midas_u | R Documentation |
Estimate unrestricted MIDAS regression using OLS. This function is a wrapper for lm
.
midas_u(formula, data, ...)
formula |
MIDAS regression model formula |
data |
a named list containing data with mixed frequencies |
... |
further arguments, which could be passed to |
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.
Virmantas Kvedaras, Vaidotas Zemlys
Kvedaras V., Zemlys, V. Testing the functional constraints on parameters in regressions with variables of different frequency Economics Letters 116 (2012) 250-254
##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))
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