Author: Patrick Schmidt
Date: 2019-02-12

# Precipitation forecasts

#### Patrick Schmidt

#### 2019-02-12

# Data

    library(PointFore)
    library(ggplot2)
    library(lubridate)
    #>
    #> Attaching package: 'lubridate'
    #> The following object is masked from 'package:base':
    #>
    #>     date

    precipitation$Date <- as.Date(row.names(precipitation),format = "%d-%m-%Y")

    ggplot(subset(precipitation, month(Date)< 7 & year(Date)==2013))+
      geom_line(aes(x=Date,y=Y))+
      geom_point(aes(x=Date,y=X), color = 'red', size = 2, shape=4)

Time series of one-day ahead HRES Precipitation forecasts (crosses) and respective observations (solid line) over London.

For more information on the data see `?precipitation`.

# Analysis

Now, let us analyse the forecasts. We begin with the constant expectile model.

    instruments <- c("lag(lag(Y))","X")

    res <- estimate.functional(iden.fct = expectiles, model = constant,
                               instruments = instruments,
                               Y = precipitation$Y, X=precipitation$X)
    #> Drop  2 case(s) because of chosen instruments
    #> Choose parameter theta0 automatically.
    summary(res)
    #> $call
    #> estimate.functional(iden.fct = expectiles, model = constant,
    #>     Y = precipitation$Y, X = precipitation$X, instruments = instruments)
    #>
    #> $coefficients
    #>           Estimate Std. Error  t value      Pr(>|t|)
    #> Theta[1] 0.5419114   0.020512 26.41924 8.237429e-154
    #>
    #> $Jtest
    #>
    #>  ##  J-Test: degrees of freedom is 2  ##
    #>
    #>                 J-test      P-value
    #> Test E(g)=0:    3.9107e+01  3.2220e-09
    plot(res,hline = TRUE)

Constant expectile analysis.

Optimality is rejected with a p-value of 0. On average the forecast tends to overestimation compared to an optimal mean forecast with an expectile level of 0.54.

Next, we consider state-dependent forecasting behavior. Instead of using the conventional state-dependence models we rely on the linear probit specification model but enforce an expectile level of \(0\) for the forecast \(0\). This is a logical consequence of precipitation being a positive random variable.

    probit0 <- function(stateVariable,theta) probit_linear(stateVariable, theta)*(stateVariable>0)

    res <- estimate.functional(iden.fct =   expectiles ,
                               model = probit0,
                               theta0 = c(0,0),
                               instruments = instruments,
                               state = precipitation$X,
                               Y = precipitation$Y, X=precipitation$X)
    #> Drop  2 case(s) because of chosen instruments
    summary(res)
    #> $call
    #> estimate.functional(iden.fct = expectiles, model = probit0, theta0 = c(0,
    #>     0), Y = precipitation$Y, X = precipitation$X, stateVariable = precipitation$X,
    #>     instruments = instruments)
    #>
    #> $coefficients
    #>             Estimate Std. Error   t value     Pr(>|t|)
    #> Theta[1] -0.26720297 0.06458326 -4.137341 3.513543e-05
    #> Theta[2]  0.08056457 0.01530689  5.263287 1.415021e-07
    #>
    #> $Jtest
    #>
    #>  ##  J-Test: degrees of freedom is 1  ##
    #>
    #>                 J-test   P-value
    #> Test E(g)=0:    0.11280  0.73698

To replicate the result plot in the paper , we need to adjust the standard plot function of the PointFore package to the probit0 specification model.

    plot(res,limits = c(0.001,15),hline = TRUE)+
      geom_point(data=data.frame(x=c(0,0),y=c(0,.395),shape=c(1,2)),
                 aes(x=x,y=y,shape=as.factor(shape)),
                 ,size=3,show.legend = FALSE)+
      scale_shape_manual(values=c(16,1))

Plot linear probit model.