curvepred | R Documentation |
Provides the predicted values for the reserve demand curve of choice. For general use prefer the predict() function, which handles the constant internally.
curvepred(x, w, type = "logistic", dummy = NULL)
x |
A matrix with the inputs. If there is a constant in the estimated curve, then the first column in |
w |
The vector of weights for the desired curve. Estimated using the |
type |
The type of the reserve demand curve. This can be any of |
dummy |
Optional input to signify a regime change (vertical shifts in the curve). Must be a vector of equal length to the rows of |
For a description of the parametric curves, see the provided reference. Below we list their functions:
logisitc
(Logistic)
r_i = \alpha + \kappa / (1 - \beta e^{g(\bm{C}_i)}) + \varepsilon_i
redLogistic
(Reduced logistic)
r_i = \alpha + 1 / (1 - \beta e^{g(\bm{C}_i)}) + \varepsilon_i
fixLogistic
(Fixed logistic)
r_i = \alpha + 1 / (1 - e^{g(\bm{C}_i)}) + \varepsilon_i
doubleExp
(Double exponential)
r_i = \alpha + \beta e^{\rho e^{g(\bm{C}_i)}} + \varepsilon_i
exponential
(Exponential)
r_i = \alpha + \beta e^{g(\bm{C}_i)} + \varepsilon_i
fixExponential
(Fixed exponential)
r_i = \beta e^{g(\bm{C}_i)} + \varepsilon_i
arctan
(Arctangent)
r_i = \alpha + \beta \arctan ( g(\bm{C}_i)) + \varepsilon_i
linear
(Linear)
r_i = g(\bm{C}_i) + \varepsilon_i
And g(\bm{C}) = c + \bm{C} w_g
, where \alpha
, \beta
, \kappa
, \rho
are curve parameters,
c
is a constant togglable by constant
, \bm{C}
are the regressors including the excess reserves. w_g
their coefficients, and finally \varepsilon_i
is the error term of the curve.
Returns a vector of the predicted values.
Nikolaos Kourentzes, nikolaos@kourentzes.com
Chen, Z., Kourentzes, N., & Veyrune, R. (2023). Modeling the Reserve Demand to Facilitate Central Bank Operations. IMF Working Papers, 2023(179).
curve
, and curveopt
.
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