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#+TITLE: Quantitative Methods
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#+PROPERTY: header-args:R :session acj :eval never-export
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#+STARTUP: hideall inlineimages hideblocks
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#+HTML_HEAD: <style>#content{max-width:1200px;} </style>
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* Title slide :slide:
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#+BEGIN_SRC emacs-lisp-slide
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(org-show-animate '("Quantitative Methods, Part-II" "Vikas Rawal" "Prachi Bansal" "" "" ""))
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#+END_SRC
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* Day 2
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** Paul Krugman on Fiscal Austerity :slide:
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#+attr_html: :width 1200px
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[[file:krugman1.png]]
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** Paul Krugman on Fiscal Austerity :slide:
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"Here’s what fiscal policy should do: it should support demand when the economy is weak, and it should pull that support back when the economy is strong. As John Maynard Keynes said, “The boom, not the slump, is the right time for austerity.” And up until 2010 the U.S. more or less followed that prescription. Since then, however, fiscal policy has become perverse: first austerity despite high unemployment, now expansion despite low unemployment.
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** Unemployment and Fiscal Austerity :slide:
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#+RESULTS: graph
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[[file:krugman2.png]]
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#+attr_html :width 500px
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[[file:krugman2.png]]
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#+NAME: graph
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#+BEGIN_SRC R :results output graphics :exports results :file krugman2.png :width 3774 :height 3774 :res 600
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library(data.table)
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library(ggplot2)
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fread("~/ssercloud/acj2018/krugmandata.csv")->a
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as.Date(a$date,format=c("%m/%d/%Y"))->a$date
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factor(ifelse(a$date<"10-01-01","2000-2009","2010-2018"))->a$Period
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melt(a,id=c("date","Period"),m=c("impact","unemployment"))->t
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levels(t$variable)<-c("Fiscal stimulus","Unemployment rate")
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ggplot(t,aes(x=date,y=value,group=variable,colour=Period))->p
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p+geom_line(size=1.2)+facet_wrap(~variable,scales="free_y",ncol=1)->p
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p+scale_y_continuous("Per cent")+theme(legend.position="bottom")
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#+END_SRC
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** Sampling Distributions :slide:
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#+RESULTS: sampling
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[[file:bsample.png]]
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#+NAME: sampling
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#+BEGIN_SRC R :results output graphics :exports results :file bsample.png :width 4500 :height 3000 :res 600
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library(data.table)
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readRDS("plfsdata/plfsacjdata.rds")->worker
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worker$standardwage->worker$wage
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#read.table("~/ssercloud/acj2018/worker.csv",sep=",",header=T)->worker
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c(1:nrow(worker))->worker$SamplingFrameOrder
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worker[sex!=3,]->worker
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library(ggplot2)
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ggplot(worker,aes(wage))+geom_density(colour="black",size=1)+scale_y_continuous(limits=c(0,0.05))+scale_x_continuous(limits=c(0,1000),breaks=c(0,mean(worker$wage),1000))->p
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# p+facet_wrap(~sex)->p
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p+annotate("text",x=520,y=0.045,
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label=paste("Population mean = ",round(mean(worker$wage)),sep=""))->p
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p+theme_bw()->p
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p
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sample(1:nrow(worker),5, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t1
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for (i in c(1:9999)) {
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sample(1:nrow(worker),5, replace=FALSE)->a1
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worker[a1,]->s1
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c(t1,mean(s1$wage))->t1
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}
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data.frame(sno=c(1:10000),meancol=t1)->t1
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p+geom_density(data=t1,aes(meancol),colour="blue",size=1)-> p
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paste("Distribution of sample means (5): mean = ",
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round(mean(t1$meancol)),
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"; stdev = ",
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round(sqrt(var(t1$meancol))),sep="")->lab
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p+annotate("text",x=700,y=0.033,label=lab,colour="blue")->p
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p
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sample(1:nrow(worker),20, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t0
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for (i in c(1:9999)) {
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sample(1:nrow(worker),20, replace=FALSE)->a1
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worker[a1,]->s1
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c(t0,mean(s1$wage))->t0
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}
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data.frame(sno=c(1:10000),meancol=t0)->t0
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p+geom_density(data=t0,aes(meancol),colour="darkolivegreen",size=1)-> p
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paste("Distribution of sample means (20): mean = ",
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round(mean(t0$meancol)),
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"; stdev = ",
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round(sqrt(var(t0$meancol))),sep="")->lab
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p+annotate("text",x=700,y=0.036,label=lab,colour="darkolivegreen")->p
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p
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sample(1:nrow(worker),50, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t
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for (i in c(1:9999)) {
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sample(1:nrow(worker),50, replace=FALSE)->a1
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worker[a1,]->s1
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c(t,mean(s1$wage))->t
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}
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data.frame(sno=c(1:10000),meancol=t)->t
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p+geom_density(data=t,aes(meancol),colour="red",size=1)-> p
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paste("Distribution of sample means (50): mean = ",
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round(mean(t$meancol)),
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"; stdev = ",
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round(sqrt(var(t$meancol))),sep="")->lab
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p+annotate("text",x=700,y=0.039,label=lab,colour="red")->p
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p
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sample(1:nrow(worker),200, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t4
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for (i in c(1:9999)) {
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sample(1:nrow(worker),200, replace=FALSE)->a1
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worker[a1,]->s1
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c(t4,mean(s1$wage))->t4
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}
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data.frame(sno=c(1:10000),meancol=t4)->t4
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p+geom_density(data=t4,aes(meancol),colour="pink",size=1)-> p
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paste("Distribution of sample means (200): mean = ",
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round(mean(t4$meancol)),
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"; stdev = ",
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round(sqrt(var(t4$meancol))),sep="")->lab
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p+annotate("text",x=700,y=0.042,label=lab,colour="pink")->p
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p
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#+end_src
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#+NAME: sampling2
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#+BEGIN_SRC R :results output graphics :exports results :file bsample2.png :width 4500 :height 3000 :res 600
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library(data.table)
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readRDS("plfsdata/plfsacjdata.rds")->worker
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worker$standardwage->worker$wage
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#read.table("~/ssercloud/acj2018/worker.csv",sep=",",header=T)->worker
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c(1:nrow(worker))->worker$SamplingFrameOrder
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worker[sex!=3,]->worker
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library(ggplot2)
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ggplot(worker,aes(wage))+geom_density(colour="black",size=1)+scale_y_continuous(limits=c(0,0.05))+scale_x_continuous(limits=c(0,600),breaks=c(0,mean(worker$wage),1000))->p
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# p+facet_wrap(~sex)->p
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p+annotate("text",x=380,y=0.045,
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label=paste("Population mean = ",round(mean(worker$wage)),sep=""))->p
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p+annotate("text",x=400,y=0.042,
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label="Distribution of sample means:")->p
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p+theme_bw()->p
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p
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sample(1:nrow(worker),5, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t1
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for (i in c(1:9999)) {
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sample(1:nrow(worker),5, replace=FALSE)->a1
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worker[a1,]->s1
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c(t1,mean(s1$wage))->t1
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}
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data.frame(sno=c(1:10000),meancol=t1)->t1
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p+geom_density(data=t1,aes(meancol),colour="blue",size=1)-> p
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paste("Sample size 5: mean = ",
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round(mean(t1$meancol)),
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"; stdev = ",
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round(sqrt(var(t1$meancol))),sep="")->lab
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p+annotate("text",x=450,y=0.030,label=lab,colour="blue")->p
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p
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sample(1:nrow(worker),20, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t0
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for (i in c(1:9999)) {
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sample(1:nrow(worker),20, replace=FALSE)->a1
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worker[a1,]->s1
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c(t0,mean(s1$wage))->t0
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}
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data.frame(sno=c(1:10000),meancol=t0)->t0
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p+geom_density(data=t0,aes(meancol),colour="darkolivegreen",size=1)-> p
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paste("Sample size 20: mean = ",
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round(mean(t0$meancol)),
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"; stdev = ",
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round(sqrt(var(t0$meancol))),sep="")->lab
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p+annotate("text",x=450,y=0.033,label=lab,colour="darkolivegreen")->p
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p
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sample(1:nrow(worker),50, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t
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for (i in c(1:9999)) {
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sample(1:nrow(worker),50, replace=FALSE)->a1
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worker[a1,]->s1
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c(t,mean(s1$wage))->t
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}
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data.frame(sno=c(1:10000),meancol=t)->t
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p+geom_density(data=t,aes(meancol),colour="red",size=1)-> p
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paste("Sample size 50: mean = ",
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round(mean(t$meancol)),
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"; stdev = ",
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round(sqrt(var(t$meancol))),sep="")->lab
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p+annotate("text",x=450,y=0.036,label=lab,colour="red")->p
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p
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sample(1:nrow(worker),200, replace=FALSE)->a1
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worker[a1,]->s1
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mean(s1$wage)->t4
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for (i in c(1:9999)) {
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sample(1:nrow(worker),200, replace=FALSE)->a1
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worker[a1,]->s1
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c(t4,mean(s1$wage))->t4
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}
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data.frame(sno=c(1:10000),meancol=t4)->t4
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p+geom_density(data=t4,aes(meancol),colour="pink",size=1)-> p
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paste("Sample size 200: mean = ",
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round(mean(t4$meancol)),
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"; stdev = ",
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round(sqrt(var(t4$meancol))),sep="")->lab
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p+annotate("text",x=450,y=0.039,label=lab,colour="pink")->p
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p
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#+end_src
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#+RESULTS: sampling2
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[[file:bsample2.png]]
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