time series - Error when attempting to estimate GARCH model in R -


here code i'm using create time series:

#violent crimes time series violent <- crime[,1] viol.ts <- ts(violent, start=1960, end=2013, frequency=1) viol.train <- window(viol.ts, start=1960, end=2003) viol.test <- window(viol.ts, start=2004) ts.plot(viol.ts, type="l", xlab="year", ylab="total crimes", main="violent crime, 1960-2013")

here code i'm using estimate garch model:

#garch viol.g <- garchfit(~arma(1,1) + garch(1,1), viol.ts) summary(viol.g) plot(viol.g) plot(viol.g@h.t, type="l") plot (viol.g@fitted, type="l")

i keep getting error

error in solve.default(fit$hessian) : system computationally singular: reciprocal condition number = 3.27071e-20

and have no idea going wrong.

system computationally singular means not possible obtain inverse of second derivative of log likelihood w.r.t. parameters (fit$hessian). mle estimates parameters minimize log likelihood function but, without it, not possible satisfy second order condition - in theory , actual implementation of package may different.

with random sample, function works without problem shown below , may check data.

library(fgarch)  #violent crimes time series set.seed(1237) violent <- sample(log(10:30), 53, replace = true) viol.ts <- ts(violent, start=1960, end=2013, frequency=1) viol.train <- window(viol.ts, start=1960, end=2003) viol.test <- window(viol.ts, start=2004) ts.plot(viol.ts, type="l", xlab="year", ylab="total crimes", main="violent crime, 1960-2013")  #garch viol.g <- garchfit(~arma(1,1) + garch(1,1), viol.ts) summary(viol.g) plot(viol.g) plot(viol.g@h.t, type="l") plot (viol.g@fitted, type="l")  title:   garch modelling   call:   garchfit(formula = ~arma(1, 1) + garch(1, 1), data = viol.ts)   mean , variance equation:   data ~ arma(1, 1) + garch(1, 1) <environment: 0x0000000030109de0>   [data = viol.ts]  conditional distribution:   norm   coefficient(s):   mu          ar1          ma1        omega       alpha1        beta1   5.42739444  -0.83160492   0.90173149   0.06406353   0.00000001   0.39089064

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