Description Usage Arguments Details Value Author(s) References See Also Examples
Computes confidence intervals for nonlinear functions of a model parameters. Delta method is used to compute standard errors. Applicable after any model provided estimates of parameters and their covariance matrix are available.
1 2 3 4 5 6 7 8 | nlConfint(obj = NULL, texts, level = 0.95, coeff = NULL,
Vcov = NULL, df2 = NULL, x = NULL)
# Standard:
# nlConfint(obj, texts) # based on z-statistics
# nlWaldtest(obj, texts, df2 = T) # based on z-statistics
# If coef(obj) and vcov(obj) are not available
# nlWaldtest(texts = funcions, coeff = vector, Vcov = matrix)
|
obj |
model object of any class, for which |
texts |
function(s) of parameters, b[i], as string or vector of strings.
Several functions can be inputted as a string, separated by semicolon, or as a
character vector, e.g. |
level |
confidence level, a number in (0, 1). Default is 0.95. |
coeff |
vector of parameter estimates. If missing, it is set for |
Vcov |
covariance matrix of parameters. If missing, it is set to |
df2 |
defines whether CI will be computed based on z (the default method) or t statistics. To compute t-based intervals, one can use |
x |
number, or numeric vector. Provides a way to supply cumbersome coefficients
into functions, e.g. |
The function should be applicable after (almost) any regression-type model, estimated using cross-section, time series, or panel data. If there are no methods for coef(obj)
and/or vcov(obj)
, coeff
and Vcov
arguments should be inputted directly. To realize the delta-method, the function first tries to compute analytical derivatives using deriv
. If failed, it computes numerical derivatives, calling numericDeriv
.
an r by 3 matrix, where r is the number of functions in texts
argument. The first column is formed of values of the functions computed at
parameters estimates. The two last columns are confidence bounds.
Oleh Komashko
Greene, W.H. (2011). Econometric Analysis, 7th edition. Upper Saddle River, NJ: Prentice Hall
1 2 3 4 5 |
value 2.5 % 97.5 %
b[2]^3+b[3]*b[1] -0.01258957 -0.07624856 0.05106942
b[2] 0.02359922 -0.31708313 0.36428156
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