knitr::opts_chunk$set(echo = TRUE) library(entropy) library(tidyverse) library(ati) # to access the pre-defined functions below ## Total word count should be 1500 words excluding references ## When you knitr the document the references should automatically populate at the end of the document
refs.bib
file to include any extra references you have. Knit
to see what the output will look like
4 . Save the .Rmd file using the naming convention specified in classThis should be introduce the statement to be critiqued in the context of modern financial research practices.
Use high-quality literature to illustrate the pros and cons of the argument.
Some examples of how to use the .bib
to add references
In line @Jensen2021
renders as Jensen (2021)
And [@Jensen]
renders as (Jensen 2021)
Simulate some data to illustrate how FML can reveal results where traditional econometrics fail OR vice versa. You are free to use the lecture material as a starting point here. For example
# package which will do much of the heavy lifting set.seed(1234) # set seed for random number generation size=1000 df<-tibble(x=rnorm(size),y=0*x+rnorm(size)) bXY=numBins(nrow(df), corr=cor(df)[1,2]) y2D=discretize2d(df$x,df$y,numBins1 = bXY,numBins2 = bXY) Hx=entropy(rowSums(y2D)) Hy=entropy(colSums(y2D)) nmi=mi.empirical(y2D)/min(Hx,Hy) cor(df)[1,2]->cor_coef
# use ggplot2 hist2d geom to extract ggplot(df,aes(x=x,y=y)) + geom_point() + labs(title = "y= 0x + e") + annotate("text",label=paste0("nmi=",round(nmi,4)),x=-2.5,y=3) + annotate("text",label=paste0("cor_coef=",round(cor_coef,4)),x=-2.5,y=2.5)
Summarise and state your critical assessment using any evidence you have provided in the previous sections.
This is where the citied references should appear when you render the document to a pdf
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