midas_nlpr: Non-linear parametric MIDAS regression
In midasr: Mixed Data Sampling Regression

Description Usage Arguments Details Value Author(s)

Estimate restricted MIDAS regression using non-linear least squares.

1	midas_nlpr(formula, data, start, Ofunction = "optim", ...)

`formula`	formula for restricted MIDAS regression or `midas_r` object. Formula must include `fmls` function
`data`	a named list containing data with mixed frequencies
`start`	the starting values for optimisation. Must be a list with named elements.
`Ofunction`	the list with information which R function to use for optimisation. The list must have element named `Ofunction` which contains character string of chosen R function. Other elements of the list are the arguments passed to this function. The default optimisation function is `optim` with arguments `method="Nelder-Mead"` and `control=list(maxit=5000)`. Other supported functions are `nls`, `optimx`.
`...`	additional arguments supplied to optimisation function

Given MIDAS regression:

y_t = ∑_{j=1}^pα_jy_{t-j} +∑_{i=0}^{k}∑_{j=0}^{l_i}β_{j}^{(i)}x_{tm_i-j}^{(i)} + u_t,

estimate the parameters of the restriction

β_j^{(i)}=g^{(i)}(j,λ).

Such model is a generalisation of so called ADL-MIDAS regression. It is not required that all the coefficients should be restricted, i.e the function g^{(i)} might be an identity function. Model with no restrictions is called U-MIDAS model. The regressors x_τ^{(i)} must be of higher (or of the same) frequency as the dependent variable y_t.

a midas_r object which is the list with the following elements:

`coefficients`	the estimates of parameters of restrictions
`midas_coefficients`	the estimates of MIDAS coefficients of MIDAS regression
`model`	model data
`unrestricted`	unrestricted regression estimated using `midas_u`
`term_info`	the named list. Each element is a list with the information about the term, such as its frequency, function for weights, gradient function of weights, etc.
`fn0`	optimisation function for non-linear least squares problem solved in restricted MIDAS regression
`rhs`	the function which evaluates the right-hand side of the MIDAS regression
`gen_midas_coef`	the function which generates the MIDAS coefficients of MIDAS regression
`opt`	the output of optimisation procedure
`argmap_opt`	the list containing the name of optimisation function together with arguments for optimisation function
`start_opt`	the starting values used in optimisation
`start_list`	the starting values as a list
`call`	the call to the function
`terms`	terms object
`gradient`	gradient of NLS objective function
`hessian`	hessian of NLS objective function
`gradD`	gradient function of MIDAS weight functions
`Zenv`	the environment in which data is placed
`nobs`	the number of effective observations
`convergence`	the convergence message
`fitted.values`	the fitted values of MIDAS regression
`residuals`	the residuals of MIDAS regression