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From |
[email protected] (Vince Wiggins, StataCorp) |

To |
[email protected] |

Subject |
Re: st: Predict dynamic model |

Date |
Mon, 27 Oct 2003 17:58:40 -0600 |

Martin Rio, Martin <[email protected]> asks how to do a dynamic prediction after estimating a regression model with a lagged endogenous variable, > I am using reg to estimate a time series regression of the form > > y(t) = a + by(t-1) + cx(t) +dz(t) + u(t), > > where y(t-1) was generated by lagging y one time period > > I want to be able to predict values of y for the later portion of my > sample and compare these with the actual values. In models with no > lagged independent variables I would use predict. In this model I > want to forecast recursively, using the previous forecast to > estimate the next, and not the predefined y(t-1) variable. If I > use: > > predict yhat, bx > > stata uses the predefined y(t-1) variable rather than recursively > estimating y and feeding it back into the model. What command in > stata would do the recursive trick? -predict- after -regress- does not truly "understand" the dynamic nature of time-series data. But all is not lost, -predict- after -arima- does understand time, and -arima- is happy to estimate simple regression models without ARMA components. If Martin's regression command were . regress y L.y x1 x2 he could instead type, . arima y L.y x1 x2 Let's further assume that Martin's data is monthly and he wants to begin dynamic forecasts in April 1995. He would get the predictions by typing, . predict y_dynhat , dyn(1995m4) Martin can find out more in in the discussion of the dyn() and t0() options in [R] arima. As a side-bar, Martin may want to specify the -hessian- option on the -arima- command to produce negative inverse Hessian estimates of the covariance matrix (VCE), and thus the standard errors. These VCE estimates differ from the small-sample estimates of -regress- by only a scale factor. When -hessian- is not specified, the default covariance estimate from -arima- is the outer produce of gradients (OPG) which differs form the Hessian estimate in finite samples. Regardless, the parameter estimates are the same. -- Vince [email protected] * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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