Using mlrv to anaylze data

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Loading data

Hong Kong circulatory and respiratory data.

library(mlrv)
library(foreach)
library(magrittr)

data(hk_data)
colnames(hk_data) = c("SO2","NO2","Dust","Ozone","Temperature",
                      "Humidity","num_circu","num_respir","Hospital Admission",
                      "w1","w2","w3","w4","w5","w6")
n = nrow(hk_data)
t = (1:n)/n
hk = list()

hk$x = as.matrix(cbind(rep(1,n), scale(hk_data[,1:3])))
hk$y = hk_data$`Hospital Admission`

Test for long memory

pvmatrix = matrix(nrow=2, ncol=4)
###inistialization
setting = list(B = 5000, gcv = 1, neighbour = 1)
setting$lb = floor(10/7*n^(4/15)) - setting$neighbour 
setting$ub = max(floor(25/7*n^(4/15))+ setting$neighbour,             
                  setting$lb+2*setting$neighbour+1)

Using the plug-in estimator for long-run covariance matrix function.

setting$lrvmethod =0. 

i=1
# print(rule_of_thumb(y= hk$y, x = hk$x))
for(type in c("KPSS","RS","VS","KS")){
  setting$type = type
  print(type)
  result_reg = heter_covariate(list(y= hk$y, x = hk$x), setting, mvselect = -2)
  print(paste("p-value",result_reg))
  pvmatrix[1,i] = result_reg
  i = i + 1
}

## [1] "KPSS"
## [1] "p-value 0.2838"
## [1] "RS"
## [1] "p-value 0.2896"
## [1] "VS"
## [1] "p-value 0.1148"
## [1] "KS"
## [1] "p-value 0.4124"

Debias difference-based estimator for long-run covariance matrix function.

setting$lrvmethod =1

i=1
for(type in c("KPSS","RS","VS","KS"))
{
  setting$type = type
  print(type)
  result_reg = heter_covariate(list(y= hk$y, x = hk$x), setting, mvselect = -2)
  print(paste("p-value",result_reg))
  pvmatrix[2,i] = result_reg
  i = i + 1
}

## [1] "KPSS"
## [1] "p-value 0.6866"
## [1] "RS"
## [1] "p-value 0.8066"
## [1] "VS"
## [1] "p-value 0.5126"
## [1] "KS"
## [1] "p-value 0.8346"

Output

rownames(pvmatrix) = c("plug","diff")
colnames(pvmatrix) = c("KPSS","RS","VS","KS")
knitr::kable(pvmatrix,type="latex")

	KPSS	RS	VS	KS
plug	0.2838	0.2896	0.1148	0.4124
diff	0.6866	0.8066	0.5126	0.8346

xtable::xtable(pvmatrix, digits = 3)

## % latex table generated in R 4.4.1 by xtable 1.8-4 package
## % Tue Jul 30 21:23:49 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
##   \hline
##  & KPSS & RS & VS & KS \\ 
##   \hline
## plug & 0.284 & 0.290 & 0.115 & 0.412 \\ 
##   diff & 0.687 & 0.807 & 0.513 & 0.835 \\ 
##    \hline
## \end{tabular}
## \end{table}

Sensitivity Check

Using parameter `shift’ to multiply the GCV selected bandwidth by a factor. - Shift = 1.2 with plug-in estimator.

pvmatrix = matrix(nrow=2, ncol=4)
setting$lrvmethod = 0
i=1
for(type in c("KPSS","RS","VS","KS")){
  setting$type = type
  print(type)
  result_reg = heter_covariate(list(y= hk$y, x = hk$x),
                                             setting,
                                        mvselect = -2, shift = 1.2)
  print(paste("p-value",result_reg))
  pvmatrix[1,i] = result_reg
  i = i + 1
}

## [1] "KPSS"
## [1] "p-value 0.4256"
## [1] "RS"
## [1] "p-value 0.3638"
## [1] "VS"
## [1] "p-value 0.118"
## [1] "KS"
## [1] "p-value 0.561"

Shift = 1.2 with debias difference-based estimator. Choosing verbose_dist = TRUE can print more intermediate results, the exact values of smoothing parameters, the test statistic, the summary of bootstrap distribution and the p-value.

setting$lrvmethod =1
i=1
for(type in c("KPSS","RS","VS","KS"))
{
  setting$type = type
  print(type)
  result_reg = heter_covariate(list(y= hk$y, x = hk$x),
                                             setting,
                                        mvselect = -2, verbose_dist = TRUE, shift = 1.2)
  print(paste("p-value",result_reg))
  pvmatrix[2,i] = result_reg
  i = i + 1
}

## [1] "KPSS"
## [1] "gcv 0.193398841583897"
## [1] "m 14 tau_n 0.332134206312301"
## [1] "test statistic: 141.654657280933"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   13.81  107.33  215.16  368.80  460.94 5186.84 
## [1] "p-value 0.6462"
## [1] "RS"
## [1] "gcv 0.193398841583897"
## [1] "m 15 tau_n 0.332134206312301"
## [1] "test statistic: 1067.76713443354"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   535.2  1023.8  1232.0  1302.6  1508.2  3383.7 
## [1] "p-value 0.6994"
## [1] "VS"
## [1] "gcv 0.193398841583897"
## [1] "m 17 tau_n 0.332134206312301"
## [1] "test statistic: 103.342038019402"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.94   70.04  110.26  152.36  187.41 1230.74 
## [1] "p-value 0.538"
## [1] "KS"
## [1] "gcv 0.193398841583897"
## [1] "m 17 tau_n 0.382134206312301"
## [1] "test statistic: 671.676091515897"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   352.6   720.4   923.3  1007.3  1213.0  3363.9 
## [1] "p-value 0.8108"

Results of shift = 1.2

rownames(pvmatrix) = c("plug","diff")
colnames(pvmatrix) = c("KPSS","RS","VS","KS")
knitr::kable(pvmatrix,type="latex")

	KPSS	RS	VS	KS
plug	0.4256	0.3638	0.118	0.5610
diff	0.6462	0.6994	0.538	0.8108

xtable::xtable(pvmatrix, digits = 3)

## % latex table generated in R 4.4.1 by xtable 1.8-4 package
## % Tue Jul 30 21:25:13 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
##   \hline
##  & KPSS & RS & VS & KS \\ 
##   \hline
## plug & 0.426 & 0.364 & 0.118 & 0.561 \\ 
##   diff & 0.646 & 0.699 & 0.538 & 0.811 \\ 
##    \hline
## \end{tabular}
## \end{table}

Shift = 0.8 with plug-in estimator.

pvmatrix = matrix(nrow=2, ncol=4)
setting$lrvmethod =0

i=1
for(type in c("KPSS","RS","VS","KS")){
  setting$type = type
  print(type)
  result_reg = heter_covariate(list(y= hk$y, x = hk$x),
                                             setting,
                                        mvselect = -2,  shift = 0.8)
  print(paste("p-value",result_reg))
  pvmatrix[1,i] = result_reg
  i = i + 1
}

## [1] "KPSS"
## [1] "p-value 0.2458"
## [1] "RS"
## [1] "p-value 0.1662"
## [1] "VS"
## [1] "p-value 0.1234"
## [1] "KS"
## [1] "p-value 0.2726"

Shift = 0.8 with the debias difference-based estimator.

setting$lrvmethod =1

i=1
for(type in c("KPSS","RS","VS","KS"))
{
  setting$type = type
  print(type)
  result_reg = heter_covariate(list(y= hk$y, x = hk$x),
                                             setting,
                                        mvselect = -2, verbose_dist = TRUE, shift = 0.8)
  print(paste("p-value",result_reg))
  pvmatrix[2,i] = result_reg
  i = i + 1
}

## [1] "KPSS"
## [1] "gcv 0.128932561055931"
## [1] "m 8 tau_n 0.382134206312301"
## [1] "test statistic: 166.543448031107"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.78   99.10  200.38  331.58  421.82 3152.45 
## [1] "p-value 0.571"
## [1] "RS"
## [1] "gcv 0.128932561055931"
## [1] "m 18 tau_n 0.382134206312301"
## [1] "test statistic: 998.08124125936"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   699.6  1276.2  1535.9  1614.1  1874.8  3730.1 
## [1] "p-value 0.9464"
## [1] "VS"
## [1] "gcv 0.128932561055931"
## [1] "m 9 tau_n 0.332134206312301"
## [1] "test statistic: 78.0587445148255"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.07   63.32  110.68  157.21  199.91 1498.85 
## [1] "p-value 0.6586"
## [1] "KS"
## [1] "gcv 0.128932561055931"
## [1] "m 9 tau_n 0.332134206312301"
## [1] "test statistic: 709.345279801765"
## [1] "Bootstrap distribution"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   290.6   695.8   910.2   986.8  1200.7  2953.0 
## [1] "p-value 0.7344"

Results of shift = 0.8

rownames(pvmatrix) = c("plug","diff")
colnames(pvmatrix) = c("KPSS","RS","VS","KS")
knitr::kable(pvmatrix,type="latex")

	KPSS	RS	VS	KS
plug	0.2458	0.1662	0.1234	0.2726
diff	0.5710	0.9464	0.6586	0.7344

xtable::xtable(pvmatrix, digits = 3)

## % latex table generated in R 4.4.1 by xtable 1.8-4 package
## % Tue Jul 30 21:26:26 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
##   \hline
##  & KPSS & RS & VS & KS \\ 
##   \hline
## plug & 0.246 & 0.166 & 0.123 & 0.273 \\ 
##   diff & 0.571 & 0.946 & 0.659 & 0.734 \\ 
##    \hline
## \end{tabular}
## \end{table}

Test for structure stability

Test if the coefficient function of “SO2”,“NO2”,“Dust” of the second year is constant.

hk$x = as.matrix(cbind(rep(1,n), (hk_data[,1:3])))
hk$y = hk_data$`Hospital Admission`
setting$type = 0
setting$bw_set = c(0.1, 0.35)
setting$eta = 0.2
setting$lrvmethod = 1
setting$lb  = 10
setting$ub  = 15
hk1 = list()
hk1$x = hk$x[366:730,]
hk1$y = hk$y[366:730]
p1 <- heter_gradient(hk1, setting, mvselect = -2, verbose = T)

## [1] "m 12 tau_n 0.364293094094381"
## [1] 10464.35
##        V1       
##  Min.   : 1537  
##  1st Qu.: 3809  
##  Median : 4838  
##  Mean   : 5239  
##  3rd Qu.: 6296  
##  Max.   :15552

p1

## [1] 0.0158

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