Econometrics-I-15

0064 7.25281 yl|.01779.01959.908.3640 45.6830

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Unformatted text preview: 0064 7.25281 YL| .01779 .01959 .908 .3640 45.6830--------+------------------------------------------------------------- FGLS ˜˜˜˜˜™ ™ 38/45 Part 15: Generalized Regression Applications----------------------------------------------------------- Constrained MLE for Multivariate Regression Model First iteration: 0 F= -48.2305 log|W|= -7.72939 gtinv(H)g= 2.0977 Last iteration: 5 F= 508.8056 log|W|= -16.78689 gtinv(H)g= .0000 Number of observations used in estimation = 123 Model: ONE PK PL PKK PLL PKL Y YY YK YL C B0 BK BL CKK CLL CKL CY CYY CYK CYL SK BK CKK CKL CYK SL BL CKL CLL CYL--------+-------------------------------------------------- Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] (FGLS) (OLS)--------+-------------------------------------------------- B0| -6.71218*** .21594 -31.084 .0000 -9.51337 -7.79653 CY| .58239*** .02737 21.282 .0000 .48204 .42610 CYY| .05016*** .00371 13.528 .0000 .04449 .05606 BK| .22965*** .06757 3.399 .0007 2.48099 2.80754 BL| -.13562* .07948 -1.706 .0879 .61358 -.02630 CKK| .11603*** .01817 6.385 .0000 .65620 .69161 CLL| .07801*** .01563 4.991 .0000 -.03048 .10325 CKL| -.01200 .01343 -.894 .3713 -.42610 -.48223 CYK| -.00473* .00250 -1.891 .0586 -.06761 -.07676 CYL| -.01792*** .00211 -8.477 .0000 .01779 .01473--------+-------------------------------------------------- Maximum Likelihood Estimates ˜˜˜˜˜™ ™ 39/45 Part 15: Generalized Regression Applications Vector Autoregression The vector autoregression (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate autoregressive model to dynamic multivariate time series. The VAR model has proven to be especially useful for describing the dynamic behavior of economic and financial time series and for forecasting. It often provides superior forecasts to those from univariate time series models and elaborate theory-based simultaneous equations models. Forecasts from VAR models are quite flexible because they can be made conditional on the potential future paths of specified variables in the model. In addition to data description and forecasting, the VAR model is also used for structural inference and policy analysis. In structural analysis, certain assumptions about the causal structure of the data under investigation are imposed, and the resulting causal impacts of unexpected shocks or innovations to specified variables on the variables in the model are summarized....
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