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Jewish style I have a problem with parameter estimation and prediction of a GARCH model. I have a time series of volatility starting in 1996 and starting in 2009. I try to estimate the parameters using the ugarchspecsum ugarchfitfunction : garch1.1 <- ugarchsp
cool df=structure(list(X = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
json_data.time.updated = structure(1:41, .Label = c("Jan 19
cool df=structure(list(X = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
json_data.time.updated = structure(1:41, .Label = c("Jan 19
jalaji pasak How do I interpret the coefficients of t garch in the rugarch package? Which is the parameter of the dummy variable? And which one is the coefficient of the arch and garch parameters I have the result but I am confused about the dummy variable par
NSAIDs I am trying to predict a Copula Garch model. I tried to use the dccforecast function with cGARCHfit but it turned out to be an error saying there is no 'dccforecast' method for cgARCHfit class objects. So, how do we actually predict the dcc copula garch
news I have a time series return like this: ret <- rnorm(50, -0.8 , 0.8)
I use the GARCH model with the rugarchpackage : library(rugarch)
comtspec <- ugarchspec(mean.model=list(
armaOrder=c(1,0)), distribution="std",
news I have a time series return like this: ret <- rnorm(50, -0.8 , 0.8)
I use the GARCH model with the rugarchpackage : library(rugarch)
comtspec <- ugarchspec(mean.model=list(
armaOrder=c(1,0)), distribution="std",
Karl I am new to R. I'm trying to use Holt's method for prediction, but I'm getting this weird error. I am using the forecasting package V-7.1 with R (version 3.2.5) and Rstudio (version 0.99.896). I reinstalled all from R to Rstudio, but it didn't work. From
xander27 How can I use the results of a randomForrest call in R to predict labels on some unlabeled data (e.g. real-world input to be classified)? Code: train_data = read.csv("train.csv")
input_data = read.csv("input.csv")
result_forest = randomForest(Label ~
username I'm trying to forecast using "Principles and Practices of Forecasting" by Hyndman and Athanasopoulos, and I'm having some annoying issues trying to forecast with my own data (using a forecastsoftware package) . my question is: The resulting prediction
username I'm trying to forecast using "Principles and Practices of Forecasting" by Hyndman and Athanasopoulos, and I'm having some annoying issues trying to forecast with my own data (using a forecastsoftware package) . my question is: The resulting prediction
username I'm trying to forecast using "Principles and Practices of Forecasting" by Hyndman and Athanasopoulos, and I'm having some annoying issues trying to forecast with my own data (using a forecastsoftware package) . my question is: The resulting prediction
username I have only recently started using the random forest package in R. After expanding the forest, I tried to predict the response using the same dataset (i.e. the training dataset), which gave me a different confusion matrix than when I printed it. The f
username I have only recently started using the random forest package in R. After expanding the forest, I tried to predict the response using the same dataset (i.e. the training dataset), which gave me a different confusion matrix than when I printed it. The f
username I have only recently started using the random forest package in R. After expanding the forest, I tried to predict the response using the same dataset (i.e. the training dataset), which gave me a different confusion matrix than when I printed it. The f
will I am trying to validate the MLEs for $\alpha$ , $\beta$ and $\lambda$ obtained for the Logistic-Lomax distribution by Zubair et al. in their paper titled A Study of Logistic-Lomax Distribution when using Dataset 1 . The paper uses the following code to do
world sports I am interested in using this nlsto help fit the Langmuir equation, Y =(Qmax*k*X)/(1+(k*X))similar to fitting nonlinear Langmuir isotherms in R in this article . The equation parameters of my interest correspond to Qmaxthe horizontal asymptotes (g
username I am trying to estimate an EGARCH model on two different time series using the excellent rugarchpackage , but the solver fails to converge. I don't want to use the "mixed" solver option because randomness is introduced when it loops through the "gosol
username I am trying to estimate an EGARCH model on two different time series using the excellent rugarchpackage , but the solver fails to converge. I don't want to use the "mixed" solver option because randomness is introduced when it loops through the "gosol
username I am trying to estimate an EGARCH model on two different time series using the excellent rugarchpackage , but the solver fails to converge. I don't want to use the "mixed" solver option because randomness is introduced when it loops through the "gosol
Pelumi I'm trying to backtest my arch model using ugarchroll but I get this warning message "警告信息:在 .rollfdensity(spec = spec, data = data, n.ahead = n.ahead, forecast.length = forecast.length, : There is a non-convergent estimation window...resubmit object wi
Kal I am new to R. I was trying to predict using holt method but getting this strange error. I am using forecast package V-7.1 with R (version 3.2.5) and Rstudio (Version 0.99.896). I reinstall all from R to Rstudio but did not work. Only h from 1 to 10 works.
edb500 I'm using R with the Neuronet package, see the docs ( https://cran.r-project.org/web/packages/neuralnet/neuralnet.pdf ). I have used the neural network function to build and train my model. Now that I have my model built, I want to test it on real data.
Andre I am using the plm package for panel data for instrumental variable estimation. However, it seems that computing cluster robust standard errors by using the vcovHC() function is not supported. More specifically, when I use the vcovHC() function, the foll
Andre I am using the plm package for panel data for instrumental variable estimation. However, it seems that computing cluster robust standard errors by using the vcovHC() function is not supported. More specifically, when I use the vcovHC() function, the foll
Andre I am using the plm package for panel data for instrumental variable estimation. However, it seems that computing cluster robust standard errors by using the vcovHC() function is not supported. More specifically, when I use the vcovHC() function, the foll
Lucca coding I have unbalanced panel data and I want to fit this type of regression: Pr(y=1|xB) = G(xB+a)
where "y" is a binary variable, "x" is a vector of explanatory variables, and "B" is my coefficient. I want to implement a random effects model with maxi
Andre I am using the plm package for panel data for instrumental variable estimation. However, it seems that computing cluster robust standard errors by using the vcovHC() function is not supported. More specifically, when I use the vcovHC() function, the foll
Andre I am using the plm package for panel data for instrumental variable estimation. However, it seems that computing cluster robust standard errors by using the vcovHC() function is not supported. More specifically, when I use the vcovHC() function, the foll