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HAMILTON.RPF uses the @MSVARSETUP procedures to estimate Hamilton’s switching model for GDP growth (Hamilton, 1994, Chapter 22).

Detailed Description

GIV.RPF is an example of generalized instrumental variables (nonlinear GMM). This example runs through the calculations for the single return models in Hansen and Singleton (1982) on a constructed data set.

Detailed Description

GIV.RPF is an example of generalized instrumental variables (nonlinear GMM). This example runs through the calculations for the single return models in Hansen and Singleton (1982) on a constructed data set.

Detailed Description

GIBBSPROBITDYNAMIC.RPF estimates using Bayesian methods a probit model with the latent index variable assumed to follow an AR(1) process. This uses the typical Gibbs sampling technique of drawing the continuous latent variable given the observable 0-1's, but, because of the dynamic process for the ...

G10XRATE is one of the data files used in Pritsker, Matthew (2006), "The hidden dangers of historical simulation," Journal of Banking & Finance, vol. 30, no. 2, pp 561-582 which might be helpful for you, since it looks at calculations of VaR.

.7, .8, correlations are big numbers; with a data set that size, you would get around .30 as marginal at the .05 level, so those are more than 4 standard deviations. If those are raw data (which seems to be the case here), it wouldn't be surprising for high correlations like that if they have common...

bok1234 wrote: ↑Tue Aug 27, 2024 7:43 pm I also have another problem, 'font broken'. How can I fix it?

I'm not sure where you are getting that, but we don't do any handling of fonts; we merely request fonts from Windows.

I've attached a procedure for doing that. Just replace

CMOM(CORR,PRINT)
# Y1 Y2 Y3

@fancycorr
# y1 y2 y3

To me, it looks pretty cluttered and it's not clear what value pairwise correlation p-values have in a three, four or five way set of variables.

Isn't a short on USD/JPY identical to a long on JPY/USD?

And (for the third time), the deterministic parts drop out of the variance, so the variance is just that for a garden-variety AR(1). Calculating the (time-varying) mean of the process out of this form requires being able to work with power series. Which is why you probably can't find it. And again, ...

THE p-value? A joint test of all three off-diagonal values? Or the P-VALUES, individually. The individual correlations are asymptotically N(0,1/obs). So

dec symm pvalues(%ncmom,%ncmom)
ewise pvalues(i,j)=%if(i==j,%na,%ztest(%cmom(i,j)*sqrt(%nobs)))
?pvalues

I'm not sure what it is that you're trying to do, but the calculations of the mean are much more complicated than that---you need the sum of (phi^n)(t-n) where you're forgetting the interaction between the phi^n and n. The mean of that process isn't a pleasant expression in the underlying parameters...

log(1/x)=-log(x)

You can do the whole thing in one go by inputting an equation which incorporates the differencing into the AR: equation(ar=1,ma=1,const,variance=1.0) yeq y set y 1 500 = 0.0 set u 1 500 = 0.0 * * Coefficients are in order: * constant * AR(1) (here 1.0 to make this an ARIMA(0,1,1) * MA(1) * associate...

For the variance of an AR(1) process? Effectively any time series book you could think of. The deterministic terms drop out in computing the variance.