Hi, I think you must first explain more precisely, what is not good with the results (e.g. model error is not approximately a white noise, or model fit is bad, or performance measures, such as MAE, MAPE, RMSE, are not good, or coefficients are not significant , and so). Did you check if the time series is really stationary (has no trend)? If so, and there are not any unit roots, you should use the ARMA model (without I part). What about stationarity, stability and invertibility? Did you set the appropriate order of AR and MA part (through the investigation of acf, pacf, etc).

hi Mr. Dragan and thank you for your interest

let X be the time series we are talking about

1- i checked the stationarity using KPSS test

2- results are not good in term of MAE, MAPE,..etc. and most importantly, in my model the foretasted value (of var X) is integrated into a procedure that forecast another variable (Y). when i use the real value of X i get amazing result of forecasting Y, however, when i use the foretasted value of X i get poor results for forecasting Y!

3- i did check acf, pacf,

i will try to use ARMA model.

thank you for your help.

I think that the structure of model, which forecasts the X variable and gives Xfor, is not adequate. If it would be, then the Xfor would fit well X, so most likely Yfor would be better. Try to derive more adequate structure of ARMA model. Regards, Dejan

Dear Boryana

i applied the ADF test (result quoted below)

would you please send me a link that explain your note concerning "ACF decay"?

regards;

> summary(ur.df(cv$rsi, lags=16, type='trend'))

###############################################

# Augmented Dickey-Fuller Test Unit Root Test #

###############################################

Test regression trend

Call:

lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)

Residuals:

Min 1Q Median 3Q Max

-1.0420348 -0.8069637 -0.3113093 0.2617201 3.0884406

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 2.38802420181 0.12172847444 19.61763 < 0.000000000000000222 ***

z.lag.1 -1.41437451726 0.06991137820 -20.23096 < 0.000000000000000222 ***

tt -0.00001231467 0.00001134956 -1.08504 0.27796

z.diff.lag1 0.43341854441 0.06688454039 6.48010 0.00000000010159203909 ***

z.diff.lag2 0.42664837233 0.06378196054 6.68917 0.00000000002521523079 ***

z.diff.lag3 0.43209041194 0.06046915014 7.14563 0.00000000000104149335 ***

z.diff.lag4 0.42293543579 0.05703911514 7.41483 0.00000000000014494384 ***

z.diff.lag5 0.40688301682 0.05336425183 7.62464 0.00000000000002971888 ***

z.diff.lag6 0.40019663881 0.04948777850 8.08678 0.00000000000000078244 ***

z.diff.lag7 0.38081547655 0.04693392119 8.11386 0.00000000000000062827 ***

z.diff.lag8 0.37999408379 0.04445187163 8.54844 < 0.000000000000000222 ***

z.diff.lag9 0.34707984784 0.04200925069 8.26199 < 0.000000000000000222 ***

z.diff.lag10 0.30049064826 0.03935206889 7.63596 0.00000000000002725154 ***

z.diff.lag11 0.02784732263 0.03654104940 0.76208 0.44605

z.diff.lag12 0.00959422782 0.03324000993 0.28863 0.77287

z.diff.lag13 0.00830523009 0.02969271226 0.27971 0.77972

z.diff.lag14 -0.00241471224 0.02574341467 -0.09380 0.92527

z.diff.lag15 0.00618586749 0.02103733342 0.29404 0.76874

z.diff.lag16 -0.00622889846 0.01499598680 -0.41537 0.67789

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9803803 on 4460 degrees of freedom

Multiple R-squared: 0.520556, Adjusted R-squared: 0.518621

F-statistic: 269.0245 on 18 and 4460 DF, p-value: < 0.00000000000000022204

Value of test-statistic is: -20.231 136.438 204.6522

Critical values for test statistics:

1pct 5pct 10pct

tau3 -3.96 -3.41 -3.12

phi2 6.09 4.68 4.03

phi3 8.27 6.25 5.34

Dear all, I completely agree with Miss Boryana's claims, it is very recommended to see some good introductory time series books. There are plenty of them, and some are even allowed to be free downloadable through the google. Best regards, Dejan

Dear Juehui,

thank you for your valuable comment. i downloaded your working paper. i think i need to read it carefully before getting back to you. regards;