New R user here. I have a relatively large panel of countries, and I want to identify recession episodes.
labor_pivoted_crisis=labor_pivoted%>%
group_by(country)%>%
mutate(crisis=ifelse(dplyr::lag(gdp_growth,1)<0 &dplyr::lag(gdp_growth,2)<0)&
dplyr::lag(gdp_growth,3)<0 & dplyr::lag(gdp_growth,4)<0 &dplyr::lag(gdp_growth,5)<0,1,0 )
This is my attempt. The idea is that for each country, I want to create a variable called crisis if there are 6 consecutive months of negative gdp growth. The code I have fails pretty badly. I don't know if this is the correct of way of approaching this, it certainly seemed the most intuitive. Moreover, I'd like to have all periods marked with 1. That is, if for australia, from 1980:1 - 1980:7 there's negative gdp growth, have crisis=1 for all
Here's a sample of my data
structure(list(country = structure(c("AUS", "AUS", "AUS", "AUS",
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100.495236018581, 100.579856488046, 100.623698364593, 100.634182465959,
100.616937320912, 100.572142389189), label = "gdp_value", format.stata = "%9.0g"),
gdp_growth = structure(c(-0.00568693496756367, 0.0481677528503255,
0.0969520827574399, 0.142477589379286, 0.183575244017042,
0.212728780257709, 0.229805623458734, 0.239742877669435,
0.251106285466385, 0.259192154068977, 0.245132206151855,
0.175239068853816, 0.0476400065019256, -0.103302945536399,
-0.1984139857651, -0.205500135803316, -0.132725634990472,
-0.0283052103898562, 0.0754700379702378, 0.154681083127084,
0.186279934105471, 0.166288921631352, 0.107265442619564,
0.0394785119016916, -0.0195213584303744, -0.0600136304472976,
-0.0758471682154038, -0.0659851810888235, -0.0333287356328427,
0.0146166379952462, 0.0700246895442014, 0.121333099689558,
0.149293591831423, 0.15161889028219, 0.140795257876025, 0.143738805690852,
0.171421668470817, 0.218701500342267, 0.262373585417119,
0.285290488726719, 0.273086737819828, 0.212392462588795,
0.103616186554678, -0.0349843388628202, -0.159781215970956,
-0.249231422413843, -0.294918630611551, -0.295144107974021,
-0.261095565574454, -0.216909130248055, -0.202207268743049,
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-0.657022951158215, -0.624133994881237, -0.553849920293725,
-0.447243310222056, -0.305682309823929, -0.138805571899692,
0.0439551507041003, 0.221126026719176, 0.354965096640836,
0.431642594810189, 0.459909686208881, 0.464711985156097,
0.459125462500776, 0.443520626095673, 0.405480311478534,
0.341030568230138, 0.256627509901985, 0.173057004047283,
0.0998740125143449, 0.041074133079988, 2.98923835906351e-05,
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0.0464189885195587, 0.111389019814112, 0.179141545384098,
0.239056946120986, 0.27998883403102, 0.285678714560067, 0.250152385175362,
0.176831236268438, 0.0806191231395914, -0.0268903581315705,
-0.130714064644533, -0.204958260710519, -0.243817887386921,
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0.274865119640921, 0.229361359462299, 0.152428111730885,
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-0.0927116631965319, -0.0977128949322115, -0.122323275416036,
-0.155918451405768, -0.190803892718172, -0.219617187302834,
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-0.382433630701229, -0.379961906369797, -0.34188104583972,
-0.287394339505642, -0.228690024438905, -0.17490910535658,
-0.136436179686061, -0.113878016746316, -0.100953380345814,
-0.0862575423671963, -0.0638263465138067, -0.0343519131440928,
-0.00696788256160374, 0.016092091103026, 0.0372794213934213,
0.0618160897212228, 0.0940143500455903, 0.135249271193505,
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0.259113914909825, 0.220593149588844, 0.151802990216066,
0.0771207875394531, 0.00923958258969293, -0.0457547238942979,
-0.0837932616572168, -0.101645087689306, -0.0944558075982371,
-0.0547333852348508, 0.0160955509610261, 0.104597052346023,
0.180011248031686, 0.228493625786186, 0.247210893961292,
0.240379119779366, 0.213386263432813, 0.172865976704806,
0.128227372783041, 0.0841680323562244, 0.0435796248957843,
0.0104185747481722, -0.0171379370286218, -0.0445301827790345
), label = "gdp_growth", format.stata = "%9.0g"), banking_crisis = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), systemic_crisis = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), currency_crisis = c(1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), fin_crisis = c(1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), row.names = c(NA, -200L), class = c("tbl_df", "tbl",
"data.frame"), label = "Written by R.")
EDIT- this approach now separates out each streak into grp, and marks as crisis any grp that is declines and length >= 6.
library(dplyr)
labor_pivoted |>
mutate(decline = gdp_growth < 0,
switch = decline != lag(decline, 1, FALSE),
grp = cumsum(switch), .by = country) |>
mutate(crisis = decline & n() >= 6, .by = c(country, grp))
Result
# A tibble: 200 × 18
country time hourly_earnings indpro unrate retail_trade tot_manuf year gdp_value gdp_growth banking_crisis systemic_crisis currency_crisis fin_crisis decline switch grp crisis
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <int> <lgl>
1 AUS 1978-02 NA NA 6.65 NA NA 1978 98.5 -0.00569 0 0 1 1 TRUE TRUE 1 FALSE
2 AUS 1978-03 NA NA 6.30 NA NA 1978 98.5 0.0482 0 0 1 1 FALSE TRUE 2 FALSE
3 AUS 1978-04 NA NA 6.27 NA NA 1978 98.6 0.0970 0 0 1 1 FALSE FALSE 2 FALSE
4 AUS 1978-05 NA NA 6.21 NA NA 1978 98.8 0.142 0 0 1 1 FALSE FALSE 2 FALSE
5 AUS 1978-06 NA NA 6.30 NA NA 1978 98.9 0.184 0 0 1 1 FALSE FALSE 2 FALSE
6 AUS 1978-07 NA NA 6.22 NA NA 1978 99.1 0.213 0 0 1 1 FALSE FALSE 2 FALSE
7 AUS 1978-08 NA NA 6.49 NA NA 1978 99.4 0.230 0 0 1 1 FALSE FALSE 2 FALSE
8 AUS 1978-09 NA NA 6.25 NA NA 1978 99.6 0.240 0 0 1 1 FALSE FALSE 2 FALSE
9 AUS 1978-10 NA NA 6.16 NA NA 1978 99.9 0.251 0 0 1 1 FALSE FALSE 2 FALSE
10 AUS 1978-11 NA NA 6.33 NA NA 1978 100. 0.259 0 0 1 1 FALSE FALSE 2 FALSE
# ℹ 190 more rows
# ℹ Use `print(n = ...)` to see more rows