search for: backcast

Hi all, Forecasting from an arima model is easy with predict. But I can't manage to backcast : invent data from the model before the begining of the sample. The theory is easy : take your parameters, reverse your data, forecast, and then reverse the forecast I've tried to adapt the predict function to do that (i'm not sure that the statistical procedure is fine (with the residuals)...

...) values for ARIMA models. But I have not found a command that suggests reasonable values for the seasonal (p,d,q) values. GOAL #3 Use the ARIMA analysis to fill in for NA values. (I'm not sure how to do this yet. For example, I do not know if I will need to use windowing to smooth my backcasted data. I would appreciate any pointers, references, or code examples. Also, the terminology of "backcasting" and "interpolation" is not perfectly clear to me. I'm certainly looking to do more than linear interpolation between data points ... that's why I'm ho...

...one lag of LCPIH data This is eview result > > *Dependent Variable: DLCPIH > **Method: Least Squares > **Date: 08/12/11 Time: 12:44 > **Sample (adjusted): 1970Q2 2010Q2 > **Included observations: 161 after adjustments > **Convergence achieved after 14 iterations > **MA Backcast: 1969Q4 1970Q1 > ** > **Variable Coefficient Std. Error t-Statistic Prob. > ** > **C 0.003361 0.001814 1.853352 0.0657 > **DLCPIH(-1) -0.100150 0.053160 -1.883917 0.0614 > **DLCPIH(-2) 0.870456 0.052466 16.59075 0.0000 > **MA(1)...

...nly open to other suggestions, especially if they are easy to implement. My specific questions: 1. Presumably, once I get ARIMA working, I still have the problem of predicting the past missing values -- I've only seen examples of predicting into the future. 2. When predicting the past (backcasting), I also want to take reasonable steps to make the data look smooth. I guess I'm looking for a really good example in a textbook or white paper (or just an R guru with some experience in this area) that can offer some guidance. Venables and Ripley was a great start (Modern Applied St...

...used. Here are the estimations: EViews: Dependent Variable: DSPOT Method: Least Squares Date: 07/23/08 Time: 14:37 Sample (adjusted): 2 518 Included observations: 517 after adjustments Convergence achieved after 8 iterations White Heteroskedasticity-Consistent Standard Errors & Covariance Backcast: 0 1 Variable Coefficient Std. Error t-Statistic Prob. X(-1) 3.419048 1.185199 2.884787 0.0041 MA(1) -0.049565 0.079305 -0.624994 0.5323 MA(2) -0.249748 0.100952 -2.473914 0.0137 R-squared 0.044155 Mean dependent var 0.613926 Adjusted R-squared 0.040436 S.D. dependent var 12.36165 S...