For this assignment, I am trying to find the covariates of lumber and test the lead-lag relationships. To test, I am thinking to do a linear regression and see if the two are correlated using either...

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For this assignment, I am trying to find the covariates of lumber and test the lead-lag relationships.

To test, I am thinking to do a linear regression and see if the two are correlated using either prices or returns (both would be ideal). You can use the close price ($). If expert feels something like an ANCOVA is necessary, that is also fine.



Also check to see if one leads/ lags the other.



Please run the regression for those against Lumber (IE you don’t need to run a linear regression on corn vs oil, but run it on corn vs. lumber and oil vs. lumber). Thank you!



CL=F is oil, ZC=F is Corn, HG=F is copper, LBS=f is Lumber, ETH is Ethereum (the cryptocurrency, GC=F is gold, and ALI=f is aluminum.



The last thing I want to test as a covariate is number of homes sold and the median price of those homes sold. I am unsure where to get that data – but maybe zillow, FRED, or census may have it. Let me know if expert is able to get this data. The test here would be to see if there is a correlation between lumber prices and number of homes sold, or lumber price and median price of the homes sold. I know the housing data only comes monthly - so I included LBS=Fmonthly as the monthly data. All the other data sets are daily. Thank you!






Please include interpretation for all of the results, as well as a conclusion of which is the best covariate, and whether there is a lead-lag relationship between any of them.

Answered Same DayMay 11, 2021

Answer To: For this assignment, I am trying to find the covariates of lumber and test the lead-lag...

Mohd answered on May 12 2021
139 Votes
Regression
Regression
-
5/12/2021
knitr::opts_chunk$set(echo = TRUE,cache = TRUE,warning = FALSE,message = FALSE,dpi = 180,fig.width = 8,fig.height = 5)
We have run first regression model for mspus and lumber. The value of adjusted r squ
are is 0.3297, that means 32.97 percent variability in response variable (close price) can explain by median house price. Beta coefficient is 0.0016, which is low and statistically significant: p value <0.05. We have built second regression model for corn and lumber. The value of adjusted r square is 0.612, that means 61.2 percent variability in response variable (close price) can explain by corn close price. Beta coefficient is 2.613 and statistically significant: p value <0.05.
We have run third regression model for copper and lumber. The value of adjusted r square is 73.5, that means 73.5 percent variability in response variable (close price) can explain by copper close price. Beta coefficient is 385.60 and statistically significant: p value <0.05. Above three regression models haven’t shown lead lag reletionaship, as we can see from regression table, beta coefficient for all predictors is positive and significant. They have positive influence on each other, of one increases then another one will also increase. Third model has highest adjusted r square value, hence third model is best among three of them.
library(readxl)
library(stargazer)
library(readr)
lum_reg <- read_excel("data/lum_reg.xlsx",
col_types = c("numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"))
str(lum_reg)
## tibble [1,257 × 6] (S3: tbl_df/tbl/data.frame)
## $ Close_oil: num [1:1257] 43.4 44.7 46.2 46.7 46.2 ...
## $ Close_alu: num [1:1257] 1630 1625 1633 1612 1600 ...
## $ Close_cop: num [1:1257] 2.1 2.09 2.1 2.07 2.08 ...
## $ Close_lum: num [1:1257] 305 308 307 303 306 ...
## $ Close_cor: num [1:1257] 368 378 374 385 382 ...
## $ Close_go : num [1:1257] 1266 1264 1275 1270 1272 ...
mod3<-lm(Close_lum~Close_cop,data=lum_reg)
stargazer(mod3,type = "text")
##
## ===============================================
## Dependent variable:
## ---------------------------
## Close_lum
## -----------------------------------------------
## Close_cop 385.601***
## (6.564) ...
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