Otneim and Tjøstheim (2017a) describes a new method for estimating multivariate density functions using the concept of local Gaussian correlations. Otneim and Tjøstheim (2017b) expands the idea to the estimation of conditional density functions. This package, written for the R programming language, provides a simple interface for implementing these methods in practical problems.
Let us illustrate the use of this package by looking at the built-in data set of daily closing prices of 4 major European stock indices in the period 1991-1998. We load the data and transform them to daily returns:
data(EuStockMarkets) x <- apply(EuStockMarkets, 2, function(x) diff(log(x))) # Remove the days where at least one index did not move at all x <- x[!apply(x, 1, function(x) any(x == 0)),]
When using this package, the first task is always to create an lg-object using the lg_main()-function. This object contains all the estimation parameters that will be used in the estimation step, including the bandwidths. There are three main parameters that one can tune:
See the documentation of the lg_main()-function for further details. We can now construct the lg-object using the default configuration by running
library(lg) lg_object <- lg_main(x)
We can then specify a set of grid points and estimate the probability density function of x using the dlg()-function. We choose a set of grid ponts that go diagonally through R^4, estimate, and plot the result as follows:
grid <- matrix(rep(seq(-.03, .03, length.out = 100), 4), ncol = 4, byrow = FALSE) density_estimate <- dlg(lg_object = lg_object, grid = grid) # plot(grid[,1], density_estimate$f_est, type = "l", # xlab = "Diagonal grid point", ylab = "Estimated density")
If we want to calculate conditional density functions, we must take care to notice the order of the columns in our data set. This is because the estimation routine, implemented in the clg()-function, will always assume that the independent variables come first. Looking at the top of our data set:
head(x) #> DAX SMI CAC FTSE #> [1,] -0.009326550 0.006178360 -0.012658756 0.006770286 #> [2,] -0.004422175 -0.005880448 -0.018740638 -0.004889587 #> [3,] 0.009003794 0.003271184 -0.005779182 0.009027020 #> [4,] -0.001778217 0.001483372 0.008743353 0.005771847 #> [5,] -0.004676712 -0.008933417 -0.005120160 -0.007230164 #> [6,] 0.012427042 0.006737244 0.011714353 0.008517217
we see that DAX comes first. Say that we want to estimate the conditional density of DAX, given that SMI = CAC = FTSE = 0. We do that by running
grid <- matrix(seq(-.03, .03, length.out = 100), ncol = 1) # The grid must be a matrix condition <- c(0, 0, 0) # Value of dependent variables cond_dens_est <- clg(lg_object = lg_object, grid = grid, condition = condition) # plot(grid, cond_dens_est$f_est, type = "l", # xlab = "DAX", ylab = "Estimated conditional density")
If we want to estimate the conditional density of CAC and FTSE given DAX and SMI, for example, we must first shuffle the data so that CAC and FTSE come first, and supply the conditional value for DAX and SMI through the vector condition, now having two elements.
Berentsen, Geir Drage, Tore Selland Kleppe, and Dag Tjøstheim. “Introducing localgauss, an R package for estimating and visualizing local Gaussian correlation.” Journal of Statistical Software 56.1 (2014): 1-18.
Otneim, Håkon, and Dag Tjøstheim. “The locally Gaussian density estimator for multivariate data.” Statistics and Computing 27, no. 6 (2017a): 1595-1616.
Otneim, Håkon, and Dag Tjøstheim. “Conditional density estimation using the local Gaussian correlation” Statistics and Computing (2017b): 1-19.
Tjøstheim, Dag, & Hufthammer, Karl Ove (2013). Local Gaussian correlation: a new measure of dependence. Journal of Econometrics, 172(1), 33-48.