Bai-Perron JAE 2003—Use of @BAIPERRON

[TomDoan]

BaiPerronJAE2003.zip includes the two empirical examples from Bai & Perron(2003), "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, vol. 18, no. 1, pages 1-22. The U.S. real rates example is a regression on constant alone, while the UKPhillips example does an AR(1) model and a Phillips curve with two fixed regressors.

The newest version of the @BaiPerron procedure is needed by this.

[iloverats]

The attached zip includes the two empirical examples from Bai & Perron(2003), "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, vol. 18, no. 1, pages 1-22. The U.S. real rates example is a regression on constant alone, while the UKPhillips example does an AR(1) model and a Phillips curve with two fixed regressors.

BaiPerronJAE2003.zip

The new version of the BaiPerron procedure needed by this is at:

http://www.estima.com/forum/viewtopic.php?f=7&t=500

dear
why are the results form the Rats so different form the paper?


results form the Rats
Breaks RSS BIC LWZ F(m) F(m|m-1)
0 1214.9219 2.51 2.55
1 644.9955 1.92 2.00 89.24 89.24
2 455.9502 1.62* 1.74* 83.23 41.46
3 445.1819 1.64 1.79 57.06 2.39
4 444.8797 1.69 1.88 42.41 0.07
5 449.6395 1.74 1.97 33.02 -1.03

paper results:
supf(1) =57.91
supf(2)= 43.01
supf(3)= 33.22
supf(4)=24.77
supf(5)=18.33
....
which is right?

[TomDoan]

Both. The results in the paper use F statistics computed with HAC covariance matrices using a very specific procedure for estimating the long-run variance. Computing that requires a great deal of number-crunching that the dynamic programming algorithm is trying to avoid, but which is feasible here with a relatively small data set. RATS is using the simpler standard F. They both give the same results asymptotically.

[iloverats]

Both. The results in the paper use F statistics computed with HAC covariance matrices using a very specific procedure for estimating the long-run variance. Computing that requires a great deal of number-crunching that the dynamic programming algorithm is trying to avoid, but which is feasible here with a relatively small data set. RATS is using the simpler standard F. They both give the same results asymptotically.

if i use Rats , are the critical values in their papers(1998)" Estimating and Testing Linear Models with Multiple structural Changes" ok?
thank you very much

[irfansystem]

While replicating the results, I observed that the standard errors related to the coefficients are not same as mentioned by Bai Perron (2003)...plz see table 1 and section 3....., what could be the cause. Where as using the same data on R (structural break package)...surprisingly I am having the exact results as indicated by Bai Perron.
Second, could we produce the graphs of BIC as on page 3 of the same paper.

[TomDoan]

While replicating the results, I observed that the standard errors related to the coefficients are not same as mentioned by Bai Perron (2003)...plz see table 1 and section 3....., what could be the cause. Where as using the same data on R (structural break package)...surprisingly I am having the exact results as indicated by Bai Perron.
Second, could we produce the graphs of BIC as on page 3 of the same paper.

The standard errors in Bai-Perron are computed using HAC. I'm not convinced that that's a good idea since the partitions can be rather short, making accurate estimation of HAC standard errors rather unlikely.

[irfansystem]

I do not understand the F(m) and F(m|m-1)....what do they exactly represent??

Breaks RSS BIC LWZ F(m) F(m|m-1)
0 1214.9219 2.51 2.55
1 644.9955 1.92 2.00 89.24 89.24
2 455.9502 1.62* 1.74* 83.23 41.46
3 445.1819 1.64 1.79 57.06 2.39
4 444.8797 1.69 1.88 42.41 0.07
5 449.6395 1.74 1.97 33.02 -1.03

is there a way to get values for SupFt(1)........UDmax and WDmax? as given in the Bai Perron (2003) page 59.

PS: I do realize that F(m) and F(m/m-1) has got to do something with SupFt but it is not clear to me what is what actually!!

[Ana_Rita]

Check this paper by them "Estimating and testing linear models with multiple structural changes" source Econometrica, vol.66, no. 1 (Jan, 1998), pp.47-78.

They show tables of their estimated critical values for the tests. I did try and mach them with the results they present in their 2003 paper but with no succes though...maybe you'll be more lucky then me!

Good luck!

[irfansystem]

I went through quite lots of paper which apply the Bai and Perron (1998, 2003), mostly they just mention the confidence interval dates around the break date. Very few articles mention other statistics (all sets of F statistics). I spent quite lots of time in trying to understand the code presented here and the BP articles. It is first of all difficult to compare the BP article due to differences in the results. Further there is no guide, or simple explanation/instructions of what is what and how they are presented in BP. If some one can do it, it would be a great help for people who are at the learning stage.

[TomDoan]

I do not understand the F(m) and F(m|m-1)....what do they exactly represent??

Breaks RSS BIC LWZ F(m) F(m|m-1)
0 1214.9219 2.51 2.55
1 644.9955 1.92 2.00 89.24 89.24
2 455.9502 1.62* 1.74* 83.23 41.46
3 445.1819 1.64 1.79 57.06 2.39
4 444.8797 1.69 1.88 42.41 0.07
5 449.6395 1.74 1.97 33.02 -1.03

F(m) is a test for m breaks vs 0 breaks. F(m|m-1) is a test for m breaks vs m-1 breaks.

[TomDoan]

Both. The results in the paper use F statistics computed with HAC covariance matrices using a very specific procedure for estimating the long-run variance. Computing that requires a great deal of number-crunching that the dynamic programming algorithm is trying to avoid, but which is feasible here with a relatively small data set. RATS is using the simpler standard F. They both give the same results asymptotically.

if i use Rats , are the critical values in their papers(1998)" Estimating and Testing Linear Models with Multiple structural Changes" ok?
thank you very much

Yes. Both the HAC and the standard F are covered by the same theorem that lays out the asympotics.