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, macrolife 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/te
aching/
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.