我正在研究R中的以下数据：
# Getting stock data
library(quantmod)
tickers <- c("ASC.OL", "AFG.OL", "AKA.OL", "AKER.OL", "AKERBP.OL", "AKSO.OL", "AKVA.OL", "AMSC.OL", "APP.OL", "AQUA.OL", "ARCHER.OL", "ARCUS.OL", "AFK.OL", "ASETEK.OL", "ATEA.OL", "ATLA-NOK.OL", "AURG.OL", "AUSS.OL", "AVANCE.OL", "AVM.OL", "AXA.OL", "B2H.OL", "BAKKA.OL", "BEL.OL", "BERGEN.OL", "BGBIO.OL", "BIOTEC.OL", "BON.OL", "BOR.OL", "BRG.OL", "BOUVET.OL", "BWLPG.OL", "BWO.OL", "BMA.OL", "COV.OL", "CXENSE.OL", "DAT.OL", "DESSC.OL", "DNB.OL", "DNO.OL", "DOF.OL", "EIOF.OL", "EKO.OL", "EMGS.OL", "EMAS.OL", "ENTRA.OL", "EPR.OL", "FAR.OL", "FOE.OL", "FRO.OL", "FUNCOM.OL", "GIG.OL", "RISH.OL", "GJF.OL", "GOGL.OL", "GOD.OL", "GSF.OL", "GYL.OL", "HNA.OL", "HNB.OL", "HAVI.OL", "HYARD.OL", "HELG.OL", "HEX.OL", "HIDDN.OL", "HLNG.OL", "HSPG.OL", "IMSK.OL", "IDEX.OL", "INC.OL", "ISSG.OL", "INSR.OL", "IOX.OL", "ITX.OL", "ITE.OL", "JIN.OL", "JAEREN.OL", "KID.OL", "KIT.OL", "KOA.OL", "KOG.OL", "KVAER.OL", "LSG.OL", "LINK.OL", "MHG.OL", "MEDI.OL", "MELG.OL", "MULTI.OL", "NAPA.OL", "NAVA.OL", "NEL.OL", "NEXT.OL", "NGT.OL", "NANO.OL", "NOD.OL", "NHY.OL", "NSG.OL", "NRS.OL", "NAS.OL", "NOR.OL", "NOFI.OL", "NPRO.OL", "NRC.OL", "NTS.OL", "OCY.OL", "OTS.OL", "ODL.OL", "ODF.OL", "ODFB.OL", "OLT.OL", "OPERA.OL", "ORK.OL", "PEN.OL", "PARB.OL", "PGS.OL", "PDR.OL", "PHO.OL", "PLCS.OL", "POL.OL", "PRS.OL", "PROTCT.OL", "QFR.OL", "QEC.OL", "RAKP.OL", "REACH.OL", "REC.OL", "RENO.OL", "SDSD.OL", "SAFE.OL", "SALM.OL", "SADG.OL", "SAS-NOK.OL", "SSO.OL", "SCHA.OL", "SCHB.OL", "SBX.OL", "SDRL.OL", "SBO.OL", "SEVDR.OL", "SEVAN.OL", "SIOFF.OL", "SKBN.OL", "SKI.OL", "SKUE.OL", "SOLON.OL", "SOFF.OL", "SOFFB.OL", "SOLV.OL", "SONG.OL", "SBVG.OL", "NONG.OL", "RING.OL", "MING.OL", "SRBANK.OL", "SOAG.OL", "MORG.OL", "SOR.OL", "SVEG.OL", "SPOG.OL", "SPU.OL", "STL.OL", "SNI.OL", "STB.OL", "STORM.OL", "STRONG.OL", "SUBC.OL", "TIL.OL", "TRVX.OL", "TEAM.OL", "TECH.OL", "TEL.OL", "TGS.OL", "SSC.OL", "THIN.OL", "TOM.OL", "TOTG.OL", "TRE.OL", "TTS.OL", "VEI.OL", "VVL.OL", "WWL.OL", "WEIFA.OL", "WRL.OL", "WWI.OL", "WWIB.OL", "WILS.OL", "XXL.OL", "YAR.OL", "ZAL.OL")
dataEnv <- new.env()
out <- sapply(tickers, function(s) tryCatch({ getSymbols(s , env = dataEnv) }, error = function(e) NA))
plist <- eapply(dataEnv, Ad)
pframe <- do.call(merge, plist)
names(pframe) <- gsub(".Adjusted", "", names(pframe))
names(pframe) <- gsub(".X", "", names(pframe))
pframe <- log(pframe)
pframe <- diff(pframe, lag = 1)
pframe<-t(pframe)
library(reshape2)
pframe<- melt(pframe)
library(data.table)
setnames(pframe, old=c("Var1","Var2", "value"), new=c("Ticker", "Date", "Returns"))
# Getting index data
index <- c("^GSPC")
dataEnv1 <- new.env()
out1 <- sapply(index, function(s) tryCatch({ getSymbols(s , env = dataEnv1) }, error = function(e) NA))
index <- eapply(dataEnv1, Ad)
index <- do.call(merge, index)
names(index) <- gsub(".Adjusted", "", names(index))
names(index) <- gsub(".X", "", names(index))
index <- log(index)
index <- diff(index, lag = 1)
index<-t(index)
index<- melt(index)
library(data.table)
setnames(index, old=c("Var1","Var2", "value"), new=c("Index", "Date", "Index_Returns"))
# Getting curr data
curr <- c("NOK=X")
dataEnv1 <- new.env()
out1 <- sapply(curr, function(s) tryCatch({ getSymbols(s , env = dataEnv1) }, error = function(e) NA))
curr <- eapply(dataEnv1, Ad)
curr <- do.call(merge, curr)
names(curr) <- gsub(".Adjusted", "", names(curr))
names(curr) <- gsub(".X", "", names(curr))
curr <- log(curr)
curr <- diff(curr, lag = 1)
curr<-t(curr)
curr<- melt(curr)
library(data.table)
setnames(curr, old=c("Var1","Var2", "value"), new=c("NOK", "Date", "NOK_Returns"))
NOK_index <- merge(index, curr, by="Date")
NOK_index$Index <- NULL
NOK_index$NOK <- NULL
Final <- merge(pframe, NOK_index, by="Date")
Final <- na.omit(Final)
我试图在名为＆＃34; Final＆＃34;的数据集中按组运行滚动时间序列回归。 group-id是名为＆＃34; Ticker＆＃34;的变量。我想要的窗口是基于至少120个观察，最多240个。我想倒退＆＃34;返回＆＃34;在＆＃34; Market_ret＆＃34;。目前正在处理下面的回归代码，试图让回归滚动。有什么建议吗？
library(dplyr)
library(magrittr)
library(broom)
## Fit models
fitted_model <- Final %>% group_by(Ticker) %>% do(tidy(lm(Returns ~ Index_Returns + NOK_Returns, data = .)))
我尝试了下面的代码，但它需要花费很多时间（得到以下消息：剩余1％~5小时，在完成之前取消它。）
library(dplyr)
library(magrittr)
library(broom)
## Fit models
fitted_model <- Final %>% group_by(Ticker) %>% do(tidy(rollapply(. , width=262, FUN = function(Z) { t = lm(formula=Returns ~ Index_Returns + NOK_Returns, data = as.data.frame(Z), na.rm=T); return(t$coef) }, by.column=FALSE, align="right")))
答案 0 :(得分：0)
如果要对组运行OLS回归，可以尝试使用dplyr包。使用do（）函数将运行模型并将它们存储在名为model的变量中。
library(dplyr)
library(magrittr)
## Fit models
fitted_model <- Final %>%
group_by(Ticker) %>%
do(model = lm(Returns ~ Market_ret, data = .))
要将系数检查提取到R中的broom包中。运行tidy(model)应该可以解决问题。