14.384: Time Series Analysis.
Final Exam.
due Wednesday, December 19 (1:30PM - 4:30PM)
You may turn it in earlier by sending me an e-mail or stopping by my oce. The
exam should take you approximately 3 hours. Late works will not be accepted. Good
luck!
1.
HAC
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 11, 2008
Recitation 2
HAC
Goal: estimate J =
variance J )
Methods:
which has asymptotic
k (or, more generally, do inferen
Filtering
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 21, 2007
Recitation 3
Filtering
In lecture 4, we introduced ltering. Here well spend a bit more time deriving some c
Empirical Process Theory
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 17, 2008
Recitation 7
Empirical Process Theory
Let xt be a real-valued random k 1 vector. Consider some
Variance Decomposition
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 5, 2007
Recitation 5
Variance Decomposition
Suppose we have a VAR and we have some way to identify orthon
Fundamentalness
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 12, 2007
Recitation 6
Fundamentalness
This section is largely based on a review paper by Alessi, Barigozzi, and
Spectrum Estimation
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 26, 2008
Recitation 4
Spectrum Estimation
We have a stationary series, cfw_zt with covariances j and spec
Consumption, Income, Wealth and Cointegration
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 31, 2008
Recitation 9
Consumption, Income, Wealth and Cointegration
There is a lon
Filtering
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
November 14, 2008
Recitation 11
Filtering
Kalman ltering is a fancy name for a simple idea. Many people think that Kalman lter
Review of the Asymptotics of Extremum Estimators
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
November 21, 2008
Recitation 12
Review of the Asymptotics of Extremum Estimators
These
GMM Estimation of the NKPC
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
November 7, 2008
Recitation 10
GMM Estimation of the NKPC
One popular use of GMM in applied macro has been es
Time Series in Matlab
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 11, 2008
Recitation 2: Time Series in Matlab
Time Series in Matlab
In problem set 1, you need to estimat
Stationarity
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 5, 2008
Recitation 1
Stationarity
Denition 1. White noise cfw_et s.t. Eet = 1, Eet es = 0, Ee2t = 2
Remark 2. cf
Review of the Asymptotics of Extremum Estimators
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
November 21, 2008
Recitation 12
Review of the Asymptotics of Extremum Estimators
These
Filtering
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
November 14, 2008
Recitation 11
Filtering
Kalman ltering is a fancy name for a simple idea. Many people think that Kalman lter
14.384: Time Series Analysis.
Bank of sample problems for 14.384 Time series
Disclaimer. The problems below do not constitute the full set of problems given as
homework assignments for the course. Some of the problems are well-known folklore,
some were in
Stationarity
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 5, 2008
Recitation 1
Stationarity
Denition 1. White noise cfw_et s.t. E et = 1, E et es = 0, E e2 = 2
t
Remark 2
HAC
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 11, 2008
Recitation 2
HAC
Goal: estimate J =
variance J )
Methods:
k (or, more generally, do inference on , which has asym
Filtering
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 21, 2007
Recitation 3
Filtering
In lecture 4, we introduced ltering. Here well spend a bit more time deriving some c
Spectrum Estimation
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 26, 2008
Recitation 4
Spectrum Estimation
We have a stationary series, cfw_zt with covariances j and spec
Variance Decomposition
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 5, 2007
Recitation 5
Variance Decomposition
Suppose we have a VAR and we have some way to identify orthon
Fundamentalness
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 12, 2007
Recitation 6
Fundamentalness
This section is largely based on a review paper by Alessi, Barigozzi, and
Empirical Process Theory
1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 17, 2008
Recitation 7
Empirical Process Theory
Let xt be a real-valued random k 1 vector. Consider some
More Empirical Process Theory
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 24, 2008
Recitation 8
More Empirical Process Theory
This section of notes, especially the subsecti
Consumption, Income, Wealth and Cointegration
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 31, 2008
Recitation 9
Consumption, Income, Wealth and Cointegration
There is a lon
GMM Estimation of the NKPC
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
November 7, 2008
Recitation 10
GMM Estimation of the NKPC
One popular use of GMM in applied macro has been es
More Empirical Process Theory
1
14.384 Time Series Analysis, Fall 2008
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
October 24, 2008
Recitation 8
More Empirical Process Theory
This section of notes, especially the subsecti