About eric_sas_1

eric_sas_1 · ‎04-15-2019

Thanks. I'm trying to fit a model to time-series data such that y_t is a function of y_{t-1} and a series of contemporaneous exogenous factors x, with a model for autocorrelated disturbances, v_t. Model detail: y_t = x'B + v_t v_t = e_t - B(v_{t-1}) - ... B(v_{t-n}) I'm using PROC AUTOREG with the following code: PROC AUTOREG data=test; model y = lagy x1 x2 x3 / lagdep=lagy nlag=12 backstep method=ml; output out=out_test predicted=p residual=r; run; I would like to forecast this model using PROC AUTOREG. Future values of the exogenous variables are provided, except for the lagged dependent variable. I can easily estimate and forecast a static model with no lagged dependent variable. Is PROC AUTOREG capable of dynamic forecasting? What is the best alternative in SAS to do so? Currently, there are two options I have been using to forecast (see below), would be nice if AUTOREG had this capability. Manual calculation in data step, making use of the retain function PROC SIMLIN can forecast dynamic models Sample dataset: data input_data; input y lagy x1 x2 x3 datalines; 212 204 0 18223 2.282 187 212 1 18121 2.212 180 187 0 18507 4.063 . 180 0 17660 3.011 . . 0 17782 2.799 . . 0 17887 2.399 . . 1 17998 2.500 ;

eric_sas_1 · ‎04-11-2019

Thanks for the response. One of the nice features if that if you build a model with an autoregressive error model for (v_t) in PROC AUTOREG using the nlag option, the forecasting engine will forecast the fully specified model including the model for the errors, with the aforementioned limitation that it cannot handle a dynamic forecast. From what I understand about PROC VARMAX, this isn't easily accomplished, but I may not be well versed enough in that PROC.

eric_sas_1 · ‎04-11-2019

I have built a simply dynamic model in PROC AUTOREG, such that y is a function of the previous month's (y_{t-1}), and some exogenous regressors. I see that PROC AUTOREG can handle forecasting of static models, but when a model with a lagged dependent variable is specified PROC AUTOREG cannot dynamically forecast that model. Are there other, simpler options for forecasting dynamic models aside from writing the model out and forecasting via a datastep or using PROC SIMLIN? It would be nice if PROC AUTOREG had the capability...Suggestions would be helpful. Working with SAS 9.4.

eric_sas_1 · ‎03-21-2017

Thanks for the reply. The data is illustrative. In reality, I have data that can be bucketed in any number of ways, depending on the question/analysis to be performed. Given that you've stated that PROC GENMOD is appropriate for panel estimation, I have two follow up questions. 1) Is GENMOD capable or handling random effects, of just FE (using the CLASS statement)? 2) Is there any straightforward way to test for spatial and/or temporal autocorrelation using GENMOD? Is not, are that any alternative PROCs that would be of value here? Thanks!

eric_sas_1 · ‎11-04-2016

I have panel data such that two cross sections of a firm are analyzed over time, and the response variable takes on non-negative integer values (i.e. count data.). I'd like to estimate this model using Poisson Regression: The data looks like this: data model_data; input date counts x1 x2 x3; datalines; January-01 14 0 6.5 .45 January-01 21 1 6.5 .45...March-04 24 0 4.5 .55March-04 26 1 4.5 .55 ; ln(counts) = B0 + B1 x1 + B2 x2 + B3 x3 ... I've estimated this model in SAS with the following code: proc genmod data = model_data; class x1 / param=glm; model counts = x1 x2 x3 / dist=poisson link=log scale=pearson type1 type3 ; output put=poisson_out; Where x1 is the cross section of the data analyzed over time, and x2 and x3 are continuous predictor variables, using a pearson correction for overdispersion. Is PROC GENMOD able to analyze this type of data? I have found plenty of examples for purely cross sectional data, but there is an absence of discussion about using this for panel data. If PROC GENMOD is not capable of analyzing panel data, is PROC TCOUNTREG a viable alternative?

eric_sas_1 · ‎11-03-2016

I have panel data such that two cross sections of a firm are analyzed over time, and the response variable takes on non-negative integer values (i.e. count data.). I'd like to estimate this model using Poisson Regression: The data looks like this: data model_data; input date counts x1 x2 x3; datalines; January-01 14 0 6.5 .45 January-01 21 1 6.5 .45 ... March-04 24 0 4.5 .55 March-04 26 1 4.5 .55 ; ln(counts) = B0 + B1 x1 + B2 x2 + B3 x3 ... I've estimated this model in SAS with the following code: proc genmod data = model_data; class x1 / param=glm; model counts = x1 x2 x3 / dist=poisson link=log scale=pearson type1 type3 ; output put=poisson_out; Where x1 is the cross section of the data analyzed over time, and x2 and x3 are continuous predictor variables, using a pearson correction for overdispersion. Is PROC GENMOD able to analyze this type of data? I have found plenty of examples for purely cross sectional data, but there is an absence of discussion about using this for panel data. If PROC GENMOD is not capable of analyzing panel data, is PROC TCOUNTREG a viable alternative?

eric_sas_1 · ‎10-24-2016

Thanks for the clarification. That confirmed what I previously thought. Quick follow up: Is it possible to forecast that type of dynamic autoregressive error model in PROC SIMLIN, or any other SAS procedures besides PROC ARIMA? It's a cumbersome syntax for what is really a quite simple model.

eric_sas_1 · ‎10-24-2016

I see your point. However, it is possible to produce multistep forecasts for dynamic models whereby the forecasted values are produced using lagged forecasted values rather than lagged actual values (i.e. this can be done in PROC SIMLIN). However, with the model of the error series (in this case AR3), is it possible to forecast this in PROC SIMLIN or PROC ARIMA? Thanks!

eric_sas_1 · ‎10-19-2016

I forgot to include that in the above model. My fault. There proper model should be: Y_t = 3.6435 + 0.0422 Y_(t-1) + 0.0263 x_1 -0.2567 x_2 - 0.1329 x_3 + v_t, with the same AR(3) autoregressive error structure.

eric_sas_1 · ‎10-19-2016

Thanks. What about the lagged dependent variable? Shouldn't that be specified as an AR(1)?

eric_sas_1 · ‎10-17-2016

I estimated the following model in PROC AUTOREG using maximum likelihood (method=ml), specifying nlag=12 and the backstep option to automtically select the autoregressive order. where y_(t-1) is a lagged dependent variable and z_t is an array of explanatory variables. Because this model was estimated with nlag and backstep, SAS automatically selects the autoregressive error model for the noise series. Finally, the estimated model is: This model was estimated approx 1 yr ago, and I want to forecast this model in PROC ARIMA without re-estimating the model, thus using the NOEST option. I'm wondering how to forecast this model, including the AR(3) parameter for v_t, estimated by PROC AUTOREG using PROC ARIMA without re-estimating the model?

eric_sas_1 · ‎10-14-2016

I'm wondering how to forecast a published autoregressive error model, estimated in PROC AUTOREG, using PROC ARIMA. I have some data I would like to a monthly time series, for example: data inputs; input month y var1 var2 var3 var4 datalines 1 200 150 3 6 8 2 300 200 8 4 8 3 270 300 4 3 1 4 250 270 7 4 5 ; Where var 1 is a lagged dependent variable. Suppose I estimate the following model proc autoreg data=some_data outest=params; model y = var1 var2 var3 var4 / lagdep=var1 method=ml nlag=12 backstep; output out=model p=yhat r=residuals; run; where var1 is a lagged dependent variable, and var2-var4 are some explanatory variables. The final model contains an AR(3) parameter for the model of the error series. If this model was estimated 1 year ago, and I want to produce a multistep forecast of the model today, I might use PROC ARIMA coupled with the "Noest" option to suppress estimation of the model: proc arima data=some_forecast_data plots(unpack)=forecast(all); identify var=y crosscorr(=var2 var3 var4); estimate p=(1) ar=0.2343 mu=7.9534 input=(/(1)var2 /(1)var3 /(1)var4) initval=(-0.564$/(0.2343)var2 0.0041$/(0.2343)var3 0.1753$/(0.2343)var4 ) noest; forecast id=month interval=month lead=25 out=forecast_data nooutall; run; But this does not include the AR(3) parameter of the autoregessive error model. How can I include the additional AR terms for the autoregressive error model? How does PROC ARIMA handle the model of the residual series if the model is not re-estimated?

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