Update: Definin Markets- Determine Distance Limit and No. of the nearestest neighbors sdfsss
Jung Hwan Koh
9/21/2019
Summary
Load data
Use hou14 for the following analysis:
- Determine the distance limit in the distance band approach
- Determine the number of the nearest neighbors in the kth nearest neighbor approach.
load(file="df_hou4.Rdata")
hou14 <- df_hou4
save(hou14, file="hou14.Rdata")
load(file="hou14.Rdata")Create data frame for low and high quality hotels
hou14$s_rating <- hou14$s_rating/10
# sort data by market id variables (qtr)
hou14 <- hou14[order(hou14$qtr),]
# If s_rating >= 3.5, hotels are considered as high quality
# If s_rating < 3.5, hotels are considered as low quality
hou14.h <- hou14[hou14$s_rating>3, ]
hou14.l <- hou14[hou14$s_rating<3.5, ]1. Analysis with all hotels in Houston, TX
- Define reasonable markets for each hotels: 1) Distance band approach and 2) the kth nearest neighbor approach.
Source R functions
source(file="price_competition_functions.R")
source(file="MMC_functions1.R")
source(file="avmmc.new.R")Determine the distance limit and the number of the nearest neighbors
# All hotels in Houston, TX
dist.mat.list.all <- list()
for(i in 1:4){
temp.data <- hou14[hou14$qtr==i, c("lat", "lon")]
temp.dist <- GeoDistMat(temp.data)
dist.mat.list.all[[i]] <- temp.dist
}## Loading required package: Imapwp.temp <- get.wp(mkt.id.fld ="qtr", id.brand.fld ="id.brand", id.chain.fld="id.chain",
price.fld ="adr", q.fl="room.sold", rating.fld="s_rating",
dist.limit = 20, dat=hou14, dis.mat = dist.mat.list.all , n.rival=5)## market id 1 completed
## market id 2 completed
## market id 3 completed
## market id 4 completedwp.temp2 <- lapply(wp.temp, as.data.frame)
wp.all <- do.call(rbind.fill, wp.temp2)
hou14.wp <- cbind(hou14, wp.all)
# OLS regression with weighted matrix function (hotel within 20 miles from the focal hotel)
ols.hou.all <- lm(adr ~ wp.d + s_rating + hi.sales + room + cbd + air + factor(chain),
data= hou14.wp)
summary(ols.hou.all)##
## Call:
## lm(formula = adr ~ wp.d + s_rating + hi.sales + room + cbd +
## air + factor(chain), data = hou14.wp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.730 -16.069 -1.153 13.458 161.832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -22.612746 3.617521 -6.251 5.32e-10 ***
## wp.d 0.061512 0.011371 5.410 7.35e-08 ***
## s_rating 40.334736 1.348540 29.910 < 2e-16 ***
## hi.sales -60.045650 13.018845 -4.612 4.33e-06 ***
## room -0.001327 0.007773 -0.171 0.864516
## cbd 18.785077 3.734819 5.030 5.51e-07 ***
## air -17.266029 3.530002 -4.891 1.11e-06 ***
## factor(chain)1 39.506478 2.523429 15.656 < 2e-16 ***
## factor(chain)2 43.807732 3.321344 13.190 < 2e-16 ***
## factor(chain)3 25.799594 2.759086 9.351 < 2e-16 ***
## factor(chain)4 -3.804409 3.137594 -1.213 0.225504
## factor(chain)5 9.226447 2.839177 3.250 0.001181 **
## factor(chain)7 16.775244 6.429069 2.609 0.009164 **
## factor(chain)11 -4.289352 4.803931 -0.893 0.372064
## factor(chain)12 20.314653 4.892639 4.152 3.48e-05 ***
## factor(chain)13 -10.228061 9.601733 -1.065 0.286944
## factor(chain)14 -24.809914 9.623609 -2.578 0.010032 *
## factor(chain)15 2.225962 8.000029 0.278 0.780863
## factor(chain)16 37.778298 4.678231 8.075 1.38e-15 ***
## factor(chain)17 -9.578927 3.885334 -2.465 0.013798 *
## factor(chain)18 159.058680 13.655595 11.648 < 2e-16 ***
## factor(chain)19 204.099757 27.186823 7.507 1.03e-13 ***
## factor(chain)22 -25.356534 4.961507 -5.111 3.63e-07 ***
## factor(chain)23 20.260527 3.801172 5.330 1.13e-07 ***
## factor(chain)24 -7.708359 4.741516 -1.626 0.104222
## factor(chain)26 15.985344 9.995926 1.599 0.109991
## factor(chain)27 -6.963797 6.168733 -1.129 0.259128
## factor(chain)28 -0.731744 13.798716 -0.053 0.957715
## factor(chain)29 -6.461710 9.591100 -0.674 0.500594
## factor(chain)30 -29.260780 7.872338 -3.717 0.000209 ***
## factor(chain)40 25.203952 3.251210 7.752 1.66e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.78 on 1490 degrees of freedom
## Multiple R-squared: 0.7509, Adjusted R-squared: 0.7458
## F-statistic: 149.7 on 30 and 1490 DF, p-value: < 2.2e-16gamma.a <- ols.hou.all$coefficients["wp.d"]
gamma.a.lw <- confint(ols.hou.all, "wp.d")[1]
gamma.a.up <- confint(ols.hou.all, "wp.d")[2]
distance <- as.matrix(seq(0, 20, by=0.1))
price.effect.a <- data.frame()
price.effect.a <- as.data.frame(cbind(distance, gamma.a/distance, gamma.a.lw/distance, gamma.a.up/distance))
price.effect.a <- price.effect.a[-1,]
names(price.effect.a) <- c("distance", "mean", "lower", "upper")
ggplot(price.effect.a, aes(x=distance, y=mean)) + geom_line(aes(color="Price Effect"), size=1) +
geom_ribbon(aes(ymin=lower, ymax=upper, x=distance, fill="CI Interval"), alpha=0.3 ) +
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12") +
xlim(0,3) +
labs(x="Distance (Miles)", y="Price Effect = b") +
theme_grey(base_size = 18)
ggplot(price.effect.a, aes(x=distance, y=mean)) + geom_line(aes(color="Price Effect"), size=1) +
geom_ribbon(aes(ymin=lower, ymax=upper, x=distance, fill="CI Interval"), alpha=0.3 ) +
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12") +
xlim(0,10) +
labs(x="Distance (Miles)", y="Price Effect = b") +
theme_grey(base_size = 18)
# High Quality Hotels
dim(hou14.h)## [1] 253 155## Create Distance Matrix
dist.mat.high.list <- list()
for(i in 1:4){
temp.data <- hou14.h[hou14.h$qtr==i, ]
temp.dist <- GeoDistMat(temp.data)
dist.mat.high.list[[i]] <- temp.dist
}
wp.temp.h <- get.wp(mkt.id.fld ="qtr", id.brand.fld ="id.brand", id.chain.fld="id.chain",
price.fld ="adr", q.fl="room.sold", rating.fld="s_rating",
dist.limit = 20, dat=hou14.h, dis.mat = dist.mat.high.list , n.rival=5)## market id 1 completed
## market id 2 completed
## market id 3 completed
## market id 4 completedwp.temp2 <- lapply(wp.temp.h, as.data.frame)
wp.high <- do.call(rbind.fill, wp.temp2)
hou14.h.wp <- cbind(hou14.h, wp.high)
# OLS regression with weighted matrix function (hotel within 20 miles from the focal hotel)
ols.hou.high <- lm(adr ~ wp.d + s_rating + room + hi.sales + cbd + air + factor(chain),
data= hou14.h.wp)
summary(ols.hou.high)##
## Call:
## lm(formula = adr ~ wp.d + s_rating + room + hi.sales + cbd +
## air + factor(chain), data = hou14.h.wp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -89.315 -19.192 1.704 21.292 74.028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.407e+02 3.164e+01 -4.446 1.35e-05 ***
## wp.d -9.257e-03 3.182e-02 -0.291 0.771347
## s_rating 8.294e+01 7.477e+00 11.092 < 2e-16 ***
## room -2.838e-02 1.339e-02 -2.120 0.035029 *
## hi.sales 5.693e+01 6.039e+01 0.943 0.346830
## cbd 1.148e+01 7.614e+00 1.507 0.133037
## air -4.310e+01 1.773e+01 -2.431 0.015832 *
## factor(chain)1 -1.784e+01 8.173e+00 -2.183 0.030039 *
## factor(chain)2 1.485e+00 7.903e+00 0.188 0.851149
## factor(chain)3 -3.264e+01 9.368e+00 -3.484 0.000591 ***
## factor(chain)4 -4.928e+01 1.348e+01 -3.657 0.000315 ***
## factor(chain)7 -3.479e+01 1.412e+01 -2.464 0.014481 *
## factor(chain)14 -9.373e+01 1.824e+01 -5.139 5.85e-07 ***
## factor(chain)16 -1.059e+01 9.798e+00 -1.081 0.280943
## factor(chain)17 -1.274e+02 1.795e+01 -7.099 1.51e-11 ***
## factor(chain)18 1.283e+02 1.841e+01 6.972 3.20e-11 ***
## factor(chain)19 1.170e+02 3.526e+01 3.319 0.001049 **
## factor(chain)26 -3.826e+01 1.371e+01 -2.791 0.005686 **
## factor(chain)28 -4.047e+01 1.824e+01 -2.219 0.027479 *
## factor(chain)40 4.158e+00 9.626e+00 0.432 0.666156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33.79 on 233 degrees of freedom
## Multiple R-squared: 0.6609, Adjusted R-squared: 0.6333
## F-statistic: 23.9 on 19 and 233 DF, p-value: < 2.2e-16gamma.a <- ols.hou.high$coefficients["wp.d"]
gamma.a.lw <- confint(ols.hou.high, "wp.d")[1]
gamma.a.up <- confint(ols.hou.high, "wp.d")[2]
distance <- as.matrix(seq(0, 20, by=0.1))
price.effect.a <- data.frame()
price.effect.a <- as.data.frame(cbind(distance, gamma.a/distance, gamma.a.lw/distance, gamma.a.up/distance))
price.effect.a <- price.effect.a[-1,]
names(price.effect.a) <- c("distance", "mean", "lower", "upper")
ggplot(price.effect.a, aes(x=distance, y=mean)) + geom_line(aes(color="Price Effect"), size=1) +
geom_ribbon(aes(ymin=lower, ymax=upper, x=distance, fill="CI Interval"), alpha=0.3 ) +
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12") +
xlim(0,3) +
labs(x="Distance (Miles)", y="Price Effect = b") +
theme_grey(base_size = 18)## Warning: Removed 170 rows containing missing values (geom_path).
ggplot(price.effect.a, aes(x=distance, y=mean)) + geom_line(aes(color="Price Effect"), size=1) +
geom_ribbon(aes(ymin=lower, ymax=upper, x=distance, fill="CI Interval"), alpha=0.3 ) +
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12") +
xlim(0,10) +
labs(x="Distance (Miles)", y="Price Effect = b") +
theme_grey(base_size = 18)## Warning: Removed 100 rows containing missing values (geom_path).
# Find distance limit for low-quality hotels
# Low Quality Hotel for sdfd
dim(hou14.l)## [1] 1268 155## Create Distance Matrix
dist.mat.low.list <- list()
for(i in 1:4){
temp.data <- hou14.l[hou14.l$qtr==i, c("lat", "lon") ]
temp.dist <- GeoDistMat(temp.data)
dist.mat.low.list[[i]] <- temp.dist
}
wp.temp.l <- get.wp(mkt.id.fld ="qtr", id.brand.fld ="id.brand", id.chain.fld="id.chain",
price.fld ="adr", q.fl="room.sold", rating.fld="s_rating",
dist.limit = 20, dat=hou14.l, dis.mat = dist.mat.low.list , n.rival=5)## market id 1 completed
## market id 2 completed
## market id 3 completed
## market id 4 completedwp.temp2 <- lapply(wp.temp.l, as.data.frame)
wp.low <- do.call(rbind.fill, wp.temp2)
hou14.l.wp <- cbind(hou14.l, wp.low)
# OLS regression with weighted matrix function (hotel within 20 miles from the focal hotel)
ols.hou.low <- lm(adr ~ wp.d + s_rating + room + hi.sales + cbd + air + factor(chain),
data= hou14.l.wp)
summary(ols.hou.low)##
## Call:
## lm(formula = adr ~ wp.d + s_rating + room + hi.sales + cbd +
## air + factor(chain), data = hou14.l.wp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.261 -10.824 -1.127 9.359 63.161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.648201 3.024099 10.796 < 2e-16 ***
## wp.d 0.021554 0.005775 3.732 0.000198 ***
## s_rating 9.656766 1.305493 7.397 2.56e-13 ***
## room -0.070768 0.012839 -5.512 4.32e-08 ***
## hi.sales -28.756608 8.638386 -3.329 0.000897 ***
## cbd 36.267682 3.756057 9.656 < 2e-16 ***
## air -11.514882 2.299656 -5.007 6.32e-07 ***
## factor(chain)1 80.020627 2.120504 37.737 < 2e-16 ***
## factor(chain)2 95.432251 3.103585 30.749 < 2e-16 ***
## factor(chain)3 65.119268 2.217987 29.360 < 2e-16 ***
## factor(chain)4 13.756830 2.110204 6.519 1.03e-10 ***
## factor(chain)5 30.546504 1.892087 16.144 < 2e-16 ***
## factor(chain)7 59.003923 5.281202 11.172 < 2e-16 ***
## factor(chain)11 5.123167 3.033882 1.689 0.091538 .
## factor(chain)12 46.229191 3.173130 14.569 < 2e-16 ***
## factor(chain)13 2.198423 6.138281 0.358 0.720292
## factor(chain)14 34.458273 8.505356 4.051 5.41e-05 ***
## factor(chain)15 45.444903 5.254176 8.649 < 2e-16 ***
## factor(chain)16 82.804755 4.642674 17.836 < 2e-16 ***
## factor(chain)17 22.563913 2.729696 8.266 3.52e-16 ***
## factor(chain)22 -0.767523 3.325875 -0.231 0.817529
## factor(chain)23 48.172860 2.583622 18.645 < 2e-16 ***
## factor(chain)24 4.990006 3.149757 1.584 0.113391
## factor(chain)27 11.619069 3.983740 2.917 0.003602 **
## factor(chain)29 5.752112 6.103177 0.942 0.346131
## factor(chain)30 -1.563541 5.066021 -0.309 0.757653
## factor(chain)40 9.771918 2.348895 4.160 3.40e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.81 on 1241 degrees of freedom
## Multiple R-squared: 0.8208, Adjusted R-squared: 0.817
## F-statistic: 218.6 on 26 and 1241 DF, p-value: < 2.2e-16gamma.a <- ols.hou.low$coefficients["wp.d"]
gamma.a.lw <- confint(ols.hou.low, "wp.d")[1]
gamma.a.up <- confint(ols.hou.low, "wp.d")[2]
distance <- as.matrix(seq(0, 20, by=0.1))
price.effect.a <- data.frame()
price.effect.a <- as.data.frame(cbind(distance, gamma.a/distance, gamma.a.lw/distance, gamma.a.up/distance))
price.effect.a <- price.effect.a[-1,]
names(price.effect.a) <- c("distance", "mean", "lower", "upper")
ggplot(price.effect.a, aes(x=distance, y=mean)) + geom_line(aes(color="Price Effect"), size=1) +
geom_ribbon(aes(ymin=lower, ymax=upper, x=distance, fill="CI Interval"), alpha=0.3 ) +
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12") +
xlim(0,3) +
labs(x="Distance (Miles)", y="Price Effect = b") +
theme_grey(base_size = 18)## Warning: Removed 170 rows containing missing values (geom_path).
ggplot(price.effect.a, aes(x=distance, y=mean)) + geom_line(aes(color="Price Effect"), size=1) +
geom_ribbon(aes(ymin=lower, ymax=upper, x=distance, fill="CI Interval"), alpha=0.3 ) +
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12") +
xlim(0,10) +
labs(x="Distance (Miles)", y="Price Effect = b") +
theme_grey(base_size = 18)## Warning: Removed 100 rows containing missing values (geom_path).
# loop for check the change in ramma withs the increase of number of rivals
ols.r.all <- list()
ols.r.big <- list()
ols.r.small <- list()
gamma.mat <- matrix(NA, 20, 3)
for (i in 1:20){
wp.fld <- names(hou14.wp)[i+159]
ols.eq <- paste("adr ~ ", wp.fld, " + s_rating + room + hi.sales + cbd + air + factor(chain)" )
est.all <- lm(ols.eq, data=hou14.wp, na.action = na.exclude)
est.big <- lm(ols.eq, data=hou14.h.wp, na.action = na.exclude)
est.small <- lm(ols.eq, data=hou14.l.wp, na.action = na.exclude)
ols.r.all[[i]] <- est.all
ols.r.big[[i]] <- est.big
ols.r.small[[i]] <- est.small
gamma.mat[i,1] <- est.all$coefficients[wp.fld]
gamma.mat[i,2] <- est.big$coefficients[wp.fld]
gamma.mat[i,3] <- est.small$coefficients[wp.fld]
}
gamma.mat <- as.data.frame(gamma.mat)
colnames(gamma.mat) <- c("all", "high", "low")
n.rival <- c(1:20)
gamma.mat <- cbind(n.rival, gamma.mat)
gamma.mat <- as.data.frame(gamma.mat)
ggplot(gamma.mat) + geom_line(aes(x=n.rival, y=all, color="All"), size=1) +
geom_line(aes(x=n.rival, y=high, color="High"), size=1) +
geom_line(aes(x=n.rival, y=low, color="Low"), size=1) +
scale_colour_manual("",values= c("black", "red", "blue" ))+
xlab("No. of Rivals") + ylab("Gamma")
Reduced Form Analysis
Use the following estimates to define the markets under the two market definition approach * item Distance Limit: 2.5 and 5 miles * item Number of Rivals: 5 and 10 rivals
# data set: hou14, hou14.h, hou14.l
# all hotels (hou14, dist.mat.list.all)
mmc.all1<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
id.chain.fld="id.chain",price.fld ="adr",
q.fld = "room.sold",
revpar.fld ="revpar", rating.fld = "s_rating",
room.fld = "room", lat.fld="lat", lon.fld="lon",
dist.limit = 2.5, dist.mat=dist.mat.list.all,
data =hou14)## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 1## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 2## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 3## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 4mmc.all2<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
id.chain.fld="id.chain",price.fld ="adr",
q.fld = "room.sold",
revpar.fld ="revpar", rating.fld = "s_rating",
room.fld = "room", lat.fld="lat", lon.fld="lon",
dist.limit = 5, dist.mat=dist.mat.list.all, data =hou14)## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 1## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 2## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 3## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 4hou14.all1 <- hou14
for(i in names(mmc.all1)){
hou14.all1[, i] <- unlist(mmc.all1[[i]])
}
hou14.all2 <- hou14
for(i in names(mmc.all2)){
hou14.all2[, i] <- unlist(mmc.all2[[i]])
}
# regression
y.var <- c("adr")
inst.var <- c("rival.dist", "sum.rating", "n.same.brand", "n.same.chain", "n.brand.city", "n.chain.city")
# model 1
x.var <- c("avmmc", "hi.sales","s_rating", "cbd","air", "factor(chain)")
endog.var <- c("avmmc")
est.all1 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.all1[hou14.all1$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")## Loading required package: AER## Loading required package: car## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## Loading required package: lmtest## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Loading required package: survival##
## =======================================================================
## Dependent variable:
## ---------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## -----------------------------------------------------------------------
## avmmc 0.159*** 0.138***
## (0.037) (0.051)
##
## hi.sales -46.335*** -44.654***
## (12.932) (13.223)
##
## s_rating 33.431*** 33.340***
## (1.437) (1.445)
##
## cbd 31.493*** 31.234***
## (3.423) (3.450)
##
## air -17.537*** -17.432***
## (3.358) (3.363)
##
## factor(chain)2 7.811*** 7.614***
## (2.821) (2.840)
##
## factor(chain)3 -14.227*** -14.404***
## (2.561) (2.578)
##
## factor(chain)4 -43.256*** -43.924***
## (3.222) (3.403)
##
## factor(chain)5 -31.851*** -32.300***
## (2.875) (2.968)
##
## factor(chain)7 -16.091*** -16.947***
## (5.620) (5.792)
##
## factor(chain)11 -49.410*** -49.728***
## (4.495) (4.525)
##
## factor(chain)12 -24.076*** -23.952***
## (4.369) (4.375)
##
## factor(chain)13 -51.182*** -52.286***
## (8.620) (8.809)
##
## factor(chain)14 -55.142*** -56.174***
## (8.482) (8.649)
##
## factor(chain)15 -32.232*** -32.783***
## (6.978) (7.037)
##
## factor(chain)16 3.833 3.163
## (4.170) (4.312)
##
## factor(chain)17 -49.801*** -50.112***
## (3.571) (3.608)
##
## factor(chain)18 130.201*** 129.175***
## (11.770) (11.891)
##
## factor(chain)19 173.826*** 173.320***
## (23.327) (23.345)
##
## factor(chain)22 -65.300*** -66.064***
## (4.682) (4.847)
##
## factor(chain)23 -26.982*** -26.474***
## (3.588) (3.683)
##
## factor(chain)24 -51.270*** -51.800***
## (4.489) (4.572)
##
## factor(chain)26 -15.995* -16.473*
## (8.544) (8.581)
##
## factor(chain)27 -49.137*** -49.626***
## (5.573) (5.631)
##
## factor(chain)28 -24.454** -25.304**
## (11.813) (11.896)
##
## factor(chain)29 -47.486*** -48.474***
## (8.574) (8.726)
##
## factor(chain)30 -67.055*** -67.884***
## (7.020) (7.151)
##
## factor(chain)40 -15.997*** -16.669***
## (3.433) (3.605)
##
## Constant 30.298*** 31.443***
## (5.253) (5.578)
##
## -----------------------------------------------------------------------
## Observations 1,141 1,141
## R2 0.788 0.788
## Adjusted R2 0.783 0.783
## Residual Std. Error (df = 1112) 22.982 22.986
## F Statistic 147.963*** (df = 28; 1112)
## =======================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01est.all2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.all2[hou14.all2$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## =======================================================================
## Dependent variable:
## ---------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## -----------------------------------------------------------------------
## avmmc 0.090*** 0.063**
## (0.023) (0.029)
##
## hi.sales -166.721*** -154.404***
## (23.271) (24.590)
##
## s_rating 32.483*** 32.295***
## (1.430) (1.436)
##
## cbd 29.964*** 29.474***
## (3.325) (3.342)
##
## air -14.453*** -13.987***
## (3.362) (3.377)
##
## factor(chain)2 7.201*** 6.952**
## (2.773) (2.779)
##
## factor(chain)3 -16.944*** -16.912***
## (2.523) (2.525)
##
## factor(chain)4 -43.761*** -44.807***
## (3.103) (3.177)
##
## factor(chain)5 -33.069*** -33.870***
## (2.821) (2.869)
##
## factor(chain)7 -18.327*** -19.711***
## (5.485) (5.560)
##
## factor(chain)11 -47.148*** -48.257***
## (4.511) (4.570)
##
## factor(chain)12 -24.716*** -24.526***
## (4.320) (4.324)
##
## factor(chain)13 -54.834*** -56.824***
## (8.477) (8.578)
##
## factor(chain)14 -56.186*** -58.202***
## (8.371) (8.476)
##
## factor(chain)15 -33.824*** -34.671***
## (6.865) (6.891)
##
## factor(chain)16 0.950 -0.181
## (4.035) (4.102)
##
## factor(chain)17 -51.597*** -52.336***
## (3.563) (3.596)
##
## factor(chain)18 128.882*** 126.933***
## (11.618) (11.693)
##
## factor(chain)19 173.932*** 173.124***
## (23.056) (23.077)
##
## factor(chain)22 -68.422*** -69.509***
## (4.490) (4.547)
##
## factor(chain)23 -26.637*** -25.947***
## (3.492) (3.523)
##
## factor(chain)24 -52.475*** -53.598***
## (4.448) (4.509)
##
## factor(chain)26 -16.074* -16.991**
## (8.443) (8.469)
##
## factor(chain)27 -49.722*** -50.581***
## (5.489) (5.520)
##
## factor(chain)28 -27.099** -28.552**
## (11.644) (11.689)
##
## factor(chain)29 -52.787*** -54.124***
## (8.391) (8.440)
##
## factor(chain)30 -70.013*** -71.056***
## (6.847) (6.884)
##
## factor(chain)40 -17.387*** -18.664***
## (3.348) (3.449)
##
## Constant 38.936*** 40.843***
## (5.102) (5.249)
##
## -----------------------------------------------------------------------
## Observations 1,141 1,141
## R2 0.793 0.793
## Adjusted R2 0.788 0.788
## Residual Std. Error (df = 1112) 22.722 22.736
## F Statistic 152.287*** (df = 28; 1112)
## =======================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01avmmc.all.knn1 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
id.chain.fld ="id.chain",price.fld ="adr",
ocu.fld ="ocu",
room.fld ="room", rating.fld ="s_rating",
lat.fld="lat", lon.fld="lon", knn =5,
data = hou14)## Loading required package: dbscanavmmc.all.knn2 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
id.chain.fld ="id.chain",price.fld ="adr",
ocu.fld ="ocu",
room.fld ="room", rating.fld ="s_rating",
lat.fld="lat", lon.fld="lon", knn =10,
data = hou14)
hou14.all3 <- hou14
for(i in names(avmmc.all.knn1)){
hou14.all3[,i] <- unlist(avmmc.all.knn1[[i]])
}
hou14.all4 <- hou14
for(i in names(avmmc.all.knn1)){
hou14.all4[,i] <- unlist(avmmc.all.knn2[[i]])
}
# model 3
x.var <- c("avmmc.knn", "hi.sales","s_rating", "cbd","air", "factor(chain)")
endog.var <- c("avmmc.knn")
est.all.knn1 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.all3[hou14.all3$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## =======================================================================
## Dependent variable:
## ---------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## -----------------------------------------------------------------------
## avmmc.knn -0.025 0.624*
## (0.155) (0.321)
##
## hi.sales -27.077** -27.852**
## (10.992) (11.083)
##
## s_rating 33.216*** 34.455***
## (1.463) (1.569)
##
## cbd 30.821*** 31.918***
## (3.347) (3.406)
##
## air -16.414*** -16.387***
## (3.374) (3.401)
##
## factor(chain)2 6.255** 6.218**
## (2.824) (2.846)
##
## factor(chain)3 -15.145*** -15.104***
## (2.570) (2.590)
##
## factor(chain)4 -47.426*** -45.453***
## (3.081) (3.220)
##
## factor(chain)5 -34.728*** -32.845***
## (2.836) (2.973)
##
## factor(chain)7 -22.850*** -20.072***
## (5.508) (5.680)
##
## factor(chain)11 -50.042*** -52.995***
## (4.626) (4.834)
##
## factor(chain)12 -22.794*** -23.918***
## (4.414) (4.475)
##
## factor(chain)13 -60.751*** -57.047***
## (8.523) (8.738)
##
## factor(chain)14 -62.403*** -58.717***
## (8.409) (8.624)
##
## factor(chain)15 -34.996*** -32.668***
## (6.987) (7.114)
##
## factor(chain)16 -1.346 -0.008
## (4.053) (4.125)
##
## factor(chain)17 -51.977*** -50.640***
## (3.571) (3.646)
##
## factor(chain)18 123.350*** 126.132***
## (11.771) (11.924)
##
## factor(chain)19 168.967*** 169.047***
## (23.512) (23.697)
##
## factor(chain)22 -73.113*** -73.595***
## (4.488) (4.528)
##
## factor(chain)23 -23.056*** -26.646***
## (3.612) (3.958)
##
## factor(chain)24 -54.760*** -51.738***
## (4.484) (4.705)
##
## factor(chain)26 -19.980** -18.609**
## (8.585) (8.673)
##
## factor(chain)27 -52.101*** -48.335***
## (5.623) (5.897)
##
## factor(chain)28 -29.484** -28.091**
## (11.810) (11.918)
##
## factor(chain)29 -53.618*** -49.110***
## (8.566) (8.851)
##
## factor(chain)30 -72.728*** -69.492***
## (6.969) (7.162)
##
## factor(chain)40 -22.131*** -22.676***
## (3.237) (3.270)
##
## Constant 40.527*** 33.312***
## (5.485) (6.348)
##
## -----------------------------------------------------------------------
## Observations 1,141 1,141
## R2 0.785 0.781
## Adjusted R2 0.779 0.776
## Residual Std. Error (df = 1112) 23.184 23.367
## F Statistic 144.708*** (df = 28; 1112)
## =======================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01est.all.knn2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.all4[hou14.all4$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## =======================================================================
## Dependent variable:
## ---------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## -----------------------------------------------------------------------
## avmmc.knn 0.172** 0.438***
## (0.079) (0.118)
##
## hi.sales -75.880*** -78.408***
## (13.982) (14.077)
##
## s_rating 33.540*** 34.298***
## (1.434) (1.462)
##
## cbd 31.046*** 32.551***
## (3.321) (3.374)
##
## air -13.780*** -12.850***
## (3.352) (3.383)
##
## factor(chain)2 6.710** 7.477***
## (2.797) (2.822)
##
## factor(chain)3 -15.811*** -15.478***
## (2.538) (2.553)
##
## factor(chain)4 -46.575*** -44.028***
## (3.092) (3.217)
##
## factor(chain)5 -33.595*** -31.599***
## (2.824) (2.912)
##
## factor(chain)7 -19.473*** -15.365***
## (5.542) (5.729)
##
## factor(chain)11 -51.828*** -51.792***
## (4.445) (4.467)
##
## factor(chain)12 -26.082*** -28.570***
## (4.419) (4.515)
##
## factor(chain)13 -59.670*** -54.452***
## (8.537) (8.747)
##
## factor(chain)14 -58.144*** -53.372***
## (8.389) (8.574)
##
## factor(chain)15 -34.406*** -30.991***
## (6.960) (7.083)
##
## factor(chain)16 -0.917 1.692
## (4.029) (4.138)
##
## factor(chain)17 -50.470*** -49.923***
## (3.513) (3.535)
##
## factor(chain)18 125.651*** 129.912***
## (11.668) (11.808)
##
## factor(chain)19 169.151*** 171.036***
## (23.242) (23.367)
##
## factor(chain)22 -73.375*** -71.663***
## (4.472) (4.529)
##
## factor(chain)23 -25.604*** -29.228***
## (3.632) (3.837)
##
## factor(chain)24 -50.791*** -48.264***
## (4.456) (4.554)
##
## factor(chain)26 -18.984** -16.007*
## (8.527) (8.625)
##
## factor(chain)27 -48.955*** -46.215***
## (5.552) (5.651)
##
## factor(chain)28 -29.143** -25.482**
## (11.725) (11.845)
##
## factor(chain)29 -40.588*** -35.611***
## (8.761) (8.953)
##
## factor(chain)30 -72.530*** -68.181***
## (6.988) (7.165)
##
## factor(chain)40 -18.292*** -15.976***
## (3.298) (3.399)
##
## Constant 41.550*** 34.660***
## (5.441) (5.912)
##
## -----------------------------------------------------------------------
## Observations 1,141 1,141
## R2 0.790 0.788
## Adjusted R2 0.784 0.782
## Residual Std. Error (df = 1112) 22.910 23.025
## F Statistic 149.144*** (df = 28; 1112)
## =======================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01# hotels with high (hou14.h, dist.mat.list.high)
mmc.high1<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
id.chain.fld="id.chain",price.fld ="adr",
q.fld = "room.sold",
revpar.fld ="revpar", rating.fld = "s_rating",
room.fld = "room", lat.fld="lat", lon.fld="lon",
dist.limit = 2.5, dist.mat=dist.mat.high.list, data =
hou14.h)## Market id = 1
## Market id = 2
## Market id = 3## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 4mmc.high2<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
id.chain.fld="id.chain",price.fld ="adr",
q.fld = "room.sold",
revpar.fld ="revpar", rating.fld = "s_rating",
room.fld = "room", lat.fld="lat", lon.fld="lon",
dist.limit = 5, dist.mat=dist.mat.high.list, data =
hou14.h)## Market id = 1
## Market id = 2
## Market id = 3## Warning in brand.mkt * dum.hotel.incl[i, ]: longer object length is not a
## multiple of shorter object length## Market id = 4hou14.high1 <- hou14.h
for(i in names(mmc.high1)){
hou14.high1[, i] <- unlist(mmc.high1[[i]])
}
hou14.high2 <- hou14.h
for(i in names(mmc.high2)){
hou14.high2[, i] <- unlist(mmc.high2[[i]])
}
# regression
y.var <- c("adr")
inst.var <- c("rival.dist", "sum.rating", "n.same.brand", "n.same.chain", "n.brand.city", "n.chain.city")
# model 1
x.var <- c("avmmc", "hi.sales","s_rating", "cbd","air", "factor(chain)")
endog.var <- c("avmmc")
est.high1 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.high1[hou14.high1$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## ====================================================================
## Dependent variable:
## -------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## --------------------------------------------------------------------
## avmmc 0.010 -0.687
## (0.321) (0.604)
##
## hi.sales -40.987*** -40.096***
## (8.538) (8.667)
##
## s_rating 47.929*** 47.912***
## (7.605) (7.697)
##
## cbd 5.416 3.335
## (6.018) (6.278)
##
## air -42.461** -26.802
## (16.511) (20.249)
##
## factor(chain)2 15.663** 12.765*
## (6.288) (6.707)
##
## factor(chain)3 -18.252** -24.604***
## (8.002) (9.334)
##
## factor(chain)4 -38.529*** -44.493***
## (11.549) (12.475)
##
## factor(chain)7 -22.076* -29.112**
## (11.735) (12.941)
##
## factor(chain)14 -83.125*** -90.246***
## (15.568) (16.593)
##
## factor(chain)16 15.360* 9.798
## (7.847) (8.921)
##
## factor(chain)17 -98.000*** -105.046***
## (15.461) (16.474)
##
## factor(chain)18 126.615*** 119.648***
## (15.668) (16.655)
##
## factor(chain)19 174.767*** 169.976***
## (30.071) (30.637)
##
## factor(chain)26 -11.793 -16.063
## (11.610) (12.158)
##
## factor(chain)28 -34.917** -41.806**
## (15.581) (16.553)
##
## factor(chain)40 27.819*** 21.666**
## (8.806) (9.982)
##
## Constant -16.533 -9.634
## (30.020) (30.800)
##
## --------------------------------------------------------------------
## Observations 211 211
## R2 0.716 0.709
## Adjusted R2 0.691 0.683
## Residual Std. Error (df = 193) 28.488 28.835
## F Statistic 28.623*** (df = 17; 193)
## ====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01est.high2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.high2[hou14.high2$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## ====================================================================
## Dependent variable:
## -------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## --------------------------------------------------------------------
## avmmc -0.356 1.620*
## (0.428) (0.835)
##
## hi.sales -56.363 -166.581***
## (35.654) (54.282)
##
## s_rating 47.863*** 44.541***
## (8.057) (8.573)
##
## cbd 10.783* 14.172**
## (5.974) (6.410)
##
## air -16.714 -24.760
## (18.319) (19.517)
##
## factor(chain)2 9.229 13.439*
## (6.606) (7.121)
##
## factor(chain)3 -27.738*** -16.153*
## (8.293) (9.662)
##
## factor(chain)4 -37.618*** -41.517***
## (11.700) (12.408)
##
## factor(chain)7 -23.890* -3.394
## (12.471) (15.027)
##
## factor(chain)14 -85.235*** -60.007***
## (16.655) (19.710)
##
## factor(chain)16 4.216 13.273
## (7.903) (8.929)
##
## factor(chain)17 -100.816*** -81.313***
## (16.202) (18.428)
##
## factor(chain)18 125.369*** 138.470***
## (16.239) (17.736)
##
## factor(chain)19 169.401*** 179.950***
## (31.090) (32.979)
##
## factor(chain)26 -14.784 -7.781
## (11.936) (12.823)
##
## factor(chain)28 -35.099** -24.675
## (16.058) (17.324)
##
## factor(chain)40 24.522*** 35.002***
## (9.066) (10.255)
##
## Constant -16.184 -8.496
## (32.331) (34.182)
##
## --------------------------------------------------------------------
## Observations 211 211
## R2 0.696 0.663
## Adjusted R2 0.669 0.633
## Residual Std. Error (df = 193) 29.467 31.055
## F Statistic 26.011*** (df = 17; 193)
## ====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01avmmc.high.knn1 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
id.chain.fld ="id.chain",price.fld ="adr",
ocu.fld ="ocu",
room.fld ="room", rating.fld ="s_rating",
lat.fld="lat", lon.fld="lon", knn =5,
data = hou14.h)
avmmc.high.knn2 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
id.chain.fld ="id.chain",price.fld ="adr",
ocu.fld ="ocu",
room.fld ="room", rating.fld ="s_rating",
lat.fld="lat", lon.fld="lon", knn =10,
data = hou14.h)
hou14.high3 <- hou14.h
for(i in names(avmmc.high.knn1)){
hou14.high3[,i] <- unlist(avmmc.high.knn1[[i]])
}
hou14.high4 <- hou14.h
for(i in names(avmmc.high.knn1)){
hou14.high4[,i] <- unlist(avmmc.high.knn2[[i]])
}
# model 3
x.var <- c("avmmc.knn", "hi.sales","s_rating", "cbd","air", "factor(chain)")
endog.var <- c("avmmc.knn")
est.high.knn1 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.high3[hou14.high3$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## ====================================================================
## Dependent variable:
## -------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## --------------------------------------------------------------------
## avmmc.knn 0.433 -1.245
## (0.723) (1.079)
##
## hi.sales 67.892 71.274
## (73.977) (75.021)
##
## s_rating 52.088*** 57.578***
## (8.339) (8.843)
##
## cbd 15.861*** 13.600**
## (5.967) (6.143)
##
## air -52.488*** -45.787***
## (16.549) (17.074)
##
## factor(chain)2 15.132** 12.100*
## (6.609) (6.852)
##
## factor(chain)3 -18.641** -24.684***
## (8.268) (8.855)
##
## factor(chain)4 -30.552** -39.489***
## (12.421) (13.281)
##
## factor(chain)7 -16.528 -28.420**
## (12.966) (14.294)
##
## factor(chain)14 -77.085*** -86.134***
## (16.587) (17.351)
##
## factor(chain)16 10.375 5.684
## (7.974) (8.382)
##
## factor(chain)17 -92.591*** -102.765***
## (16.524) (17.428)
##
## factor(chain)18 133.963*** 128.366***
## (16.630) (17.067)
##
## factor(chain)19 175.574*** 161.627***
## (32.301) (33.404)
##
## factor(chain)26 -8.788 -18.649
## (12.860) (13.844)
##
## factor(chain)28 -20.408 -28.791*
## (16.506) (17.196)
##
## factor(chain)40 35.398*** 24.772**
## (9.931) (11.249)
##
## Constant -63.123* -73.941**
## (36.317) (37.174)
##
## --------------------------------------------------------------------
## Observations 211 211
## R2 0.684 0.675
## Adjusted R2 0.656 0.647
## Residual Std. Error (df = 193) 30.049 30.466
## F Statistic 24.579*** (df = 17; 193)
## ====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01est.high.knn2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.high4[hou14.high4$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## ====================================================================
## Dependent variable:
## -------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## --------------------------------------------------------------------
## avmmc.knn -0.737 -1.394**
## (0.475) (0.612)
##
## hi.sales 410.717*** 492.831***
## (139.145) (147.750)
##
## s_rating 57.146*** 61.955***
## (8.550) (9.035)
##
## cbd 19.126*** 18.977***
## (5.815) (5.845)
##
## air -58.649*** -60.357***
## (15.916) (16.026)
##
## factor(chain)2 13.521** 13.657**
## (6.371) (6.403)
##
## factor(chain)3 -21.430*** -22.911***
## (7.774) (7.860)
##
## factor(chain)4 -32.851*** -36.415***
## (11.991) (12.227)
##
## factor(chain)7 -32.095** -40.393***
## (13.203) (14.117)
##
## factor(chain)14 -84.029*** -89.048***
## (16.184) (16.523)
##
## factor(chain)16 8.473 6.766
## (7.680) (7.781)
##
## factor(chain)17 -100.922*** -108.435***
## (16.580) (17.225)
##
## factor(chain)18 116.221*** 109.179***
## (17.187) (17.750)
##
## factor(chain)19 164.638*** 154.812***
## (31.912) (32.574)
##
## factor(chain)26 -13.351 -17.755
## (12.368) (12.690)
##
## factor(chain)28 -23.298 -26.707
## (15.961) (16.162)
##
## factor(chain)40 23.727** 16.820
## (9.935) (10.760)
##
## Constant -107.247*** -128.352***
## (36.508) (38.684)
##
## --------------------------------------------------------------------
## Observations 211 211
## R2 0.696 0.693
## Adjusted R2 0.669 0.666
## Residual Std. Error (df = 193) 29.478 29.624
## F Statistic 25.984*** (df = 17; 193)
## ====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01# hotels with low (hou14.l, dist.mat.list.high)
mmc.low1<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
id.chain.fld="id.chain",price.fld ="adr",
q.fld = "room.sold",
revpar.fld ="revpar", rating.fld = "s_rating",
room.fld = "room", lat.fld="lat", lon.fld="lon",
dist.limit = 2.5, dist.mat=dist.mat.low.list, data =
hou14.l)## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 1## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 2## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 3## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 4mmc.low2<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
id.chain.fld="id.chain",price.fld ="adr",
q.fld = "room.sold",
revpar.fld ="revpar", rating.fld = "s_rating",
room.fld = "room", lat.fld="lat", lon.fld="lon",
dist.limit = 5, dist.mat=dist.mat.low.list, data =
hou14.l)## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 1## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 2## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 3## Warning in brand.mkt * dum.hotel.incl[i, ]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.## Market id = 4hou14.low1 <- hou14.l
for(i in names(mmc.low1)){
hou14.low1[, i] <- unlist(mmc.low1[[i]])
}
hou14.low2 <- hou14.l
for(i in names(mmc.low2)){
hou14.low2[, i] <- unlist(mmc.low2[[i]])
}
# regression
y.var <- c("adr")
inst.var <- c("rival.dist", "sum.rating", "n.same.brand", "n.same.chain", "n.brand.city", "n.chain.city")
# model 1
x.var <- c("avmmc", "hi.sales","s_rating", "cbd","air", "factor(chain)")
endog.var <- c("avmmc")
est.low1 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.low1[hou14.low1$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## =====================================================================
## Dependent variable:
## --------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## ---------------------------------------------------------------------
## avmmc 0.318*** 0.493***
## (0.036) (0.057)
##
## hi.sales -2.450 -19.827*
## (9.374) (10.450)
##
## s_rating 17.761*** 17.551***
## (2.110) (2.138)
##
## cbd 40.330*** 39.756***
## (4.231) (4.290)
##
## air -10.516*** -10.659***
## (2.661) (2.697)
##
## factor(chain)2 15.567*** 16.516***
## (2.923) (2.971)
##
## factor(chain)3 -14.018*** -13.799***
## (2.158) (2.187)
##
## factor(chain)4 -51.265*** -47.323***
## (2.713) (2.922)
##
## factor(chain)5 -38.359*** -35.939***
## (2.295) (2.403)
##
## factor(chain)7 -11.878** -5.405
## (5.505) (5.810)
##
## factor(chain)11 -63.135*** -61.801***
## (3.656) (3.720)
##
## factor(chain)12 -33.332*** -35.644***
## (3.394) (3.488)
##
## factor(chain)13 -61.999*** -54.776***
## (6.690) (7.017)
##
## factor(chain)14 -22.903** -15.996*
## (8.929) (9.213)
##
## factor(chain)15 -27.257*** -22.936***
## (5.339) (5.518)
##
## factor(chain)16 8.295* 14.275***
## (4.737) (5.029)
##
## factor(chain)17 -48.871*** -46.472***
## (2.901) (3.000)
##
## factor(chain)22 -72.562*** -67.650***
## (3.619) (3.869)
##
## factor(chain)23 -37.661*** -42.397***
## (2.875) (3.146)
##
## factor(chain)24 -63.927*** -61.494***
## (3.674) (3.773)
##
## factor(chain)27 -58.426*** -56.419***
## (4.342) (4.429)
##
## factor(chain)29 -57.681*** -52.002***
## (6.624) (6.862)
##
## factor(chain)30 -68.809*** -63.743***
## (5.345) (5.563)
##
## factor(chain)40 -50.855*** -47.602***
## (3.692) (3.829)
##
## Constant 65.606*** 60.498***
## (6.219) (6.430)
##
## ---------------------------------------------------------------------
## Observations 930 930
## R2 0.808 0.803
## Adjusted R2 0.803 0.798
## Residual Std. Error (df = 905) 17.363 17.595
## F Statistic 158.970*** (df = 24; 905)
## =====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01est.low2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.low2[hou14.low2$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## =====================================================================
## Dependent variable:
## --------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## ---------------------------------------------------------------------
## avmmc 0.187*** 0.209***
## (0.025) (0.031)
##
## hi.sales -132.675*** -139.129***
## (18.428) (19.219)
##
## s_rating 17.173*** 17.043***
## (2.129) (2.133)
##
## cbd 39.491*** 39.644***
## (4.291) (4.295)
##
## air -12.633*** -12.868***
## (2.679) (2.687)
##
## factor(chain)2 14.242*** 14.211***
## (2.943) (2.945)
##
## factor(chain)3 -17.729*** -18.019***
## (2.210) (2.224)
##
## factor(chain)4 -54.047*** -53.570***
## (2.673) (2.704)
##
## factor(chain)5 -39.967*** -39.607***
## (2.300) (2.320)
##
## factor(chain)7 -16.347*** -15.374***
## (5.503) (5.566)
##
## factor(chain)11 -62.327*** -61.995***
## (3.692) (3.704)
##
## factor(chain)12 -33.029*** -33.394***
## (3.418) (3.434)
##
## factor(chain)13 -65.076*** -63.999***
## (6.692) (6.755)
##
## factor(chain)14 -25.162*** -23.822***
## (9.021) (9.095)
##
## factor(chain)15 -30.321*** -29.606***
## (5.371) (5.407)
##
## factor(chain)16 5.016 5.856
## (4.714) (4.768)
##
## factor(chain)17 -51.905*** -51.489***
## (2.931) (2.953)
##
## factor(chain)22 -74.777*** -74.237***
## (3.540) (3.570)
##
## factor(chain)23 -35.191*** -35.852***
## (2.841) (2.896)
##
## factor(chain)24 -64.862*** -64.346***
## (3.718) (3.744)
##
## factor(chain)27 -59.852*** -59.495***
## (4.368) (4.380)
##
## factor(chain)29 -66.241*** -65.702***
## (6.617) (6.635)
##
## factor(chain)30 -76.484*** -76.030***
## (5.317) (5.333)
##
## factor(chain)40 -52.303*** -51.964***
## (3.668) (3.681)
##
## Constant 76.131*** 75.455***
## (6.268) (6.296)
##
## ---------------------------------------------------------------------
## Observations 930 930
## R2 0.805 0.805
## Adjusted R2 0.800 0.800
## Residual Std. Error (df = 905) 17.493 17.500
## F Statistic 156.048*** (df = 24; 905)
## =====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01avmmc.low.knn1 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
id.chain.fld ="id.chain",price.fld ="adr",
ocu.fld ="ocu",
room.fld ="room", rating.fld ="s_rating",
lat.fld="lat", lon.fld="lon", knn =5,
data = hou14.l)
avmmc.low.knn2 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
id.chain.fld ="id.chain",price.fld ="adr",
ocu.fld ="ocu",
room.fld ="room", rating.fld ="s_rating",
lat.fld="lat", lon.fld="lon", knn =10,
data = hou14.l)
hou14.low3 <- hou14.l
for(i in names(avmmc.low.knn1)){
hou14.low3[,i] <- unlist(avmmc.low.knn1[[i]])
}
hou14.low4 <- hou14.l
for(i in names(avmmc.low.knn1)){
hou14.low4[,i] <- unlist(avmmc.low.knn2[[i]])
}
# model 3
x.var <- c("avmmc.knn", "hi.sales","s_rating", "cbd","air", "factor(chain)")
endog.var <- c("avmmc.knn")
est.low.knn1 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.low3[hou14.low3$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## =====================================================================
## Dependent variable:
## --------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## ---------------------------------------------------------------------
## avmmc.knn 0.536*** 1.854***
## (0.118) (0.260)
##
## hi.sales 19.378 -16.148
## (18.319) (20.488)
##
## s_rating 20.148*** 23.399***
## (2.214) (2.429)
##
## cbd 39.654*** 37.063***
## (4.377) (4.692)
##
## air -11.112*** -10.132***
## (2.732) (2.920)
##
## factor(chain)2 15.411*** 18.419***
## (3.040) (3.285)
##
## factor(chain)3 -13.666*** -12.356***
## (2.235) (2.396)
##
## factor(chain)4 -55.715*** -48.918***
## (2.751) (3.162)
##
## factor(chain)5 -40.062*** -33.604***
## (2.391) (2.784)
##
## factor(chain)7 -20.024*** -10.884*
## (5.599) (6.180)
##
## factor(chain)11 -67.280*** -70.595***
## (3.809) (4.105)
##
## factor(chain)12 -31.041*** -35.179***
## (3.503) (3.805)
##
## factor(chain)13 -68.989*** -58.327***
## (6.821) (7.508)
##
## factor(chain)14 -32.551*** -22.195**
## (9.171) (9.949)
##
## factor(chain)15 -32.069*** -24.911***
## (5.501) (5.999)
##
## factor(chain)16 0.427 7.045
## (4.783) (5.230)
##
## factor(chain)17 -51.788*** -47.997***
## (2.992) (3.259)
##
## factor(chain)22 -79.174*** -77.557***
## (3.579) (3.830)
##
## factor(chain)23 -30.811*** -36.143***
## (2.846) (3.174)
##
## factor(chain)24 -64.574*** -56.902***
## (3.875) (4.342)
##
## factor(chain)27 -58.265*** -49.612***
## (4.571) (5.101)
##
## factor(chain)29 -62.840*** -51.993***
## (6.862) (7.558)
##
## factor(chain)30 -75.143*** -65.550***
## (5.490) (6.088)
##
## factor(chain)40 -54.698*** -54.655***
## (3.778) (4.032)
##
## Constant 63.830*** 52.264***
## (7.935) (8.700)
##
## ---------------------------------------------------------------------
## Observations 930 930
## R2 0.795 0.766
## Adjusted R2 0.789 0.760
## Residual Std. Error (df = 905) 17.970 19.177
## F Statistic 145.901*** (df = 24; 905)
## =====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01est.low.knn2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var,
data = hou14.low4[hou14.low4$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")##
## =====================================================================
## Dependent variable:
## --------------------------------------
## adr
## OLS instrumental
## variable
## (1) (2)
## ---------------------------------------------------------------------
## avmmc.knn 0.280*** 0.838***
## (0.069) (0.102)
##
## hi.sales 81.375** 9.409
## (33.348) (35.793)
##
## s_rating 19.181*** 21.237***
## (2.191) (2.285)
##
## cbd 40.791*** 40.628***
## (4.358) (4.513)
##
## air -10.883*** -8.258***
## (2.750) (2.868)
##
## factor(chain)2 15.353*** 16.427***
## (3.027) (3.137)
##
## factor(chain)3 -14.249*** -14.957***
## (2.229) (2.310)
##
## factor(chain)4 -56.253*** -51.283***
## (2.746) (2.917)
##
## factor(chain)5 -40.949*** -36.720***
## (2.374) (2.519)
##
## factor(chain)7 -19.202*** -9.085
## (5.651) (6.000)
##
## factor(chain)11 -66.768*** -68.236***
## (3.773) (3.911)
##
## factor(chain)12 -32.087*** -38.279***
## (3.552) (3.767)
##
## factor(chain)13 -67.087*** -56.151***
## (6.872) (7.258)
##
## factor(chain)14 -29.910*** -18.272*
## (9.210) (9.658)
##
## factor(chain)15 -31.644*** -26.224***
## (5.485) (5.724)
##
## factor(chain)16 3.104 10.151**
## (4.805) (5.061)
##
## factor(chain)17 -52.578*** -51.835***
## (2.956) (3.063)
##
## factor(chain)22 -77.562*** -74.075***
## (3.596) (3.751)
##
## factor(chain)23 -32.053*** -39.821***
## (2.962) (3.232)
##
## factor(chain)24 -64.802*** -60.205***
## (3.826) (4.007)
##
## factor(chain)27 -58.976*** -54.069***
## (4.512) (4.716)
##
## factor(chain)29 -62.945*** -52.418***
## (6.853) (7.229)
##
## factor(chain)30 -72.982*** -63.715***
## (5.536) (5.859)
##
## factor(chain)40 -53.576*** -48.396***
## (3.791) (3.983)
##
## Constant 58.808*** 50.941***
## (7.508) (7.842)
##
## ---------------------------------------------------------------------
## Observations 930 930
## R2 0.796 0.781
## Adjusted R2 0.790 0.775
## Residual Std. Error (df = 905) 17.917 18.553
## F Statistic 146.988*** (df = 24; 905)
## =====================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01