SimData {SpatialRegimes}

Simulated data for estimating spatial regimes.

Description

Simulated production function like data for estimating spatial regimes; data has been generated for the paper "F. Vidoli, G. Pignataro, R. Benedetti, F. Pammolli, "Spatially constrained cluster-wise regression: optimal territorial areas in Italian health care", forthcoming.

Usage

data(SimData)

Format

SimData is a simulated dataset with 500 observations and 7 variables.

long

Longitude

lat

Latitude

A

Land input

L

Labour input

K

Capital input

clu

Real regime

y_ols

Production output

500 units (100 units for each of the 5 regimes) are generated and, for each unit, the longitude and latitude coordinates are randomly drawn by using two Uniform distributions from 0 to 50 and from -70 to 20, i.e. U(0,50) and U(-70,20), respectively. Consequently, we set the matrix of covariates which include the constant, A, L and K variables by drawing from U(1.5,4). For each regime, finally, a different (in the coefficients) spatial function is set assuming a linear functional form. More in particular, we set 5 different vectors of parameters (including the intercept): beta1 = (13,0.5,0.3,0.2), beta2 = (11,0.8,0.1,0.1), beta3 = (9,0.3,0.2,0.5), beta4 = (7,0.4,0.3,0.3) and beta5 = (5,0.2,0.6,0.2) and a normally distributed error term in N(0,1).

Author(s)

Vidoli F.

References

F. Vidoli, G. Pignataro and R. Benedetti "Identification of spatial regimes of the production function of Italian hospitals through spatially constrained cluster-wise regression", Socio-Economic Planning Sciences (in press) https://doi.org/10.1016/j.seps.2022.101223

Examples

data(SimData)