snapshot

Examples/Data/klein.data

@@ -1,22 +1,23 @@
-1920 39.8 12.7 28.8  2.7 180.1 44.9 2.2  4.6  3.4
-1921 41.9 12.4 25.5 -0.2 182.8 45.6 2.7  6.6  7.7
-1922 45.0 16.9 29.3  1.9 182.6 50.1 2.9  6.1  3.9
-1923 49.2 18.4 34.1  5.2 184.5 57.2 2.9  5.7  4.7
-1924 50.6 19.4 33.9  3.0 189.7 57.1 3.1  6.6  3.8
-1925 52.6 20.1 35.4  5.1 192.7 61.0 3.2  6.5  5.5
-1926 55.1 19.6 37.4  5.6 197.8 64.0 3.3  6.6  7.0
-1927 56.2 19.8 37.9  4.2 203.4 64.4 3.6  7.6  6.7
-1928 57.3 21.1 39.2  3.0 207.6 64.5 3.7  7.9  4.2
-1929 57.8 21.7 41.3  5.1 210.6 67.0 4.0  8.1  4.0
-1930 55.0 15.6 37.9  1.0 215.7 61.2 4.2  9.4  7.7
-1931 50.9 11.4 34.5 -3.4 216.7 53.4 4.8 10.7  7.5
-1932 45.6  7.0 29.0 -6.2 213.3 44.3 5.3 10.2  8.3
-1933 46.5 11.2 28.5 -5.1 207.1 45.1 5.6  9.3  5.4
-1934 48.7 12.3 30.6 -3.0 202.0 49.7 6.0 10.0  6.8
-1935 51.3 14.0 33.2 -1.3 199.0 54.4 6.1 10.5  7.2
-1936 57.7 17.6 36.8  2.1 197.7 62.7 7.4 10.3  8.3
-1937 58.7 17.3 41.0  2.0 199.8 65.0 6.7 11.0  6.7
-1938 57.5 15.3 38.2 -1.9 201.8 60.9 7.7 13.0  7.4
-1939 61.6 19.0 41.6  1.3 199.9 69.5 7.8 14.4  8.9
-1940 65.0 21.1 45.0  3.3 201.2 75.7 8.0 15.4  9.6
-1941 69.7 23.5 53.3  4.9 204.5 88.4 8.5 22.3 11.6
+YEAR,C,P,WP,I,KLAG,X,WG,G,T
+1920,39.8,12.7,28.8,2.7,180.1,44.9,2.2,4.6,3.4
+1921,41.9,12.4,25.5,-0.2,182.8,45.6,2.7,6.6,7.7
+1922,45.0,16.9,29.3,1.9,182.6,50.1,2.9,6.1,3.9
+1923,49.2,18.4,34.1,5.2,184.5,57.2,2.9,5.7,4.7
+1924,50.6,19.4,33.9,3.0,189.7,57.1,3.1,6.6,3.8
+1925,52.6,20.1,35.4,5.1,192.7,61.0,3.2,6.5,5.5
+1926,55.1,19.6,37.4,5.6,197.8,64.0,3.3,6.6,7.0
+1927,56.2,19.8,37.9,4.2,203.4,64.4,3.6,7.6,6.7
+1928,57.3,21.1,39.2,3.0,207.6,64.5,3.7,7.9,4.2
+1929,57.8,21.7,41.3,5.1,210.6,67.0,4.0,8.1,4.0
+1930,55.0,15.6,37.9,1.0,215.7,61.2,4.2,9.4,7.7
+1931,50.9,11.4,34.5,-3.4,216.7,53.4,4.8,10.7,7.5
+1932,45.6,7.0,29.0,-6.2,213.3,44.3,5.3,10.2,8.3
+1933,46.5,11.2,28.5,-5.1,207.1,45.1,5.6,9.3,5.4
+1934,48.7,12.3,30.6,-3.0,202.0,49.7,6.0,10.0,6.8
+1935,51.3,14.0,33.2,-1.3,199.0,54.4,6.1,10.5,7.2
+1936,57.7,17.6,36.8,2.1,197.7,62.7,7.4,10.3,8.3
+1937,58.7,17.3,41.0,2.0,199.8,65.0,6.7,11.0,6.7
+1938,57.5,15.3,38.2,-1.9,201.8,60.9,7.7,13.0,7.4
+1939,61.6,19.0,41.6,1.3,199.9,69.5,7.8,14.4,8.9
+1940,65.0,21.1,45.0,3.3,201.2,75.7,8.0,15.4,9.6
+1941,69.7,23.5,53.3,4.9,204.5,88.4,8.5,22.3,11.6

Examples/GMM/portfolio.jl

@@ -49,9 +49,9 @@ d = data[:,3]
 r = (p + d) ./ lag(p,1) .- 1.0
 c = c ./ lag(c,1); # ensure stationarity
 
-## choose the instruments
-inst = [lag(c,1) r d p]
-maxlag = 2
+## choose the instruments: note - the code is off by one w.r.t. the notes
+inst = [lag(c,1) lag(r,1)]
+maxlag = 5
 inst = [inst lags(inst, maxlag)]
 inst = inst[maxlag+2:end,:]  # drop first two obs, due to two lags of c
 inst, junk, junk = stnorm(inst) # for numeric stability
@@ -64,7 +64,7 @@ moments = θ -> portfolio_moments(θ, data)
 weight = 1.0
 names = ["beta","gamma"]
 # initial consistent estimate
-θstart  = [0.95, 0.5]
+θstart  = [0.95, 2.]
 θhat, obj_value, D, ms, convergence = gmm(moments, θstart , weight)
 # second step with efficient weight matrix
 Ω  = cov(ms)

Examples/Simeq/Klein2SLS.jl

@@ -1,32 +1,25 @@
-# Estimates the Klein consumption equation by 2SLS
-using Econometrics, DelimitedFiles, Statistics
+## Estimates the Klein consumption equation by 2SLS
 
-
-function main()
+## read in the data and create needed variables	
+using Econometrics, CSV, DataFrames, DataFramesMeta, Statistics, Term
 cd(@__DIR__)
-data = readdlm("klein.data")
-# construct missing lags, and drop first row that has missing data
-profits = data[:,3]
-output = data[:,7]
-data = [data lag(profits,1) lag(output,1)]
-data = data[2:end,:]
-n = size(data,1)
+klein = CSV.read("klein.data", DataFrame; header=true)
+## construct missing lags, and drop first row that has missing data
+klein.lagP = vcat(missing, [klein.P[i-1] for i=2:size(klein,1)])
+klein.lagX = vcat(missing,[klein.X[i-1] for i=2:size(klein,1)])
+klein.WAGES = klein.WP + klein.WG
+klein = dropmissing(klein)
+
 # define instruments
-exogs = [1, 6, 8, 9, 10, 11, 12]
-exogs = data[:,exogs]
+n = size(klein,1)
+exogs = Matrix(@select(klein, :YEAR, :WG, :G, :T, :lagP,:lagX))
 exogs = [ones(n,1) exogs]
 
 # CONSUMPTION
 println("CONSUMPTION EQUATION")
 # define variables in consumption equation
-y = data[:,2]
-profits = data[:,3]
-lagprofits = data[:,11]
-wp = data[:,4]
-wg = data[:,8]
-wages = wp + wg
-# regressors in consumption equation
-x = [profits lagprofits wages]
+y = Matrix(@select(klein, :C))
+x = Matrix(@select(klein, :P, :lagP, :WAGES))
 x = [ones(n,1) x]
 # 2SLS estimation
 names = ["Constant", "Profits", "Profits-1", "Wages"]
@@ -35,6 +28,3 @@ printstyled("\nNote that 2SLS does not give the same results as does GMM.\n", co
 printstyled("This is because the equation is overidentified. GMM is\n", color=:green)
 printstyled("using an estimate of the efficient weight matrix. 2SLS does not.\n", color=:green)
 
-return
-end
-main()

Examples/Simeq/KleinCUE.jl

@@ -1,35 +1,31 @@
-# Estimates the Klein consumption equation by GMM
-using Econometrics, DelimitedFiles, Statistics
+# Estimates the Klein consumption equation by CUE GMM
+using Econometrics, CSV, DataFrames, DataFramesMeta, Statistics, Term
+
 function KleinMoments(θ, y, x, z)
 	e = y - x*θ
 	m = e.*z
 end
 
 function main()
+## read in the data and create needed variables	
 cd(@__DIR__)
-data = readdlm("klein.data")
-# construct missing lags, and drop first row that has missing data
-profits = data[:,3]
-output = data[:,7]
-data = [data lag(profits,1) lag(output,1)]
-data = data[2:end,:]
-n = size(data,1)
-# define instruments
-exogs = [1, 6, 8, 9, 10, 11, 12]
-exogs = data[:,exogs]
-exogs = [ones(n,1) exogs]
+klein = CSV.read("klein.data", DataFrame; header=true)
+## construct missing lags, and drop first row that has missing data
+klein.lagP = vcat(missing, [klein.P[i-1] for i=2:size(klein,1)])
+klein.lagX = vcat(missing,[klein.X[i-1] for i=2:size(klein,1)])
+klein.WAGES = klein.WP + klein.WG
+klein = dropmissing(klein)
+
 # CONSUMPTION
 println("CONSUMPTION EQUATION")
+# define instruments
+n = size(klein,1)
+exogs = Matrix(@select(klein, :YEAR, :WG, :G, :T, :lagP,:lagX))
+exogs = [ones(n) exogs]
 # define variables in consumption equation
-y = data[:,2]
-profits = data[:,3]
-lagprofits = data[:,11]
-wp = data[:,4]
-wg = data[:,8]
-wages = wp + wg
-# regressors in consumption equation
-x = [profits lagprofits wages]
-x = [ones(n,1) x]
+y = Matrix(@select(klein, :C))
+x = Matrix(@select(klein, :P, :lagP, :WAGES))
+x = [ones(n) x]
 # GMM estimation using CUE
 θstart = x\y # ols start values
 names = ["Constant", "Profits", "Profits-1", "Wages"]

Examples/Simeq/KleinGMM.jl

@@ -1,35 +1,32 @@
 # Estimates the Klein consumption equation by GMM
-using Econometrics, DelimitedFiles, Statistics
-function KleinMoments(theta, y, x, z)
-	e = y - x*theta
+using Econometrics, CSV, DataFrames, DataFramesMeta, Statistics, Term
+
+function KleinMoments(θ, y, x, z)
+	e = y - x*θ
 	m = e.*z
 end
 
 function main()
+## read in the data and create needed variables	
 cd(@__DIR__)
-data = readdlm("klein.data")
-# construct missing lags, and drop first row that has missing data
-profits = data[:,3]
-output = data[:,7]
-data = [data lag(profits,1) lag(output,1)]
-data = data[2:end,:]
-n = size(data,1)
-# define instruments
-exogs = [1, 6, 8, 9, 10, 11, 12]
-exogs = data[:,exogs]
-exogs = [ones(n,1) exogs]
+klein = CSV.read("klein.data", DataFrame; header=true)
+## construct missing lags, and drop first row that has missing data
+klein.lagP = vcat(missing, [klein.P[i-1] for i=2:size(klein,1)])
+klein.lagX = vcat(missing,[klein.X[i-1] for i=2:size(klein,1)])
+klein.WAGES = klein.WP + klein.WG
+klein = dropmissing(klein)
+
 # CONSUMPTION
 println("CONSUMPTION EQUATION")
+# define instruments
+n = size(klein,1)
+exogs = Matrix(@select(klein, :YEAR, :WG, :G, :T, :lagP,:lagX))
+exogs = [ones(n) exogs]
 # define variables in consumption equation
-y = data[:,2]
-profits = data[:,3]
-lagprofits = data[:,11]
-wp = data[:,4]
-wg = data[:,8]
-wages = wp + wg
-# regressors in consumption equation
-x = [profits lagprofits wages]
-x = [ones(n,1) x]
+y = Matrix(@select(klein, :C))
+x = Matrix(@select(klein, :P, :lagP, :WAGES))
+x = [ones(n) x]
+
 # GMM estimation
 theta = x\y  # ols start values
 weight = 1.0