* * Martin, Hurn, Harris, "Econometric Modelling with Time Series" * Examples 13-24, 13-25, 13-26, 13-27 from pp 492-500 * VAR Estimates of a U.S. Macroeconomic Model * open data sims_data.dat calendar(m) 1959:1 data(format=prn,nolabels,org=columns) 1959:01 1998:12 ffunds exchrate commpri m1 cpi ip * set lex = log(exchrate) set lcp = log(commpri) set lm = log(m1) set lp = log(cpi) set lo = log(ip) * set r = ffunds set mgrow = 100.0*(lm-lm{12}) set infl = 100.0*(lp-lp{12}) set ygrow = 100.0*(lo-lo{12}) * system(model=var2) variables r mgrow infl ygrow lags 1 2 det constant end(system) * estimate disp "Log Likelihood (value in text is divided by T)" %logl * * Example 13.25 Causality tests * * Causality test statistics are included in the ESTIMATE output. Note * that what are provided in the text are chi-squared while ESTIMATE uses * the F form, so (with two degrees of freedom) the ESTIMATE ones are * roughly half the size though the p-values should be almost identical * given the high denominator degrees of freedom on the F. * * Example 13.26 Impulse responses * * The simple IMPULSE instruction below will (by default), do the * Choleski factor in the order of listing on the VARIABLES instruction. * This gives the same results as in the text, except the matrices are * organized differently---reading across a row in the IMPULSE output is * the same as reading down a column in the matrices in the text. * impulse(model=var2,steps=2,print) * * The variance decomposition done by ERRORS scales everything to sum to * 100% for each variable and horizon. The text shows the values before * doing that. For any row, if you square the standard error to get the * forecast variance and multiply the table percentage by the * variance/100, you'll get the values in the text. * errors(model=var2,steps=2)