Загрузка пакетов
library(ggplot2)
library(lmtest)
## Загрузка требуемого пакета: zoo
##
## Присоединяю пакет: 'zoo'
## Следующие объекты скрыты от 'package:base':
##
## as.Date, as.Date.numeric
library(foreign)
library(xtable)
library(readr)
library(margins)
library(dplyr)
##
## Присоединяю пакет: 'dplyr'
## Следующие объекты скрыты от 'package:stats':
##
## filter, lag
## Следующие объекты скрыты от 'package:base':
##
## intersect, setdiff, setequal, union
library(car)
## Загрузка требуемого пакета: carData
##
## Присоединяю пакет: 'car'
## Следующий объект скрыт от 'package:dplyr':
##
## recode
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
library(broom)
library(writexl)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.1.8
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ car::recode() masks dplyr::recode()
## ✖ purrr::some() masks car::some()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(readxl)
library(tidyr)
library(plm)
##
## Присоединяю пакет: 'plm'
##
## Следующие объекты скрыты от 'package:dplyr':
##
## between, lag, lead
library(openxlsx)
library(tseries)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
Загрузка данных для 3 гипотезы
lavka3 <- read_excel("D:/Рабочий стол/4 курс/диплом/фин_данные/lavka_hyp3.xlsx")
magnit3 <- read_excel("D:/Рабочий стол/4 курс/диплом/фин_данные/magnit_hyp3.xlsx")
pyat3 <- read_excel("D:/Рабочий стол/4 курс/диплом/фин_данные/5ka_hyp3.xlsx")
#perek3 <- read_csv("D:/Рабочий стол/4 курс/диплом/фин_данные/perek_hyp3.csv")
#lenta3 <- read_csv("D:/Рабочий стол/4 курс/диплом/фин_данные/lenta_hyp3.csv")
lavka3 <- lavka3[!duplicated(lavka3), ]
magnit3 <- magnit3[!duplicated(magnit3), ] #тут нет дубликатов
pyat3 <- pyat3[!duplicated(pyat3), ] #тут нет дубликатов
#perek3 <- perek3[!duplicated(perek3), ]
#lenta3 <- lenta3[!duplicated(lenta3), ]
Загрузка данных для 4-5 гипотез
lavka4_5 <- read_excel("D:/Рабочий стол/4 курс/диплом/фин_данные/lavka_hope2.xlsx")
magnit4_5 <- read_excel("D:/Рабочий стол/4 курс/диплом/фин_данные/magnit_hope_cool.xlsx")
pyat4_5 <- read_excel("D:/Рабочий стол/4 курс/диплом/фин_данные/5ka_hope.xlsx")
lavka4_5 <- lavka4_5[!duplicated(lavka4_5), ]
magnit4_5 <- magnit4_5[!duplicated(magnit4_5), ]
pyat4_5 <- pyat4_5[!duplicated(pyat4_5), ]
#perek <- perek[!duplicated(perek), ]
#lenta <- lenta[!duplicated(lenta), ]
Проверка 3 гипотезы (крайняя левая цифра)
Лавка
logit_3hyp_left_lavka <- glm(factor(left_number_changed) ~ factor(price_increase) + right_number_changed + middle_number_changed + left_number_prev_changed + middle_number_prev_changed + price_diff + factor(category), data = lavka3, family = binomial(link = "logit"), x = TRUE)
sum_hyp3_left_lavka <- coeftest(logit_3hyp_left_lavka)
sum_hyp3_left_lavka
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) -3.60499010 0.01778224 -202.7298
## factor(price_increase)1 0.63045700 0.01619708 38.9241
## right_number_changed 0.51893950 0.01461875 35.4982
## middle_number_changed 2.57948982 0.01782710 144.6949
## left_number_prev_changed 2.21260934 0.01596831 138.5626
## middle_number_prev_changed -0.91034447 0.01504248 -60.5182
## price_diff -0.00825048 0.00031768 -25.9709
## factor(category)Безалкогольные напитки 0.21981973 0.02239066 9.8175
## factor(category)Бытовая химия -0.00857185 0.03931433 -0.2180
## factor(category)Замороженные продукты -0.12981128 0.03614690 -3.5912
## factor(category)Красота и здоровье -0.29816777 0.02548410 -11.7001
## factor(category)Красота и здорровье -0.92114777 0.77235642 -1.1926
## factor(category)Молоко и яйца 0.15601084 0.02018038 7.7308
## factor(category)Мясо -0.59589841 0.03323265 -17.9311
## factor(category)Овощи 0.73568746 0.03914526 18.7938
## factor(category)Рыба -0.05151611 0.04213510 -1.2226
## factor(category)Сладости к чаю 0.09055596 0.01856407 4.8780
## factor(category)Фрукты и ягоды -0.02189728 0.05649786 -0.3876
## factor(category)Хлебобулочные изделия -0.34104936 0.05561099 -6.1328
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## right_number_changed < 2.2e-16 ***
## middle_number_changed < 2.2e-16 ***
## left_number_prev_changed < 2.2e-16 ***
## middle_number_prev_changed < 2.2e-16 ***
## price_diff < 2.2e-16 ***
## factor(category)Безалкогольные напитки < 2.2e-16 ***
## factor(category)Бытовая химия 0.8274028
## factor(category)Замороженные продукты 0.0003291 ***
## factor(category)Красота и здоровье < 2.2e-16 ***
## factor(category)Красота и здорровье 0.2330081
## factor(category)Молоко и яйца 1.069e-14 ***
## factor(category)Мясо < 2.2e-16 ***
## factor(category)Овощи < 2.2e-16 ***
## factor(category)Рыба 0.2214652
## factor(category)Сладости к чаю 1.072e-06 ***
## factor(category)Фрукты и ягоды 0.6983290
## factor(category)Хлебобулочные изделия 8.636e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Магнит
logit_3hyp_left_magnit <- glm(factor(left_number_changed) ~ factor(price_increase) + right_number_changed + middle_number_changed + left_number_prev_changed + middle_number_prev_changed + price_diff + factor(category), data = magnit3, family = binomial(link = "logit"), x = TRUE)
sum_hyp3_left_magnit <- coeftest(logit_3hyp_left_magnit)
sum_hyp3_left_magnit
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) -4.05405710 0.02920424 -138.8174
## factor(price_increase)1 0.52765810 0.02762029 19.1040
## right_number_changed -0.13977233 0.02258098 -6.1898
## middle_number_changed 3.33525105 0.02849187 117.0597
## left_number_prev_changed 2.02881848 0.02728761 74.3494
## middle_number_prev_changed -0.82534527 0.02485388 -33.2079
## price_diff -0.00370913 0.00022494 -16.4893
## factor(category)Безалкогольные напитки -0.06768878 0.04526935 -1.4952
## factor(category)Бытовая химия -0.01901656 0.04667221 -0.4074
## factor(category)Замороженные продукты 0.23148541 0.04689554 4.9362
## factor(category)Красота и здоровье 0.15727213 0.03810916 4.1269
## factor(category)Молоко и яйца 0.16142189 0.03242141 4.9789
## factor(category)Мясо 0.43144964 0.03285310 13.1327
## factor(category)Овощи -0.27120928 0.06519105 -4.1602
## factor(category)Рыба 0.13801630 0.04450653 3.1010
## factor(category)Сладости к чаю -0.02736665 0.03639252 -0.7520
## factor(category)Фрукты и ягоды -0.35391672 0.07640857 -4.6319
## factor(category)Хлебобулочные изделия 0.65686951 0.10400494 6.3158
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## right_number_changed 6.023e-10 ***
## middle_number_changed < 2.2e-16 ***
## left_number_prev_changed < 2.2e-16 ***
## middle_number_prev_changed < 2.2e-16 ***
## price_diff < 2.2e-16 ***
## factor(category)Безалкогольные напитки 0.134850
## factor(category)Бытовая химия 0.683678
## factor(category)Замороженные продукты 7.966e-07 ***
## factor(category)Красота и здоровье 3.677e-05 ***
## factor(category)Молоко и яйца 6.396e-07 ***
## factor(category)Мясо < 2.2e-16 ***
## factor(category)Овощи 3.179e-05 ***
## factor(category)Рыба 0.001928 **
## factor(category)Сладости к чаю 0.452060
## factor(category)Фрукты и ягоды 3.623e-06 ***
## factor(category)Хлебобулочные изделия 2.688e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Пятерочка
logit_3hyp_left_pyat <- glm(factor(left_number_changed) ~ factor(price_increase) + right_number_changed + middle_number_changed + left_number_prev_changed + middle_number_changed + price_diff + factor(category), data = pyat3, family = binomial(link = "logit"), x = TRUE)
sum_hyp3_left_pyat <- coeftest(logit_3hyp_left_pyat)
sum_hyp3_left_pyat
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) -3.7715729 0.0176851 -213.2634
## factor(price_increase)1 0.6458246 0.0159713 40.4366
## right_number_changed 0.5050271 0.0143423 35.2125
## middle_number_changed 2.3386240 0.0172100 135.8873
## left_number_prev_changed 1.7512339 0.0135059 129.6640
## price_diff -0.0082850 0.0003167 -26.1607
## factor(category)Безалкогольные напитки 0.2368169 0.0221024 10.7145
## factor(category)Бытовая химия -0.0192872 0.0388598 -0.4963
## factor(category)Замороженные продукты -0.1658701 0.0356236 -4.6562
## factor(category)Красота и здоровье -0.3097574 0.0251628 -12.3101
## factor(category)Красота и здорровье -0.8389950 0.7580904 -1.1067
## factor(category)Молоко и яйца 0.1636596 0.0198744 8.2347
## factor(category)Мясо -0.6587756 0.0327784 -20.0979
## factor(category)Овощи 0.7182385 0.0385290 18.6415
## factor(category)Рыба -0.0631052 0.0415821 -1.5176
## factor(category)Сладости к чаю 0.0854880 0.0182670 4.6799
## factor(category)Фрукты и ягоды -0.1385470 0.0547592 -2.5301
## factor(category)Хлебобулочные изделия -0.3066914 0.0550510 -5.5710
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## right_number_changed < 2.2e-16 ***
## middle_number_changed < 2.2e-16 ***
## left_number_prev_changed < 2.2e-16 ***
## price_diff < 2.2e-16 ***
## factor(category)Безалкогольные напитки < 2.2e-16 ***
## factor(category)Бытовая химия 0.6197
## factor(category)Замороженные продукты 3.221e-06 ***
## factor(category)Красота и здоровье < 2.2e-16 ***
## factor(category)Красота и здорровье 0.2684
## factor(category)Молоко и яйца < 2.2e-16 ***
## factor(category)Мясо < 2.2e-16 ***
## factor(category)Овощи < 2.2e-16 ***
## factor(category)Рыба 0.1291
## factor(category)Сладости к чаю 2.870e-06 ***
## factor(category)Фрукты и ягоды 0.0114 *
## factor(category)Хлебобулочные изделия 2.532e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Перекрёсток
#logit_3hyp_perek <- glm(factor(left_number_changed) ~ factor(price_increase) + right_number_changed + middle_number_changed + left_number_prev_changed + middle_number_prev_changed + right_number_prev_changed + price_diff + factor(category), data = perek3, family = binomial(link = "logit"), x = TRUE)
#sum_hyp3_perek <- coeftest(logit_3hyp_perek)
#sum_hyp3_perek
Лента
#logit_3hyp_lenta <- glm(factor(left_number_changed) ~ factor(price_increase) + right_number_changed + middle_number_changed + left_number_prev_changed + middle_number_prev_changed + right_number_prev_changed + price_diff + factor(category), data = lenta3, family = binomial(link = "logit"), x = TRUE)
#sum_hyp3_lenta <- coeftest(logit_3hyp_lenta)
#sum_hyp3_lenta
T-статистика (3 гипотеза - крайняя левая цифра)
#Лавка
0.63045700/0.01619708
## [1] 38.92411
#Магнит
0.52765810/0.02762029
## [1] 19.104
#Пятерочка
0.6458246/0.0159713
## [1] 40.43657
Проверка 3 гипотезы (серединная цифра)
Лавка
logit_3hyp_mid_lavka <- glm(factor(middle_number_changed) ~ factor(price_increase) + right_number_changed + left_number_changed + left_number_prev_changed + middle_number_prev_changed + price_diff + factor(category), data = lavka3, family = binomial(link = "logit"), x = TRUE)
sum_hyp3_mid_lavka <- coeftest(logit_3hyp_mid_lavka)
sum_hyp3_mid_lavka
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) -2.35811248 0.01348295 -174.8959
## factor(price_increase)1 4.12137402 0.02268349 181.6904
## right_number_changed 2.16789311 0.02073417 104.5565
## left_number_changed 2.10165797 0.01863834 112.7599
## left_number_prev_changed -0.89774750 0.01760424 -50.9961
## middle_number_prev_changed 1.51273080 0.01327544 113.9496
## price_diff -0.04434713 0.00047666 -93.0381
## factor(category)Безалкогольные напитки -0.26519207 0.02334213 -11.3611
## factor(category)Бытовая химия 0.15076214 0.03560834 4.2339
## factor(category)Замороженные продукты 0.11137563 0.03381607 3.2936
## factor(category)Красота и здоровье 0.15286131 0.02169486 7.0460
## factor(category)Красота и здорровье -0.08534466 0.47839375 -0.1784
## factor(category)Молоко и яйца -0.07430058 0.02054116 -3.6172
## factor(category)Мясо 0.37188852 0.02626507 14.1591
## factor(category)Овощи -0.19968318 0.04541237 -4.3971
## factor(category)Рыба 0.05303568 0.03750575 1.4141
## factor(category)Сладости к чаю 0.03244488 0.01816315 1.7863
## factor(category)Фрукты и ягоды 0.59902859 0.05845543 10.2476
## factor(category)Хлебобулочные изделия -0.18304474 0.04834922 -3.7859
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## right_number_changed < 2.2e-16 ***
## left_number_changed < 2.2e-16 ***
## left_number_prev_changed < 2.2e-16 ***
## middle_number_prev_changed < 2.2e-16 ***
## price_diff < 2.2e-16 ***
## factor(category)Безалкогольные напитки < 2.2e-16 ***
## factor(category)Бытовая химия 2.297e-05 ***
## factor(category)Замороженные продукты 0.0009892 ***
## factor(category)Красота и здоровье 1.842e-12 ***
## factor(category)Красота и здорровье 0.8584101
## factor(category)Молоко и яйца 0.0002979 ***
## factor(category)Мясо < 2.2e-16 ***
## factor(category)Овощи 1.097e-05 ***
## factor(category)Рыба 0.1573420
## factor(category)Сладости к чаю 0.0740503 .
## factor(category)Фрукты и ягоды < 2.2e-16 ***
## factor(category)Хлебобулочные изделия 0.0001532 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Магнит
logit_3hyp_mid_magnit <- glm(factor(middle_number_changed) ~ factor(price_increase) + right_number_changed + left_number_changed + left_number_prev_changed + middle_number_prev_changed + price_diff + factor(category), data = magnit3, family = binomial(link = "logit"), x = TRUE)
sum_hyp3_mid_magnit <- coeftest(logit_3hyp_mid_magnit)
sum_hyp3_mid_magnit
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) -2.69376806 0.02158316 -124.8088
## factor(price_increase)1 3.92290502 0.03096761 126.6777
## right_number_changed 2.71292382 0.03014829 89.9860
## left_number_changed 2.89703545 0.02902923 99.7972
## left_number_prev_changed -0.53638455 0.02895116 -18.5272
## middle_number_prev_changed 0.86408468 0.02204903 39.1892
## price_diff -0.02490743 0.00035908 -69.3645
## factor(category)Безалкогольные напитки -0.00746563 0.04240685 -0.1760
## factor(category)Бытовая химия 0.21933865 0.04339289 5.0547
## factor(category)Замороженные продукты 0.01137694 0.04606173 0.2470
## factor(category)Красота и здоровье -0.19287572 0.03623392 -5.3231
## factor(category)Молоко и яйца 0.01830607 0.03172542 0.5770
## factor(category)Мясо 0.18478387 0.03404769 5.4272
## factor(category)Овощи 0.35428593 0.06299498 5.6240
## factor(category)Рыба 0.16408236 0.04261470 3.8504
## factor(category)Сладости к чаю -0.18947494 0.03414209 -5.5496
## factor(category)Фрукты и ягоды 0.24271785 0.07923416 3.0633
## factor(category)Хлебобулочные изделия -0.55161002 0.11714846 -4.7086
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## right_number_changed < 2.2e-16 ***
## left_number_changed < 2.2e-16 ***
## left_number_prev_changed < 2.2e-16 ***
## middle_number_prev_changed < 2.2e-16 ***
## price_diff < 2.2e-16 ***
## factor(category)Безалкогольные напитки 0.8602565
## factor(category)Бытовая химия 4.310e-07 ***
## factor(category)Замороженные продукты 0.8049133
## factor(category)Красота и здоровье 1.020e-07 ***
## factor(category)Молоко и яйца 0.5639287
## factor(category)Мясо 5.724e-08 ***
## factor(category)Овощи 1.865e-08 ***
## factor(category)Рыба 0.0001179 ***
## factor(category)Сладости к чаю 2.863e-08 ***
## factor(category)Фрукты и ягоды 0.0021891 **
## factor(category)Хлебобулочные изделия 2.494e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Пятерочка
logit_3hyp_mid_pyat <- glm(factor(middle_number_changed) ~ factor(price_increase) + right_number_changed + left_number_changed + left_number_prev_changed + middle_number_prev_changed + price_diff + factor(category), data = pyat3, family = binomial(link = "logit"), x = TRUE)
sum_hyp3_mid_pyat <- coeftest(logit_3hyp_mid_pyat)
sum_hyp3_mid_pyat
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) -2.35811248 0.01348295 -174.8959
## factor(price_increase)1 4.12137402 0.02268349 181.6904
## right_number_changed 2.16789311 0.02073417 104.5565
## left_number_changed 2.10165797 0.01863834 112.7599
## left_number_prev_changed -0.89774750 0.01760424 -50.9961
## middle_number_prev_changed 1.51273080 0.01327544 113.9496
## price_diff -0.04434713 0.00047666 -93.0381
## factor(category)Безалкогольные напитки -0.26519207 0.02334213 -11.3611
## factor(category)Бытовая химия 0.15076214 0.03560834 4.2339
## factor(category)Замороженные продукты 0.11137563 0.03381607 3.2936
## factor(category)Красота и здоровье 0.15286131 0.02169486 7.0460
## factor(category)Красота и здорровье -0.08534466 0.47839375 -0.1784
## factor(category)Молоко и яйца -0.07430058 0.02054116 -3.6172
## factor(category)Мясо 0.37188852 0.02626507 14.1591
## factor(category)Овощи -0.19968318 0.04541237 -4.3971
## factor(category)Рыба 0.05303568 0.03750575 1.4141
## factor(category)Сладости к чаю 0.03244488 0.01816315 1.7863
## factor(category)Фрукты и ягоды 0.59902859 0.05845543 10.2476
## factor(category)Хлебобулочные изделия -0.18304474 0.04834922 -3.7859
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## right_number_changed < 2.2e-16 ***
## left_number_changed < 2.2e-16 ***
## left_number_prev_changed < 2.2e-16 ***
## middle_number_prev_changed < 2.2e-16 ***
## price_diff < 2.2e-16 ***
## factor(category)Безалкогольные напитки < 2.2e-16 ***
## factor(category)Бытовая химия 2.297e-05 ***
## factor(category)Замороженные продукты 0.0009892 ***
## factor(category)Красота и здоровье 1.842e-12 ***
## factor(category)Красота и здорровье 0.8584101
## factor(category)Молоко и яйца 0.0002979 ***
## factor(category)Мясо < 2.2e-16 ***
## factor(category)Овощи 1.097e-05 ***
## factor(category)Рыба 0.1573420
## factor(category)Сладости к чаю 0.0740503 .
## factor(category)Фрукты и ягоды < 2.2e-16 ***
## factor(category)Хлебобулочные изделия 0.0001532 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
T-статистика (3 гипотеза - серединная цифра)
#Лавка
4.12137402/0.02268349
## [1] 181.6905
#Магнит
3.92290502/0.03096761
## [1] 126.6777
#Пятерочка
4.12137402/0.02268349
## [1] 181.6905
Проверка 4 гипотезы
Лавка
logit_4_lavka <- glm(factor(end_nine) ~ factor(price_increase) + factor(prev_price_nine) + price_diff + factor(category) + factor(weekdays) + factor(holiday), data = lavka4_5, family = binomial(link = "logit"), x = TRUE)
sum_hyp4_lavka <- coeftest(logit_4_lavka)
sum_hyp4_lavka
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) -1.19467730 0.09999731 -11.9471
## factor(price_increase)1 0.31222086 0.02040178 15.3036
## factor(prev_price_nine)1 3.84464139 0.01353858 283.9766
## price_diff 0.01765488 0.00032158 54.9004
## factor(category)Бакалея -0.00919095 0.09903218 -0.0928
## factor(category)Безалкогольные напитки -0.25461654 0.10119073 -2.5162
## factor(category)Бытовая химия 0.50280654 0.10534286 4.7730
## factor(category)Замороженные продукты 0.45676785 0.10064082 4.5386
## factor(category)Красота и здоровье 0.47218098 0.10149055 4.6525
## factor(category)Молоко и яйца -0.32441389 0.09960921 -3.2569
## factor(category)Мясо 0.33171069 0.10212061 3.2482
## factor(category)Напитки 0.26693396 0.10226527 2.6102
## factor(category)Овощи 0.49986460 0.10816096 4.6215
## factor(category)Рыба 0.38399145 0.10674724 3.5972
## factor(category)Сладости к чаю -0.09607730 0.09978569 -0.9628
## factor(category)Фрукты и ягоды 0.99939595 0.11304585 8.8406
## factor(category)Хлебобулочные изделия -0.17138481 0.10605384 -1.6160
## factor(weekdays)понедельник -0.00600306 0.02070193 -0.2900
## factor(weekdays)пятница -0.01247833 0.02058626 -0.6061
## factor(weekdays)среда 0.46988560 0.03362481 13.9744
## factor(weekdays)четверг 0.58433180 0.08137625 7.1806
## factor(holiday)1 -0.33008658 0.01268247 -26.0270
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## factor(prev_price_nine)1 < 2.2e-16 ***
## price_diff < 2.2e-16 ***
## factor(category)Бакалея 0.9260563
## factor(category)Безалкогольные напитки 0.0118626 *
## factor(category)Бытовая химия 1.815e-06 ***
## factor(category)Замороженные продукты 5.663e-06 ***
## factor(category)Красота и здоровье 3.280e-06 ***
## factor(category)Молоко и яйца 0.0011265 **
## factor(category)Мясо 0.0011613 **
## factor(category)Напитки 0.0090486 **
## factor(category)Овощи 3.810e-06 ***
## factor(category)Рыба 0.0003217 ***
## factor(category)Сладости к чаю 0.3356296
## factor(category)Фрукты и ягоды < 2.2e-16 ***
## factor(category)Хлебобулочные изделия 0.1060906
## factor(weekdays)понедельник 0.7718346
## factor(weekdays)пятница 0.5444162
## factor(weekdays)среда < 2.2e-16 ***
## factor(weekdays)четверг 6.940e-13 ***
## factor(holiday)1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hyp4_lavka_tidy_result <- tidy(sum_hyp4_lavka)
write.xlsx(hyp4_lavka_tidy_result, "D:/Рабочий стол/4 курс/диплом/R/hyp4_lavka_tidy_result.xlsx", rowNames = TRUE)
eff_lavka_23 <- summary(margins(logit_4_lavka, variables = "price_increase"))
eff_lavka_23
## factor AME SE z p lower upper
## price_increase1 0.0337 0.0022 15.3208 0.0000 0.0294 0.0380
st_lavka <- 0.31222086/0.02040178
st_lavka #больше чем Tcr -> отвергаем нулевую гипотезу
## [1] 15.30361
Магнит
logit_4_magnit <- glm(factor(end_nine) ~ factor(price_increase) + factor(prev_price_nine) + price_diff + factor(category) + factor(weekdays) + factor(holiday), data = magnit4_5, family = binomial(link = "logit"), x = TRUE)
sum_hyp4_magnit <- coeftest(logit_4_magnit)
sum_hyp4_magnit
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) 1.5783e-01 2.7148e-02 5.8138
## factor(price_increase)1 -6.0969e-01 4.0043e-02 -15.2258
## factor(prev_price_nine)1 5.7780e+00 4.2061e-02 137.3725
## price_diff -1.3566e-03 3.7629e-04 -3.6053
## factor(category)Безалкогольные напитки 8.3409e-02 4.4800e-02 1.8618
## factor(category)Бытовая химия 4.1261e-01 5.6688e-02 7.2787
## factor(category)Замороженные продукты -8.4813e-02 5.5770e-02 -1.5208
## factor(category)Красота и здоровье 5.6683e-01 3.9111e-02 14.4930
## factor(category)Молоко и яйца -1.3813e-01 3.5426e-02 -3.8992
## factor(category)Мясо -3.8467e-01 4.8722e-02 -7.8952
## factor(category)Овощи -8.2978e-01 8.4646e-02 -9.8029
## factor(category)Рыба -1.8442e-01 5.8816e-02 -3.1355
## factor(category)Сладости к чаю -8.2154e-02 4.0247e-02 -2.0412
## factor(category)Фрукты и ягоды -1.2634e+00 1.1944e-01 -10.5775
## factor(category)Хлебобулочные изделия -8.9154e-01 8.6326e-02 -10.3276
## factor(weekdays)понедельник -3.0786e+00 2.6698e-02 -115.3145
## factor(weekdays)пятница -2.0931e+01 8.6347e+01 -0.2424
## factor(weekdays)среда -2.1777e+01 2.1989e+02 -0.0990
## factor(weekdays)четверг -2.1338e+01 8.7210e+01 -0.2447
## factor(holiday)1 1.1681e+00 2.5728e-02 45.4039
## Pr(>|z|)
## (Intercept) 6.107e-09 ***
## factor(price_increase)1 < 2.2e-16 ***
## factor(prev_price_nine)1 < 2.2e-16 ***
## price_diff 0.0003118 ***
## factor(category)Безалкогольные напитки 0.0626302 .
## factor(category)Бытовая химия 3.370e-13 ***
## factor(category)Замороженные продукты 0.1283195
## factor(category)Красота и здоровье < 2.2e-16 ***
## factor(category)Молоко и яйца 9.651e-05 ***
## factor(category)Мясо 2.899e-15 ***
## factor(category)Овощи < 2.2e-16 ***
## factor(category)Рыба 0.0017153 **
## factor(category)Сладости к чаю 0.0412276 *
## factor(category)Фрукты и ягоды < 2.2e-16 ***
## factor(category)Хлебобулочные изделия < 2.2e-16 ***
## factor(weekdays)понедельник < 2.2e-16 ***
## factor(weekdays)пятница 0.8084633
## factor(weekdays)среда 0.9211105
## factor(weekdays)четверг 0.8067059
## factor(holiday)1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eff_magnit_23 <- summary(margins(logit_4_magnit, variables = "price_increase"))
eff_magnit_23
## factor AME SE z p lower upper
## price_increase1 -0.0211 0.0013 -16.5662 0.0000 -0.0236 -0.0186
t_st_magnit <- -6.0969e-01/4.0043e-02
t_st_magnit #меньше tcr -> не отвергаем нулевую гипотезу
## [1] -15.22588
Пятерочка
logit_4_pyat <- glm(factor(end_nine) ~ factor(price_increase) + factor(prev_price_nine) + price_diff + factor(category) + factor(weekdays) + factor(holiday), data = pyat4_5, family = binomial(link = "logit"), x = TRUE)
sum_hyp4_pyat <- coeftest(logit_4_pyat)
sum_hyp4_pyat
##
## z test of coefficients:
##
## Estimate Std. Error z value
## (Intercept) 1.05312162 0.10323753 10.2010
## factor(price_increase)1 -1.28756986 0.03738787 -34.4382
## factor(prev_price_nine)1 3.27963362 0.03298031 99.4422
## price_diff 0.00488868 0.00064004 7.6380
## factor(category)Безалкогольные напитки 0.13709974 0.05417567 2.5307
## factor(category)Бытовая химия 0.82790735 0.12415685 6.6682
## factor(category)Замороженные продукты 0.35102295 0.09233074 3.8018
## factor(category)Красота и здоровье 1.27382468 0.09231436 13.7988
## factor(category)Красота и здорровье 0.43874575 1.04586643 0.4195
## factor(category)Молоко и яйца 1.36891726 0.08035655 17.0355
## factor(category)Мясо 0.08002979 0.06915550 1.1572
## factor(category)Овощи -0.54368002 0.07551990 -7.1992
## factor(category)Рыба 0.46538213 0.11563027 4.0247
## factor(category)Сладости к чаю -0.34558856 0.03869372 -8.9314
## factor(category)Фрукты и ягоды -0.98187706 0.08549377 -11.4848
## factor(category)Хлебобулочные изделия 0.26555111 0.12593442 2.1086
## factor(weekdays)вторник 0.89864262 0.11791688 7.6210
## factor(weekdays)понедельник 0.70854326 0.10335168 6.8557
## factor(weekdays)пятница 0.68920522 0.10106713 6.8193
## factor(weekdays)среда 1.08044451 0.17891863 6.0387
## factor(weekdays)суббота 0.25895747 0.11764492 2.2012
## factor(holiday)1 -0.17515943 0.03508705 -4.9921
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## factor(price_increase)1 < 2.2e-16 ***
## factor(prev_price_nine)1 < 2.2e-16 ***
## price_diff 2.205e-14 ***
## factor(category)Безалкогольные напитки 0.0113851 *
## factor(category)Бытовая химия 2.589e-11 ***
## factor(category)Замороженные продукты 0.0001436 ***
## factor(category)Красота и здоровье < 2.2e-16 ***
## factor(category)Красота и здорровье 0.6748474
## factor(category)Молоко и яйца < 2.2e-16 ***
## factor(category)Мясо 0.2471726
## factor(category)Овощи 6.058e-13 ***
## factor(category)Рыба 5.704e-05 ***
## factor(category)Сладости к чаю < 2.2e-16 ***
## factor(category)Фрукты и ягоды < 2.2e-16 ***
## factor(category)Хлебобулочные изделия 0.0349752 *
## factor(weekdays)вторник 2.517e-14 ***
## factor(weekdays)понедельник 7.099e-12 ***
## factor(weekdays)пятница 9.150e-12 ***
## factor(weekdays)среда 1.553e-09 ***
## factor(weekdays)суббота 0.0277234 *
## factor(holiday)1 5.971e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eff_pyat_23 <- summary(margins(logit_4_pyat, variables = "price_increase"))
eff_pyat_23
## factor AME SE z p lower upper
## price_increase1 -0.0288 0.0010 -27.8791 0.0000 -0.0308 -0.0268
t_st_pyat <- -1.28756986/0.03738787
t_st_pyat #меньше tcr -> не отвергаем нулевую гипотезу
## [1] -34.43817
Проверка 5 гипотезы
Лавка
hyp5_lavka_fixed <- plm(price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine)*factor(price_increase) + price_diff + factor(category) + fx + factor(holiday) + factor(weekdays), index = c("Наименование", "Дата"), model = "within", data = lavka4_5)
sum_hyp5_lavka_fixed <- summary(hyp5_lavka_fixed)
sum_hyp5_lavka_fixed
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) *
## factor(price_increase) + price_diff + factor(category) +
## fx + factor(holiday) + factor(weekdays), data = lavka4_5,
## model = "within", index = c("Наименование", "Дата"))
##
## Unbalanced Panel: n = 17862, T = 1-168, N = 242430
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.8351111 -0.0019590 0.0055348 0.0159341 42.4315884
##
## Coefficients: (1 dropped because of singularities)
## Estimate Std. Error
## factor(prev_price_nine)1 8.3422e-03 1.1533e-03
## factor(price_increase)1 1.7802e-01 1.8512e-03
## price_diff 4.4043e-03 1.5014e-05
## factor(category)Бакалея -4.2464e-02 2.1240e-01
## factor(category)Бытовая химия -8.2562e-02 2.9086e-01
## factor(category)Замороженные продукты -9.5598e-02 2.5651e-01
## factor(category)Молоко и яйца -1.1587e-02 6.1207e-02
## factor(category)Мясо -9.1743e-02 3.7174e-01
## factor(category)Напитки -4.1239e-02 2.8515e-01
## fx -1.4196e-04 4.6377e-05
## factor(holiday)1 1.0500e-03 8.3568e-04
## factor(weekdays)понедельник -3.1336e-03 1.4154e-03
## factor(weekdays)пятница 9.1305e-04 2.0100e-03
## factor(weekdays)среда 5.5876e-03 2.6878e-03
## factor(weekdays)четверг -5.1772e-03 4.8464e-03
## factor(prev_price_nine)1:factor(price_increase)1 -6.9064e-02 2.4467e-03
## t-value Pr(>|t|)
## factor(prev_price_nine)1 7.2332 4.732e-13 ***
## factor(price_increase)1 96.1638 < 2.2e-16 ***
## price_diff 293.3455 < 2.2e-16 ***
## factor(category)Бакалея -0.1999 0.841538
## factor(category)Бытовая химия -0.2839 0.776525
## factor(category)Замороженные продукты -0.3727 0.709380
## factor(category)Молоко и яйца -0.1893 0.849853
## factor(category)Мясо -0.2468 0.805070
## factor(category)Напитки -0.1446 0.885008
## fx -3.0609 0.002207 **
## factor(holiday)1 1.2565 0.208945
## factor(weekdays)понедельник -2.2139 0.026839 *
## factor(weekdays)пятница 0.4543 0.649637
## factor(weekdays)среда 2.0788 0.037633 *
## factor(weekdays)четверг -1.0683 0.285409
## factor(prev_price_nine)1:factor(price_increase)1 -28.2275 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 13605
## Residual Sum of Squares: 8127.6
## R-Squared: 0.40259
## Adj. R-Squared: 0.35503
## F-statistic: 9457.75 on 16 and 224552 DF, p-value: < 2.22e-16
hyp5_lavka_random <- plm(price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine)*factor(price_increase) + price_diff + factor(category) + fx + factor(holiday) + factor(weekdays), index = c("Наименование", "Дата"), model = "random", data = lavka4_5)
sum_hyp5_lavka_random <- summary(hyp5_lavka_random)
sum_hyp5_lavka_random
## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) *
## factor(price_increase) + price_diff + factor(category) +
## fx + factor(holiday) + factor(weekdays), data = lavka4_5,
## model = "random", index = c("Наименование", "Дата"))
##
## Unbalanced Panel: n = 17862, T = 1-168, N = 242430
##
## Effects:
## var std.dev share
## idiosyncratic 0.036195 0.190250 0.925
## individual 0.002931 0.054142 0.075
## theta:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.03819 0.31543 0.47819 0.42166 0.53599 0.73834
##
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.784 0.001 0.006 0.000 0.012 43.374
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -8.2274e-04 1.0617e-02
## factor(prev_price_nine)1 6.1221e-03 9.9101e-04
## factor(price_increase)1 1.7779e-01 1.7723e-03
## price_diff 4.3849e-03 1.4582e-05
## factor(category)Бакалея -5.2711e-04 1.0163e-02
## factor(category)Безалкогольные напитки 1.4675e-02 1.0452e-02
## factor(category)Бытовая химия -2.5729e-03 1.0733e-02
## factor(category)Замороженные продукты 6.2492e-04 1.0285e-02
## factor(category)Красота и здоровье 2.0047e-04 1.0383e-02
## factor(category)Молоко и яйца 2.0190e-03 1.0228e-02
## factor(category)Мясо -2.9331e-03 1.0449e-02
## factor(category)Напитки -2.3867e-03 1.0438e-02
## factor(category)Овощи -3.4342e-05 1.0968e-02
## factor(category)Рыба -6.5747e-03 1.0891e-02
## factor(category)Сладости к чаю 8.5887e-04 1.0243e-02
## factor(category)Фрукты и ягоды -2.6939e-03 1.1101e-02
## factor(category)Хлебобулочные изделия 4.3636e-03 1.0876e-02
## fx -1.1657e-04 4.1739e-05
## factor(holiday)1 1.0008e-03 7.9837e-04
## factor(weekdays)понедельник -2.3645e-03 1.3025e-03
## factor(weekdays)пятница -1.7850e-03 1.5151e-03
## factor(weekdays)среда 3.5375e-03 2.4689e-03
## factor(weekdays)четверг -8.0975e-03 4.4619e-03
## factor(prev_price_nine)1:factor(price_increase)1 -6.7469e-02 2.3448e-03
## z-value Pr(>|z|)
## (Intercept) -0.0775 0.938234
## factor(prev_price_nine)1 6.1776 6.507e-10 ***
## factor(price_increase)1 100.3182 < 2.2e-16 ***
## price_diff 300.7137 < 2.2e-16 ***
## factor(category)Бакалея -0.0519 0.958634
## factor(category)Безалкогольные напитки 1.4041 0.160301
## factor(category)Бытовая химия -0.2397 0.810548
## factor(category)Замороженные продукты 0.0608 0.951552
## factor(category)Красота и здоровье 0.0193 0.984595
## factor(category)Молоко и яйца 0.1974 0.843522
## factor(category)Мясо -0.2807 0.778924
## factor(category)Напитки -0.2287 0.819127
## factor(category)Овощи -0.0031 0.997502
## factor(category)Рыба -0.6037 0.546055
## factor(category)Сладости к чаю 0.0838 0.933176
## factor(category)Фрукты и ягоды -0.2427 0.808266
## factor(category)Хлебобулочные изделия 0.4012 0.688271
## fx -2.7929 0.005223 **
## factor(holiday)1 1.2535 0.210011
## factor(weekdays)понедельник -1.8154 0.069459 .
## factor(weekdays)пятница -1.1782 0.238735
## factor(weekdays)среда 1.4328 0.151917
## factor(weekdays)четверг -1.8148 0.069553 .
## factor(prev_price_nine)1:factor(price_increase)1 -28.7741 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 13878
## Residual Sum of Squares: 8369.4
## R-Squared: 0.39693
## Adj. R-Squared: 0.39687
## Chisq: 159664 on 23 DF, p-value: < 2.22e-16
#Hausman test
lavka_hausman <- phtest(hyp5_lavka_fixed, hyp5_lavka_random)
lavka_hausman
##
## Hausman Test
##
## data: price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) * ...
## chisq = 51.317, df = 16, p-value = 1.411e-05
## alternative hypothesis: one model is inconsistent
#выбираем модель fixed effects
Магнит
hyp5_magnit_fixed <- plm(price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine)*factor(price_increase) + price_diff + factor(category) + fx + factor(holiday) + factor(weekdays), index = c("Наименование", "Дата"), model = "within", data = magnit4_5)
sum_hyp5_magnit_fixed <- summary(hyp5_magnit_fixed)
sum_hyp5_magnit_fixed
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) *
## factor(price_increase) + price_diff + factor(category) +
## fx + factor(holiday) + factor(weekdays), data = magnit4_5,
## model = "within", index = c("Наименование", "Дата"))
##
## Unbalanced Panel: n = 22701, T = 1-77, N = 209219
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.566758 -0.010457 0.012936 0.031841 4.712719
##
## Coefficients:
## Estimate Std. Error
## factor(prev_price_nine)1 4.4596e-03 1.0446e-03
## factor(price_increase)1 2.4656e-01 9.2724e-04
## price_diff 3.0186e-03 9.0243e-06
## factor(category)Безалкогольные напитки -6.6900e-04 1.4359e-01
## factor(category)Замороженные продукты -5.4220e-03 1.0240e-01
## factor(category)Красота и здоровье 5.7313e-04 1.3431e-01
## factor(category)Молоко и яйца 2.2474e-02 1.2818e-01
## factor(category)Овощи 2.5685e-02 4.8765e-02
## factor(category)Рыба 2.0916e-02 2.7063e-02
## factor(category)Сладости к чаю -2.1010e-03 3.9168e-02
## factor(category)Хлебобулочные изделия 1.3516e-02 7.6471e-02
## fx 9.3647e-05 4.1251e-05
## factor(holiday)1 -2.0640e-03 6.5490e-04
## factor(weekdays)понедельник 1.3845e-03 9.9865e-04
## factor(weekdays)пятница -2.0569e-03 2.0550e-03
## factor(weekdays)среда -9.3132e-04 2.0121e-03
## factor(weekdays)четверг 7.2167e-04 2.0629e-03
## factor(prev_price_nine)1:factor(price_increase)1 -5.8769e-02 1.8776e-03
## t-value Pr(>|t|)
## factor(prev_price_nine)1 4.2693 1.962e-05 ***
## factor(price_increase)1 265.9070 < 2.2e-16 ***
## price_diff 334.5008 < 2.2e-16 ***
## factor(category)Безалкогольные напитки -0.0047 0.996283
## factor(category)Замороженные продукты -0.0530 0.957771
## factor(category)Красота и здоровье 0.0043 0.996595
## factor(category)Молоко и яйца 0.1753 0.860816
## factor(category)Овощи 0.5267 0.598395
## factor(category)Рыба 0.7729 0.439605
## factor(category)Сладости к чаю -0.0536 0.957222
## factor(category)Хлебобулочные изделия 0.1768 0.859704
## fx 2.2702 0.023197 *
## factor(holiday)1 -3.1516 0.001624 **
## factor(weekdays)понедельник 1.3864 0.165629
## factor(weekdays)пятница -1.0009 0.316875
## factor(weekdays)среда -0.4629 0.643464
## factor(weekdays)четверг 0.3498 0.726465
## factor(prev_price_nine)1:factor(price_increase)1 -31.3000 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 8083
## Residual Sum of Squares: 2883.8
## R-Squared: 0.64323
## Adj. R-Squared: 0.59977
## F-statistic: 18680 on 18 and 186500 DF, p-value: < 2.22e-16
hyp5_magnit_random <- plm(price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine)*factor(price_increase) + price_diff + factor(category) + fx + factor(holiday) + factor(weekdays), index = c("Наименование", "Дата"), model = "random", data = magnit4_5)
sum_hyp5_magnit_random <- summary(hyp5_magnit_random)
sum_hyp5_magnit_random
## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) *
## factor(price_increase) + price_diff + factor(category) +
## fx + factor(holiday) + factor(weekdays), data = magnit4_5,
## model = "random", index = c("Наименование", "Дата"))
##
## Unbalanced Panel: n = 22701, T = 1-77, N = 209219
##
## Effects:
## var std.dev share
## idiosyncratic 0.01546 0.12435 1
## individual 0.00000 0.00000 0
## theta:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.5570189 0.0042208 0.0178198 0.0242647 5.0144668
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.0263e-02 2.2441e-03
## factor(prev_price_nine)1 3.4671e-03 9.2052e-04
## factor(price_increase)1 2.3846e-01 8.7138e-04
## price_diff 3.0357e-03 8.7217e-06
## factor(category)Безалкогольные напитки -1.4058e-04 1.0736e-03
## factor(category)Бытовая химия -6.2427e-04 1.4597e-03
## factor(category)Замороженные продукты 5.0506e-03 1.4120e-03
## factor(category)Красота и здоровье 8.0207e-03 1.0642e-03
## factor(category)Молоко и яйца -6.8835e-03 8.4374e-04
## factor(category)Мясо 7.7333e-03 1.1216e-03
## factor(category)Овощи -1.9282e-02 1.8395e-03
## factor(category)Рыба 5.4604e-04 1.4014e-03
## factor(category)Сладости к чаю 6.7539e-03 8.7294e-04
## factor(category)Фрукты и ягоды -3.6568e-02 2.5376e-03
## factor(category)Хлебобулочные изделия 4.3435e-03 1.9070e-03
## fx 2.9678e-04 3.0061e-05
## factor(holiday)1 -3.4083e-03 6.0900e-04
## factor(weekdays)понедельник -5.1155e-04 9.0628e-04
## factor(weekdays)пятница -6.2390e-03 1.1232e-03
## factor(weekdays)среда -4.0727e-03 1.8550e-03
## factor(weekdays)четверг -7.6863e-04 1.1771e-03
## factor(prev_price_nine)1:factor(price_increase)1 -5.8880e-02 1.7820e-03
## z-value Pr(>|z|)
## (Intercept) -17.9417 < 2.2e-16 ***
## factor(prev_price_nine)1 3.7664 0.0001656 ***
## factor(price_increase)1 273.6600 < 2.2e-16 ***
## price_diff 348.0660 < 2.2e-16 ***
## factor(category)Безалкогольные напитки -0.1309 0.8958213
## factor(category)Бытовая химия -0.4277 0.6688954
## factor(category)Замороженные продукты 3.5769 0.0003477 ***
## factor(category)Красота и здоровье 7.5369 4.814e-14 ***
## factor(category)Молоко и яйца -8.1583 3.397e-16 ***
## factor(category)Мясо 6.8951 5.383e-12 ***
## factor(category)Овощи -10.4820 < 2.2e-16 ***
## factor(category)Рыба 0.3896 0.6968058
## factor(category)Сладости к чаю 7.7370 1.018e-14 ***
## factor(category)Фрукты и ягоды -14.4101 < 2.2e-16 ***
## factor(category)Хлебобулочные изделия 2.2777 0.0227447 *
## fx 9.8728 < 2.2e-16 ***
## factor(holiday)1 -5.5966 2.186e-08 ***
## factor(weekdays)понедельник -0.5644 0.5724489
## factor(weekdays)пятница -5.5546 2.783e-08 ***
## factor(weekdays)среда -2.1956 0.0281237 *
## factor(weekdays)четверг -0.6530 0.5137680
## factor(prev_price_nine)1:factor(price_increase)1 -33.0418 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 8464.2
## Residual Sum of Squares: 3150
## R-Squared: 0.62784
## Adj. R-Squared: 0.6278
## Chisq: 352922 on 21 DF, p-value: < 2.22e-16
#Hausman test
magnit_hausman <- phtest(hyp5_magnit_fixed, hyp5_magnit_random)
magnit_hausman
##
## Hausman Test
##
## data: price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) * ...
## chisq = 905.73, df = 18, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent
#выбираем модель fixed effects
Пятерочка
hyp5_pyat_fixed <- plm(price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine)*factor(price_increase) + price_diff + factor(category) + fx + factor(holiday) + factor(weekdays), index = c("Наименование", "Дата"), model = "within", data = pyat4_5)
sum_hyp5_pyat_fixed <- summary(hyp5_pyat_fixed)
sum_hyp5_pyat_fixed
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) *
## factor(price_increase) + price_diff + factor(category) +
## fx + factor(holiday) + factor(weekdays), data = pyat4_5,
## model = "within", index = c("Наименование", "Дата"))
##
## Unbalanced Panel: n = 11741, T = 1-114, N = 255887
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.8426656 -0.0178954 0.0045973 0.0159257 7.6153216
##
## Coefficients:
## Estimate Std. Error
## factor(prev_price_nine)1 -4.4071e-03 9.8143e-04
## factor(price_increase)1 2.9337e-01 3.0695e-03
## price_diff 6.0827e-03 1.2250e-05
## factor(category)Безалкогольные напитки 7.4322e-03 1.5826e-02
## factor(category)Бытовая химия -2.2178e-02 2.6647e-02
## factor(category)Замороженные продукты -8.6789e-03 1.0673e-02
## factor(category)Красота и здоровье 5.7240e-04 2.3258e-02
## factor(category)Молоко и яйца -6.8349e-03 5.8036e-03
## factor(category)Мясо 6.4888e-03 1.2741e-02
## factor(category)Овощи 9.3689e-03 1.2665e-02
## factor(category)Рыба -4.0152e-03 5.8673e-03
## factor(category)Сладости к чаю -3.4425e-03 4.7660e-03
## factor(category)Фрукты и ягоды -1.3209e-03 2.1691e-02
## factor(category)Хлебобулочные изделия 2.9450e-03 8.6115e-03
## fx 2.9002e-05 2.5818e-05
## factor(holiday)1 7.3699e-04 4.7878e-04
## factor(weekdays)вторник 3.3107e-02 1.9345e-03
## factor(weekdays)понедельник 2.7394e-02 1.7797e-03
## factor(weekdays)пятница 2.2318e-02 1.7082e-03
## factor(weekdays)среда 2.4199e-02 2.4423e-03
## factor(weekdays)суббота 2.7065e-02 2.0503e-03
## factor(prev_price_nine)1:factor(price_increase)1 -2.0840e-01 3.0789e-03
## t-value Pr(>|t|)
## factor(prev_price_nine)1 -4.4905 7.109e-06 ***
## factor(price_increase)1 95.5772 < 2.2e-16 ***
## price_diff 496.5559 < 2.2e-16 ***
## factor(category)Безалкогольные напитки 0.4696 0.6386
## factor(category)Бытовая химия -0.8323 0.4052
## factor(category)Замороженные продукты -0.8131 0.4161
## factor(category)Красота и здоровье 0.0246 0.9804
## factor(category)Молоко и яйца -1.1777 0.2389
## factor(category)Мясо 0.5093 0.6105
## factor(category)Овощи 0.7398 0.4594
## factor(category)Рыба -0.6843 0.4938
## factor(category)Сладости к чаю -0.7223 0.4701
## factor(category)Фрукты и ягоды -0.0609 0.9514
## factor(category)Хлебобулочные изделия 0.3420 0.7324
## fx 1.1233 0.2613
## factor(holiday)1 1.5393 0.1237
## factor(weekdays)вторник 17.1140 < 2.2e-16 ***
## factor(weekdays)понедельник 15.3924 < 2.2e-16 ***
## factor(weekdays)пятница 13.0653 < 2.2e-16 ***
## factor(weekdays)среда 9.9086 < 2.2e-16 ***
## factor(weekdays)суббота 13.2008 < 2.2e-16 ***
## factor(prev_price_nine)1:factor(price_increase)1 -67.6850 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 7405.4
## Residual Sum of Squares: 2672.8
## R-Squared: 0.63907
## Adj. R-Squared: 0.62168
## F-statistic: 19648 on 22 and 244124 DF, p-value: < 2.22e-16
hyp5_pyat_random <- plm(price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine)*factor(price_increase) + price_diff + factor(category) + fx + factor(holiday) + factor(weekdays), index = c("Наименование", "Дата"), model = "random", data = pyat4_5)
sum_hyp5_pyat_random <- summary(hyp5_pyat_random)
sum_hyp5_pyat_random
## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) *
## factor(price_increase) + price_diff + factor(category) +
## fx + factor(holiday) + factor(weekdays), data = pyat4_5,
## model = "random", index = c("Наименование", "Дата"))
##
## Unbalanced Panel: n = 11741, T = 1-114, N = 255887
##
## Effects:
## var std.dev share
## idiosyncratic 0.01095 0.10464 1
## individual 0.00000 0.00000 0
## theta:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -4.1807783 -0.0154887 0.0064378 0.0110892 7.8731015
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -2.9427e-02 2.6063e-03
## factor(prev_price_nine)1 -5.4766e-03 8.9169e-04
## factor(price_increase)1 2.9526e-01 2.9051e-03
## price_diff 6.1128e-03 1.2030e-05
## factor(category)Безалкогольные напитки 3.7915e-03 7.4798e-04
## factor(category)Бытовая химия 9.7137e-03 1.2501e-03
## factor(category)Замороженные продукты 3.0633e-03 1.1851e-03
## factor(category)Красота и здоровье 3.8478e-03 7.6881e-04
## factor(category)Красота и здорровье -7.5142e-03 1.6774e-02
## factor(category)Молоко и яйца -1.3236e-03 6.8838e-04
## factor(category)Мясо 8.6286e-04 9.7505e-04
## factor(category)Овощи 5.4035e-03 1.4720e-03
## factor(category)Рыба 1.7146e-03 1.3075e-03
## factor(category)Сладости к чаю 6.6991e-03 6.2044e-04
## factor(category)Фрукты и ягоды 1.1482e-02 2.0860e-03
## factor(category)Хлебобулочные изделия 2.8720e-03 1.5590e-03
## fx 2.2137e-05 2.3694e-05
## factor(holiday)1 9.7469e-04 4.7134e-04
## factor(weekdays)вторник 2.8696e-02 1.8334e-03
## factor(weekdays)понедельник 2.3215e-02 1.7183e-03
## factor(weekdays)пятница 1.8562e-02 1.6841e-03
## factor(weekdays)среда 1.9680e-02 2.3517e-03
## factor(weekdays)суббота 2.5458e-02 2.0310e-03
## factor(prev_price_nine)1:factor(price_increase)1 -2.1367e-01 2.9193e-03
## z-value Pr(>|z|)
## (Intercept) -11.2909 < 2.2e-16 ***
## factor(prev_price_nine)1 -6.1418 8.157e-10 ***
## factor(price_increase)1 101.6343 < 2.2e-16 ***
## price_diff 508.1522 < 2.2e-16 ***
## factor(category)Безалкогольные напитки 5.0689 4.000e-07 ***
## factor(category)Бытовая химия 7.7705 7.815e-15 ***
## factor(category)Замороженные продукты 2.5849 0.0097410 **
## factor(category)Красота и здоровье 5.0049 5.590e-07 ***
## factor(category)Красота и здорровье -0.4480 0.6541785
## factor(category)Молоко и яйца -1.9227 0.0545136 .
## factor(category)Мясо 0.8849 0.3761875
## factor(category)Овощи 3.6709 0.0002417 ***
## factor(category)Рыба 1.3114 0.1897236
## factor(category)Сладости к чаю 10.7973 < 2.2e-16 ***
## factor(category)Фрукты и ягоды 5.5042 3.709e-08 ***
## factor(category)Хлебобулочные изделия 1.8422 0.0654474 .
## fx 0.9343 0.3501512
## factor(holiday)1 2.0679 0.0386475 *
## factor(weekdays)вторник 15.6515 < 2.2e-16 ***
## factor(weekdays)понедельник 13.5103 < 2.2e-16 ***
## factor(weekdays)пятница 11.0222 < 2.2e-16 ***
## factor(weekdays)среда 8.3685 < 2.2e-16 ***
## factor(weekdays)суббота 12.5345 < 2.2e-16 ***
## factor(prev_price_nine)1:factor(price_increase)1 -73.1915 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 7657.4
## Residual Sum of Squares: 2805.3
## R-Squared: 0.63365
## Adj. R-Squared: 0.63362
## Chisq: 442551 on 23 DF, p-value: < 2.22e-16
#Hausman test
pyat_hausman <- phtest(hyp5_pyat_fixed, hyp5_pyat_random)
pyat_hausman
##
## Hausman Test
##
## data: price_change_pct ~ factor(prev_price_nine) + factor(prev_price_nine) * ...
## chisq = 743.66, df = 22, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent
#выбираем модель fixed effects
T-статистика (5 гипотеза)
#Лавка
-6.9064e-02/2.4467e-03
## [1] -28.22741
#Магнит
-5.8769e-02/1.8776e-03
## [1] -31.30006
#Пятерочка
-2.0840e-01/3.0789e-03
## [1] -67.68651