# midas_u: Estimate unrestricted MIDAS regression In midasr: Mixed Data Sampling Regression

## Description

Estimate unrestricted MIDAS regression using OLS. This function is a wrapper for lm.

## Usage

 1 midas_u(formula, data, ...)

## Arguments

 formula MIDAS regression model formula data a named list containing data with mixed frequencies ... further arguments, which could be passed to lm function.

## Details

MIDAS regression has the following form:

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,

where x_τ^{(i)}, i=0,...k are regressors of higher (or similar) frequency than y_t. Given certain assumptions the coefficients can be estimated using usual OLS and they have the familiar properties associated with simple linear regression.

lm object.

## Author(s)

Virmantas Kvedaras, Vaidotas Zemlys

## References

Kvedaras V., Zemlys, V. Testing the functional constraints on parameters in regressions with variables of different frequency Economics Letters 116 (2012) 250-254

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 ##The parameter function theta_h0 <- function(p, dk, ...) { i <- (1:dk-1)/100 pol <- p[3]*i + p[4]*i^2 (p[1] + p[2]*i)*exp(pol) } ##Generate coefficients theta0 <- theta_h0(c(-0.1,10,-10,-10),4*12) ##Plot the coefficients ##Do not run #plot(theta0) ##' ##Generate the predictor variable xx <- ts(arima.sim(model = list(ar = 0.6), 600 * 12), frequency = 12) ##Simulate the response variable y <- midas_sim(500, xx, theta0) x <- window(xx, start=start(y)) ##Create low frequency data.frame ldt <- data.frame(y=y,trend=1:length(y)) ##Create high frequency data.frame hdt <- data.frame(x=window(x, start=start(y))) ##Fit unrestricted model mu <- midas_u(y~fmls(x,2,12)-1, list(ldt, hdt)) ##Include intercept and trend in regression mu_it <- midas_u(y~fmls(x,2,12)+trend, list(ldt, hdt)) ##Pass data as partialy named list mu_it <- midas_u(y~fmls(x,2,12)+trend, list(ldt, x=hdt\$x))

midasr documentation built on May 29, 2017, 4:12 p.m.