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From |
"Mansour Farahani" <[email protected]> |

To |
<[email protected]> |

Subject |
Re: st: interpret xtabond one-step results |

Date |
Fri, 10 Aug 2007 21:14:19 -0400 |

In an autoregressive distributed lag (ADL) model: Yt = a0 + a1Yt-1 + b0Xt + b1Xt-1 + et We can derive the long-run equilibrium for E(Y) by considering what happens when Yt = Yt-1, Xt = Xt-1; this yields: E(Y) = a0 + a1E(Y) + b0X + b1X giving the long-run solution for E(Y) as: E(Y) = a0/(1 - a1) + {(b0 + b1)/(1 - a1)}X. (b0 + b1)/(1 - b1) measures the long-run response of Y on average to a unit change in X, whereas b0 measures the short-run response. then you use the following command nlcom (_b[X]+ _b[L.X])/(1-_b[L.Y]) Mansour >>> "Jing Tong" <[email protected]> 08/10/07 3:27 PM >>> Dear Stata users Could anyone please help me on the interpretation of the one-step results as shown in the following example: 1) how to report the coefficients and Z-values of the independent variables? I mean, instead of reporting as Wit and Wi(t-1) separately, how to get the coefficient and Z-value for W itself? And how to tell the significance level for W, if (P>|z|) for D1 is 0.000 (significant at 1% level) while (P>|z|) for LD is 0.100 (significant at 10% level)? 2) For Sargan test of over-identifying restrictions, is the (prob>chi2) the probability of accepting the null hypotheses of valid instruments? In this example, it seems to show the strong evidence against the null. Does it mean the specification is not valid at all? 3) For AR(1) and AR(2) at the bottom of the output, is the (pr>z) the probability of accepting the null hypotheses of no first-order and second-order serial correlation? If the residuals are not serially correlated, should there be evidence of significant negative first-order serial correlation (i.e., pr>z close to 0) and not rejecting the null of no second-order serial correlation (i.e., pr>z close to 1) ? . xtabond n l(0/1).w k , lags(2) Arellano-Bond dynamic panel data Number of obs = 611 Group variable (i): id Number of groups = 140 Wald chi2(5) = 350.58 Time variable (t): year min number of obs = 4 max number of obs = 6 mean number of obs = 4.364286 One-step results -------------------------------------------------------------------------- ---- n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+------------------------------------------------------------ ---- n | LD | .3751428 .1050691 3.57 0.000 .1692112 .5810745 L2D | -.0822723 .0422479 -1.95 0.051 - .1650766 .000532 w | D1 | -.4754038 .0564188 -8.43 0.000 -.5859825 - .364825 LD | .208237 .0832401 2.50 0.012 .0450894 .3713847 k | D1 | .3802498 .0352074 10.80 0.000 .3112446 .449255 _cons | -.0178497 .0041618 -4.29 0.000 -.0260068 - .0096926 -------------------------------------------------------------------------- ---- Sargan test of over-identifying restrictions: chi2(25) = 97.07 Prob > chi2 = 0.0000 Arellano-Bond test that average autocovariance in residuals of order 1 is 0: H0: no autocorrelation z = -3.02 Pr > z = 0.0026 Arellano-Bond test that average autocovariance in residuals of order 2 is 0: H0: no autocorrelation z = -0.01 Pr > z = 0.9892 Thank you very much for your help! - Jing * * 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/ * * 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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