<p>
The return data consist of 1999 daily observations in the period of 
2000/01/06 to 2008/29/01.</p>

datasets

Due to a large quantity of non-Gaussian time series and reliability data, the R-package non-Gaussian state-space with exact marginal likelihood is useful for modeling and forecasting non-Gaussian time series and reliability data via non-Gaussian state-space models with the exact marginal likelihood easily, see Gamerman, Santos and Franco (2013) <doi:10.1111/jtsa.12039> and Santos, Gamerman and Franco (2017) <doi:10.1109/TR.2017.2670142>. The package gives codes for formulating and specifying the non-Gaussian state-space models in the R language. Inferences for the parameters of the model can be made under the classical and Bayesian. Furthermore, prediction, filtering, and smoothing procedures can be used to perform inferences for the latent parameters. Applications include, e.g., count, volatility, piecewise exponential, and software reliability data.

T. dos Santos

NGSSEML

Non-Gaussian State-Space with Exact Marginal Likelihood

Rt function

<p>A data frame with 1999 rows and 1 variable.</p>

Rt: Returns of the asset PETR3 (Petrobras company) in the Brazilian stock market

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Examples