macro models and aggregate time series models

• More realistically, but less practically, macro-life is a panel.

• We won’t discuss panels here. But what we do cover involves overlapping techniques, and will provide stepping-stones.

Topics covered: 1

• Estimation using maximum likelihood=finding the model that maximises the chance of having observed the data you have.

• The Kalman Filter: using data on observables to uncover the unobservable, like the natural rate of unemployment.

• Univariate and multivariate time series models: ARs, ARMAs, VARs, VARMAs

Topics covered 2

• Forecasting• Impulse response analysis• Estimation using minimum distance and

indirect inference• VARS and their time-varying equivalents.• Structural identification of economic shocks

using VARs.• Bayesian time series econometrics.

Topics covered 3

• Stationarity, unit roots. [Not cointegration]• Enabling topics like: techniques for finding

mean and variance of autoregressive processes.

• Summability and stability of time-series processes.

• Lag operators and lag polynomials.

Teaching

• 8 two hour lectures.• 8 one hour tutorials.• Tutorials to go over non-assessed problem

sets, designed to push you a little harder than the exam.

• Course, as it develops, will emphasise discussion of applications in frontier research, particularly in macro-econometrics.

Teaching 2

• Course content being built, and progress posted on my teaching homepage:

• http://tonyyateshomepage.wordpress.com/teaching/

Exam

• 3 hour exam, 4 questions.• Same format as last year. Choose 2/3

questions in each section.• Section A on univariate time series topics.• Section B on multivariate (VAR) time series

topics.• Resit.