Looping over the same GDP variable over vintages of time

[Lisa Wilson]

Hi all
I have Gross Domestic Product, gdp, data that are in vintages (i.e. the gdp variable is revised over time, so gdp65Q4 below is the gdp data revised at a vintage date of year 1965 quarter 4. Therefore for each column below we have gdp data for each quarter measured on a specific vintage date. The next column will show how the gdp for the same quarters have been revised in a subsequent vintage date and so on. To clarify more, the first cell 306.4 on the north west corner represents gdp in quarter 1 year 1947 but as revised in quarter 4 of year 1965, the next cell to the right 307.4 represents gdp in quarter 1 year 1947 again but as revised in quarter 1 year 1966....and so on)

date	gdp65Q4	gdp66Q1	gdp66Q2	gdp66Q3
1947:Q1	306.4	307.4	309.4	302.4
1947:Q2	309	301	302	309
1947:Q3	309.6	304.6	308.6	302.6
1947:Q4	314.5	315.5	314.5	310.5
1948:Q1	317.1	311.2	314.3	319.1
1948:Q2	322.9	325.7	333.9	322.9

What I want to do is to generate a variable that represents gdp vintage shock and measured as residual of a regression of gdp at a given vintage date on the same gdp at a previous vintage date.
I do not know how a loop can estimate that ?

Thank you

[Jesse Wursten]

You can use Markus Eberhardt's excellent xtmg command for this. This will perform separate time series regressions for each panel unit. You can install it through ssc I think (ssc install xtmg) and then replace the regress part by the following.

*** Regress
xtset date prediction
gen L_routput = L.routput
xtmg routput L_routput, res(shock)

[Lisa Wilson]

Hi Jesse
Well, yes that is what I wanted initially. I should have been clear about that. Sorry!
I want the regression to be estimated per each gdp data (not vintage date), so time series regressions. It will make more sense to regress the gdp variable in a given vintage date on the same gdp variable but at a previous vintage date on a time series basis without pooling all data. This might give better results.

Is this possible?

[Jesse Wursten]

Haha, thanks :D

At the moment there's just one regression, i.e. all the info is pooled to estimate one coefficient. So I don't know if it'd be a good idea to avoid short timeframes? Unless you want to perform separate regressions per gdp date?

[Lisa Wilson]

Thanks a lot. I gave you "Likes"
Jesse, I noted that some regressions may not have enough observations because some gdp data would be missing ( an obvious example would be gdp variable of, say, quarter 4 year 1990 in a previous vintage date , say year 1990 quarter 3 or 1990 quarter 2 and so on)
Can you adjust your code to be restricted to regressions with a certain number of observations, for example 20 obs?

[Jesse Wursten]

I think so, yes. Just be careful if you were to run this again with different data, as 15q3 might no longer correspond to prediction 200. Of course, if its possible, you will always want to do a manual check/eyeball it to verify you're getting what you intended.

[Lisa Wilson]

Hi Jesse
Many many thanks. That seems to be helpful.

I followed your suggestions:
I got the following stata output:

routput65q4 became routput1
routput66q1 became routput2
routput66q2 became routput3
routput66q3 became routput4
routput66q4 became routput5
routput67q1 became routput6
routput67q2 became routput7
routput67q3 became routput8
routput67q4 became routput9
routput68q1 became routput10
routput68q2 became routput11
routput68q3 became routput12
routput68q4 became routput13
routput69q1 became routput14
routput69q2 became routput15
routput69q3 became routput16
routput69q4 became routput17
routput70q1 became routput18
routput70q2 became routput19
routput70q3 became routput20
routput70q4 became routput21
routput71q1 became routput22
routput71q2 became routput23
routput71q3 became routput24
routput71q4 became routput25
routput72q1 became routput26
routput72q2 became routput27
routput72q3 became routput28
routput72q4 became routput29
routput73q1 became routput30
routput73q2 became routput31
routput73q3 became routput32
routput73q4 became routput33
routput74q1 became routput34
routput74q2 became routput35
routput74q3 became routput36
routput74q4 became routput37
routput75q1 became routput38
routput75q2 became routput39
routput75q3 became routput40
routput75q4 became routput41
routput76q1 became routput42
routput76q2 became routput43
routput76q3 became routput44
routput76q4 became routput45
routput77q1 became routput46
routput77q2 became routput47
routput77q3 became routput48
routput77q4 became routput49
routput78q1 became routput50
routput78q2 became routput51
routput78q3 became routput52
routput78q4 became routput53
routput79q1 became routput54
routput79q2 became routput55
routput79q3 became routput56
routput79q4 became routput57
routput80q1 became routput58
routput80q2 became routput59
routput80q3 became routput60
routput80q4 became routput61
routput81q1 became routput62
routput81q2 became routput63
routput81q3 became routput64
routput81q4 became routput65
routput82q1 became routput66
routput82q2 became routput67
routput82q3 became routput68
routput82q4 became routput69
routput83q1 became routput70
routput83q2 became routput71
routput83q3 became routput72
routput83q4 became routput73
routput84q1 became routput74
routput84q2 became routput75
routput84q3 became routput76
routput84q4 became routput77
routput85q1 became routput78
routput85q2 became routput79
routput85q3 became routput80
routput85q4 became routput81
routput86q1 became routput82
routput86q2 became routput83
routput86q3 became routput84
routput86q4 became routput85
routput87q1 became routput86
routput87q2 became routput87
routput87q3 became routput88
routput87q4 became routput89
routput88q1 became routput90
routput88q2 became routput91
routput88q3 became routput92
routput88q4 became routput93
routput89q1 became routput94
routput89q2 became routput95
routput89q3 became routput96
routput89q4 became routput97
routput90q1 became routput98
routput90q2 became routput99
routput90q3 became routput100
routput90q4 became routput101
routput91q1 became routput102
routput91q2 became routput103
routput91q3 became routput104
routput91q4 became routput105
routput92q1 became routput106
routput92q2 became routput107
routput92q3 became routput108
routput92q4 became routput109
routput93q1 became routput110
routput93q2 became routput111
routput93q3 became routput112
routput93q4 became routput113
routput94q1 became routput114
routput94q2 became routput115
routput94q3 became routput116
routput94q4 became routput117
routput95q1 became routput118
routput95q2 became routput119
routput95q3 became routput120
routput95q4 became routput121
routput96q1 became routput122
routput96q2 became routput123
routput96q3 became routput124
routput96q4 became routput125
routput97q1 became routput126
routput97q2 became routput127
routput97q3 became routput128
routput97q4 became routput129
routput98q1 became routput130
routput98q2 became routput131
routput98q3 became routput132
routput98q4 became routput133
routput99q1 became routput134
routput99q2 became routput135
routput99q3 became routput136
routput99q4 became routput137
routput00q1 became routput138
routput00q2 became routput139
routput00q3 became routput140
routput00q4 became routput141
routput01q1 became routput142
routput01q2 became routput143
routput01q3 became routput144
routput01q4 became routput145
routput02q1 became routput146
routput02q2 became routput147
routput02q3 became routput148
routput02q4 became routput149
routput03q1 became routput150
routput03q2 became routput151
routput03q3 became routput152
routput03q4 became routput153
routput04q1 became routput154
routput04q2 became routput155
routput04q3 became routput156
routput04q4 became routput157
routput05q1 became routput158
routput05q2 became routput159
routput05q3 became routput160
routput05q4 became routput161
routput06q1 became routput162
routput06q2 became routput163
routput06q3 became routput164
routput06q4 became routput165
routput07q1 became routput166
routput07q2 became routput167
routput07q3 became routput168
routput07q4 became routput169
routput08q1 became routput170
routput08q2 became routput171
routput08q3 became routput172
routput08q4 became routput173
routput09q1 became routput174
routput09q2 became routput175
routput09q3 became routput176
routput09q4 became routput177
routput10q1 became routput178
routput10q2 became routput179
routput10q3 became routput180
routput10q4 became routput181
routput11q1 became routput182
routput11q2 became routput183
routput11q3 became routput184
routput11q4 became routput185
routput12q1 became routput186
routput12q2 became routput187
routput12q3 became routput188
routput12q4 became routput189
routput13q1 became routput190
routput13q2 became routput191
routput13q3 became routput192
routput13q4 became routput193
routput14q1 became routput194
routput14q2 became routput195
routput14q3 became routput196
routput14q4 became routput197
routput15q1 became routput198
routput15q2 became routput199
routput15q3 became routput200
routput15q4 became routput201
routput16q1 became routput202
routput16q2 became routput203
routput16q3 became routput204

I then identified prediction 200 to be the shocks pertaining to vintage 2015 quarter 3 ( as estimated from regressions of two subsequent vintages as we know)

I then did the following:

keep if prediction==200
tsset date

I got the following as an example from dataex;

* Example generated by -dataex-. To install: ssc install dataex
clear
input float date int prediction double routput float shockVi
-52 200 1934.5 -19.467176
-51 200 1932.3 -19.459435
-50 200 1930.3 -19.452394
-49 200 1960.7 -19.559393
-48 200 1989.5  -19.66076
-47 200 2021.9 -19.774796
-46 200 2033.2  -19.81457
-45 200 2035.3  -19.82196
-44 200 2007.5  -19.72411
-43 200 2000.8  -19.70053
-42 200 2022.8 -19.777964
-41 200 2004.7 -19.714256
-40 200 2084.6  -19.99548
-39 200 2147.6 -20.217216
-38 200 2230.4 -20.508644
-37 200 2273.4  -20.65999
-36 200 2304.5  -20.76945
-35 200 2344.5 -20.910236
-34 200 2392.8 -21.080236
-33 200 2398.1  -21.09889
-32 200 2423.5  -21.18829
-31 200 2428.5  -21.20589
-30 200 2446.1 -21.267834
-29 200 2526.4  -21.55046
-28 200 2573.4 -21.715887
-27 200 2593.5  -21.78663
-26 200 2578.9 -21.735245
-25 200 2539.8 -21.597626
-24 200   2528 -21.556095
-23 200 2530.7   -21.5656
-22 200 2559.4  -21.66661
-21 200 2609.3 -21.842243
-20 200 2683.8  -22.10446
-19 200 2727.5 -22.258266
-18 200 2764.1 -22.387085
-17 200 2780.8 -22.445864
-16 200   2770  -22.40785
-15 200 2792.9  -22.48845
-14 200 2790.6 -22.480356
-13 200 2836.2 -22.640854
-12 200 2854.5 -22.705263
-11 200 2848.2  -22.68309
-10 200 2875.9 -22.780584
 -9 200 2846.4 -22.676754
 -8 200 2772.7 -22.417355
 -7 200 2790.9 -22.481413
 -6 200 2855.5  -22.70878
 -5 200 2922.3 -22.943895
 -4 200 2976.6 -23.135014
 -3 200   3049  -23.38984
 -2 200 3043.1  -23.36907
 -1 200 3055.1 -23.411306
  0 200 3123.2 -23.650995
  1 200 3111.3  -23.60911
  2 200 3119.1 -23.636564
  3 200 3081.3  -23.50352
  4 200 3102.3 -23.577435
  5 200 3159.9 -23.780167
  6 200 3212.6  -23.96565
  7 200 3277.7  -24.19478
  8 200 3336.8 -24.402794
  9 200 3372.7  -24.52915
 10 200 3404.8  -24.64213
 11 200   3418  -24.68859
 12 200 3456.1  -24.82269
 13 200 3501.1 -24.981073
 14 200 3569.5  -25.22182
 15 200   3595  -25.31157
 16 200 3672.7 -25.585047
 17 200 3716.4  -25.73886
 18 200 3766.9   -25.9166
 19 200 3780.2  -25.96341
 20 200 3873.5 -26.291796
 21 200 3926.4 -26.477985
 22 200 4006.2 -26.758854
 23 200 4100.6  -27.09111
 24 200 4201.9  -27.44765
 25 200 4219.1  -27.50819
 26 200 4249.2  -27.61413
 27 200 4285.6 -27.742247
 28 200 4324.9  -27.88057
 29 200 4328.7 -27.893944
 30 200 4366.1  -28.02558
 31 200 4401.2  -28.14912
 32 200 4490.6 -28.463776
 33 200 4566.4 -28.730566
 34 200 4599.3 -28.846363
 35 200 4619.8 -28.918516
 36 200 4691.6 -29.171227
 37 200 4706.7 -29.224375
 38 200 4736.1  -29.32785
 39 200 4715.5  -29.25535
 40 200 4707.1  -29.22578
 41 200 4715.4 -29.254995
 42 200 4757.2 -29.402117
 43 200 4708.3 -29.230005
 44 200 4834.3 -29.673483
 45 200 4861.9 -29.770626
 46 200   4900 -29.904724
 47 200 4914.3 -29.955055
end
format %tq date

I think the shocks are now ready for merging and I do not need to reshape the data, correct ?

[Jesse Wursten]

Ah yes, I called it prediction, not predictdate, my bad! I've added a line of code that now tells you which vintage relates to which prediction number.

E.g. "routput68q3 became routput12" means prediction 12 relates to 68Q3.

There's probably a better way to do this, but it's non-trivial AFAIK.

*** Get data ready
use "Y:\Statalist\predictionshocks.dta", clear
gen year = real(substr(date, 1, 4))
gen quarter = real(substr(date, -1, 1))

gen statadate = yq(year, quarter)
format statadate %tq

drop date year quarter
order statadate, first
rename statadate date

destring routput*, force replace

*** Reshape
local predictdate = 1
foreach var of varlist routput* {
    di "`var' became routput`predictdate'"
    rename `var' routput`predictdate'
    local predictdate = `predictdate' + 1
}
reshape long routput, i(date) j(prediction)

*** Regress
xtset date prediction
reg routput L.routput
predict shock, r

[Lisa Wilson]

Hi
The output has a variable called prediction and not predectdate. Do you know how to figure out the 2015Q3 vintage?
In my complete dataset (not the one in dataex) this vintage variable is routput15q3

is there anyway to extract only shocks pertaining to this vintage and then reshape my data to be ready for merging?

[Jesse Wursten]

Hi

There's no 2015Q3 vintage in the sample you attached, so you'll have to figure out which predictdate it relates to. Then you can simply add

keep if predictdate == <2015q3date>

And you would have what you want I think?

[Lisa Wilson]

Jesse: Thanks a lot.
I have some questions:
1- I have done what you suggested and I have estimated the residuals now (shocks). The output looks like:
----------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. To install: ssc install dataex
clear
input float date int prediction double routput float shock
-52   1  306.4          .
-52   2  306.4  -13.73682
-52   3  306.4  -13.73682
-52   4  306.4  -13.73682
-52   5  306.4  -13.73682
-52   6  306.4  -13.73682
-52   7  306.4  -13.73682
-52   8  306.4  -13.73682
-52   9  306.4  -13.73682
-52  10  306.4  -13.73682
-52  11  306.4  -13.73682
-52  12  306.4  -13.73682
-52  13  306.4  -13.73682
-52  14  306.4  -13.73682
-52  15  306.4  -13.73682
-52  16  306.4  -13.73682
-52  17  306.4  -13.73682
-52  18  306.4  -13.73682
-52  19  306.4  -13.73682
-52  20  306.4  -13.73682
-52  21  306.4  -13.73682
-52  22  306.4  -13.73682
-52  23  306.4  -13.73682
-52  24  306.4  -13.73682
-52  25  306.4  -13.73682
-52  26  306.4  -13.73682
-52  27  306.4  -13.73682
-52  28  306.4  -13.73682
-52  29  306.4  -13.73682
-52  30  306.4  -13.73682
-52  31  306.4  -13.73682
-52  32  306.4  -13.73682
-52  33  306.4  -13.73682
-52  34  306.4  -13.73682
-52  35  306.4  -13.73682
-52  36  306.4  -13.73682
-52  37  306.4  -13.73682
-52  38  306.4  -13.73682
-52  39  306.4  -13.73682
-52  40  306.4  -13.73682
-52  41  306.4  -13.73682
-52  42    464  143.86317
-52  43    464  -14.29152
-52  44    464  -14.29152
-52  45    464  -14.29152
-52  46    464  -14.29152
-52  47    464  -14.29152
-52  48    464  -14.29152
-52  49    464  -14.29152
-52  50    464  -14.29152
-52  51    464  -14.29152
-52  52    464  -14.29152
-52  53    464  -14.29152
-52  54    464  -14.29152
-52  55    464  -14.29152
-52  56    464  -14.29152
-52  57    464  -14.29152
-52  58    464  -14.29152
-52  59    464  -14.29152
-52  60    464  -14.29152
-52  61    464  -14.29152
-52  62    466  -12.29152
-52  63    466 -14.298558
-52  64    466 -14.298558
-52  65    466 -14.298558
-52  66    466 -14.298558
-52  67    466 -14.298558
-52  68    466 -14.298558
-52  69    466 -14.298558
-52  70    466 -14.298558
-52  71    466 -14.298558
-52  72    466 -14.298558
-52  73    466 -14.298558
-52  74    466 -14.298558
-52  75    466 -14.298558
-52  76    466 -14.298558
-52  77    466 -14.298558
-52  78    466 -14.298558
-52  79    466 -14.298558
-52  80    466 -14.298558
-52  81    466 -14.298558
-52  82 1056.5   576.2014
-52  83 1056.5 -16.376917
-52  84 1056.5 -16.376917
-52  85 1056.5 -16.376917
-52  86 1056.5 -16.376917
-52  87 1056.5 -16.376917
-52  88 1056.5 -16.376917
-52  89 1056.5 -16.376917
-52  90 1056.5 -16.376917
-52  91 1056.5 -16.376917
-52  92 1056.5 -16.376917
-52  93 1056.5 -16.376917
-52  94 1056.5 -16.376917
-52  95 1056.5 -16.376917
-52  96 1056.5 -16.376917
-52  97 1056.5 -16.376917
-52  98 1056.5 -16.376917
-52  99 1056.5 -16.376917
-52 100 1056.5 -16.376917
end
format %tq date

My first questions is: If I am interested in shocks at a particular vintage (which is vintage of year 2015 quarter 3) to proxy for the arrival of new information as of that particular vintage date, where I can find those? does the predictdate in your code represent the vintage date? if so, what would be the vintage date of year 2015 quarter 3?

I will need to extract vintage 2015 Q3 shocks in all gdp variables and reshape my data to be ready for merging with other economic dataset.
The ultimate objective would be to use these shocks as proxies for information arrival in year 2015 quarter 3. Therefore, using all vintages initially was meant to estimate the vintage shocks. Now, I need to use shocks of the year 2015 q3 vintage that manifest in a revision of all gdp variables in all my sample. Does that make sense to you?

i think the steps should be something like:

Extract shocks of all gdp data variables in my sample as pertaining to a vintage date of year 2015 quarter 3 and drop all other shocks pertaining to the remaining vintages
then, reshape back again those shocks of year 2015 quarter 3, so data becomes ready for merging.

Can you help me in that please?

Thanks

[Jesse Wursten]

*** Get data ready
use "Y:\Statalist\predictionshocks.dta", clear
gen year = real(substr(date, 1, 4))
gen quarter = real(substr(date, -1, 1))

gen statadate = yq(year, quarter)
format statadate %tq

drop date year quarter
order statadate, first
rename statadate date

destring routput*, force replace

*** Reshape
local predictdate = 1
foreach var of varlist routput* {
    rename `var' routput`predictdate'
    local predictdate = `predictdate' + 1
}
reshape long routput, i(date) j(prediction)

*** Regress
xtset date prediction
reg routput L.routput
predict shock, r

Is this what you are looking for? In the first part I transform the string date to a date Stata understands. Then in the reshape part, I rename the predictions because the reshape command requires sequential variables (e.g. var1 var2, not var23q4 var24q1). Then finally we regress each routput on the output from the previous vintage and generate the shock variable as the residual from that regression.

[Lisa Wilson]

Thanks. Here you go

* Example generated by -dataex-. To install: ssc install dataex
clear
input str7 date str5(routput65q4 routput66q1 routput66q2 routput66q3 routput66q4 routput67q1 routput67q2 routput67q3 routput67q4 routput68q1 routput68q2 routput68q3 routput68q4 routput69q1 routput69q2)
"1947:Q1" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4" "306.4"
"1947:Q2" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0" "309.0"
"1947:Q3" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6" "309.6"
"1947:Q4" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5" "314.5"
"1948:Q1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1" "317.1"
"1948:Q2" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9" "322.9"
"1948:Q3" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8" "325.8"
"1948:Q4" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7" "328.7"
"1949:Q1" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5" "324.5"
"1949:Q2" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5" "322.5"
"1949:Q3" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1" "326.1"
"1949:Q4" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3" "323.3"
"1950:Q1" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6" "339.6"
"1950:Q2" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5" "348.5"
"1950:Q3" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8" "362.8"
"1950:Q4" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1" "370.1"
"1951:Q1" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8" "374.8"
"1951:Q2" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5" "381.5"
"1951:Q3" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7"
"1951:Q4" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7" "388.7"
"1952:Q1" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4" "391.4"
"1952:Q2" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6" "389.6"
"1952:Q3" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9" "393.9"
"1952:Q4" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3" "405.3"
"1953:Q1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1" "412.1"
"1953:Q2" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4" "416.4"
"1953:Q3" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7" "413.7"
"1953:Q4" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8" "408.8"
"1954:Q1" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9" "402.9"
"1954:Q2" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1" "402.1"
"1954:Q3" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2" "407.2"
"1954:Q4" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7" "415.7"
"1955:Q1" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0" "428.0"
"1955:Q2" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4" "435.4"
"1955:Q3" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1" "442.1"
"1955:Q4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4" "446.4"
"1956:Q1" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6" "443.6"
"1956:Q2" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6" "445.6"
"1956:Q3" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5" "444.5"
"1956:Q4" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3" "450.3"
"1957:Q1" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4" "453.4"
"1957:Q2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2" "453.2"
"1957:Q3" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2" "455.2"
"1957:Q4" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2" "448.2"
"1958:Q1" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5" "437.5"
"1958:Q2" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5" "439.5"
"1958:Q3" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7" "450.7"
"1958:Q4" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6" "461.6"
"1959:Q1" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6" "468.6"
"1959:Q2" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9" "479.9"
"1959:Q3" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0" "475.0"
"1959:Q4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4" "480.4"
"1960:Q1" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2" "490.2"
"1960:Q2" "489.8" "489.8" "489.8" "489.7" "489.7" "489.7" "489.7" "489.7" "489.7" "489.7" "489.7" "489.7" "489.7" "489.7" "489.7"
"1960:Q3" "487.4" "487.4" "487.4" "487.3" "487.3" "487.3" "487.3" "487.3" "487.3" "487.3" "487.3" "487.3" "487.3" "487.3" "487.3"
"1960:Q4" "483.8" "483.8" "483.8" "483.7" "483.7" "483.7" "483.7" "483.7" "483.7" "483.7" "483.7" "483.7" "483.7" "483.7" "483.7"
"1961:Q1" "482.7" "482.7" "482.7" "482.6" "482.6" "482.6" "482.6" "482.6" "482.6" "482.6" "482.6" "482.6" "482.6" "482.6" "482.6"
"1961:Q2" "492.9" "492.9" "492.9" "492.8" "492.8" "492.8" "492.8" "492.8" "492.8" "492.8" "492.8" "492.8" "492.8" "492.8" "492.8"
"1961:Q3" "501.6" "501.6" "501.6" "501.5" "501.5" "501.5" "501.5" "501.5" "501.5" "501.5" "501.5" "501.5" "501.5" "501.5" "501.5"
"1961:Q4" "511.9" "511.9" "511.9" "511.7" "511.7" "511.7" "511.7" "511.7" "511.7" "511.7" "511.7" "511.7" "511.7" "511.7" "511.7"
"1962:Q1" "519.7" "519.7" "519.7" "519.5" "519.5" "519.5" "519.5" "519.5" "519.5" "519.5" "519.5" "519.5" "519.5" "519.5" "519.5"
"1962:Q2" "527.9" "527.9" "527.9" "527.7" "527.7" "527.7" "527.7" "527.7" "527.7" "527.7" "527.7" "527.7" "527.7" "527.7" "527.7"
"1962:Q3" "533.6" "533.6" "533.6" "533.4" "533.4" "533.4" "533.4" "533.4" "533.4" "533.4" "533.4" "533.4" "533.4" "533.4" "533.4"
"1962:Q4" "538.5" "538.5" "538.5" "538.3" "538.3" "538.3" "538.3" "538.3" "538.3" "538.3" "538.3" "538.3" "538.3" "538.3" "538.3"
"1963:Q1" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2" "541.2"
"1963:Q2" "544.9" "544.9" "544.9" "546.0" "546.0" "546.0" "546.0" "546.0" "546.0" "546.0" "546.0" "546.0" "546.0" "546.0" "546.0"
"1963:Q3" "553.7" "553.7" "553.7" "554.7" "554.7" "554.7" "554.7" "554.7" "554.7" "554.7" "554.7" "554.7" "554.7" "554.7" "554.7"
"1963:Q4" "560.0" "560.0" "560.0" "562.1" "562.1" "562.1" "562.1" "562.1" "562.1" "562.1" "562.1" "562.1" "562.1" "562.1" "562.1"
"1964:Q1" "567.1" "567.1" "567.1" "569.7" "569.7" "569.7" "569.7" "571.1" "571.1" "571.1" "571.1" "571.1" "571.1" "571.1" "571.1"
"1964:Q2" "575.9" "575.9" "575.9" "578.1" "578.1" "578.1" "578.1" "578.6" "578.6" "578.6" "578.6" "578.6" "578.6" "578.6" "578.6"
"1964:Q3" "582.6" "582.6" "582.6" "585.0" "585.0" "585.0" "585.0" "585.8" "585.8" "585.8" "585.8" "585.8" "585.8" "585.8" "585.8"
"1964:Q4" "584.7" "584.7" "584.7" "587.2" "587.2" "587.2" "587.2" "588.5" "588.5" "588.5" "588.5" "588.5" "588.5" "588.5" "588.5"
"1965:Q1" "597.5" "597.7" "597.7" "600.3" "600.3" "600.3" "600.3" "601.5" "601.5" "601.5" "601.5" "601.6" "601.6" "601.6" "601.6"
"1965:Q2" "601.4" "603.5" "603.5" "607.8" "607.8" "607.8" "607.8" "609.7" "609.7" "609.7" "609.7" "610.4" "610.4" "610.4" "610.4"
"1965:Q3" "609.1" "613.0" "613.0" "618.2" "618.2" "618.2" "618.2" "620.7" "620.7" "620.7" "620.7" "622.5" "622.5" "622.5" "622.5"
"1965:Q4" "#N/A"  "621.7" "624.4" "631.2" "631.2" "631.2" "631.2" "634.4" "634.4" "634.4" "634.4" "636.6" "636.6" "636.6" "636.6"
"1966:Q1" "#N/A"  "#N/A"  "633.8" "640.5" "640.5" "640.5" "640.5" "645.4" "645.4" "645.4" "645.4" "648.6" "648.6" "648.6" "648.6"
"1966:Q2" "#N/A"  "#N/A"  "#N/A"  "644.2" "643.5" "643.5" "643.5" "649.3" "649.3" "649.3" "649.3" "653.3" "653.3" "653.3" "653.3"
"1966:Q3" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "650.7" "649.9" "649.9" "654.8" "654.8" "654.8" "654.8" "659.5" "659.5" "659.5" "659.5"
"1966:Q4" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "657.0" "657.2" "661.1" "661.1" "661.1" "661.1" "667.1" "667.1" "667.1" "667.1"
"1967:Q1" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "657.2" "660.7" "660.7" "660.7" "660.7" "665.7" "665.7" "665.7" "665.7"
"1967:Q2" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "664.6" "664.7" "664.7" "664.7" "669.2" "669.2" "669.2" "669.2"
"1967:Q3" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "671.6" "672.0" "672.0" "675.6" "675.6" "675.6" "675.6"
"1967:Q4" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "679.4" "679.6" "681.8" "681.8" "681.8" "681.8"
"1968:Q1" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "689.7" "692.7" "692.7" "692.7" "692.7"
"1968:Q2" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "701.7" "703.4" "703.4" "703.4"
"1968:Q3" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "712.0" "712.3" "712.3"
"1968:Q4" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "719.1" "718.4"
"1969:Q1" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "723.6"
"1969:Q2" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1969:Q3" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1969:Q4" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1970:Q1" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1970:Q2" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1970:Q3" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1970:Q4" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1971:Q1" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1971:Q2" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1971:Q3" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
"1971:Q4" "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"  "#N/A"
end

I hope this helps.
The main target here is that a shock variable (measured as residual of a given period gdp variable at a given vintage date on the same period gdp variable in a previous vintage date) would be a proxy for the arrival of new information ( that led to the revision of the gdp variable). I hope this clarifies more

Look forward to hearing

[Jesse Wursten]

Can you add a data example using -dataex- (see the FAQ of this forum)? That would make it easier to try to come up with code examples. Either way you'll have to reshape your data to long first I think.

[Lisa Wilson]

Jesse: thanks a lot for your reply.
Well, the data extends vertically from 1947 Q1 to 2015:Q1 (time) . The revised GDP variable extends horizontally from gdp65Q4(revised gdp data in year 1965 Q4) to gdp16Q1 (revised gdp data in year 2016 Q1)

I think of converting the table here to shocks (residuals) measured from a regression of vintage gdp variable on a previous vintage gdp variable. For example, I want to regress gdp (of vintage year 2015 Q4) on gdp (of vintage year 2015 Q4) for each given time period, for exampleyear 1990 Q1, 1990 Q2, 1990 Q3, ....and so on.