Skip to contents

Corbae-Ouliaris

corbae_ouliaris.Rd

Corbae-Ouliaris (2006) Frequency Domain Filter

Usage

corbae_ouliaris(x, low_freq = NULL, high_freq = NULL)

Arguments

x

Vector, data.frame, matrix or any similar 1D/2D object containing values for filtering.

low_freq

Number indicating the lowest period of oscillation as fractions of \(\pi\). If low_freq > 1, indicating that the direct frequency of the data is provided, this is transformed internally into 2 / high_freq. The default is NULL, meaning that the ifelse(freq > 1, trunc(freq * 1.5), 2) will be used.

high_freq

Number indicating the highest period of oscillation as radians of \(\pi\). If high_freq > 1, indicating that the direct frequency of the data is provided, this is transformed internally into 2 / low_freq. The default is NULL, meaning that the trunc(freq * 8) will be used.

Details

This is a pure R implementation of the filtering algorithm. low_freq and high_freq are connected with characteristics of the series, for example the business circle. low_freq and high_freq must be both either between 0 and 1, meaning that they are frequencies of the period as radians, or both >1, indicating that both are starting and ending periods of the cycle.

low_freq and high_freq are used for keeping the relevant frequencies. These are meant to be the ones inside the range \([ low \_ freq, high \_ freq ]\). Therefore, values outside this range are removed.

For 2-dimensional objects x, filtering per column is applied.

Value

Filtered object with the same length/dimensions and class as the input x.

References

Corbae, D., Ouliaris, S., & Phillips, P. (2002), Band Spectral Regression with Trending-Data. Econometrica 70(3), pp. 1067-1109.

Corbae, D. & Ouliaris, S. (2006), Extracting Cycles from Nonstationary Data, in Corbae D., Durlauf S.N., & Hansen B.E. (eds.). Econometric Theory and Practice: Frontiers of Analysis and Applied Research. Cambridge: Cambridge University Press, pp. 167–177. doi:10.1017/CBO9781139164863.008 .

Shaw, E.S. (1947), Burns and Mitchell on Business Cycles. Journal of Political Economy, 55(4): pp. 281-298. doi:10.1086/256533 .

See also

dftse

Examples

# Apply on ts
data(USgdp)
res <- corbae_ouliaris(USgdp, low_freq = 0.0625, high_freq = 0.3333)
head(res)
#> [1] 220.437445 122.214673  33.554008  -3.624313  10.611337  39.376810

# Apply on vector
data(USgdp)
res <- corbae_ouliaris(USgdp, low_freq = 0.0625, high_freq = 0.3333)
head(res)
#> [1] 220.437445 122.214673  33.554008  -3.624313  10.611337  39.376810

# Apply on matrix per column
mat <- matrix(USgdp, ncol = 4)
res <- corbae_ouliaris(mat, low_freq = 0.0625, high_freq = 0.3333)
head(res)
#>           [,1]       [,2]        [,3]       [,4]
#> [1,] 122.74643 -147.02035 -113.696760   37.14300
#> [2,] 121.75519 -139.93212    1.967257   40.79558
#> [3,] 104.71525 -114.29315   90.769600  -27.41276
#> [4,]  81.59822  -71.11876  117.136438 -129.43620
#> [5,]  61.87092  -26.92243   84.482012 -212.27882
#> [6,]  46.35087   -1.33427   27.081152 -240.99583

# Apply on data.frame per column
dfmat <- as.data.frame(mat)
res <- corbae_ouliaris(dfmat, low_freq = 0.0625, high_freq = 0.3333)
head(res)
#>          V1         V2          V3         V4
#> 1 122.74643 -147.02035 -113.696760   37.14300
#> 2 121.75519 -139.93212    1.967257   40.79558
#> 3 104.71525 -114.29315   90.769600  -27.41276
#> 4  81.59822  -71.11876  117.136438 -129.43620
#> 5  61.87092  -26.92243   84.482012 -212.27882
#> 6  46.35087   -1.33427   27.081152 -240.99583