"too many variables specified" error with predict following logit - stata

I have a panel of data (firm-years) that span several countries. For each country I estimate a logit model using the first five years then I use this model to predict probabilities in subsequent years. I foreach loop over the countries and forvalues loop over the subsequent years.
The first few countries work well (both estimations and predictions), but the fifth country's first out-of-sample prediction fails with:
Country: United Kingdom
Year: 1994
too many variables specified
r(103);
The model fits and 1994 has enough data to predict a probability. My predict call is:
predict temp_`c'`y' ///
if (country == "`c'") ///
& (fyear == `y'), ///
pr
Do you have any ideas what could cause this error? I am confused because logit and predict work elsewhere in the same loop. Thanks!
FWIW, here's the .do file.
* generate table 5 from Denis and Osobov (2008 JFE)
preserve
* loop to estimate model by country
levelsof country, local(countries)
foreach c of local countries {
display "Country: `c'"
summarize fyear if (country == "`c'"), meanonly
local est_low = `r(min)'
local est_high = `=`r(min)' + 4'
local pred_low = `=`r(min)' + 5'
local pred_high = `r(max)'
logit payer size v_a_tr e_a_tr re_be_tr ///
if (country == "`c'") ///
& inrange(fyear, `est_low', `est_high')
forvalues y = `pred_low'/`pred_high' {
display "Country: `c'"
display "Year: `y'"
predict temp_`c'`y' ///
if (country == "`c'") ///
& (fyear == `y'), ///
pr
}
}
* combine fitted values and generate delta
egen payer_expected = rowfirst(temp_*)
drop temp_*
generate delta = payer - payer_expected
* table
table country fyear, ///
contents(count payer mean payer mean payer_expected)
*
restore
Update: If I drop (country == "United Kingdom"), then the same problem shifts to the United States (next and last country in panel). If I drop inlist(country, "United Kingdom", "United States") then the problem disappears and the .do file runs through.

You are using country names as part of the new variable name that predict is creating. However, when you get to "United Kingdom" your line
predict temp_`c'`y'
implies something like
predict temp_United Kingdom1812
But Stata sees that as two variable names where only one is allowed.
Otherwise put, you are being bitten by a simple rule: Stata does not allow spaces within variable names.
Clearly the same problem would bite with "United States".
The simplest fudge is to change the values so that spaces become underscores "_". Stata's OK with variable names including underscores. That could be
gen country2 = subinstr(country, " ", "_", .)
followed by a loop over country2.
Note for everyone not up in historical details. 1812 is the year that British troops burnt down the White House. Feel free to substitute "1776" or some other date of choice.
(By the way, credit for a crystal-clear question!)

Here's an another approach to your problem. Initialise your variable to hold predicted values. Then as you loop over the possibilities, replace it chunk by chunk with each set of predictions. That avoids the whole business of generating a bunch of variables with different names which you don't want to hold on to long-term.
* generate table 5 from Denis and Osobov (2008 JFE)
preserve
gen payer_expected = .
* loop to estimate model by country
levelsof country, local(countries)
foreach c of local countries {
display "Country: `c'"
summarize fyear if (country == "`c'"), meanonly
local est_low = `r(min)'
local est_high = `=`r(min)' + 4'
local pred_low = `=`r(min)' + 5'
local pred_high = `r(max)'
logit payer size v_a_tr e_a_tr re_be_tr ///
if (country == "`c'") ///
& inrange(fyear, `est_low', `est_high')
forvalues y = `pred_low'/`pred_high' {
display "Country: `c'"
display "Year: `y'"
predict temp ///
if (country == "`c'") ///
& (fyear == `y'), pr
quietly replace payer_expected = temp if temp < .
drop temp
}
}
generate delta = payer - payer_expected
* table
table country fyear, ///
contents(count payer mean payer mean payer_expected)
*
restore

Related

Reordering panels by another variable in twoway, by() graphs

Suppose I make the following chart showing the weight of 9 pigs over time:
webuse pig
tw line weight week if inrange(id,1,9), by(id) subtitle(, nospan)
Is it possible to reorder the panels by another variable while retaining the original label? I can imagine defining another variable that is sorted the right way and then labeling it with the right id, but curious if there is a less clunky way of achieving that.
I think you are right: you need a new ordering variable. Positively, you can order on any criterion of choice. Watch out for ties on the variable used to order, which can always broken by referring to the original identifier. Here we sort on final weights, by default smallest first. (For largest first, negate the weight variable.)
webuse pig, clear
keep if id <= 9
bysort id (week) : gen last = weight[_N]
egen newid = group(last id)
bysort newid : gen toshow = strofreal(id) + " (" + strofreal(last, "%2.1f") + ")"
* search labmask for download links
labmask newid , values(toshow)
set scheme s1color
line weight week, by(newid, note("")) sort xla(1/9)
Short papers discussing the principles here are already in train for publication in the Stata Journal in 2021.

Looping with distance matching

I want to match treated firms to control firms by industry and year considering firms that are the closest in terms of profitability (roa). I want a 1:1 match. I am using a distance measure (mahalanobis).
I have 530,000 firm-year observations in my sample, namely 267,000 treated observations and 263,000 control observations approximatively. Here is my code:
gen neighbor1 = .
gen idobs = .
levelsof industry
local a = r(levels)
levelsof year
local b = r(levels)
foreach i in `a' {
foreach j in `b'{
capture noisily psmatch2 treat if industry == `i' & year == `j', mahalanobis(roa)
capture noisily replace neighbor1 = _n1 if industry == `i' & year == `j'
capture noisily replace idobs = _id if industry == `i' & year == `j'
drop _treated _support _weight _id _n1 _nn
}
}
Treat is my treatment variable. It takes the value of 1 for treated observations and 0 for non-treated observations.
The command psmatch2 creates the variable _n1 and _id among others. _n1 is the id number of the matched observation (closest neighbor) and _id is an id number (1 - 530,000) that is unique to each observation.
The code 'works', i.e. I get no error message. My variable neighbor1 has 290,724 non-missing observations.
However, these 290,724 observations vary between 1 and 933 which is odd. The variable neighbor1 should provide me the observation id number of the matched observation, which can vary between 1 and 530,000.
It seems that the code erases or ignores the result of the matching process in different subgroups. What am I doing wrong?
Edit:
I found a public dataset and adapted my previous code so that you can run my code with this dataset and see more clearly what the problem could be.
I am using Vella and Verbeek (1998) panel data on 545 men worked every year from 1980-1987 from this website: https://www.stata.com/texts/eacsap/
Let's say that I want to match treated observations, i.e. people, to control observations by marriage status (married) and year considering people that worked a similar number of hours (hours), i.e. the shortest distance.
I create a random treatment variable (treat) for the sake of this example.
use http://www.stata.com/data/jwooldridge/eacsap/wagepan.dta
gen treat = round(runiform())
gen neighbor1 = .
gen idobs = .
levelsof married
local a = r(levels)
levelsof year
local b = r(levels)
foreach i in `a' {
foreach j in `b'{
capture noisily psmatch2 treat if married == `i' & year == `j', mahalanobis(hours)
capture noisily replace neighbor1 = _n1 if married == `i' & year == `j'
capture noisily replace idobs = _id if married == `i' & year == `j'
drop _treated _support _weight _id _n1 _nn
}
}
What this code should do is to look at each subgroup of observations: 444 observations in 1980 that are not married, 101 observations in 1980 that are married, ..., and 335 observations in 1987 that are married. In each of these subgroups, I would like to match a treated observation to a control observation considering the shortest distance in the number of hours worked.
There are two problems that I see after running the code.
First, the variable idobs should take a unique number between 1 and 4360 because there are 4360 observations in this dataset. It is just an ID number. It is not the case. A few observations can have an ID number 1, 2 and so on.
Second, neighbor1 varies between 1 and 204 meaning that the matched observations have only ID numbers varying from 1 to 204.
What is the problem with my code?
Here is a solution using the command iematch, installed through the package ietoolkit -> ssc install ietoolkit. For disclosure, I wrote this command. psmatch2 is great if you want the ATT. But if all you want is to match observations across two groups using nearest neighbor, then iematch is cleaner.
In both commands you need to make each industry-year match in a subset, then combine that information. In both commands the matched group ID will restart from 1 in each subset.
Using your example data, this creates one matchID var for each subset, then you will have to find a way to combine these to a single matchID without conflicts across the data set.
* use data set and keep only vars required for simplicity
use http://www.stata.com/data/jwooldridge/eacsap/wagepan.dta, clear
keep year married hour
* Set seed for replicability. NEVER use the 123456 seed in production, randomize a new seed
set seed 123456
*Generate mock treatment
gen treat = round(runiform())
*generate vars to store results
gen matchResult = .
gen matchDiff = .
gen matchCount = .
*Create locals for loops
levelsof married
local married_statuses = r(levels)
levelsof year
local years = r(levels)
*Loop over each subgroup
foreach year of local years {
foreach married_status of local married_statuses {
*This command is similar to psmatch2, but a simplified version for
* when you are not looking for the ATT.
* This command is only about matching.
iematch if married == `married_status' & year == `year', grp(treat) match(hour) seedok m1 maxmatch(1)
*These variables list meta info about the match. See helpfile for docs,
*but this copy info from each subset in this loop to single vars for
*the full data set. Then the loop specfic vars are dropped
replace matchResult = _matchResult if married == `married_status' & year == `year'
replace matchDiff = _matchDiff if married == `married_status' & year == `year'
replace matchCount = _matchCount if married == `married_status' & year == `year'
drop _matchResult _matchDiff _matchCount
*For each loop you will get a match ID restarting at 1 for each loop.
*Therefore we need to save them in one var for each loop and combine afterwards.
rename _matchID matchID_`married_status'_`year'
}
}

Calculate the number of firms at a given month

I'm working on a dataset in Stata
The first column is the name of the firm. the second column is the start date of this firm and the third column is the expiration date of this firm. If the expdate is missing, this firm is still in business. I want to create a variable that will record the number of firms at a given time. (preferably to be a monthly variable)
I'm really lost here. Please help!
Next time, try using dataex (ssc install dataex) rather than a screen shot, this is recommended in the Stata tag wiki, and will help others help you!
Here is an example for how to count the number of firms that are alive in each period (I'll use years, but point out where you can switch to month). This example borrows from Nick Cox's Stata journal article on this topic.
First, load the data:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long(firmID dt_start dt_end)
3923155 20080123 99991231
2913168 20070630 99991231
3079566 20000601 20030212
3103920 20020805 20070422
3357723 20041201 20170407
4536020 20120201 20170407
2365954 20070630 20190630
4334271 20110721 20191130
4334338 20110721 20170829
4334431 20110721 20190429
end
Note that my in my example data my dates are not in Stata format, so I'll convert them here:
tostring dt_start, replace
generate startdate=date(dt_start, "YMD")
tostring dt_end, replace
generate enddate=date(dt_end, "YMD")
format startdate enddate
Next make a variable with the time interval you'd like to count within:
generate startyear = year(startdate)
generate endyear = year(enddate)
In my dataset I have missing end dates that begin with '9999' while you have them as '.' I'll set these to the current year, the assumption being that the dataset is current. You'll have to decide whether this is appropriate in your data.
replace endyear = year(date("$S_DATE","DMY")) if endyear == 9999
Next create an observation for the first and last years (or months) that the firm is alive:
expand 2
by firmID, sort: generate year = cond(_n == 1, startyear, endyear)
keep firmID year
duplicates drop // keeps one observation for firms that die in the period they were born
Now expand the dataset to have an observation for every period between the start and end date. For this I use tsfill.
xtset firmID year
tsfill
Now I have one observation per existing firm in each period. All that remains is to count the observations by year:
egen entities = count(firmID), by(year)
drop firmID
duplicates drop

Computing and plotting difference in group means

In what follows I plot the mean of an outcome of interest (price) by a grouping variable (foreign) for each possible value taken by the fake variable time:
sysuse auto, clear
gen time = rep78 - 3
bysort foreign time: egen avg_p = mean(price)
scatter avg_p time if (foreign==0 & time>=0) || ///
scatter avg_p time if (foreign==1 & time>=0), ///
legend(order(1 "Domestic" 2 "Foreign")) ///
ytitle("Average price") xlab(#3)
What I would like to do is to plot the difference in the two group means over time, not the two separate means.
I am surely missing something, but to me it looks complicated because the information about the averages is stored "vertically" (in avg_p).
The easiest way to do this is to arguably use linear regression to estimate the differences:
/* Regression Way */
drop if time < 0 | missing(time)
reg price i.foreign##i.time
margins, dydx(foreign) at(time =(0(1)2))
marginsplot, noci title("Foreign vs Domestic Difference in Price")
If regression is hard to wrap your mind around, the other is involves mangling the data with a reshape:
/* Transform the Data */
keep price time foreign
collapse (mean) price, by(time foreign)
reshape wide price, i(time) j(foreign)
gen diff = price1-price0
tw connected diff time
Here is another approach. graph dot will happily plot means.
sysuse auto, clear
set scheme s1color
collapse price if inrange(rep78, 3, 5), by(foreign rep78)
reshape wide price, i(rep78) j(foreign)
rename price0 Domestic
label var Domestic
rename price1 Foreign
label var Foreign
graph dot (asis) Domestic Foreign, over(rep78) vertical ///
marker(1, ms(Oh)) marker(2, ms(+))

How to replace a zero-valued answer by its respective average value?

I have a household data set which includes expenditures for various foods. I categorized them into main food groups and price is obtained by dividing the expenditure value by quantity. For some households price comes as zero since their consumption with respect to the corresponding food group is zero. In such cases, I want to get the price as the average price of the corresponding city, district & province, which that non-consumed household is selected.
How could I do it using STATA?
The mean of the positive values is
egen mean_price = mean(price / (price > 0)), by(province district city)
and you can replace zeros in a clone by
gen price2 = cond(price > 0, price, mean_price)
The division trick can be explained like this. If price > 0 is true, then that expression evaluates to 1; and if false to 0. Dividing by 1 clearly leaves values unchanged. Dividing by 0 creates missings, which egen's mean() function will ignore, which is precisely what is wanted.
There is more discussion of related technique in the article referred to in http://www.stata-journal.com/article.html?article=dm0055
P.S. Stata is the correct spelling. It is an invented word, and was never an acronym.
P.S. You have yet to acknowledge an answer at How to get the difference of two variables, when there are missing values?
LATER:
In this case another way is
egen total = total(price), by(province district city)
egen number = total(price > 0), by(province district city)
gen price2 = cond(price > 0, price, total/number)
as zero prices make no difference to the total. Use doubles throughout.