About jaweriahh

jaweriahh · ‎06-21-2016

Thank you I was able to change the preference and get the journal format.

jaweriahh · ‎06-20-2016

The code that I use is data metals; input METAL_INDICES @@; year = intnx( 'year', '1jan1960'd, _n_-1 ); format year year4.; datalines; 66.22394646972511 63.384704520079 61.4419993429769 62.9757221934923 73.6304533994784 82.5734655655989 84.1483788908113 73.3390991255459 77.4431122955925 82.4270327336745 78.7169960742791 68.8143144975537 61.8121521989829 69.5780120704402 70.9516888908576 55.8389544132356 59.8840130129157 61.9357151684358 57.7552833219016 65.8560733079746 65.7405466406374 51.88053840411849 45.6068461282871 55.758026125387 51.2150888852897 47.315645402171 41.2951031311713 47.9488331430076 70.4396617071417 64.7588119558383 54.840202297415 47.0899987683978 44.7236678825118 37.2367586934329 46.0601800629501 51.090465430714 44.4227938184707 48.0554952320341 41.090861817545 41.6011233159068 48.0762406145175 44.9000941006041 43.71436526851619 46.26060606160901 57.9601902078588 68.5299063421553 102.220610510998 113.302733959249 99.4066215642484 70.9260523446916 100 104.177006984306 89.34210534803179 85.62180156384051 80.0811093609334 ; RUN; data metals; set metals; LOGMETALS=log(METAL_INDICES); data metals; set metals; level1988= ( year >= '1jan1988'd ); run; ODS HTML style=Journal; proc ucm data = metals; id year interval = year; model LOGMETALS=level1988; irregular; leveL variance=0 noest; slope plot=smooth; cycle plot=smooth; CYCLE plot=smooth; estimate plot=acf plot=cusum plot=cusumsq plot=histogram plot=loess plot=pacf plot=model plot=qq plot=wn Back=6; forecast back=6 lead=6 plot=decomp outfor = results; RUN; data results; set results; passf = exp(forecast + 0.5*std**2); passlcl = exp(lcl); passucl = exp(ucl); if _n_ > 55; keep obs forecast std lcl ucl passf passlcl passucl; run; proc print data = results; run; And the error that I get is NOTE: ODS statements in the SAS Studio environment may disable some output features ODS HTML style=Journal; NOTE: Writing HTML Body file: sashtml6.htm ERROR: Insufficient authorization to access /opt/sasinside/SASConfig/Lev1/SASApp/sashtml6.htm. ERROR: No body file. HTML output will not be created.

jaweriahh · ‎06-20-2016

Thank you for your answer. I require the journal option but do not know how to include it in my SAS code. The code that I am using is. proc ucm data = metals; id year interval = year; model logtin=level1986; irregular; level variance=0 noest; slope plot=smooth; cycle plot=smooth; cycle plot=smooth; estimate plot=acf plot=cusum plot=cusumsq plot=histogram plot=loess plot=pacf plot=model plot=qq plot=wn back=5; forecast back=5 lead=5 plot=decomp; run; If I specify the option ODS HTML style=journal before proc ucm I get an error. Please advise

jaweriahh · ‎06-18-2016

I am running the Unobserved Components Model. I generate graphical results for forecasts. Is there any command which can help me to change the colour of the confidence intervals for the forecasts (which is blue by default)?

jaweriahh · ‎06-03-2016

Thank you very much i used the code and it worked.

jaweriahh · ‎06-02-2016

I have a very simple question. I am running an Unobserved Components Model to decompose and forecast my data. I transform my data into log and then run the model. My code is given below proc ucm data = metals; id year interval = year; model metal_indices=level1988; level variance=0 noest; slope plot=smooth; cycle plot=smooth; cycle plot=smooth; estimate back=6; forecast back=6 lead=6 plot=decomp; run; As I am using the log form of data after I obtain my forecasts and parameters I need to transform the data back. What code should I use for it?

jaweriahh · ‎04-12-2016

Thank you very much for your reply. My data has two breaks detected in 1973 and in 1992. I include step intervention variables for them and run the model proc ucm data = metals; id year interval = year; model AL=BREAK1973 BREAK1992; irregular; level variance=0 noest Plot=smooth; slope; cycle; cycle; estimate plot=all; run; However I am stuck about the forecasting. i am not sure if i should hold out sample points (my data pertains to commodity markets and since mid 2000 markets have been volatile). Can I estimate my model using the whole sample (1958 to 2012) and then forecast from 2008 till 2020 (Using the command given below) ? Is there any other option that i can use?? proc ucm data = metals; id year interval = year; model AL=BREAK1973 BREAK1992; irregular; level variance=0 noest Plot=smooth; slope; cycle; cycle; estimate plot=all; forecast back=5 lead=13 plot=decomp; run; Thank you

jaweriahh · ‎04-12-2016

Thank you for the replies. The blog was helpful. My data is annual time series from 1958 to 2012 and I want to forecast till 2020. This is what I understood. I should use data points 1958 to 2004 to estimate my model (I am using UNOBSERVED COMPONENTS MODEL) Then I should use the same model to forecast from 2005 to 2012 and compare the actual and the forecasted value. Then based in the best performing model I should make forecasts from 2012 to 2020 using the same model (estimated using data points 1958 to 2004) Is this procedure right? However my data shows extreme values from 2004 till 2009. I am attaching my data and the Graph .

jaweriahh · ‎04-10-2016

I have a data set of 54 data points. my objective is to (1) decompose my data into trend and cycles using UCM and (2) to forecast. My question is that as I only have 54 data points is it necessary for me to HOLD some sample points for out-of-sample forecasting? It is right if I first estimate my model using all the data points and then use the entire sample to forecast.

jaweriahh · ‎03-16-2016

I have a very simply question about Unobserved Components Model. I have a data set of 63 years. The data is declining for the entire time period but the slope is stochastic. The parameter of my slope term is negative but the level is positive [in the Trend Information (Based on the Final State) table]. I can interpret the slope but should the level not be negative if the data is decreasing? I use the command proc ucm data = lan; id year interval = year; model Eco ; irregular ; level variance=0 noest; slope ; run ;

jaweriahh · ‎02-18-2016

I have annual time series data for 63 years (attached). The data demonstrates a U shaped curve. I am trying to estimate the Unobserved Components Model with spline regression. the command I use is proc ucm data=land; id year interval = year; model es; irregular; level; slope; splinereg annual KNOTS=1958 1962 1977 variance=0 noest; estimate; run; However the Spline terms are not significant. is there any other method to incorporate a U shaped trend in PROC. My data is attached below. Kindly let me know what I am doing wrong UCM?

jaweriahh · ‎02-08-2016

Thank you for your reply. I require some common diagnostic statistics such as (Predicted error variance the one step ahead prediction errors, the normality statistic based on the third and fourth moments, heteroscedasticity statistic based on the ratio of the sample for the first one third of the prediction errors, the Box-Ljung serial correlation statistics, Durbin Watson) I have looked into the outfor=option but could not get these statistics (though I was able to get several diagnostic graphs). Think you

jaweriahh · ‎12-14-2015

When using the Unobserved Components Model is it possible to include an independent vaiable and generate out of sample forecasts? I am using the UCM but whenever I include an independent variable (along with a fixed level stochastic slope and cycle) i get an error and the results do not contain out of sample forecasts.

jaweriahh · ‎11-26-2015

I try the program (below) and then add the intervention (below). However my r square reduces (instead of improving) while model selection criteria, AIC and BIC criteria improve and the break is significant. So is the model without the intervention better than the one with the intervention? Is the r square deteriorating as my data set is small (55 points). proc ucm data=metals; model al; irregular; level variance=0 noest checkbreak; slope; cycle plot=smooth; estimate plot=panel; forecast plot=decomp; run; proc ucm data=metals; model al=break1973; irregular; level variance=0 noest checkbreak; slope; cycle plot=smooth; estimate plot=panel; forecast plot=decomp; run;

jaweriahh · ‎11-20-2015

Thank you sir your reply and pointing out the above important points. It, and references (especially Harvey 1989 chapter 5 page 268 and 1992), helped me to clarify my doubts I. As you pointed out, I will not try to overfit the model and drop the outliers. However sir I still have one issue. I want to incorporate the 1973 structural break to show its impact on the data (real metal prices which were distorted due to the oil price shock). As you mentioned the theory also supports this inclusion. Could you suggest how I can include and show the structural break in the model? Thank you once again.

Online Status	Offline
Date Last Visited	‎06-24-2016 05:58 AM

Re: Graphical Results in Unobserved Components Model

Re: Graphical Results in Unobserved Components Model

Re: Graphical Results in Unobserved Components Model

Graphical Results in Unobserved Components Model

Re: Forecasts with Unobserved Components Model

Forecasts with Unobserved Components Model

Re: Out-of-Sample Forecasting with Unobserved Components Model

Re: Out-of-Sample Forecasting with Unobserved Components Model

Out-of-Sample Forecasting with Unobserved Components Model

Unobserved Components Models and Trend Term

Re: Unobserved Components Model: Negetive R square and missing Adjuste...

Re: Unobserved Components Model: Negetive R square and missing Adjuste...

Re: Unobserved Components Model

Re: Unobserved Components Model Model Diagnostic

Re: Graphical Results in Unobserved Components Model

Re: Graphical Results in Unobserved Components Model

Re: Graphical Results in Unobserved Components Model

Graphical Results in Unobserved Components Model

Re: Forecasts with Unobserved Components Model

Forecasts with Unobserved Components Model

Re: Out-of-Sample Forecasting with Unobserved Components Model

Re: Out-of-Sample Forecasting with Unobserved Components Model

Out-of-Sample Forecasting with Unobserved Components Model

Unobserved Components Models and Trend Term

Unobserved Components Model with Spline

Re: Prediction Error Variance

Re: Unobserved Components Model Model Diagnostic

Re: Unobserved Components Model Model Diagnostic

Re: Unobserved Components Model Model Diagnostic