Hi there, merely to straighten out a wee point first of all, I ask the following by way of trying to help. Are you aware (for example) that in their books, P Brockwell and R Shumway pose the ARIMA equations in different form re the positive and negative coefficient signage. Their models are different and the signage results are different but when the coefficients are inserted in their models, the results are the same. Just asking that you properly know the model being computed is all I'm asking. rjfhud(a)powerup.com.au ----- Original Message ----- From: 不提供 不提供 To: gretl-users(a)lists.wfu.edu Sent: Sunday, January 09, 2011 10:54 PM Subject: [Gretl-users] Some questions about X-12-ARIMA Dear all: I make my question clearer. ARIMA and X-12-ARIMA have almost the same outcomes under most combinations of AR and MA. For example, Using the same sample, the output of ARIMA(1,1,1)(1,1,0 ): Function evaluations: 22 Evaluations of gradient: 8 Model 5: ARIMA, using observations 1982:03-1989:12 (T = 94) Estimated using BHHH method (conditional ML) Dependent variable: (1-L)(1-Ls) z coefficient std. error z p-value --------------------------------------------------------- phi_1 0.0386387 0.490287 0.07881 0.9372 Phi_1 -0.547450 0.103980 -5.265 1.40e-07 *** theta_1 0.134454 0.505469 0.2660 0.7902 Mean dependent var -595.9894 S.D. dependent var 35113.05 Mean of innovations -657.4065 S.D. of innovations 29171.20 Log-likelihood -1099.788 Akaike criterion 2207.577 Schwarz criterion 2217.750 Hannan-Quinn 2211.686 Real Imaginary Modulus Frequency ----------------------------------------------------------- AR Root 1 25.8808 0.0000 25.8808 0.0000 AR (seasonal) Root 1 -1.8266 0.0000 1.8266 0.5000 MA Root 1 -7.4375 0.0000 7.4375 0.5000 ----------------------------------------------------------- the output of X-12-ARIMA(1,1,1)(1,1,0 ): Model 6: ARIMA, using observations 1982:03-1989:12 (T = 94) Estimated using X-12-ARIMA (conditional ML) Dependent variable: (1-L)(1-Ls) z coefficient std. error z p-value --------------------------------------------------------- phi_1 0.0383739 0.602274 0.06371 0.9492 Phi_1 -0.547423 0.0911210 -6.008 1.88e-09 *** theta_1 0.134554 0.597619 0.2252 0.8219 Mean dependent var -595.9894 S.D. dependent var 35113.05 Mean of innovations -657.4774 S.D. of innovations 29171.20 Log-likelihood -1099.788 Akaike criterion 2207.577 Schwarz criterion 2217.750 Hannan-Quinn 2211.686 Real Imaginary Modulus Frequency ----------------------------------------------------------- AR Root 1 26.0594 0.0000 26.0594 0.0000 AR (seasonal) Root 1 -1.8267 0.0000 1.8267 0.5000 MA Root 1 -7.4320 0.0000 7.4320 0.5000 ----------------------------------------------------------- The outcomes of ARIMA(1,1,1)(1,1,0 ) and X-12-ARIMA(1,1,1)(1,1,0 ) are almost the same. But there are a few exceptions. For example, under the same sample, the output of ARIMA(1,1,2)(2,1,0 ): Model 7: ARIMA, using observations 1983:03-1989:12 (T = 82) Estimated using BHHH method (conditional ML) Dependent variable: (1-L)(1-Ls) z coefficient std. error z p-value ------------------------------------------------------- phi_1 -0.590308 0.200862 -2.939 0.0033 *** Phi_1 -0.683313 0.134247 -5.090 3.58e-07 *** Phi_2 -0.240713 0.113586 -2.119 0.0341 ** theta_1 0.873512 0.207170 4.216 2.48e-05 *** theta_2 0.361254 0.0966288 3.739 0.0002 *** Mean dependent var -1074.305 S.D. dependent var 36698.54 Mean of innovations -1019.087 S.D. of innovations 28580.42 Log-likelihood -957.7121 Akaike criterion 1927.424 Schwarz criterion 1941.864 Hannan-Quinn 1933.222 Real Imaginary Modulus Frequency ----------------------------------------------------------- AR Root 1 -1.6940 0.0000 1.6940 0.5000 AR (seasonal) Root 1 -1.4194 -1.4628 2.0382 -0.3726 Root 2 -1.4194 1.4628 2.0382 0.3726 MA Root 1 -1.2090 -1.1430 1.6638 -0.3795 Root 2 -1.2090 1.1430 1.6638 0.3795 ----------------------------------------------------------- the output of X-12-ARIMA(1,1,2)(2,1,0 ): Model 8: ARIMA, using observations 1983:03-1989:12 (T = 82) Estimated using X-12-ARIMA (conditional ML) Dependent variable: (1-L)(1-Ls) z coefficient std. error z p-value ------------------------------------------------------- phi_1 0.653709 0.209156 3.125 0.0018 *** Phi_1 -0.675406 0.113095 -5.972 2.34e-09 *** Phi_2 -0.244173 0.113191 -2.157 0.0310 ** theta_1 -0.566737 0.220105 -2.575 0.0100 ** theta_2 -0.222901 0.115118 -1.936 0.0528 * Mean dependent var -1074.305 S.D. dependent var 36698.54 Mean of innovations -2724.431 S.D. of innovations 29295.00 Log-likelihood -959.7371 Akaike criterion 1931.474 Schwarz criterion 1945.914 Hannan-Quinn 1937.272 Real Imaginary Modulus Frequency ----------------------------------------------------------- AR Root 1 1.5297 0.0000 1.5297 0.0000 AR (seasonal) Root 1 -1.3830 1.4774 2.0237 0.3698 Root 2 -1.3830 -1.4774 2.0237 -0.3698 MA Root 1 1.1990 0.0000 1.1990 0.0000 Root 2 -3.7416 0.0000 3.7416 0.5000 ----------------------------------------------------------- The outcomes of ARIMA(1,1,2)(2,1,0 ) and X-12-ARIMA(1,1,2)(2,1,0 ) are hugely different. The question above puzzles me. I also want to know When I choose the options Model/Time series/ARIMA/Using X-12-ARIMA to run the X-12-ARIMA model. Is the set of equation of X-12-ARIMA in gretl the same as RegARIMA(X-12-ARIMA – Reference Manual, Version 0.3. U.S. Census Bureau): φ(B)Φ(B)▽^d ▽_s^D[y-Σβ_i x_it]= θ(B)Θ(B)a_t I can not see the outcome of any seasonality adjusting regression variables(the part of y-Σβ_i x_it, such as length-of-month、Trend constant、Trading day、level shift at t_0 and so on). Thanks a lot _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users