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TA的文库&&
本帖最后由 Lisrelchen 于
08:00 编辑
Winrats资源总汇 Software (4.44 MB) (23.8 MB, 售价: 10 个论坛币) (2.61 MB)&&& r
User’s Guide·& && &&&Winrats 8.0
(11.23 MB, 售价:5 个论坛币) ·& && &&&Winrats6.2 User’s Guide
(6.66 MB, 售价: 20 个论坛币) ·& && &&&RATS 6.0 Getting Started
Winrats - Related Textbook·& && &&& ·& && &&&·& && &&&·& && &&&·& && &&&Applied Econometric Time Series, 3rd WalterEnders·& && &&&Econometric Analysis of Panel Data, 4th Badi Baltag ·& && &&&Introductory Econometrics for Finance,2nd Chris Brooks·& && &&& Chris Brooks·& && &&& Commandeur and Koopman·& && &&& Durbin and Koopman·& && &&& JamesD. Hamilton·& && &&& Katarina Juselius·& && && & GaryKoop ·& && && &·& && &&& Jeffrey Wooldridge·& && &&&·& && && & & .
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Cats in Rats - Cointegration Analysis of Time Series Version 2.0
本帖最后由 Nicolle 于
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Cats in Rats - Cointegration Analysis of Time Series Version 2.0
(2.72 MB, 售价: 25 个论坛币)
00:22:45 上传
Cats in Rats - Cointegration Analysis of Time Series Version 2.0
售价: 25 个论坛币
本帖最后由 Nicolle 于
01:26 编辑
RATS Handbook to Accompany Introductory Econometrics for Finance [Paperback] (Author)
Kim and Nelson, &State-space Models with Regime Switching&
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Kim and Nelson, &State-space Models with Regime Switching&
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Kim and Nelson, &State-space Models with Regime Switching&的RATS代码和数据文件
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总评分:&论坛币 + 20&
wangyulan 发表于
Kim and Nelson, &State-space Models with Regime Switching&
/forum/viewtopic. ...您好，我下载了然后试着运行& & 有些程序可以有些则不行，是不是缺失了一些文件啊 ？&&比如localdlminit& &MSINIT&&MSFILTERINIT等，谢谢啊，我是初学者。
wangyulan 发表于
Kim and Nelson, &State-space Models with Regime Switching&
/forum/viewtopic. ...我运行Kimnp111&&kimnp115的程序都出现下面的问题：
The Error Occurred At Location 170, Line 13 of %MSINIT
Called From Location 127, Line 13 of MSFILTERINIT
## MAT14. Non-invertible Matrix. Using Generalized Inverse for SYMMETRIC.
这可能是因为什么呢？& &谢谢
VAR: SVAR with short-run restrictions
本帖最后由 Nicolle 于
20:33 编辑
*
* Martin, Hurn, Harris, &Econometric Modelling with Time Series&
* Examples 14.13, 14.14, 14.17 from pp 528-534
* Estimating an SVAR with short-run restrictions
*
open data mcnew.dat
calendar(m) 1946:12
data(format=free,org=columns) 1:2 r0 r1 r3 r6 r9 r120
*
system(model=var1)
variables r0 r1 r3
lags 1
det constant
end(system)
*
estimate(sigma)
*
* The structural model is an A-type model
*
nonlin b21 b31
dec frml[rect] afrml
frml afrml = ||1.0,0.0,0.0|$
& && && && && &b21,1.0,0.0|$
& && && && && &b31,0.0,1.0||
cvmodel(a=afrml,factor=f) %sigma
disp &Impact responses& f
*
* Example 14.14. Long-run restrictions
* Note that this is technically incorrect. An overidentified model with
* long-run restrictions can't be estimated (properly) in two stages
* (with the lags model first, then the structural model given the
* estimated lag coefficients) because the long-run restrictions place
* non-linear restrictions on the lags. This isn't a problem with a
* just-identified model with either long-run restrictions or
* short-and-long-run restrictions.
*
dec rect lr(3,3)
input lr
. 0 .
*
* This uses the @SHORTANDLONG procedure with the options NOESTIMATE and
* RPERP to remap an underlying set of parameters to produce an impact
* response matrix which has the restrictions.
*
compute masums=inv(%varlagsums)
@shortandlong(lr=lr,masums=masums,rperp=rp,noestimate) %sigma
*
* RP is a 9 x 5 matrix. RP x theta, where theta is a 5-vector will
* produce the &vec& of a 3x3 impact matrix which will satisfy the long
* run restrictions (and this will span the space of all such impact
* matrices).
*
dec vect theta(%cols(rp))
dec frml[rect] lrfrml
*
frml lrfrml = %vectorect(rp*theta,3)
compute [vect] theta=%ginv(rp)*%vec(%decomp(%sigma))
*
nonlin theta
cvmodel(b=lrfrml,factor=f,dmatrix=identity) %sigma
disp &Impact responses with long-run restrictions& f
disp &Long run responses& masums*f
*
* Example 14.17
*
* These are included in the output already
*复制代码
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