CODEWITHSUNDEEP
rloopsregressionlmrepeat
Consti
Consti

Reputation: 87

How to create a loop for Regression

I just started using R for statistical purposes and I appreciate any kind of help.

My task is to make calculations on one index and 20 stocks from the index. The data contains 22 columns (DATE, INDEX, S1 .... S20) and about 4000 rows (one row per day).

Firstly I imported the .csv file, called it "dataset" and calculated log returns this way and did it for all stocks "S1-S20" plus the INDEX.

n <- nrow(dataset)
S1 <- dataset$S1
S1_logret <- log(S1[2:n])-log(S1[1:(n-1)])

Secondly, I stored the data in a data.frame:

logret_data <- data.frame(INDEX_logret, S1_logret, S2_logret, S3_logret, S4_logret, S5_logret, S6_logret, S7_logret, S8_logret, S9_logret, S10_logret, S11_logret, S12_logret, S13_logret, S14_logret, S15_logret, S16_logret, S17_logret, S18_logret, S19_logret, S20_logret)

Then I ran the regression (S1 to S20) using the log returns:

S1_Reg1 <- lm(S1_logret~INDEX_logret)

I couldn't figure out how to write the code in a more efficient way and use some function for repetition.

In a further step I have to run a cross sectional regression for each day in a selected interval. It is impossible to do it manually and R should provide some quick solution. I am quite insecure about how to do this part. But I would also like to use kind of loop for the previous calculations.

Yet I lack the necessary R coding knowledge. Any kind of help top the point or advise for literature or tutorial is highly appreciated! Thank you!

Upvotes: 3

Views: 617

Answers (1)

LyzandeR
LyzandeR

Reputation: 37889

You could provide all the separate dependent variables in a matrix to run your regressions. Something like this:

#example data
Y1 <- rnorm(100)
Y2 <- rnorm(100)
X  <- rnorm(100)
df <- data.frame(Y1, Y2, X)

#run all models at once
lm(as.matrix(df[c('Y1', 'Y2')]) ~ X)

Out:

Call:
lm(formula = as.matrix(df[c("Y1", "Y2")]) ~ df$X)

Coefficients:
             Y1        Y2      
(Intercept)  -0.15490  -0.08384
df$X         -0.15026  -0.02471

Upvotes: 1

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