dynrq {quantreg} | R Documentation

## Dynamic Linear Quantile Regression

### Description

Interface to `rq.fit` and `rq.wfit` for fitting dynamic linear quantile regression models. The interface is based very closely on Achim Zeileis's `dynlm` package. In effect, this is mainly “syntactic sugar” for formula processing, but one should never underestimate the value of good, natural sweeteners.

### Usage

    dynrq(formula, tau = 0.5, data, subset, weights, na.action, method = "br",
      contrasts = NULL, start = NULL, end = NULL, ...)

### Arguments

`formula` | a `"formula"` describing the linear model to be fit. For details see below and `rq`.
`tau` | the quantile(s) to be estimated, may be vector valued, but all all values must be in (0,1).
`data` | an optional `"data.frame"` or time series object (e.g., `"ts"` or `"zoo"`), containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `rq` is called.
`subset` | an optional vector specifying a subset of observations to be used in the fitting process.
`weights` | an optional vector of weights to be used in the fitting process. If specified, weighted least squares is used with weights `weights` (that is, minimizing `sum(w*e^2)`); otherwise ordinary least squares is used.
`na.action` | a function which indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset. The “factory-fresh” default is `na.omit`. Another possible value is `NULL`, no action. Note, that for time series regression special methods like `na.contiguous`, `na.locf` and `na.approx` are available.
`method` | the method to be used; for fitting, by default `method = "br"` is used; `method = "fn"` employs the interior point (Frisch-Newton) algorithm. The latter is advantageous for problems with sample sizes larger than about 5,000.
`contrasts` | an optional list. See the `contrasts.arg` of `model.matrix.default`.
`start` | start of the time period which should be used for fitting the model.
`end` | end of the time period which should be used for fitting the model.
`...` | additional arguments to be passed to the low level regression fitting functions.

### Details

The interface and internals of `dynrq` are very similar to `rq`, but currently `dynrq` offers two advantages over the direct use of `rq` for time series applications of quantile regression: extended formula processing, and preservation of time series attributes. Both features have been shamelessly lifted from Achim Zeileis's package `"dynlm"`.

For specifying the `formula` of the model to be fitted, there are several functions available which allow for convenient specification of dynamics (via `d()` and `L()`) or linear/cyclical patterns (via `trend()`, `season()`, and `harmon()`). These new formula functions require that their arguments are time series objects (i.e., `"ts"` or `"zoo"`).

Dynamic models: An example would be `d(y) ~ L(y, 2)`, where `d(x, k)` is `diff(x, lag = k)` and `L(x, k)` is `lag(x, lag = -k)`, note the difference in sign. The default for `k` is in both cases `1`. For `L()`, it can also be vector-valued, e.g., `y ~ L(y, 1:4)`.

Trends: `y ~ trend(y)` specifies a linear time trend where `(1:n)/freq` is used by default as the covariate, `n` is the number of observations and `freq` is the frequency of the series (if any, otherwise `freq = 1`). Alternatively, `trend(y, scale = FALSE)` would employ `1:n` and `time(y)` would employ the original time index.

Seasonal/cyclical patterns: Seasonal patterns can be specified via `season(x, ref = NULL)` and harmonic patterns via `harmon(x, order = 1)`. `season(x, ref = NULL)` creates a factor with levels for each cycle of the season. Using the `ref` argument, the reference level can be changed from the default first level to any other. `harmon(x, order = 1)` creates a matrix of regressors corresponding to `cos(2 * o * pi * time(x))` and `sin(2 * o * pi * time(x))` where `o` is chosen from `1:order`.

See below for examples.

Another aim of `dynrq` is to preserve time series properties of the data. Explicit support is currently available for `"ts"` and `"zoo"` series. Internally, the data is kept as a `"zoo"` series and coerced back to `"ts"` if the original dependent variable was of that class (and no internal `NA`s were created by the `na.action`).

### See Also

`zoo`, `dynlm`, `merge.zoo`

### Examples

    ###########################
    ## Dynamic Linear Quantile Regression Models ##
    ###########################

    require(zoo)
    ## multiplicative median SARIMA(1,0,0)(1,0,0)_12 model fitted to UK seatbelt data
         data("UKDriverDeaths", package = "datasets")
         uk <- log10(UKDriverDeaths)
         dfm <- dynrq(uk ~ L(uk, 1) + L(uk, 12))
         dfm

         dfm3 <- dynrq(uk ~ L(uk, 1) + L(uk, 12),tau = 1:3/4)
         summary(dfm3)
     ## explicitly set start and end
         dfm1 <- dynrq(uk ~ L(uk, 1) + L(uk, 12), start = c(1975, 1), end = c(1982, 12))
     ## remove lag 12
         dfm0 <- update(dfm1, . ~ . - L(uk, 12))
         tuk1  <- anova(dfm0, dfm1)
     ## add seasonal term
         dfm1 <- dynrq(uk ~ 1, start = c(1975, 1), end = c(1982, 12))
         dfm2 <- dynrq(uk ~ season(uk), start = c(1975, 1), end = c(1982, 12))
         tuk2 <- anova(dfm1, dfm2)
     ## regression on multiple lags in a single L() call
         dfm3 <- dynrq(uk ~ L(uk, c(1, 11, 12)), start = c(1975, 1), end = c(1982, 12))
         anova(dfm1, dfm3)

    ###############################
    ## Time Series Decomposition ##
    ###############################

    ## airline data
    data("AirPassengers", package = "datasets")
    ap <- log(AirPassengers)
    fm <- dynrq(ap ~ trend(ap) + season(ap), tau = 1:4/5)
    sfm <- summary(fm)
    plot(sfm)

    ## Alternative time trend specifications:
    ##   time(ap)                  1949 + (0, 1, ..., 143)/12
    ##   trend(ap)                 (1, 2, ..., 144)/12
    ##   trend(ap, scale = FALSE)  (1, 2, ..., 144)

    ###############################
    ## An Edgeworth (1886) Problem##
    ###############################
    # DGP
    fye <- function(n, m = 20){
        a <- rep(0,n)
        s <- sample(0:9, m, replace = TRUE)
        a[1] <- sum(s)
        for(i in 2:n){
           s[sample(1:20,1)] <- sample(0:9,1)
           a[i] <- sum(s)
        }
        zoo::zoo(a)
    }
    x <- fye(1000)
    f <- dynrq(x ~ L(x,1))
    plot(x,cex = .5, col = "red")
    lines(fitted(f), col = "blue")

[Package quantreg version 5.36 Index]