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3.RegressionsSD_2023.R
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3.RegressionsSD_2023.R
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##########################################################
######## Description: regressions for SD database ########
## Load libraries used
library(fixest)
library(data.table)
library(ggplot2)
# - Aggregate regressions ------------------------------------
# Load database aggregate data
db <- fread("../../Bases/processed/2023.Aggregate.csv", data.table = F)
db$pop <- db$num.ocup + db$num.unem
db$ur <- db$num.unem / db$pop
# * - Dispersion ----------------------------------------------------------
reg1 = feols((SD.PReal*100) ~ PAve + Time, weights = ~num_strs_comp+1, cluster = "Product^Time", data = db)
summary(reg1)
reg2 = feols((SD.PReal*100) ~ PAve + Time | Product + Month, weights = ~num_strs_comp+1, cluster = "Product^Time", data = db)
summary(reg2)
reg3 = feols((SD.PReal*100) ~ PAve + Time | city2 + Month, weights = ~num_strs_comp+1,cluster = "Product^Time", data = db)
summary(reg3)
reg4 = feols((SD.PReal*100) ~ PAve + Time | Product + city2 + Month, weights = ~num_strs_comp+1, cluster = "Product^Time", data = db)
summary(reg4)
reg5 = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 + Month, weights = ~num_strs_comp+1, cluster = "Product^Time", data = db)
summary(reg5)
setFixest_dict(c("PAve" = "Av. Price",
"SD.PReal*100" = "SD (in %)",
"city2"="Market"))
etable(reg1,reg2,reg3,reg4,reg5,
digits = 4)
# For paper
etable(reg1,reg2,reg3,reg4,reg5,
title = "Convergence Baseline Estimation.",
label = "tab:congergence",
placement = "ht",
fontsize = "small",
digits = 4,
tex = T)
# write(paper1,file = "../../Lyx/regressions/reg1.tex")
# Presentation
pres1 <- etable(reg1,reg2,reg3,reg4,reg5,
placement = "ht",
fontsize = "tiny",
digits = 4,
coef.style = .("\\color{red} :coef_se:" = "Time"),
tex = T)
#write(pres1,file = "../../Presentaciones/texoutput/pres1.tex")
rm(reg1,reg2,reg3,reg4,reg5)
# Above and below median time
db$time_median <- db$Time - median(db$Time)
db$time2_median <- db$time_median * db$time_median
#db$UntilMT <- ifelse(db$Time <median(db$Time),0,1)
reg4b = feols((SD.PReal*100) ~ PAve + Time | Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db[db$Time <= median(db$Time),])
summary(reg4b)
reg4a = feols((SD.PReal*100) ~ PAve + time_median | Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db[db$Time > median(db$Time),])
summary(reg4a)
reg5b = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db[db$Time <= median(db$Time),])
summary(reg5b)
reg5a = feols((SD.PReal*100) ~ PAve + time_median + time2_median| Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db[db$Time > median(db$Time),])
summary(reg5a)
setFixest_dict(c("PAve" = "Av. Price",
"SD.PReal*100" = "SD (in %)",
"city2"="Market",
"time_median" = "Time",
"time2_median"= "Time2"))
etable(reg4b,reg5b,reg4a,reg5a,
digits = 4)
# For paper
# Note: sustituir (1) & (2)
# por \multicolumn{2}{c}{Until Median Time}
# (3) & (4)
# \multicolumn{2}{c}{After Median Time}
etable(reg4b,reg5b,reg4a,reg5a,
title = "Convergence Baseline Estimation. Until and After Median Time",
label = "tab:convergence-split-sample",
placement = "ht",
# group=list("Until Median Time"=c("1", "2")),
digits = 4,
tex = T)
# Presentation
etable(reg4b,reg5b,reg4a,reg5a,
placement = "ht",
fontsize = "tiny",
digits = 4,
group=list("Until Median Time"=c("1", "2")),
coef.style = .("\\color{red} :coef_se:" = "Time"),
tex = T)
rm(reg4b,reg5b,reg4a,reg5a)
# * - Plot coefficients ---------------------------------------------------
dbplot <- as.data.frame(matrix(0,nrow = 190,ncol = 2))
colnames(dbplot) <- c("time","time2")
dbplot$time <- c(1:190)
dbplot$time2 <- dbplot$time*dbplot$time
# Information for figures in the paper
# Columns (4) and (5) of Table 2
f1 <- function(x) 0.0172*x
f2 <- function(x) +.0027*x + 7.08*10^(-5)*x^2
ggplot() +
xlim(0,190) +
ylim(0,4) +
labs(x = "Time", y = "Dispersion (in %)") +
geom_function(fun = f1, aes(color = "Linear Trend")) +
geom_function(fun = f2, aes(color = "Quadratic Trend")) +
geom_hline(yintercept=0) + # add horizontal axes
geom_vline(xintercept=0) + # add vertical axes
labs(color="Trends") +# change name of box
theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# Columns (1) and (2), and (3) and (4) of Table 3
f1 <- function(x) {
(x <= 89) * (0.0095*x) + (x > 89) * (0.0165*(x-89))
}
f2 <- function(x) {
(x <= 89) * (-.0063*x + 0.0001*x^2) + (x > 89) * (-.0493*(x-89) + 0.0008*(x-89)^2)
}
ggplot() +
xlim(0,200) +
ylim(-2,5) +
labs(x = "Time", y = "Dispersion (in %)") +
geom_function(fun = f1, aes(color = "Linear Trend")) +
geom_function(fun = f2, aes(color = "Quadratic Trend")) +
geom_hline(yintercept=0) + # add horizontal axes
geom_vline(xintercept=0) + # add vertical axes
labs(color="Trends") +# change name of box
theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# * - Dispersion: original stores -----------------------------------------
# Load database aggregate data
dbo <- fread("../../Bases/processed/2023.Aggregate.Original.csv", data.table = F)
dbo$pop <- dbo$num.ocup + dbo$num.unem
dbo$ur <- dbo$num.unem / dbo$pop
reg4o = feols((SD.PReal*100) ~ PAve + Time | Product + city2 + Month,
weights = ~num_strs_comp+1, cluster = "Product^Time", data = dbo)
summary(reg4o)
reg5o = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 + Month,
weights = ~num_strs_comp+1, cluster = "Product^Time", data = dbo)
summary(reg5o)
# setFixest_dict(c("PAve" = "Av. Price",
# "SD.PReal*100" = "SD (in %)",
# "city2"="Market"))
etable(reg4o,reg5o,
digits = 4)
# Above and below median time
dbo$time_median <- dbo$Time - median(dbo$Time)
dbo$time2_median <- dbo$time_median * dbo$time_median
reg4bo = feols((SD.PReal*100) ~ PAve + Time | Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = dbo[dbo$Time <= median(dbo$Time),])
summary(reg4bo)
reg4ao = feols((SD.PReal*100) ~ PAve + time_median | Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = dbo[dbo$Time > median(dbo$Time),])
summary(reg4ao)
reg5bo = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = dbo[dbo$Time <= median(dbo$Time),])
summary(reg5bo)
reg5ao = feols((SD.PReal*100) ~ PAve + time_median + time2_median| Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = dbo[dbo$Time > median(dbo$Time),])
summary(reg5ao)
setFixest_dict(c("PAve" = "Av. Price",
"SD.PReal*100" = "SD (in %)",
"city2"="Market"))
etable(reg4bo,reg5bo,reg4ao,reg5ao,
digits = 4)
# For paper
# Note: sustituir (3) & (4)
# por \multicolumn{2}{c}{Until June, 2015}
# (5) & (5)
# \multicolumn{2}{c}{After June, 2015}
etable(reg4o,reg5o,reg4bo,reg5bo,reg4ao,reg5ao,
title = "Convergence Baseline Estimation. Stores in the year 2007.",
label = "tab:original-stores",
placement = "h!",
fontsize = "small",
# group=list("Until Median Time"=c("1", "2")),
digits = 4,
tex = T)
# Presentation
etable(reg4b,reg5b,reg4a,reg5a,
placement = "ht",
fontsize = "tiny",
digits = 4,
group=list("Until Median Time"=c("1", "2")),
coef.style = .("\\color{red} :coef_se:" = "Time"),
tex = T)
rm(reg4o,reg5o,reg4bo,reg5bo,reg4ao,reg5ao)
# * - Plot coefficients ---------------------------------------------------
dbplot <- as.data.frame(matrix(0,nrow = 190,ncol = 2))
colnames(dbplot) <- c("time","time2")
dbplot$time <- c(1:190)
dbplot$time2 <- dbplot$time*dbplot$time
#### Information for figures in the paper
## Columns (1) and (2) of Table 2
f1 <- function(x) 0.012*x
f2 <- function(x) -.0101*x + .0001*x^2
ggplot() +
xlim(0,190) +
ylim(-1,3) +
labs(x = "Time", y = "Dispersion (in %)") +
geom_function(fun = f1, aes(color = "Linear Trend")) +
geom_function(fun = f2, aes(color = "Quadratic Trend")) +
geom_hline(yintercept=0) + # add horizontal axes
geom_vline(xintercept=0) + # add vertical axes
labs(color="Trends") +# change name of box
theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# Columns (3) and (5), and (4) and (6) of Table 4
f1 <- function(x) {
(x <= 89) * (0.0012*x) + (x > 89) * (0.0163*(x-89))
}
f2 <- function(x) {
(x <= 89) * (-.0240*x + 0.0002*x^2) + (x > 89) * (-.0403*(x-89) + 0.0006*(x-89)^2)
}
ggplot() +
xlim(0,200) +
ylim(-1,3) +
labs(x = "Time", y = "Dispersion (in %)") +
geom_function(fun = f1, aes(color = "Linear Trend")) +
geom_function(fun = f2, aes(color = "Quadratic Trend")) +
geom_hline(yintercept=0) + # add horizontal axes
geom_vline(xintercept=0) + # add vertical axes
labs(color="Trends") +# change name of box
theme(axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# * - Explanations --------------------------------------------------------
### Short Run
regExpSR = feols((SD.PReal*100) ~ PAve + Time + CatDisp + num_strs_comp + SD.shProd +
log(pop) + ur + log(income) + sd_income|
Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db)
summary(regExpSR)
regExpSR_bm = feols((SD.PReal*100) ~ PAve + Time + CatDisp + num_strs_comp + SD.shProd +
log(pop) + ur + log(income) + sd_income|
Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db[db$Time <= median(db$Time),])
summary(regExpSR_bm)
regExpSR_am = feols((SD.PReal*100) ~ PAve + time_median + CatDisp + num_strs_comp + SD.shProd +
log(pop) + ur + log(income) + sd_income|
Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db[db$Time > median(db$Time),])
summary(regExpSR_am)
setFixest_dict(c("PAve" = "Av. Price",
"SD.PReal*100" = "SD (in %)",
"city2"="Market",
"CatDisp" = "Cat. Entropy",
"num_strs_comp" = "Num. Comp. Stores",
"SD.shProd" = "SD Sh. Prod.",
"log(pop)" = "Log Pop.",
"ur" = "Unemp. Rate",
"log(income)" = "Log Income",
"sd_income" = "SD Income",
"time_median" = "Time"))
etable(regExpSR,regExpSR_bm,regExpSR_am,
digits = 4)
# Paper: short run
etable(regExpSR,regExpSR_bm,regExpSR_am,
title = "Sources of Price Convergence: Short-run.",
label = "tab:source-sr",
# fontsize = "small",
placement = "!h",
digits = 4,
tex = T)
# # Presentation: short run
# pres2 <- etable(reg4catcompprod,
# placement = "ht",
# fontsize = "tiny",
# digits = 4,
# # coef.style = .("\\color{red} :coef_se:" = "Time"),
# tex = T)
# write(pres2,file = "../../Presentaciones/texoutput/pres2.tex")
rm(regExpSR,regExpSR_bm,regExpSR_am)
#### Long Run
regExpLR = feols((SD.PReal*100) ~ PAve + Time * (CatDisp + num_strs_comp + SD.shProd +
log(pop) + ur + log(income) + sd_income)| Product + city2 + Month,
weights = ~num_strs_comp+1, cluster = "Product^Time", data = db)
summary(regExpLR)
# Paper: long run (only trends)
etable(regExpLR,
title = "Sources of Price Convergence: Long-run effects.",
label = "tab:source-lr",
placement = "ht",
fontsize = "small",
digits = 4,
drop = "!Time",
# highlight = .("rowcol" = ("Time:CatDisp")),#"Time x Number Comp. Stores","Time x SD Sh. Prod.")),
tex = T)
# Presentation: long run
# pres31 <- etable(reg3catcompshprodint,
# placement = "ht",
# fontsize = "tiny",
# digits = 4,
# drop = "Time",
# # highlight = .("rowcol" = ("Time:CatDisp")),#"Time x Number Comp. Stores","Time x SD Sh. Prod.")),
# tex = T)
# write(pres31,file = "../../Presentaciones/texoutput/pres31.tex")
#
# pres32 <- etable(reg3catcompshprodint,
# placement = "ht",
# fontsize = "tiny",
# digits = 4,
# drop = "!Time",
# # highlight = .("rowcol" = ("Time:CatDisp")),#"Time x Number Comp. Stores","Time x SD Sh. Prod.")),
# tex = T)
# write(pres32,file = "../../Presentaciones/texoutput/pres32.tex")
rm(regExpLR)
rm(dbo)
# ---------------- Chains ------------------------------------------
# Load database between chains
db_bch <- fread("../../Bases/processed/2023.Between.chains.csv", data.table = F)
db_bch$pop <- db_bch$num.ocup + db_bch$num.unem
db_bch$ur <- db_bch$num.unem / db_bch$pop
db_bch$time_median <- db_bch$Time - median(db_bch$Time)
db_bch$time2_median <- db_bch$time_median * db_bch$time_median
# Load database within chains
db_wch <- fread("../../Bases/processed/2023.Within.chains.csv", data.table = F)
db_wch$pop <- db_wch$num.ocup + db_wch$num.unem
db_wch$ur <- db_wch$num.unem / db_wch$pop
db_wch <- db_wch[db_wch$is.chain == "Chains",] # pick only chains, exclude independent stores
db_wch$time_median <- db_wch$Time - median(db_wch$Time)
db_wch$time2_median <- db_wch$time_median * db_wch$time_median
# * - Dispersion: within ----------------------------------------------------------
reg1w = feols((SD.PReal*100) ~ PAve + Time | Product + city2 ^ chain + Month,
weights = ~num_stores_chain, cluster = "Product^Time", data = db_wch)
summary(reg1w)
reg2w = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 ^ chain + Month,
weights = ~num_stores_chain, cluster = "Product^Time", data = db_wch)
summary(reg2w)
# setFixest_dict(c("PAve" = "Av. Price",
# "SD.PReal*100" = "SD (in %)",
# "city2"="Market"))
etable(reg1w,reg2w,
digits = 4)
# Above and below median time
reg3wb = feols((SD.PReal*100) ~ PAve + Time | Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch[db_wch$Time <= median(db_wch$Time),])
summary(reg3wb)
reg3wa = feols((SD.PReal*100) ~ PAve + time_median | Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch[db_wch$Time > median(db_wch$Time),])
summary(reg3wa)
reg4wb = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch[db_wch$Time <= median(db_wch$Time),])
summary(reg4wb)
reg4wa = feols((SD.PReal*100) ~ PAve + time_median + time2_median| Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch[db_wch$Time > median(db_wch$Time),])
summary(reg4wa)
# setFixest_dict(c("PAve" = "Av. Price",
# "SD.PReal*100" = "SD (in %)",
# "city2"="Market"))
etable(reg3wb,reg4wb,reg3wa,reg4wa,
digits = 4)
# * - Dispersion: between ----------------------------------------------------------
reg1b = feols((SD.PReal*100) ~ PAve + Time | Product + city2 + Month,
weights = ~num_strs_comp+1, cluster = "Product^Time", data = db_bch)
summary(reg1b)
reg2b = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 + Month,
weights = ~num_strs_comp+1, cluster = "Product^Time", data = db_bch)
summary(reg2b)
# setFixest_dict(c("PAve" = "Av. Price",
# "SD.PReal*100" = "SD (in %)",
# "city2"="Market"))
etable(reg1b,reg2b,
digits = 4)
# Above and below median time
reg3bb = feols((SD.PReal*100) ~ PAve + Time | Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch[db_bch$Time <= median(db_bch$Time),])
summary(reg3bb)
reg3ba = feols((SD.PReal*100) ~ PAve + time_median | Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch[db_bch$Time > median(db_bch$Time),])
summary(reg3ba)
reg4bb = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch[db_bch$Time < median(db_bch$Time),])
summary(reg4bb)
reg4ba = feols((SD.PReal*100) ~ PAve + Time + Time2| Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch[db_bch$Time > median(db_bch$Time),])
summary(reg4ba)
# setFixest_dict(c("PAve" = "Av. Price",
# "SD.PReal*100" = "SD (in %)",
# "city2"="Market"))
etable(reg3bb,reg4bb,reg3ba,reg4ba,
digits = 4)
########## Results tables
setFixest_dict(c("PAve" = "Av. Price",
"SD.PReal*100" = "SD (in %)",
"city2"="Market",
"CatDisp" = "Cat. Entropy",
"num_strs_comp" = "Num. Comp. Stores",
"num_stores_chain" = "Num. Chain Own Stores",
"SD.shProd" = "SD Sh. Prod.",
"log(pop)" = "Log Pop.",
"ur" = "Unemp. Rate",
"log(income)" = "Log Income",
"sd_income" = "SD Income"))
# For paper: table in paper
etable(reg1w,reg2w,reg1b,reg2b,
title = "Price Convergence: Within and Between Chains.",
label = "tab:convergence-chains",
fontsize = "small",
placement = "!h",
digits = 4,
tex = T)
# For paper: appendix table
etable(reg3wb,reg4wb,reg3wa,reg4wa,reg3bb,reg4bb,reg3ba,reg4ba,
title = "Price Convergence: Within and Between Chains, Until and After Median Period.",
label = "tab:appendix-convergence-chains",
fontsize = "footnotesize",
placement = "!htbp",
digits = 4,
tex = T)
# For presentation
# pres4 <- etable(reg1,reg2,reg3,reg4,#reg5,
# # title = "Convergence Estimation by Chains.",
# # label = "chain-base-estimation",
# fontsize = "tiny",
# placement = "!h",
# coef.style = .("\\color{red} :coef_se:" = "Time"),
# digits = 4,
# tex = T)
# write(pres4,file = "../../Presentaciones/texoutput/pres4.tex")
rm(reg1w,reg2w,reg1b,reg2b,reg3wb,reg4wb,reg3wa,reg4wa,reg3bb,reg4bb,reg3ba,reg4ba)
# * - Explanations: WCh --------------------------------------------------------
### Short Run
regExpWSR = feols((SD.PReal*100) ~ PAve + Time + CatDisp + num_strs_comp + num_stores_chain
+ SD.shProd + log(pop) + ur + log(income) + sd_income|
Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch)
summary(regExpWSR)
regExpWSR_bm = feols((SD.PReal*100) ~ PAve + Time + CatDisp + num_strs_comp + num_stores_chain
+ SD.shProd + log(pop) + ur + log(income) + sd_income|
Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch[db_wch$Time <= median(db_wch$Time),])
summary(regExpWSR_bm)
regExpWSR_am = feols((SD.PReal*100) ~ PAve + time_median + CatDisp + num_strs_comp + num_stores_chain
+ SD.shProd + log(pop) + ur + log(income) + sd_income|
Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch[db_wch$Time > median(db_wch$Time),])
summary(regExpWSR_am)
etable(regExpWSR,regExpWSR_bm,regExpWSR_am,
digits = 4)
### Long Run
regExpWLR = feols((SD.PReal*100) ~ PAve + Time * (CatDisp + num_strs_comp + num_stores_chain
+ SD.shProd + log(pop) + ur + log(income) + sd_income)|
Product + city2 ^ chain + Month, weights = ~num_stores_chain,
cluster = "Product^Time", data = db_wch)
summary(regExpWLR)
# * - Explanations: BCh --------------------------------------------------------
### Short Run
regExpBSR = feols((SD.PReal*100) ~ PAve + Time + CatDisp + num_strs_comp
+ SD.shProd + log(pop) + ur + log(income) + sd_income|
Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch)
summary(regExpBSR)
regExpBSR_bm = feols((SD.PReal*100) ~ PAve + Time + CatDisp + num_strs_comp
+ SD.shProd + log(pop) + ur + log(income) + sd_income|
Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch[db_bch$Time <= median(db_bch$Time),])
summary(regExpBSR_bm)
regExpBSR_am = feols((SD.PReal*100) ~ PAve + time_median + CatDisp + num_strs_comp
+ SD.shProd + log(pop) + ur + log(income) + sd_income|
Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch[db_bch$Time > median(db_bch$Time),])
summary(regExpBSR_am)
etable(regExpBSR,regExpBSR_bm,regExpBSR_am,
digits = 4)
### Long Run
regExpBLR = feols((SD.PReal*100) ~ PAve + Time * (CatDisp + num_strs_comp
+ SD.shProd + log(pop) + ur + log(income) + sd_income)|
Product + city2 + Month, weights = ~num_strs_comp+1,
cluster = "Product^Time", data = db_bch)
summary(regExpBLR)
# ** - Tables --------------------------------------------------------
########## Short run
# For paper: table in paper
etable(regExpWSR,regExpBSR,
title = "Sources of Price Convergence: Short-run. Within and Between Chains.",
label = "tab:sources-chains-sr",
fontsize = "small",
placement = "!h",
digits = 4,
tex = T)
# For paper: appendix table
etable(regExpWSR_bm,regExpWSR_am,regExpBSR_bm,regExpBSR_am,
title = "Sources of Price Convergence: Short-run. Within and Between Chains, Until and After Median Period.",
label = "tab:appendix-sources-chains-sr",
fontsize = "small",
placement = "!htbp",
digits = 4,
tex = T)
########## Long run
# For paper: table in paper
etable(regExpWLR,regExpBLR,
title = "Sources of Price Convergence: Long-run. Within and Between Chains.",
label = "tab:sources-chains-lr",
fontsize = "small",
placement = "!h",
drop = "!Time",
digits = 4,
tex = T)
rm(egExpWSR_bm,regExpWSR_am,regExpBSR_bm,regExpBSR_am,regExpWLR,regExpBLR)
rm(regExpBSR,regExpBSR_bm,regExpBSR_am)
## End of script ------------