Replication of Knittel (2011) 链接到标题

对 Knittel (2011) 部分内容进行复制，暂无稳健性检验部分，原始为 Stata 代码。

library(tidyverse)
library(ggthemes)
library(here)
library(fixest)
library(modelsummary)

# config
mytheme <-theme_classic() + theme(legend.position="bottom")
myscale <- scale_colour_stata("mono")
mystars <- c("***"=0.01, "**"=0.05, "*"=0.10)

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[31mx[39m [34mdplyr[39m::[32mlag()[39m    masks [34mstats[39m::lag()
here() starts at D:/zhou/projects/rep_knittel_2011

# load data
steroids <- haven::read_dta(here("original", "data", "Steroids_AER_data_post.dta"))

vehicle_80_06 <- steroids |>
    mutate(type_year = case_when(
        d_truck == 0 & year == 1980 ~ "Cars in 1980",
        d_truck == 0 & year == 2006 ~ "Cars in 2006",
        d_truck == 1 & year == 1980 ~ "Trucks in 1980",
        d_truck == 1 & year == 2006 ~ "Trucks in 2006",
        TRUE ~ "others"
    )) |>
    filter(fuel == "G" & outlier == 0 & type_year != "others")

cars <- steroids |> filter(outlier == 0 & d_truck == 0)
trucks <- steroids |> filter(outlier == 0 & d_truck == 1)



datawizard::data_codebook(steroids)

Density Plots 链接到标题

Fuel Economy

vehicle_80_06 |>
    ggplot() + geom_density(aes(mpg, linetype = d_truck == 1, color = year == 1980)) +
    labs(x = "Fuel Economy", y = "Density") + mytheme

png

Curb Weight

vehicle_80_06 |>
    ggplot() + geom_density(aes(curbwt, linetype = d_truck == 1, color = year == 1980)) +
    labs(x = "Curb Weight", y = "Density") + mytheme

png

Hoursepwer

vehicle_80_06 |>
    ggplot() + geom_density(aes(hp, linetype = year == 1980, color = d_truck == 1)) +
    labs(x = "Horsepower", y = "Density") + mytheme

png

Acceleration

vehicle_80_06 |>
    ggplot() + geom_density(aes(accel, linetype = year == 1980, color = d_truck == 1)) +
    labs(x = "Acceration", y = "Density") + mytheme

png

Isoquants 链接到标题

Cars 链接到标题

Fuel economy v.s. Weight

cars_mpglt50 <- vehicle_80_06 |>
    filter(mpg < 50 & d_truck == 0) |>
    mutate(Year = as.character(year))

ggplot(cars_mpglt50, aes(curbwt, mpg, 
                         shape = Year, color = Year)) + 
    geom_point() +
    geom_smooth(se = FALSE) +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Fuel economy v.s. Horsepower

ggplot(cars_mpglt50, aes(hp, mpg, shape = Year, color = Year)) +
    geom_point() +
    geom_smooth(se = FALSE) +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Fuel economy v.s. Torque

ggplot(cars_mpglt50, aes(torque, mpg, shape = Year, color = Year)) +
    geom_point() +
    geom_smooth(se = FALSE) +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Fuel economy v.s. Acceleration

ggplot(cars_mpglt50, aes(accel, mpg, shape = Year, color = Year)) +
    geom_point() +
    geom_smooth() +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Trucks 链接到标题

Fuel economy v.s. Weight

trucks_mpglt50 <- vehicle_80_06 |>
    filter(d_truck == 1 & mpg < 50) |>
    mutate(Year = as.character(year))

ggplot(trucks_mpglt50, aes(curbwt, mpg, shape = Year, color = Year)) +
    geom_point() +
    geom_smooth() +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Fuel economy v.s. Horsepower

ggplot(trucks_mpglt50, aes(hp, mpg, shape = Year, color = Year)) +
    geom_point() +
    geom_smooth(se = FALSE) +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Fuel economy v.s. Torque

ggplot(trucks_mpglt50, aes(torque, mpg, shape = Year, color = Year)) +
    geom_point() +
    geom_smooth(se = FALSE) +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Fuel economy v.s. Acceleration

ggplot(trucks_mpglt50, aes(accel, mpg, shape = Year, color = Year)) +
    geom_point() +
    geom_smooth(se = FALSE) +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Cars v. Trucks 链接到标题

vehicle_80_06 |>
    ggplot(aes(curbwt, mpg, color = as.factor(d_truck), shape = as.factor(year), linetype = as.factor(year))) +
    geom_point() +
    geom_smooth(se = FALSE) +
    mytheme

[1m[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

png

Cars 链接到标题

Cobb-Douglass

# use feols for speed
# use dummy variable on purpose
cars_cd1 <- feols(lmpg ~ lcurbwt + lhp + ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year), cars)
cars_cd2 <- feols(lmpg ~ lcurbwt + lhp + ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year) + i(mfr), cars)
cars_cd3 <- feols(lmpg ~ lcurbwt + lhp + ltorque + d_manual + time_d_manual + d_diesel + i(year) + i(mfr), cars)

Translog

cars_tl1 <- feols(lmpg ~ lcurbwt + lhp + ltorque + lhp2 + lcurbwt2 + ltorque2 + lcurbwt_lhp + lcurbwt_ltorque +  lhp_ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year), cars)
cars_tl2 <- feols(lmpg ~ lcurbwt + lhp + ltorque + lhp2 + lcurbwt2 + ltorque2 + lcurbwt_lhp + lcurbwt_ltorque +  lhp_ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year) + i(mfr), cars)
cars_tl3 <- feols(lmpg ~ lcurbwt + lhp + ltorque + lhp2 + lcurbwt2 + ltorque2 + lcurbwt_lhp + lcurbwt_ltorque +  lhp_ltorque + d_manual + time_d_manual + d_diesel + i(year) + i(mfr), cars)

Table 2 链接到标题

modelsummary(
    list(cars_cd1, cars_cd2, cars_cd3, cars_tl1, cars_tl2, cars_tl3),
    output = "jupyter",
    stars = mystars,
    vcov = ~mfr,
    drop = "year|mfr"
)

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
(Intercept)	7.857***	7.686***	7.828***	10.061**	11.451***	12.183***
	(0.254)	(0.200)	(0.193)	(4.040)	(3.887)	(3.744)
lcurbwt	-0.398***	-0.383***	-0.419***	0.462	0.197	-0.043
	(0.046)	(0.032)	(0.029)	(1.271)	(1.261)	(1.254)
lhp	-0.324***	-0.268***	-0.262***	-2.549***	-3.092***	-2.937***
	(0.047)	(0.039)	(0.043)	(0.803)	(0.693)	(0.774)
ltorque	-0.019	-0.064**	-0.045	-0.041	0.212	0.191
	(0.038)	(0.030)	(0.035)	(0.757)	(0.583)	(0.673)
d_manual	0.087***	0.101***	0.102***	0.076***	0.086***	0.087***
	(0.013)	(0.013)	(0.013)	(0.013)	(0.012)	(0.012)
time_d_manual	-0.004***	-0.004***	-0.004***	-0.003***	-0.003***	-0.003***
	(0.0007)	(0.0007)	(0.0007)	(0.0007)	(0.0007)	(0.0007)
d_diesel	0.196***	0.212***	0.229***	0.257***	0.248***	0.272***
	(0.018)	(0.017)	(0.023)	(0.019)	(0.025)	(0.033)
d_turbo	0.025**	0.051***		0.017**	0.051***
	(0.010)	(0.010)		(0.007)	(0.009)
d_super	0.055***	0.034***		0.057***	0.038**
	(0.017)	(0.011)		(0.020)	(0.016)
lhp2				-0.180*	-0.099*	-0.151**
				(0.106)	(0.054)	(0.060)
lcurbwt2				-0.208*	-0.165	-0.154
				(0.117)	(0.115)	(0.118)
ltorque2				-0.030	0.025	-0.022
				(0.123)	(0.065)	(0.066)
lcurbwt_lhp				0.473***	0.489***	0.477***
				(0.139)	(0.129)	(0.147)
lcurbwt_ltorque				0.016	-0.059	-0.044
				(0.108)	(0.122)	(0.137)
lhp_ltorque				0.047	-0.017	0.070
				(0.197)	(0.059)	(0.057)
Num.Obs.	14423	14423	14423	14423	14423	14423
R2	0.838	0.883	0.879	0.847	0.890	0.886
R2 Adj.	0.838	0.882	0.878	0.846	0.889	0.886
AIC	-29072.0	-33644.6	-33202.3	-29824.4	-34536.1	-34062.0
BIC	-28806.8	-33099.1	-32672.0	-29513.8	-33945.1	-33486.1
RMSE	0.09	0.07	0.08	0.09	0.07	0.07
Std.Errors	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr
* p < 0.1, p < 0.05, * p < 0.01

Table 3 链接到标题

modelsummary(
    list(cars_cd1, cars_cd2, cars_cd3, cars_tl1, cars_tl2, cars_tl3),
    output = "jupyter",
    stars = mystars,
    vcov = ~mfr,
    keep = "year"
)

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
year = 1981	0.055***	0.055***	0.055***	0.054***	0.053***	0.053***
	(0.009)	(0.009)	(0.010)	(0.009)	(0.008)	(0.009)
year = 1982	0.095***	0.095***	0.096***	0.092***	0.090***	0.091***
	(0.012)	(0.012)	(0.013)	(0.011)	(0.011)	(0.011)
year = 1983	0.134***	0.131***	0.132***	0.128***	0.125***	0.125***
	(0.013)	(0.014)	(0.015)	(0.012)	(0.013)	(0.013)
year = 1984	0.160***	0.156***	0.159***	0.150***	0.148***	0.150***
	(0.012)	(0.013)	(0.013)	(0.009)	(0.010)	(0.010)
year = 1985	0.188***	0.181***	0.185***	0.174***	0.171***	0.174***
	(0.011)	(0.011)	(0.012)	(0.010)	(0.009)	(0.009)
year = 1986	0.215***	0.211***	0.215***	0.200***	0.199***	0.201***
	(0.010)	(0.010)	(0.011)	(0.010)	(0.009)	(0.009)
year = 1987	0.225***	0.220***	0.224***	0.207***	0.209***	0.211***
	(0.012)	(0.011)	(0.012)	(0.013)	(0.011)	(0.011)
year = 1988	0.251***	0.242***	0.245***	0.233***	0.231***	0.232***
	(0.014)	(0.012)	(0.012)	(0.015)	(0.013)	(0.013)
year = 1989	0.260***	0.252***	0.255***	0.239***	0.239***	0.239***
	(0.012)	(0.010)	(0.012)	(0.012)	(0.010)	(0.010)
year = 1990	0.278***	0.267***	0.270***	0.257***	0.254***	0.254***
	(0.013)	(0.011)	(0.012)	(0.012)	(0.010)	(0.011)
year = 1991	0.291***	0.279***	0.281***	0.268***	0.265***	0.264***
	(0.013)	(0.012)	(0.014)	(0.010)	(0.011)	(0.012)
year = 1992	0.304***	0.293***	0.294***	0.280***	0.278***	0.276***
	(0.015)	(0.015)	(0.016)	(0.013)	(0.014)	(0.014)
year = 1993	0.335***	0.322***	0.322***	0.311***	0.304***	0.302***
	(0.017)	(0.016)	(0.018)	(0.014)	(0.015)	(0.015)
year = 1994	0.355***	0.340***	0.339***	0.330***	0.323***	0.320***
	(0.018)	(0.016)	(0.017)	(0.016)	(0.015)	(0.016)
year = 1995	0.386***	0.371***	0.370***	0.358***	0.351***	0.348***
	(0.017)	(0.016)	(0.018)	(0.014)	(0.015)	(0.016)
year = 1996	0.398***	0.380***	0.378***	0.368***	0.361***	0.356***
	(0.017)	(0.014)	(0.016)	(0.013)	(0.013)	(0.013)
year = 1997	0.407***	0.393***	0.392***	0.379***	0.373***	0.370***
	(0.019)	(0.017)	(0.019)	(0.015)	(0.014)	(0.015)
year = 1998	0.420***	0.409***	0.409***	0.392***	0.389***	0.387***
	(0.019)	(0.019)	(0.021)	(0.016)	(0.017)	(0.018)
year = 1999	0.417***	0.411***	0.410***	0.388***	0.392***	0.389***
	(0.019)	(0.019)	(0.021)	(0.016)	(0.017)	(0.018)
year = 2000	0.426***	0.423***	0.421***	0.396***	0.405***	0.401***
	(0.020)	(0.020)	(0.022)	(0.016)	(0.018)	(0.019)
year = 2001	0.438***	0.434***	0.434***	0.409***	0.415***	0.413***
	(0.020)	(0.020)	(0.023)	(0.017)	(0.018)	(0.020)
year = 2002	0.454***	0.451***	0.450***	0.424***	0.431***	0.429***
	(0.024)	(0.023)	(0.025)	(0.019)	(0.021)	(0.022)
year = 2003	0.474***	0.467***	0.467***	0.445***	0.447***	0.446***
	(0.024)	(0.025)	(0.028)	(0.022)	(0.023)	(0.025)
year = 2004	0.483***	0.474***	0.475***	0.455***	0.453***	0.452***
	(0.025)	(0.024)	(0.026)	(0.021)	(0.022)	(0.024)
year = 2005	0.495***	0.488***	0.489***	0.469***	0.467***	0.467***
	(0.024)	(0.024)	(0.026)	(0.021)	(0.023)	(0.024)
year = 2006	0.522***	0.512***	0.511***	0.494***	0.486***	0.485***
	(0.028)	(0.027)	(0.029)	(0.023)	(0.026)	(0.027)
Num.Obs.	14423	14423	14423	14423	14423	14423
R2	0.838	0.883	0.879	0.847	0.890	0.886
R2 Adj.	0.838	0.882	0.878	0.846	0.889	0.886
AIC	-29072.0	-33644.6	-33202.3	-29824.4	-34536.1	-34062.0
BIC	-28806.8	-33099.1	-32672.0	-29513.8	-33945.1	-33486.1
RMSE	0.09	0.07	0.08	0.09	0.07	0.07
Std.Errors	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr
* p < 0.1, p < 0.05, * p < 0.01

Trucks 链接到标题

Cobb-Douglass

tr_cd1 <- feols(lmpg ~ lcurbwt + lhp + ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year), trucks)
tr_cd2 <- feols(lmpg ~ lcurbwt + lhp + ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year) + i(mfr), trucks)
tr_cd3 <- feols(lmpg ~ lcurbwt + lhp + ltorque + d_manual + time_d_manual + d_diesel + i(year) + i(mfr), trucks)

Translog

tr_tl1 <- feols(lmpg ~ lcurbwt + lhp + ltorque + lhp2 + lcurbwt2 + ltorque2 + lcurbwt_lhp + lcurbwt_ltorque +  lhp_ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year), trucks)
tr_tl2 <- feols(lmpg ~ lcurbwt + lhp + ltorque + lhp2 + lcurbwt2 + ltorque2 + lcurbwt_lhp + lcurbwt_ltorque +  lhp_ltorque + d_manual + time_d_manual + d_diesel + d_turbo + d_super + i(year) + i(mfr), trucks)
tr_tl3 <- feols(lmpg ~ lcurbwt + lhp + ltorque + lhp2 + lcurbwt2 + ltorque2 + lcurbwt_lhp + lcurbwt_ltorque +  lhp_ltorque + d_manual + time_d_manual + d_diesel + i(year) + i(mfr), trucks)

Table 4 链接到标题

modelsummary(
    list(tr_cd1, tr_cd2, tr_cd3, tr_tl1, tr_tl2, tr_tl3),
    output = "jupyter",
    stars = mystars,
    vcov = ~mfr,
    drop = "year|mfr"
)

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
(Intercept)	7.541***	7.726***	7.728***	8.781	4.911	5.049
	(0.333)	(0.378)	(0.393)	(5.902)	(5.066)	(5.177)
lcurbwt	-0.363***	-0.355***	-0.356***	0.280	1.431	1.391
	(0.052)	(0.040)	(0.043)	(1.534)	(1.172)	(1.222)
lhp	-0.047	-0.071*	-0.071*	-0.847	-1.308**	-1.283**
	(0.043)	(0.041)	(0.041)	(0.704)	(0.561)	(0.595)
ltorque	-0.277***	-0.303***	-0.303***	-0.908	-0.780	-0.791
	(0.048)	(0.054)	(0.052)	(1.037)	(0.839)	(0.838)
d_manual	0.103***	0.094***	0.094***	0.099***	0.086***	0.086***
	(0.021)	(0.016)	(0.016)	(0.022)	(0.017)	(0.017)
time_d_manual	-0.005***	-0.005***	-0.005***	-0.005***	-0.004***	-0.004***
	(0.001)	(0.0008)	(0.0008)	(0.001)	(0.0009)	(0.0009)
d_diesel	0.267***	0.244***	0.245***	0.278***	0.252***	0.255***
	(0.019)	(0.017)	(0.023)	(0.019)	(0.016)	(0.019)
d_turbo	0.002	0.004		0.014	0.025
	(0.054)	(0.062)		(0.042)	(0.049)
d_super	-0.038	-0.007		-0.047	-0.016
	(0.042)	(0.045)		(0.038)	(0.038)
lhp2				0.011	0.055	0.052
				(0.156)	(0.146)	(0.145)
lcurbwt2				-0.197**	-0.245***	-0.242***
				(0.094)	(0.063)	(0.067)
ltorque2				-0.405***	-0.388***	-0.384***
				(0.111)	(0.130)	(0.129)
lcurbwt_lhp				-0.050	-0.064	-0.063
				(0.111)	(0.092)	(0.093)
lcurbwt_ltorque				0.505***	0.454***	0.450***
				(0.148)	(0.116)	(0.115)
lhp_ltorque				0.199	0.218	0.217
				(0.283)	(0.269)	(0.267)
Num.Obs.	12572	12572	12572	12572	12572	12572
R2	0.768	0.796	0.796	0.778	0.805	0.805
R2 Adj.	0.767	0.795	0.795	0.777	0.804	0.804
AIC	-20796.9	-22373.3	-22376.9	-21361.9	-22953.4	-22948.6
BIC	-20536.5	-21949.3	-21967.8	-21056.9	-22484.8	-22494.8
RMSE	0.11	0.10	0.10	0.10	0.10	0.10
Std.Errors	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr
* p < 0.1, p < 0.05, * p < 0.01

Table 5 链接到标题

modelsummary(
    list(tr_cd1, tr_cd2, tr_cd3, tr_tl1, tr_tl2, tr_tl3),
    output = "jupyter",
    stars = mystars,
    vcov = ~mfr,
    keep = "year"
)

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
year = 1981	0.069***	0.067***	0.067***	0.066***	0.063***	0.063***
	(0.012)	(0.011)	(0.011)	(0.012)	(0.011)	(0.011)
year = 1982	0.102***	0.098***	0.098***	0.098***	0.094***	0.094***
	(0.016)	(0.016)	(0.016)	(0.015)	(0.015)	(0.015)
year = 1983	0.132***	0.120***	0.121***	0.130***	0.117***	0.117***
	(0.016)	(0.015)	(0.015)	(0.016)	(0.015)	(0.015)
year = 1984	0.136***	0.120***	0.120***	0.136***	0.118***	0.118***
	(0.020)	(0.016)	(0.016)	(0.020)	(0.016)	(0.016)
year = 1985	0.146***	0.130***	0.130***	0.140***	0.123***	0.124***
	(0.011)	(0.009)	(0.009)	(0.013)	(0.012)	(0.012)
year = 1986	0.169***	0.155***	0.155***	0.161***	0.148***	0.148***
	(0.012)	(0.011)	(0.011)	(0.014)	(0.013)	(0.014)
year = 1987	0.174***	0.163***	0.163***	0.168***	0.157***	0.157***
	(0.013)	(0.011)	(0.011)	(0.015)	(0.013)	(0.013)
year = 1988	0.205***	0.195***	0.195***	0.201***	0.190***	0.190***
	(0.013)	(0.010)	(0.010)	(0.014)	(0.011)	(0.011)
year = 1989	0.207***	0.201***	0.201***	0.201***	0.195***	0.195***
	(0.010)	(0.008)	(0.009)	(0.011)	(0.009)	(0.009)
year = 1990	0.221***	0.215***	0.215***	0.216***	0.211***	0.211***
	(0.021)	(0.017)	(0.018)	(0.023)	(0.020)	(0.020)
year = 1991	0.222***	0.219***	0.219***	0.217***	0.214***	0.214***
	(0.011)	(0.010)	(0.010)	(0.014)	(0.013)	(0.013)
year = 1992	0.258***	0.252***	0.252***	0.250***	0.244***	0.244***
	(0.018)	(0.016)	(0.016)	(0.019)	(0.016)	(0.016)
year = 1993	0.258***	0.254***	0.254***	0.250***	0.245***	0.246***
	(0.028)	(0.025)	(0.025)	(0.029)	(0.025)	(0.026)
year = 1994	0.283***	0.277***	0.277***	0.275***	0.269***	0.270***
	(0.027)	(0.023)	(0.025)	(0.027)	(0.022)	(0.023)
year = 1995	0.283***	0.280***	0.280***	0.275***	0.272***	0.272***
	(0.032)	(0.028)	(0.030)	(0.033)	(0.028)	(0.029)
year = 1996	0.288***	0.285***	0.285***	0.301***	0.294***	0.296***
	(0.034)	(0.025)	(0.028)	(0.025)	(0.017)	(0.019)
year = 1997	0.324***	0.327***	0.327***	0.336***	0.336***	0.336***
	(0.025)	(0.019)	(0.020)	(0.015)	(0.011)	(0.011)
year = 1998	0.328***	0.324***	0.324***	0.323***	0.316***	0.317***
	(0.032)	(0.025)	(0.026)	(0.030)	(0.023)	(0.024)
year = 1999	0.342***	0.347***	0.347***	0.341***	0.342***	0.342***
	(0.036)	(0.031)	(0.031)	(0.039)	(0.032)	(0.032)
year = 2000	0.367***	0.374***	0.374***	0.353***	0.356***	0.356***
	(0.033)	(0.027)	(0.027)	(0.034)	(0.029)	(0.029)
year = 2001	0.338***	0.343***	0.343***	0.326***	0.325***	0.325***
	(0.032)	(0.027)	(0.027)	(0.034)	(0.028)	(0.028)
year = 2002	0.347***	0.352***	0.352***	0.332***	0.332***	0.332***
	(0.034)	(0.028)	(0.028)	(0.036)	(0.029)	(0.030)
year = 2003	0.369***	0.369***	0.369***	0.353***	0.347***	0.347***
	(0.044)	(0.038)	(0.038)	(0.045)	(0.038)	(0.038)
year = 2004	0.410***	0.413***	0.413***	0.390***	0.385***	0.385***
	(0.044)	(0.038)	(0.038)	(0.045)	(0.039)	(0.040)
year = 2005	0.463***	0.466***	0.467***	0.440***	0.433***	0.434***
	(0.036)	(0.029)	(0.030)	(0.036)	(0.029)	(0.030)
year = 2006	0.492***	0.497***	0.497***	0.469***	0.465***	0.466***
	(0.037)	(0.032)	(0.033)	(0.036)	(0.030)	(0.031)
Num.Obs.	12572	12572	12572	12572	12572	12572
R2	0.768	0.796	0.796	0.778	0.805	0.805
R2 Adj.	0.767	0.795	0.795	0.777	0.804	0.804
AIC	-20796.9	-22373.3	-22376.9	-21361.9	-22953.4	-22948.6
BIC	-20536.5	-21949.3	-21967.8	-21056.9	-22484.8	-22494.8
RMSE	0.11	0.10	0.10	0.10	0.10	0.10
Std.Errors	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr	by: mfr
* p < 0.1, p < 0.05, * p < 0.01

Figure 7

coeftable(cars_tl3, keep = "year") |>
    as_tibble(rownames = "year") |>
    mutate(diff = Estimate - dplyr::lag(Estimate)) |>
    extract(year, "year", "(\\d+)", convert = TRUE) |>
    ggplot(aes(year, diff)) + geom_point() + geom_line() + mytheme + ylim(-0.05, 0.1)

Warning message:
"[1m[22mRemoved 1 rows containing missing values (`geom_point()`)."
Warning message:
"[1m[22mRemoved 1 row containing missing values (`geom_line()`)."

png