ASSA (version 2.0)

bmssa: Multivariate Singular Spectrum Business Cycle Indicator

Description

Computes a business cycle indicator using multivariate singular spectrum analysis.

Usage

bmssa(y, l = 32)

Arguments

y

multivariate time series of economic activity data from which the cycle is to be extracted; the first column is reserved to Gross Domestic Product (GDP).

l

window length; by default, l = 32.

Value

cycle

time series with the business cycle indicator.

sfisher

vector with indices of elementary reconstructed components selected with Fisher \(g\) statistic; see details.

erc

time series with elementary reconstructed components resulting from targeted grouping based on a Fisher \(g\) statistic.

l

window length.

Details

The business cycle indicator produced using this routine is based on methods proposed in de Carvalho and Rua (2017). A quick summary of the method is as follows. Multivariate singular spectrum analysis is used to decompose a multivariate time series (y) into principal components, and a Fisher \(g\) statistic automatically selects elementary reconstructed components (erc) within business cycle frequencies. The indicator results from adding elementary reconstructed components within business cycle frequencies. The plot method depicts the resulting business cycle indicator, and the print method reports the business cycle indicator along with the components selected by the Fisher \(g\) statistic.

References

de Carvalho, M., Rodrigues, P., and Rua, A. (2012). Tracking the US business cycle with a singular spectrum analysis. Economics Letters, 114, 32--35.

de Carvalho, M. and Rua, A. (2017). Real-time nowcasting the US output gap: Singular spectrum analysis at work. International Journal of Forecasting, 33, 185--198.

See Also

See combplot for a chart of the selected elementary reconstructed components from which the business cycle indicator results. See bssa for a univariate version of the method.

Examples

# NOT RUN {
## Tracking the US Business Cycle (de Carvalho et al, 2017; Fig. 6) 
data(GDPIP)
fit <- bmssa(log(GDPIP))
plot(fit)
print(fit)
# }