インターネットを活用した経済分析 - フリーソフト Rを使おう

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ありあながだき
6 years ago
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1 R /64

2 2 R 3 R R RESAS /64

3 2 R 3 R R RESAS /64

4 2-4 ( ) ( (80%) (20%) /64

5 PC LAN R /64

6 R R /64

7 3-4 R /64

8 R STATA Eviews MATLAB STATA R /64

9 R /64

10 R R Windows Mac Linux R Comprehensive R Archive Network (CRAN: ) /64

11 /64

12 /64

13 R R A B Word /64

14 R > ( ) + esc /64

15 (1): > x < # x > x [1] 4 > 2.5* x [1] /64

16 (2): > y <- c(1,2,3) # y y = [ ] > y * y [1] /64

17 (2): y y = [ ] 1 2 = = 14 3 > y %*% y # % [1] 14 > z <- c(1,2,3,4,5,6) > z.mat <- matrix(z,nrow=2,ncol=3) > z.mat [,1] [,2] [,3] [1,] [2,] /64

18 > z.mat[1,] [1] > z.mat[,2] [1] /64

19 (3): > f <- expression(x^2) > D(f,"x") 2*x > h <- expression(x^2+3*x*y) > D(h,"x") 2*x+3*y /64

20 > testfunction<- function(x^2) > x<-seq(-5,10,0.1) > plot(x, testfunction(x),type="l") ggplot2 1 : y = x /64

21 R R CRAN 8, /64

22 2 : /64

23 Nippon ( ( ) /64

24 Nippon (2013) > setwd("/users/xxxx/r/gis/nipponpackage") # > library(nippon) > library(rcolorbrewer) > library(maptools) > library(classint) # > data<-read.table("nipponpackagetest.csv",sep=",",header=true) > op <- par(bg = "skyblue") # # : > p <- JapanPrefecturesMap(col = "ivory") > colleges <- data$colleges # # ( ) > cols <- rev(brewer.pal(5, "RdYlBu")) # n > c1 <- classintervals(colleges, n = 5, style = "fisher") > colcode1 <- findcolours(c1, cols, cutlabels = FALSE) # # 1 ( > legtext1 <- paste0(names(attr(colcode1,"table")), " (", attr(colcode1, "table"),")") > p <- JapanPrefecturesMap(col = colcode1) # ( # ( > legend("bottomright", legend = legtext1,fill = cols, title = "Colleges",bg = "white") /64

25 /64

26 (1) Nippon /64

27 2 R 2 R 3 R R RESAS /64

28 2 R (π) (P Y ) (C(Y )) max Y π = P Y C(Y ) (1) Y /64

29 2 R Hoover(2012, P331) Y = 9.63L 0.67 K 0.33 (2) Y L K Y = AL a K 1 a (A ) 1 Hoover, KD(2012) Applied intermediate macroeconomics, Cambridge University Press /64

30 2 R > plotfun(a*(l^0.7)*(k^0.3)~l&k, A=5,xlim=range(0,21), ylim=range(0,100), filled=false, surface=true ) mosaic A * (L^0.7) * (K^0.3) K L /64

31 2 R > library(mosaic) > plotfun(a*(l^0.7)*(k^0.3)~l,k=20,a=5,xlim=range(-1,21), ylim=range(-5,105)) 80 A * (L^0.7) * (K^0.3) L /64

32 2 R K Y > plotfun(a*(l^0.7)*(k^0.3)~l,k=20,a=5,xlim=range(-1,21), ylim=range(-5,151)) > plotfun(a*(l^0.7)*(k^0.3)~l, K=40,A=5,xlim=range(-1,21), ylim=range(-5,151), lty=2,add=true ) A * (L^0.7) * (K^0.3) L /64

33 2 R A Y > plotfun(a*(l^0.7)*(k^0.3)~l,k=20,a=5,xlim=range(-1,21), ylim=range(-5,151)) > plotfun(a*(l^0.7)*(k^0.3)~l,k=20, A=10,xlim=range(-1,21), ylim=range(-5,151), lty=2,add=true ) A * (L^0.7) * (K^0.3) L /64

34 ( ) 2 R > plotfun(a*(l^0.7)*(k^0.3)~l&k,a=5,xlim=range(0,21), ylim=range(0,100), filled=false) K L /64

35 3 R 2 R 3 R R RESAS /64

36 3 R (U) U x (x, y) p x = U y(x, y) p y (3) U x (x, y) U(x,y) x, P i i /64

37 3 R : (1) p x = 1, p y = 2 U = x + 2 x y + y (4) U x = 1 + x 1 2 y, U y = 1 + x y 1 2 (5) (3) y 1 2 x 1 2 = 1 2 (6) /64

38 3 R : (2) : p x x + p y y = m ( m ) (3) x = mp y p x (p x + p y ), and y = mp x p y (p x + p y ) (7) m = 6 x = 4 y = /64

39 3 R R max U = x + 2 x y + y (8) s.t. x + 2y = 6 (9) x = 4 y = /64

40 3 R R 1 x< seq(0,5,1) # y< seq(0,5,1) # g< function(x,y) x+2 sqrt(x y)+y # 4 z< outer(x,y,g) # x y g 5 contour(x,y,z) # x-y 6 par(new=t) # 7 8 contour(x,y,z,level=9) # x-y z=9 9 par(new=t) gg< function(x,y) x+2 y # 12 z< outer(x,y,gg) # x y gg 13 contour(x,y,z,level=6, col="red",lwd=3,xlab="x", ylab="y") abline(h=1,lty=2) # x=1 ( ) 16 abline(v=4,lty=2) # y=4 ( ) /64

41 3 R /64

42 R 2 R 3 R R RESAS /64

43 R /64

44 R (2b) /64

45 R (2c) /64

46 R 2 i, j R D GDP R ij = GDP igdp j D ij (10) ln R ij = β 0 + β 1 ln GDP i + β 2 ln GDP j β 3 ln D ij + ϵ (11) /64

47 R UN Comtrade CEPII (BACI) GDP World Development Indicators R WDI CEPII The CEPII Gravity Dataset /64

48 R I Y = α + βx (12) /64

49 R II (X i, Y i ) µ i µ i = Y i α βx i (13) /64

50 R (1) J α β α β J = n ˆµ 2 i = (Y i α βx i ) 2 (14) J α = i=1 n [ 2(Y i α βx i )] = 0 (15) i=1 J β = n [ 2X i (Y i α βx i )] = 0 (16) i= /64

51 R (2) ˆβ = n i=1 (X i X)(Y i Y ) n i=1 (X i X) 2 (17) ˆα = Y ˆβX (18) ˆα ˆβ /64

52 R : GDP WDI GDP 1 library(wdi) # WDI 2 3 WDIsearch(string="GDP") # GDP 4 5 dat < WDI(indicator= NY.GDP.MKTP.CD, country=c( JP, KR, CN ), start=1980, end=2012) 6 # GDP 7 8 library(ggplot2) # 9 10 ggplot(dat, aes(year, NY.GDP.MKTP.CD/ , color= country)) + geom line() + 11 xlab( Year ) + ylab( GDP (current billion US$) ) /64

53 R GDP 7500 GDP (current billion US$) 5000 country China Japan Korea, Rep Year /64

54 R R : (1) 1 trade.lm< lm(log(trade) log(gdp)+log(gdp.china)+log( distance), data=dataset) 2 # OLS 3 4 summary(trade.lm) # /64

55 R R : (2) 1 2 Coefficients: 3 Estimate Std. Er. t value Pr(> t ) 4 (Intercept) log(gdp) e 08 6 log(gdp.china) e 16 7 log(distance) Signif. codes: Residual standard error: on 38 degrees of freedom 12 Multiple R squared: , Adjusted R squared: F statistic: on 3 and 38 DF, p value: < 2.2e /64

56 R (1) (null hypothesis) (H 0 ) (H 1 ) /64

57 R (2) H 0 H 0 H 1 H /64

58 R 3a OLS /64

59 R R : (1) (colgpa) (hsgpa) (ACT) 1 library(foreign) # R 2 3 html< " # 4 5 gpa1< read.dta(html) # 6 7 GPAres< lm(colgpa hsgpa+act, data=gpa1) # 8 9 summary(gpares) # /64

60 R R : (2) 1 Call:lm(formula = colgpa hsgpa + ACT, data = gpa1) 2 3 Residuals: 4 Min 1Q Median 3Q Max Coefficients: 8 Estimate Std. Error t value Pr(> t ) 9 (Intercept) hsgpa e ACT Signif. codes: Residual standard error: on 138 degrees of freedom 16 Multiple R squared: , Adjusted R squared: /64

61 R 3b OLS /64

62 RESAS 2 R 3 R R RESAS /64

63 RESAS RESAS RESAS RESAS RESAS /64

64 RESAS Dayal, Vikram (2015) An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing, Springer. Heisss, Florian (2016) Using R for Introductory Econometrics, ( ) (2008). (2000). (2012) RIETI ( (2016) R - -,. (1995) /64

(lm) lm AIC 2 / 1

W707 s-taiji@is.titech.ac.jp 1 / 1 (lm) lm AIC 2 / 1 : y = β 1 x 1 + β 2 x 2 + + β d x d + β d+1 + ϵ (ϵ N(0, σ 2 )) y R: x R d : β i (i = 1,..., d):, β d+1 : ( ) (d = 1) y = β 1 x 1 + β 2 + ϵ (d > 1) y