ar+noise.oms 
 Estimating and forecasting a state space model
with an AR(1) state equation and additive Gaussian noise, using
the ltikf STSA function and both methods of numerical
optimization BHHH and NewtonRaphson with BFGS update

example01.oms 
 Simulate a stationary, zero mean Gaussian
autoregressive model of order 2 and perform various operations on
the resulting realization

example02.oms 
 Simulate a nonlinear (exponential) trend
with Gaussian MA innovations and illustrate how one can use the
functions gauss_newton and bhhh_ml with a
userspecified function for optimization and estimation of a
model's parameters

example03.oms 
 In this example we use two real economic
time series to illustrate the estimation and forecasting of a
dynamic transfer function, a vector autoregressive model.
Then, we show how to compare the forecasting performance of
various models using a trainingvalidation sample approach

example04.oms 
 Illustrate the simulation and estimation of
nonlinear time series models

example05.oms 
 Briefly illustrate the STSA functions for
exponential and simple smoothing of a time series

example06.oms 
 Illustrate the simulation, fitting and
forecasting based on Bayesian first order polynomial
DLM (dynamic linear model).
This is a class of simple, yet powerful models for shortterm forecasting.
The example also illustrates how to generate prediction intervals (bands)
that are frequently much more useful than point forecasts.

example07.oms 
 Replicate some results found in the book
Bayesian Forecasting and Dynamic Models
by Harrison and West (1997, SpringerVerlag)

example08.oms 
 The workhorse functions of the BAYES directory
are reference_tsDLM and fit_tsDLM which allow the user
to develop a wide range of DLMs and the use of reference priors that
simplify the analysis. In addition, we provide for a number of
functions that allow for the automatic specification of the trend and
seasonal components of a DLM model.

example09.oms 
 Illustrate the differences between the two available kernels for nonparametric estimation and forecasting. Then, we illustrate the differences in the fit between a linear and a nonparametric regression when the true model is
nonlinear but unknown.

example10.oms 
 Illustrate the simulation and estimation of
time series that exhibit long memory, that is slowly decaying
correlations of small magnitude (contrast this to nonstationary where
the correlations are slowly decaying but are large in magnitude).
Time series like these are frequently encountered in diverse fields
and the STSA toolbox provides a number of functions that can handle
the modeling of such series.

example11.oms 
 Illustrate the simulation, estimation and forecasting
of ARFIMAtype models. This class of models extends traditional, shortmemory
ARMA models, to incorporate the effects of longmemory. In the example, we
illustrate how to use the arfima_estimate and arfima_forecast
STSA functions and we compare the forecasting performance of
ARFIMA forecasts vs. forecasts generated by a linear AR model.

example12.oms 
 Illustrate the functions in the RNG directory;
we generate random numbers using the inverse distribution function
method and we then test whether the resulting data indeed appear
to come from their generating distributions

expdecay.oms 
 Simulate and estimate the fit of an exponential
decay model with yoffset of the form y = A*exp(B*x) + C + error

flu.oms 
 Analysis of the flu data in Shumway and Stoffer,
Time Series Analysis and its Applications, published by
SpringerVerlag.

gnp.oms 
 Analysis of the GNP data in Shumway and Stoffer,
Time Series Analysis and its Applications, published by SpringerVerlag

jj.oms 
 Analysis of the Johnson & Johnson data in Shumway and
Stoffer, Time Series Analysis and its Applications, published
by SpringerVerlag

nonpar_example01.oms 
 Nonparametric estimation of the density and
empirical distribution for a time series. 2 data series: (1) simulated data
and (2) the quarterly growth rate of the US Gross National Product
For the second series we specifically have the Real Gross National Product,
USA, quarterly 1947Q1 to 2006Q2, FRED database series GNPC96
Federal Reserve Bank of Saint Louis online database
http://research.stlouisfed.org/fred2/, in billions of chained 2000 dollars

nonpar_example02.oms 
 Nonparametric smoothing and extrapolation.
1 data series: daily closing price of General Electric from 1/02/2003 to 1/17/2006

nonpar_example03.oms 
 Various nonparametric autoregression methods,
crossvalidation and forecasting.
3 data series: (1) simulated data (2) Canadian Lynx data and
(3) US interest rate data.
The Canadian lynx data are documented in the reference book. The US interest
rate data are 3 month treasure bill rates, USA, monthly from
01/1970 to 09/2006, FRED database series TB3MS, Federal Reserve Bank of
Saint Louis online database
at http://research.stlouisfed.org/fred2/

nonpar_example04.oms 
 Various nonparametric autoregression
methods using simulated data, including a partially linear model.

optimization.oms 
 Illustrate the use of the quasinewton
optimization algorithm for solving minimization problems.
The test functions are adopted from the book Optimization:
Foundations and Applications, by Ronald E. Miller, Wiley Interscience.

pod_example01.oms 
 Constructing the trajectory matrix, applying SVD
decomposition and reconstruction of trend and seasonal.
Dataset: "unemployment" series of reference book (pp. 29, 30 and 31)

pod_example02.oms 
 Constructing the trajectory matrix, applying
SVD decomposition, reconstruction and forecasting.
Dataset: Monthly US Imports from China, Jan1990 to June2006,
FRED database series IMPCH
Federal Reserve Bank of Saint Louis online database
http://research.stlouisfed.org/fred2/

pod_example03.oms 
 Constructing the trajectory matrix, applying
SVD decomposition, reconstruction and forecasting.
Dataset: Real Gross National Product, USA, quarterly 1947Q1 to 2006Q2,
FRED database series GNPC96
Federal Reserve Bank of Saint Louis online database
http://research.stlouisfed.org/fred2/

soi_recruit.oms 
 Analysis of the SOI and Recruitment data in
Shumway and Stoffer, Time Series Analysis and its Applications,
published by SpringerVerlag

stsa04.oms 
 Illustrate the use of the HoltWinters method for forecasting

varve.oms 
 Analysis of the glacial varve data in Shumway and Stoffer,
Time Series Analysis and its Applications, published by SpringerVerlag

