search for: ar5

I'm using the fixed argument in arima. Shouldn't ar4, ar5, and ar6 display as zero in the output? Call: arima(x = window(log(hhprice), start = c(1990, 1), end = c(2003, 3)), order = c(7, 1, 0), xreg = window(ts.union(exa1 = lag(exa, -1), exa12 = lag(exa, -12), exb1 = lag(exb, -1), exc1 = lag(exc, -1), exc12 = lag(exc, -12)), start = c(1990,...

Dear all, The standard call to ARIMA in the base package such as arima(y,c(5,0,0),include.mean=FALSE) gives a full 5th order lag polynomial model with for example coeffs Coefficients: ar1 ar2 ar3 ar4 ar5 0.4715 0.067 -0.1772 0.0256 -0.2550 s.e. 0.1421 0.158 0.1569 0.1602 0.1469 Is it possible (I doubt it but am just checking) to define a more parsimonous lag1 and lag 5 model with coeff ar1 and ar5? Or do I need one of the other TS packages? thanks Gerard *********...

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811 0.0646 s.e. 0.0865 0.0885 0.1141 0.1181 0.1196 0.1220 0.1120 0.0908 0.0865 ma1 ma2 sma1 -0.0221 -0.9779 -0.7635 s.e. 0.0539 0.0534 0.0834 sigma^2 esti...

...done something wrong? I am attaching the code, thank you. Tian #GARCH analysis of monthly return rm(list=ls(all=TRUE)) sp500 <- read.csv('s&p_m90.csv', header=TRUE) sp500 <- sp500[,2] #only adjusted close n <- length(sp500) logr <- log(sp500[1:n-1] / sp500[2:n]) acf(logr) ar5 <- arima(logr, order = c(5, 0, 0), include.mean = T) logr<- ar5$res acf(logr) #fit GARCH distribution hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD') norm_fit <- normFit(logr) curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add = TRUE, col=2) t_fit <- stdFit...

...h an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078 -0.44774 0.41881 0.47624 0.01406 0.06565 -0.06167 -0.01294 0.31313 ma3 ma4 ma5 intercept -0.49027 -0.55461 -0.11520 -0.10692 > modelarima<-arima(x,c(7,1,5)) > modelarima Call: arima(x = x, order = c(7, 1, 5)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 ma3...

...a or something wrong? I am attaching the code, thank you. Muster #GARCH analysis of monthly return rm(list=ls(all=TRUE)) sp500 <- read.csv('s&p_m.csv', header=TRUE) sp500 <- sp500[,2] #only adjusted close n <- length(sp500) logr <- log(sp500[1:n-1] / sp500[2:n]) acf(logr) ar5 <- arima(logr, order = c(5, 0, 0), include.mean = T) logr<- ar5$res #remove mean acf(logr) #fit GARCH distribution hist(logr, freq = F, ylim = c(0, 12), breaks = 'FD') norm_fit <- normFit(logr) curve(dnorm(x, norm_fit$est[1], norm_fit$est[2]), -.15, .15, add = TRUE, col=2) t_fit...

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811 0.0646 s.e. 0.0865 0.0885 0.1141 0.1181 0.1196 0.1220 0.1120 0.0908 0.0865 ma1 ma2 sma1 -0.0221 -0.9779 -0.7635 s.e. 0.0539 0.0534 0.0834 sigma^2 esti...

...rder = c(7, 0, 7), xreg = isr) : possible convergence problem: optim gave code=1 In this case, when you print the results from the arima model, the error message appears again: > reg Call: arima(x = y, order = c(7, 0, 7), xreg = isr) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 -0.3356 0.0282 -0.0124 0.0604 0.1608 0.7949 0.2286 0.4461 0.0099 s.e. NaN 0.2140 0.1711 0.2545 0.1547 0.0725 NaN NaN 0.1842 ma3 ma4 ma5 ma6 ma7 intercept isr 0.0841 0.0773 -0.054 -0.7227 -0.1322...

...Std. Error z value Pr(>|z|) d 4.745e-01 0.000e+00 Inf <2e-16 *** ar1 8.897e-01 0.000e+00 Inf <2e-16 *** ar2 -3.386e-01 0.000e+00 -Inf <2e-16 *** ar3 3.339e-01 2.044e-17 1.634e+16 <2e-16 *** ar4 -4.406e-01 0.000e+00 -Inf <2e-16 *** ar5 3.924e-02 6.349e-18 6.182e+15 <2e-16 *** ar6 -5.184e-01 2.558e-17 -2.026e+16 <2e-16 *** ar7 8.988e-01 0.000e+00 Inf <2e-16 *** ar8 -7.568e-01 3.112e-16 -2.432e+15 <2e-16 *** ar9 3.442e-01 2.175e-22 1.582e+21 <2e-16 *** ma1 -1.190e-01 1.470e-18 -8.097e+16...

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on

This patch set is for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on native

This patch set is for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on native

This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect call into in-place execution or direct call. The first patch imports helper functions which themselves doesn't interesting things. The second patch replaces the indirect function calls with a

This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect call into in-place execution or direct call. The first patch imports helper functions which themselves doesn't interesting things. The second patch replaces the indirect function calls with a

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