## Summary

Use hou14 for the following analysis:

1. Determine the distance limit in the distance band approach
1. 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: Imap
wp.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  completed
wp.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-16
gamma.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  completed
wp.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-16
gamma.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  completed
wp.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-16
gamma.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",
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 = 4
mmc.all2<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
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 = 4
hou14.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
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<0.01 est.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:
##                                 ---------------------------------------
##                                            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<0.01
avmmc.all.knn1 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
ocu.fld ="ocu",
room.fld ="room", rating.fld ="s_rating",
lat.fld="lat", lon.fld="lon", knn =5,
data = hou14)
## Loading required package: dbscan
avmmc.all.knn2 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
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<0.01 est.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:
##                                 ---------------------------------------
##                                            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",
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 = 4
mmc.high2<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
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 = 4
hou14.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
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$id.brand>1, , drop=F], endog.var.fld = endog.var, inst.var.fld = inst.var, model="both") ## ## ==================================================================== ## Dependent variable: ## ------------------------------------- ## adr ## OLS instrumental ## variable ## (1) (2) ## -------------------------------------------------------------------- ## avmmc 0.010 -0.687 ## (0.321) (0.604) ## ## hi.sales -40.987*** -40.096*** ## (8.538) (8.667) ## ## s_rating 47.929*** 47.912*** ## (7.605) (7.697) ## ## cbd 5.416 3.335 ## (6.018) (6.278) ## ## air -42.461** -26.802 ## (16.511) (20.249) ## ## factor(chain)2 15.663** 12.765* ## (6.288) (6.707) ## ## factor(chain)3 -18.252** -24.604*** ## (8.002) (9.334) ## ## factor(chain)4 -38.529*** -44.493*** ## (11.549) (12.475) ## ## factor(chain)7 -22.076* -29.112** ## (11.735) (12.941) ## ## factor(chain)14 -83.125*** -90.246*** ## (15.568) (16.593) ## ## factor(chain)16 15.360* 9.798 ## (7.847) (8.921) ## ## factor(chain)17 -98.000*** -105.046*** ## (15.461) (16.474) ## ## factor(chain)18 126.615*** 119.648*** ## (15.668) (16.655) ## ## factor(chain)19 174.767*** 169.976*** ## (30.071) (30.637) ## ## factor(chain)26 -11.793 -16.063 ## (11.610) (12.158) ## ## factor(chain)28 -34.917** -41.806** ## (15.581) (16.553) ## ## factor(chain)40 27.819*** 21.666** ## (8.806) (9.982) ## ## Constant -16.533 -9.634 ## (30.020) (30.800) ## ## -------------------------------------------------------------------- ## Observations 211 211 ## R2 0.716 0.709 ## Adjusted R2 0.691 0.683 ## Residual Std. Error (df = 193) 28.488 28.835 ## F Statistic 28.623*** (df = 17; 193) ## ==================================================================== ## Note: *p<0.1; **p<0.05; ***p<0.01 est.high2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var, data = hou14.high2[hou14.high2$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")
##
## ====================================================================
##                                         Dependent variable:
##                                -------------------------------------
##                                          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<0.01
avmmc.high.knn1 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
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",
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$id.brand>1, , drop=F], endog.var.fld = endog.var, inst.var.fld = inst.var, model="both") ## ## ==================================================================== ## Dependent variable: ## ------------------------------------- ## adr ## OLS instrumental ## variable ## (1) (2) ## -------------------------------------------------------------------- ## avmmc.knn 0.433 -1.245 ## (0.723) (1.079) ## ## hi.sales 67.892 71.274 ## (73.977) (75.021) ## ## s_rating 52.088*** 57.578*** ## (8.339) (8.843) ## ## cbd 15.861*** 13.600** ## (5.967) (6.143) ## ## air -52.488*** -45.787*** ## (16.549) (17.074) ## ## factor(chain)2 15.132** 12.100* ## (6.609) (6.852) ## ## factor(chain)3 -18.641** -24.684*** ## (8.268) (8.855) ## ## factor(chain)4 -30.552** -39.489*** ## (12.421) (13.281) ## ## factor(chain)7 -16.528 -28.420** ## (12.966) (14.294) ## ## factor(chain)14 -77.085*** -86.134*** ## (16.587) (17.351) ## ## factor(chain)16 10.375 5.684 ## (7.974) (8.382) ## ## factor(chain)17 -92.591*** -102.765*** ## (16.524) (17.428) ## ## factor(chain)18 133.963*** 128.366*** ## (16.630) (17.067) ## ## factor(chain)19 175.574*** 161.627*** ## (32.301) (33.404) ## ## factor(chain)26 -8.788 -18.649 ## (12.860) (13.844) ## ## factor(chain)28 -20.408 -28.791* ## (16.506) (17.196) ## ## factor(chain)40 35.398*** 24.772** ## (9.931) (11.249) ## ## Constant -63.123* -73.941** ## (36.317) (37.174) ## ## -------------------------------------------------------------------- ## Observations 211 211 ## R2 0.684 0.675 ## Adjusted R2 0.656 0.647 ## Residual Std. Error (df = 193) 30.049 30.466 ## F Statistic 24.579*** (df = 17; 193) ## ==================================================================== ## Note: *p<0.1; **p<0.05; ***p<0.01 est.high.knn2 <- get.ols.iv.est(x.var.fld = x.var, y.var.fld=y.var, data = hou14.high4[hou14.high4$id.brand>1, , drop=F],
endog.var.fld = endog.var, inst.var.fld = inst.var,
model="both")
##
## ====================================================================
##                                         Dependent variable:
##                                -------------------------------------
##                                          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",
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 = 4
mmc.low2<- get.avmmc.new(mkt.id.fld="qtr", id.brand.fld ="id.brand",
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 = 4
hou14.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
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<0.01 est.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:
##                                --------------------------------------
##                                           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<0.01
avmmc.low.knn1 <- get.avmmc.knn(mkt.id.fld ="qtr", id.brand.fld="id.brand",
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",
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<0.01 est.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:
##                                --------------------------------------
##                                           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