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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 Newton-Raphson with BFGS update
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example01.oms |
- Simulate a stationary, zero mean Gaussian
autoregressive model of order 2 and perform various operations on
the resulting realization
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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
user-specified function for optimization and estimation of a
model's parameters
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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 training-validation sample approach
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example04.oms |
- Illustrate the simulation and estimation of
nonlinear time series models
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example05.oms |
- Briefly illustrate the STSA functions for
exponential and simple smoothing of a time series
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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 short-term forecasting.
The example also illustrates how to generate prediction intervals (bands)
that are frequently much more useful than point forecasts.
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example07.oms |
- Replicate some results found in the book
Bayesian Forecasting and Dynamic Models
by Harrison and West (1997, Springer-Verlag)
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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.
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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.
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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 non-stationary 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.
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example11.oms |
- Illustrate the simulation, estimation and forecasting
of ARFIMA-type models. This class of models extends traditional, short-memory
ARMA models, to incorporate the effects of long-memory. 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.
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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
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expdecay.oms |
- Simulate and estimate the fit of an exponential
decay model with y-offset of the form y = A*exp(B*x) + C + error
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flu.oms |
- Analysis of the flu data in Shumway and Stoffer,
Time Series Analysis and its Applications, published by
Springer-Verlag.
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gnp.oms |
- Analysis of the GNP data in Shumway and Stoffer,
Time Series Analysis and its Applications, published by Springer-Verlag
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jj.oms |
- Analysis of the Johnson & Johnson data in Shumway and
Stoffer, Time Series Analysis and its Applications, published
by Springer-Verlag
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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 1947-Q1 to 2006-Q2, FRED database series GNPC96
Federal Reserve Bank of Saint Louis online database
http://research.stlouisfed.org/fred2/, in billions of chained 2000 dollars
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nonpar_example02.oms |
- Nonparametric smoothing and extrapolation.
1 data series: daily closing price of General Electric from 1/02/2003 to 1/17/2006
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nonpar_example03.oms |
- Various nonparametric autoregression methods,
cross-validation 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/
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nonpar_example04.oms |
- Various nonparametric autoregression
methods using simulated data, including a partially linear model.
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optimization.oms |
- Illustrate the use of the quasi-newton
optimization algorithm for solving minimization problems.
The test functions are adopted from the book Optimization:
Foundations and Applications, by Ronald E. Miller, Wiley Interscience.
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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)
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pod_example02.oms |
- Constructing the trajectory matrix, applying
SVD decomposition, reconstruction and forecasting.
Dataset: Monthly US Imports from China, Jan-1990 to June-2006,
FRED database series IMPCH
Federal Reserve Bank of Saint Louis online database
http://research.stlouisfed.org/fred2/
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pod_example03.oms |
- Constructing the trajectory matrix, applying
SVD decomposition, reconstruction and forecasting.
Dataset: Real Gross National Product, USA, quarterly 1947-Q1 to 2006-Q2,
FRED database series GNPC96
Federal Reserve Bank of Saint Louis online database
http://research.stlouisfed.org/fred2/
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soi_recruit.oms |
- Analysis of the SOI and Recruitment data in
Shumway and Stoffer, Time Series Analysis and its Applications,
published by Springer-Verlag
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stsa04.oms |
- Illustrate the use of the Holt-Winters method for forecasting
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varve.oms |
- Analysis of the glacial varve data in Shumway and Stoffer,
Time Series Analysis and its Applications, published by Springer-Verlag
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stsa01.oms |
- Demonstrates the functions: acfplot,
ar_order, arma_to_ma, arma_roots, arma_simulate,
ar_yw, descriptives, Gaussianity_Ftest,
print_acf_summary, qstat, regress, and seqlags
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stsa02.oms |
- Demonstrates the functions:
acfplot, ar_order, arma_details,
arma_estimate, arma_noise, arma_residual_diagnostics,
arma_simulate ar_yw, boxplot, descriptives,
gauss_newton, Gaussianity_ADtest, and Gaussianity_CVMtest
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stsa03.oms |
- Demonstrates the functions:
acvf_spectrum, ar_order, ar_spectrum,
arma_simulate, plot_spectrum, and spectrum
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stsa05.oms |
- Illustrate the use of the updated qqplot function
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logistic.oms |
- Illustrate the use of the logistic_regression function
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