The RATS Software Forum

blanchardquahaer1989.zip has programs to do pretty much the full analysis from Blanchard-Quah(1989), "The Dynamic Effects of Aggregate Demand and Supply Disturbances", AER, vol 79, no. 4.

Detailed description

What kind of modifications do I need to make to Blanchard and Quah for more than 2 variables
Albert

asdavies6032 wrote:What kind of modifications do I need to make to Blanchard and Quah for more than 2 variables
Albert

The direct extension of BQ is fairly simple, since %BQFACTOR will work with any size VAR. However, it tends to be uninteresting, since it requires that you have one shock which has no permanent effects on y2 and another have no permanent effects on y1 and y2 which isn't a very likely pattern. Once you get above two variables, you usually need to go with a combination of short and long run restrictions. See also the Gali QJE 1992 examples.

What is gd87?

ivory4 wrote:

Code: Select all

set loggnp = log(gnp)
set loggd  = log(gd87)
set logrgnp = loggnp-loggd+log(100)
set dlogrgnp = 100.0*(logrgnp-logrgnp{1})

What is gd87?

GD87 is the GNP deflator. GNP is nominal GNP.

Hello
I am new to Rats. I tried to replicate the Blanchard Quah paper and could successfully do it. However, i have got few questions pertaining that -
1.The data involves LHMUR series. what is the full form of LHMUR? Also in the codes we use URADJUST. Does URADJUST means unempolyment detrended?
2. I want to replicate the article for UK economy.what data should i procure - Is it Level of GNP and rate of unemployment? However,the unemployment rate data of UK is nonstationary in levels and stationary in the first difference.What do you think i can do now?
3 Can i directly use the attachment named BQEXTRAS-ZIP that is uploaded in the forum?
4. I want to conduct a granger causality test for the two variables in Blanchard and Quah. Could you please hep me sending the codes for the same?

Many Thanks
Sonica

Sonica Singhi wrote:Hello
I am new to Rats. I tried to replicate the Blanchard Quah paper and could successfully do it. However, i have got few questions pertaining that -
1.The data involves LHMUR series. what is the full form of LHMUR? Also in the codes we use URADJUST. Does URADJUST means unempolyment detrended?
2. I want to replicate the article for UK economy.what data should i procure - Is it Level of GNP and rate of unemployment? However,the unemployment rate data of UK is nonstationary in levels and stationary in the first difference.What do you think i can do now?
3 Can i directly use the attachment named BQEXTRAS-ZIP that is uploaded in the forum?
4. I want to conduct a granger causality test for the two variables in Blanchard and Quah. Could you please hep me sending the codes for the same?

Many Thanks
Sonica

The data actually used in the model are adjusted quite a bit from the observed values. You'll have to check the original paper to get the sources. However, yes, URADJUST is the unemployment rate detrended and the GDP series is in growth rates, which have had separate means extracted from different ranges. The model assumes the data are stationary, so these transformations were done to get rid of what were seen as obvious problems. However, I'm not sure anyone would detrend the unemployment rate any longer.

If you have a current version of RATS, use the Blanchard Quah programs on that.

I wouldn't be concerned about whether U has a unit root. The long-run zero restriction is on the growth of GDP, not on unemployment, so the identification of the demand shock isn't affected by the long run response of unemployment.

There's nothing special about doing a Granger causality test for this pair of variables. However, the whole B-Q model isn't very interesting if U doesn't Granger cause Y---the supply shock will then have to be the innovation to Y.

Dear Mr Doan,

I was hoping to get some advice from you on how to adapt the BQ procedure to a country other than the US, and I apologise in advance for the pedestrian nature of my questions. (By the way, all I’m after really is the measure of the output gap predicted by the model, I’m aware that you’ve written a code specifically for that, but I think it would be easier for me to just use the BQ code as I’m just starting to come to grips with the software). There are three things that I’m concerned about: the NBER classification of peaks and troughs, the dummy variables, again, specific to the US, and how to obtain figures from two graphs.


How shall I extract the means without regressing the series against two dummies, i.e.:
set dummy1 = t<=1973:4
set dummy2 = t>1973:4
linreg dlogrgnp
# dummy1 dummy2
set gdpadjust = %resids
prj means_from_gnp

I thought the following lines referred to the US business cycle, obviously I’m missing something here, because if I remove them, I somehow get rid of the graph to which they apply as well.

@NBERCycles(peaks=peaks,trough=troughs)
*
* This gets the scale correct for the spikes showing the peaks and troughs
*
set peaks = .10*peaks
set troughs = -.10*troughs
graph(footer="Figure 8. Output Fluctuations Due to Demand",$
ovcount=2,overlay=spike,ovsame) 3
# histdecomp(3,1)
# peaks 1950:2 * 2
# troughs 1950:2 * 2

what shall I add to the following lines to extract the output gap numbers?

graph(footer="Figure 8. Output Fluctuations Due to Demand",$
ovcount=2,overlay=spike,ovsame) 3
# histdecomp(3,1)
# peaks 1950:2 * 2
# troughs 1950:2 * 2

…as well as to these lines to get the figures for trend GDP?

set lessdemand = histdecomp(1,1)+histdecomp(2,1)
graph(footer="Figure 7. Output Fluctuations Absent Demand")
# lessdemand


Many thanks in advance!

FRegensburg wrote:Dear Mr Doan,

I was hoping to get some advice from you on how to adapt the BQ procedure to a country other than the US, and I apologise in advance for the pedestrian nature of my questions. (By the way, all I’m after really is the measure of the output gap predicted by the model, I’m aware that you’ve written a code specifically for that, but I think it would be easier for me to just use the BQ code as I’m just starting to come to grips with the software). There are three things that I’m concerned about: the NBER classification of peaks and troughs, the dummy variables, again, specific to the US, and how to obtain figures from two graphs.


How shall I extract the means without regressing the series against two dummies, i.e.:
set dummy1 = t<=1973:4
set dummy2 = t>1973:4
linreg dlogrgnp
# dummy1 dummy2
set gdpadjust = %resids
prj means_from_gnp

diff(center) dlogrgnp / gdpadjust

would pull a single mean out of the data.

FRegensburg wrote: I thought the following lines referred to the US business cycle, obviously I’m missing something here, because if I remove them, I somehow get rid of the graph to which they apply as well.

@NBERCycles(peaks=peaks,trough=troughs)
*
* This gets the scale correct for the spikes showing the peaks and troughs
*
set peaks = .10*peaks
set troughs = -.10*troughs
graph(footer="Figure 8. Output Fluctuations Due to Demand",$
ovcount=2,overlay=spike,ovsame) 3
# histdecomp(3,1)
# peaks 1950:2 * 2
# troughs 1950:2 * 2

what shall I add to the following lines to extract the output gap numbers?

graph(footer="Figure 8. Output Fluctuations Due to Demand",$
ovcount=2,overlay=spike,ovsame) 3
# histdecomp(3,1)
# peaks 1950:2 * 2
# troughs 1950:2 * 2

…as well as to these lines to get the figures for trend GDP?

set lessdemand = histdecomp(1,1)+histdecomp(2,1)
graph(footer="Figure 7. Output Fluctuations Absent Demand")
# less demand


Many thanks in advance!

If you want to get rid of the peaks and troughs part, just do:

graph(footer="Figure 8. Output Fluctuations Due to Demand") 1
# histdecomp(3,1)

This is correct if you get rid of the extra space in "less demand" on the # line.

set lessdemand = histdecomp(1,1)+histdecomp(2,1)
graph(footer="Figure 7. Output Fluctuations Absent Demand")
# less demand

Thanks a lot for that. To a novice, there is something slightly god-like about things materialising after the right words, as it were.
In your original code, you added the means previously removed back into GDP to get the BQ equivalent of potential output, via the line:

set histdecomp(1,1) = histdecomp(1,1)+means_from_gnp

When, however, one mean is taken out, that is:

diff(center) dlogrgnp / gdpadjust

Like you indicated, how do I go about storing it so that I can add it back in, as above?

Many thanks

If you do it right away, before something else redefines %mean:

diff(center) dlogrgnp / gdpadjust
set means_from_gnp = %mean

Dear Tom,

sorry for the silly question, but I am relatively new in dealing with counterfactuals.
Hence, I apologize in advance for lack of precision and mistakes in what follows.

In the BQ (1989) code, to replicate the graph "Output fluctuations absent demand disturbances" we write the following line
If I correctly understand, this should be equivalent to a measure of potential output, that is the cumulated sum of technology shocks.

But then, to replicate the graph "Output fluctuations due to demand disturbances"
we write the following
If I correctly understand, this should be equivalent to a measure of output--gap, i.e. the output path one should have observed if only demand shocks occurred.

So, i do not get how we have the same element (histdecomp(2,1)) in both the components, output--gap and potential output.


Could you please clarify this to me?

Thank you so much, your help would be really appreciated.

Kobe

You would be correct if that's what the program did, but the second graph has histdecomp(3,1) (which is the effect of demand shocks), not histdecomp(2,1) which is the effect of supply shocks.

Makes sense right now: I misread a 3 for a 2. Sorry for wasting your time.
Probably I need a break and some coffee.

However, I have a final question. So, to wrap-up:

histdecomp(2,1) is the effect of supply shocks on output
histdecomp(3,1) is the effect of demand shocks on output

histdecomp(2,2) is the effect of supply shocks on unemployment
histdecomp(3,2) is the effect of demand shocks on unemployment

Then, histdecomp(1,1) and histdecomp(1,2) are the respective "scale factors", i.e. unconditional means?

Thanks a lot for your time and kind reply, Tom. I really appreciate it.

They are the forecasts conditional on the pre-sample information (in this case, with corrections for the detrending and de-meaning done before estimated the VAR).