This document discusses the origins and meaning of regression analysis in econometrics. It describes Francis Galton's studies in the late 1800s of genetic inheritance, where he observed that offspring traits tended to regress towards the mean trait of the general population. This concept of regression led to the development of the regression model in statistics. The document provides examples of regression analysis using data on heights from Galton's studies and discusses how regression relates to the concept of correlation.

1. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Advanced Econometrics #1 : Nonlinear Transformations*
A. Charpentier (Université de Rennes 1)
Université de Rennes 1
Graduate Course, 2018.
@freakonometrics 1

2. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Econometrics and ‘Regression’ ?
Galton (1870, Heriditary Genius, 1886, Regression to-
wards mediocrity in hereditary stature) and Pearson &
Lee (1896, On Telegony in Man, 1903 On the Laws of
Inheritance in Man) studied genetic transmission of
characterisitcs, e.g. the heigth.
On average the child of tall parents is taller than
other children, but less than his parents.
“I have called this peculiarity by the name of regres-
sion”, Francis Galton, 1886.
@freakonometrics 2

3. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Econometrics and ‘Regression’ ?
1 > library(HistData)
2 > attach(Galton)
3 > Galton$count <- 1
4 > df <- aggregate(Galton , by=list(parent ,
child), FUN=sum)[,c(1,2,5)]
5 > plot(df[,1:2], cex=sqrt(df[,3]/3))
6 > abline(a=0,b=1,lty =2)
7 > abline(lm(child~parent ,data=Galton))
8 > coefficients (lm(child~parent ,data=Galton)
)[2]
9 parent
10 0.6462906
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64 66 68 70 72
62646668707274
height of the mid−parent
heightofthechild
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q q q q
It is more an autoregression issue here :
if Yt = φYt−1 + εt, then cor[Yt, Yt+h] = φh
→ 0 as h → ∞.
@freakonometrics 3

4. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Econometrics and ‘Regression’ ?
Regression is a correlation problem.
Overall, children are not smaller than parents
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60 65 70 75
60657075
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60 65 70 75
60657075
@freakonometrics 4

5. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Overview
◦ Linear Regression Model: yi = β0 + xT
i β + εi = β0 + β1x1,i + β2x2,i + εi
• Nonlinear Transformations : smoothing techniques
h(yi) = β0 + β1x1,i + β2x2,i + εi
yi = β0 + β1x1,i + h(x2,i) + εi
• Asymptotics vs. Finite Distance : boostrap techniques
• Penalization : Parcimony, Complexity and Overfit
• From least squares to other regressions : quantiles, expectiles, etc.
@freakonometrics 5

6. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
References
Motivation
Kopczuk, W. Tax bases, tax rates and the elasticity of reported income. JPE.
References
Eubank, R.L. (1999) Nonparametric Regression and Spline Smoothing, CRC Press.
Fan, J. & Gijbels, I. (1996) Local Polynomial Modelling and Its Applications CRC
Press.
Hastie, T.J. & Tibshirani, R.J. (1990) Generalized Additive Models. CRC Press
Wand, M.P & Jones, M.C. (1994) Kernel Smoothing. CRC Press
@freakonometrics 6

7. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Deterministic or Parametric Transformations
Consider child mortality rate (y) as a function of GDP per capita (x).
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0e+00 2e+04 4e+04 6e+04 8e+04 1e+05
050100150
PIB par tête
Tauxdemortalitéinfantile
Afghanistan
Albania
Algeria
American.Samoa
Angola
Argentina
Armenia
Austria
Azerbaijan
Bahamas
Bangladesh
Belarus
Belgium
Belize
Benin
BhutanBolivia
Bosnia.and.Herzegovina
Brunei.Darussalam
Bulgaria
Burkina.Faso
Cambodia
Canada
Cape.Verde
Central.African.Republic
Chad
Channel.Islands Chile
China
Comoros
Congo
Cook.Islands
Côte.dIvoire
Cuba CyprusCzech.Republic
Korea
Democratic.Republic.of.the.Congo
Denmark
Djibouti
Egypt
El.Salvador
Equatorial.Guinea
Estonia
Fiji
FinlandFrance
French.Guiana
French.Polynesia
Gabon
Gambia
Ghana
Gibraltar
Greece
Grenada
Guam
Guatemala
Guinea
Guinea−Bissau
Guyana
Haiti
Honduras
India
Indonesia
Iran
IrelandIsrael Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kyrgyzstan
Laos
Latvia
Lesotho
Libyan.Arab.Jamahiriya
Liechtenstein
Lithuania
Luxembourg
Madagascar
Malawi
Malaysia
Malta
Marshall.Islands
Martinique
Mauritius
Micronesia.(Federated.States.of)
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Netherlands
Netherlands.Antilles
New.Caledonia
Nicaragua
Nigeria
Niue
Norway
Occupied.Palestinian.Territory
Oman
Pakistan
Papua.New.Guinea
Paraguay
Peru
Poland Puerto.Rico Qatar
Republic.of.Korea
Republic.of.Moldova
RéunionRomaniaRussian.Federation
Saint.Vincent.and.the.GrenadinesSamoa
San.Marino
Saudi.Arabia
Serbia
Sierra.Leone
Singapore
Slovakia Slovenia
Solomon.Islands
Somalia
Sri.Lanka
Sudan
Suriname
Sweden
Syrian.Arab.Republic
Tajikistan
Thailand
Macedonia
Timor−Leste
Togo
Tunisia
Turkey
Turkmenistan
Tuvalu
Ukraine
United.Arab.Emirates
United.Kingdom
United.Republic.of.Tanzania
United.States.of.America
United.States.Virgin.Islands
Uruguay
Venezuela
Viet.Nam
YemenZimbabwe
@freakonometrics 7

8. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Deterministic or Parametric Transformations
Logartihmic transformation, log(y) as a function of log(x)
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1e+02 5e+02 1e+03 5e+03 1e+04 5e+04 1e+05
25102050100
PIB par tête (log)
Tauxdemortalité(log)
Afghanistan
Albania
Algeria
American.Samoa
Angola
Argentina
Armenia
Aruba
AustraliaAustria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
BhutanBolivia
Bosnia.and.Herzegovina
Botswana
Brazil
Brunei.Darussalam
Bulgaria
Burkina.FasoBurundi
Cambodia
Cameroon
Canada
Cape.Verde
Central.African.Republic
Chad
Channel.Islands
Chile
China
Hong.Kong
Colombia
Comoros
Congo
Cook.Islands
Costa.Rica
Côte.dIvoire
Croatia
Cuba
Cyprus
Czech.Republic
Korea
Democratic.Republic.of.the.Congo
Denmark
Djibouti
Dominican.Republic
Ecuador
Egypt
El.Salvador
Equatorial.Guinea
Eritrea
Estonia
Ethiopia
Fiji
FinlandFrance
French.Guiana
French.Polynesia
Gabon
Gambia
Georgia
Germany
Ghana
Gibraltar
Greece
Greenland
Grenada
Guadeloupe
Guam
Guatemala
Guinea
Guinea−Bissau
Guyana
Haiti
Honduras
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Isle.of.Man
Israel Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kiribati
Kuwait
KyrgyzstanLaos
Latvia
Lebanon
Lesotho
Liberia
Libyan.Arab.Jamahiriya
Liechtenstein
Lithuania
Luxembourg
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Marshall.Islands
Martinique
Mauritania
Mauritius
Mexico
Micronesia.(Federated.States.of)
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands.Antilles
New.Caledonia
New.Zealand
Nicaragua
Niger Nigeria
Niue
Norway
Occupied.Palestinian.Territory
Oman
Pakistan
Palau
Panama
Papua.New.Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Puerto.Rico
Qatar
Republic.of.Korea
Republic.of.Moldova
Réunion
Romania
Russian.Federation
Rwanda
Saint.Lucia
Saint.Vincent.and.the.GrenadinesSamoa
San.Marino
Sao.Tome.and.Principe
Saudi.Arabia
Senegal
Serbia
Sierra.Leone
Singapore
Slovakia
Slovenia
Solomon.Islands
Somalia
South.Africa
Spain
Sri.Lanka
Sudan
Suriname
Swaziland
Sweden
Switzerland
Syrian.Arab.Republic
Tajikistan
Thailand
Macedonia
Timor−Leste
Togo
Tonga
Trinidad.and.Tobago
Tunisia
Turkey
Turkmenistan
Turks.and.Caicos.Islands
Tuvalu
Uganda
Ukraine
United.Arab.Emirates
United.Kingdom
United.Republic.of.Tanzania
United.States.of.America
United.States.Virgin.Islands
Uruguay
Uzbekistan
Vanuatu
Venezuela
Viet.Nam
Western.Sahara
Yemen
Zambia
Zimbabwe
@freakonometrics 8

9. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Deterministic or Parametric Transformations
Reverse transformation
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0e+00 2e+04 4e+04 6e+04 8e+04 1e+05
050100150
PIB par tête
Tauxdemortalité
Afghanistan
Albania
Algeria
American.Samoa
Angola
Argentina
Armenia
Aruba
AustraliaAustria
Azerbaijan
Bahamas
Bahrain
Bangladesh
BarbadosBelarus
Belgium
Belize
Benin
BhutanBolivia
Bosnia.and.Herzegovina
Botswana
Brazil
Brunei.Darussalam
Bulgaria
Burkina.Faso
Burundi
Cambodia
Cameroon
Canada
Cape.Verde
Central.African.Republic
Chad
Channel.Islands Chile
China
Hong.Kong
Colombia
Comoros
Congo
Cook.IslandsCosta.Rica
Côte.dIvoire
CroatiaCuba CyprusCzech.Republic
Korea
Democratic.Republic.of.the.Congo
Denmark
Djibouti
Dominican.Republic
Ecuador
Egypt
El.Salvador
Equatorial.Guinea
Eritrea
Estonia
Ethiopia
Fiji
FinlandFrance
French.Guiana
French.Polynesia
Gabon
Gambia
Georgia
Germany
Ghana
Gibraltar
GreeceGreenland
Grenada
GuadeloupeGuam
Guatemala
Guinea
Guinea−Bissau
Guyana
Haiti
Honduras
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Isle.of.Man
Israel Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kiribati
Kuwait
Kyrgyzstan
Laos
Latvia
Lebanon
Lesotho
Liberia
Libyan.Arab.Jamahiriya
Liechtenstein
Lithuania
Luxembourg
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Marshall.Islands
Martinique
Mauritania
Mauritius
Mexico
Micronesia.(Federated.States.of)
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands.Antilles
New.CaledoniaNew.Zealand
Nicaragua
NigerNigeria
Niue
Norway
Occupied.Palestinian.Territory
Oman
Pakistan
Palau
Panama
Papua.New.Guinea
Paraguay
Peru
Philippines
Poland PortugalPuerto.Rico Qatar
Republic.of.Korea
Republic.of.Moldova
RéunionRomaniaRussian.Federation
Rwanda
Saint.Lucia
Saint.Vincent.and.the.GrenadinesSamoa
San.Marino
Sao.Tome.and.Principe
Saudi.Arabia
Senegal
Serbia
Sierra.Leone
Singapore
Slovakia Slovenia
Solomon.Islands
Somalia
South.Africa
Spain
Sri.Lanka
Sudan
Suriname
Swaziland
Sweden Switzerland
Syrian.Arab.Republic
Tajikistan
Thailand
Macedonia
Timor−Leste
Togo
Tonga
Trinidad.and.Tobago
Tunisia
Turkey
Turkmenistan
Turks.and.Caicos.Islands
Tuvalu
Uganda
Ukraine
United.Arab.Emirates
United.Kingdom
United.Republic.of.Tanzania
United.States.of.America
United.States.Virgin.Islands
Uruguay
Uzbekistan
Vanuatu
Venezuela
Viet.Nam
Western.Sahara
Yemen
Zambia
Zimbabwe
@freakonometrics 9

10. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Box-Cox transformation
See Box & Cox (1964) An Analysis of Transformations ,
h(y, λ) =



yλ
− 1
λ
if λ = 0
log(y) if λ = 0
or
h(y, λ, µ) =



[y + µ]λ
− 1
λ
if λ = 0
log([y + µ]) if λ = 0
@freakonometrics 10
0 1 2 3 4
−4−3−2−1012
−1 −0.5 0 0.5 1 1.5 2

11. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Profile Likelihood
In a statistical context, suppose that unknown parameter can be partitioned
θ = (λ, β) where λ is the parameter of interest, and β is a nuisance parameter.
Consider {y1, · · · , yn}, a sample from distribution Fθ, so that the log-likelihood is
log L(θ) =
n
i=1
log fθ(yi)
θ
MLE
is defined as θ
MLE
= argmax {log L(θ)}
Rewrite the log-likelihood as log L(θ) = log Lλ(β). Define
β
pMLE
λ = argmax
β
{log Lλ(β)}
and then λpMLE
= argmax
λ
log Lλ(β
pMLE
λ ) . Observe that
√
n(λpMLE
− λ)
L
−→ N(0, [Iλ,λ − Iλ,βI−1
β,βIβ,λ]−1
)
@freakonometrics 11

12. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Profile Likelihood and Likelihood Ratio Test
The (profile) likelihood ratio test is based on
2 max L(λ, β) − max L(λ0, β)
If (λ0, β0) are the true value, this difference can be written
2 max L(λ, β) − max L(λ0, β0) − 2 max L(λ0, β) − max L(λ0, β0)
Using Taylor’s expension
∂L(λ, β)
∂λ (λ0,βλ0
)
∼
∂L(λ, β)
∂λ (λ0,β0
)
− Iβ0λ0
I−1
β0β0
∂L(λ0, β)
∂β (λ0,β0
)
Thus,
1
√
n
∂L(λ, β)
∂λ (λ0,βλ0
)
L
→ N(0, Iλ0λ0
) − Iλ0β0
I−1
β0β0
Iβ0λ0
and 2 L(λ, β) − L(λ0, βλ0
)
L
→ χ2
(dim(λ)).
@freakonometrics 12

13. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Profile Likelihood and Likelihood Ratio Test
Consider some lognormal sample, and fit a Gamma distribution,
f(x; α, β) =
xα−1
βα
e−β x
Γ(α)
with x > 0 and θ = (α, β).
1 > x=exp(rnorm (100))
Maximum-likelihood, θ = argmax{log L(θ)}.
1 > library(MASS)
2 > (F=fitdistr(x,"gamma"))
3 shape rate
4 1.4214497 0.8619969
5 (0.1822570) (0.1320717)
6 > F$estimate [1]+c(-1,1)*1.96*F$sd [1]
7 [1] 1.064226 1.778673
@freakonometrics 13

14. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Profile Likelihood and Likelihood Ratio Test
See also
1 > log_lik=function(theta){
2 + a=theta [1]
3 + b=theta [2]
4 + logL=sum(log(dgamma(x,a,b)))
5 + return(-logL)
6 + }
7 > optim(c(1 ,1),log_lik)
8 $par
9 [1] 1.4214116 0.8620311
We can also use profile likelihood,
α = argmax max
β
log L(α, β) = argmax log L(α, βα)
@freakonometrics 14

15. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Profile Likelihood and Likelihood Ratio Test
1 > prof_log_lik=function(a){
2 + b=( optim (1, function(z) -sum(log(dgamma(x,a,z)))))$par
3 + return(-sum(log(dgamma(x,a,b))))
4 + }
5
6 > vx=seq (.5,3, length =101)
7 > vl=-Vectorize(prof_log_lik)(vx)
8 > plot(vx ,vl ,type="l")
9 > optim (1,prof_log_lik)
10 $par
11 [1] 1.421094
We can use the likelihood ratio test
2 log Lp(α) − log Lp(α) ∼ χ2
(1)
@freakonometrics 15

16. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Profile Likelihood and Likelihood Ratio Test
The implied 95% confidence interval is
1 > (b1=uniroot(function(z) Vectorize(prof_log_lik)(z)+borne ,c(.5 ,1.5))
$root)
2 [1] 1.095726
3 > (b2=uniroot(function(z) Vectorize(prof_log_lik)(z)+borne ,c
(1.25 ,2.5))$root)
4 [1] 1.811809
@freakonometrics 16

17. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Box-Cox
1 > boxcox(lm(dist~speed ,data=cars))
Here h∗
∼ 0.5
Heuristally, y
1/2
i ∼ β0 + β1xi + εi
why not consider a quadratic regression...?
−0.5 0.0 0.5 1.0 1.5 2.0
−90−80−70−60−50
λ
log−Likelihood
95%
@freakonometrics 17
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18. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Uncertainty: Parameters vs. Prediction
Uncertainty on regression parameters (β0, β1)
From the output of the regression we can derive
confidence intervals for β0 and β1, usually
βk ∈ βk ± u1−α/2se[βk]
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020406080100120
Vitesse du véhicule
Distancedefreinage
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Vitesse du véhicule
Distancedefreinage
@freakonometrics 18

19. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Uncertainty: Parameters vs. Prediction
Uncertainty on a prediction, y = m(x). Usually
m(x) ∈ m(x) ± u1−α/2se[m(x)]
hence, for a linear model
xT
β ± u1−α/2σ xT[XT
X]−1x
i.e. (with one covariate)
se2
[m(x)]2
= Var[β0 + β1x]
se2
[β0] + cov[β0, β1]x + se2
[β1]x2
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Vitesse du véhicule
Distancedefreinage
1 > predict(lm(dist~speed ,data=cars),newdata=data.frame(speed=x),
interval="confidence")
@freakonometrics 19

20. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Least Squares and Expected Value (Orthogonal Projection Theorem)
Let y ∈ Rd
, y = argmin
m∈R



n
i=1
1
n
yi − m
εi
2



. It is the empirical version of
E[Y ] = argmin
m∈R



y − m
ε
2
dF(y)



= argmin
m∈R



E (Y − m
ε
)2



where Y is a 1 random variable.
Thus, argmin
m(·):Rk→R



n
i=1
1
n
yi − m(xi)
εi
2



is the empirical version of E[Y |X = x].
@freakonometrics 20

21. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
The Histogram and the Regressogram
Connections between the estimation of f(y) and E[Y |X = x].
Assume that yi ∈ [a1, ak+1), divided in k classes [aj, aj+1). The histogram is
ˆfa(y) =
k
j=1
1(t ∈ [aj, aj+1))
aj+1 − aj
1
n
n
i=1
1(yi ∈ [aj, aj+1))
Assume that aj+1 −aj = hn and hn → 0 as n → ∞
with nhn → ∞ then
E ( ˆfa(y) − f(y))2
∼ O(n−2/3
)
(for an optimal choice of hn).
1 > hist(height)
@freakonometrics 21
150 160 170 180 190
0.000.010.020.030.040.050.06

22. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
The Histogram and the Regressogram
Then a moving histogram was considered,
ˆf(y) =
1
2nhn
n
i=1
1(yi ∈ [y ± hn)) =
1
nhn
n
i=1
k
yi − y
hn
with k(x) =
1
2
1(x ∈ [−1, 1)), which a (flat) kernel
estimator.
1 > density(height , kernel = " rectangular ")
150 160 170 180 190 200
0.000.010.020.030.04
150 160 170 180 190 200
0.000.010.020.030.04
@freakonometrics 22

23. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
The Histogram and the Regressogram
From Tukey (1961) Curves as parameters, and touch
estimation, the regressogram is defined as
ˆma(x) =
n
i=1 1(xi ∈ [aj, aj+1))yi
n
i=1 1(xi ∈ [aj, aj+1))
and the moving regressogram is
ˆm(x) =
n
i=1 1(xi ∈ [x ± hn])yi
n
i=1 1(xi ∈ [x ± hn])
@freakonometrics 23
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020406080100120
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dist
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speed
dist

24. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Nadaraya-Watson and Kernels
Background: Kernel Density Estimator
Consider sample {y1, · · · , yn}, Fn empirical cumulative distribution function
Fn(y) =
1
n
n
i=1
1(yi ≤ y)
The empirical measure Pn consists in weights 1/n on each observation.
Idea: add (little) continuous noise to smooth Fn.
Let Yn denote a random variable with distribution Fn and define
˜Y = Yn + hU where U ⊥⊥ Yn, with cdf K
The cumulative distribution function of ˜Y is ˜F
˜F(y) = P[ ˜Y ≤ y] = E 1( ˜Y ≤ y) = E E 1( ˜Y ≤ y) Yn
˜F(y) = E 1 U ≤
y − Yn
h
Yn =
n
i=1
1
n
K
y − yi
h
@freakonometrics 24

25. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Nadaraya-Watson and Kernels
If we differentiate
˜f(y)=
1
nh
n
i=1
k
y − yi
h
=
1
n
n
i=1
kh (y − yi) with kh(u) =
1
h
k
u
h
˜f is the kernel density estimator of f, with kernel
k and bandwidth h.
Rectangular, k(u) =
1
2
1(|u| ≤ 1)
Epanechnikov, k(u) =
3
4
1(|u| ≤ 1)(1 − u2
)
Gaussian, k(u) =
1
√
2π
e− u2
2
1 > density(height , kernel = " epanechnikov ")
−2 −1 0 1 2
@freakonometrics 25
150 160 170 180 190 200
0.000.010.020.030.04

26. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Kernels and Statistical Properties
Consider here an i.id. sample {Y1, · · · , Yn} with density f
Given y, observe that E[ ˜f(y)] =
1
h
k
y − t
h
f(t)dt = k(u)f(y − hu)du. Use
Taylor expansion around h = 0,f(y − hu) ∼ f(y) − f (y)hu +
1
2
f (y)h2
u2
E[ ˜f(y)] = f(y)k(u)du − f (y)huk(u)du +
1
2
f (y + hu)h2
u2
k(u)du
= f(y) + 0 + h2 f (y)
2
k(u)u2
du + o(h2
)
Thus, if f is twice continuously differentiable with bounded second derivative,
k(u)du = 1, uk(u)du = 0 and u2
k(u)du < ∞,
then E[ ˜f(y)] = f(y) + h2 f (y)
2
k(u)u2
du + o(h2
)
@freakonometrics 26

27. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Kernels and Statistical Properties
For the heuristics on that bias, consider a flat kernel,
and set
fh(y) =
F(y + h) − F(y − h)
2h
then the natural estimate is
fh(y) =
F(y + h) − F(y − h)
2h
=
1
2nh
n
i=1
1(yi ∈ [y ± h])
Zi
where Zi’s are Bernoulli B(px) i.id. variables with
px = P[Yi ∈ [x ± h]] = 2h · fh(x). Thus, E(fh(y)) = fh(y), while
fh(y) ∼ f(y) +
h2
6
f (y) as h ∼ 0.
@freakonometrics 27

28. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Kernels and Statistical Properties
Similarly, as h → 0 and nh → ∞
Var[ ˜f(y)] =
1
n
E[kh(z − Z)2
] − (E[kh(z − Z)])
2
Var[ ˜f(y)] =
f(y)
nh
k(u)2
du + o
1
nh
Hence
• if h → 0 the bias goes to 0
• if nh → ∞ the variance goes to 0
@freakonometrics 28

29. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Kernels and Statistical Properties
Extension in Higher Dimension:
˜f(y) =
1
n|H|1/2
n
i=1
k H−1/2
(y − yi)
˜f(y) =
1
nhd|Σ|1/2
n
i=1
k Σ−1/2 (y − yi)
h
@freakonometrics 29
150 160 170 180 190 200
406080100120
height
weight
2e−04
4e−04
6e−04
8e−04
0.001
0.0012
0.0014
0.0016
0.0018

30. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Kernels and Convolution
Given f and g, set
(f g)(x) =
R
f(x − y)g(y)dy
Then ˜fh = ( ˆf kh), where
ˆf(y) =
ˆF(y)
dy
=
n
i=1
δyi
(y)
Hence, ˜f is the distribution of Y + ε where
Y is uniform over {y1, · · · , yn} and ε ∼ kh are independent
@freakonometrics 30
q q q q q
−0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2
0.00.20.40.60.81.0

31. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Nadaraya-Watson and Kernels
Here E[Y |X = x] = m(x). Write m as a function of densities
m(x) = yf(y|x)dy =
yf(y, x)dy
f(y, x)dy
Consider some bivariate kernel k, such that
tk(t, u)dt = 0 and κ(u) = k(t, u)dt
For the numerator, it can be estimated using
y ˜f(y, x)dy =
1
nh2
n
i=1
yk
y − yi
h
,
x − xi
h
=
1
nh
n
i=1
yik t,
x − xi
h
dt =
1
nh
n
i=1
yiκ
x − xi
h
@freakonometrics 31

32. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Nadaraya-Watson and Kernels
and for the denominator
f(y, x)dy =
1
nh2
n
i=1
k
y − yi
h
,
x − xi
h
=
1
nh
n
i=1
κ
x − xi
h
Therefore, plugging in the expression for g(x) yields
˜m(x) =
n
i=1 yiκh (x − xi)
n
i=1 κh (x − xi)
Observe that this regression estimator is a weighted
average (see linear predictor section)
˜m(x) =
n
i=1
ωi(x)yi with ωi(x) =
κh (x − xi)
n
i=1 κh (x − xi)
@freakonometrics 32
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020406080100120
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33. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Nadaraya-Watson and Kernels
One can prove that kernel regression bias is given by
E[ ˜m(x)] ∼ m(x) + h2 C1
2
m (x) + C2m (x)
f (x)
f(x)
while Var[ ˜m(x)] ∼
C3
nh
σ(x)
f(x)
. In this univariate case, one can easily get the kernel
estimator of derivatives.
Actually, ˜m is a function of bandwidth h.
Note: this can be extended to multivariate x.
@freakonometrics 33
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34. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Nadaraya-Watson and Kernels in Higher Dimension
Here mH(x) =
n
i=1 yikH(xi − x)
n
i=1 kH(xi − x)
for some symmetric positive definite
bandwidth matrix H, and kH(x) = det[H]−1
k(H−1
x). Then
E[mH(x)] ∼ m(x) +
C1
2
trace HT
m (x)H + C2
m (x)T
HHT
f(x)
f(x)
while
Var[mH(x)] ∼
C3
ndet(H)
σ(x)
f(x)
Hence, if H = hI, h ∼ Cn− 1
4+dim(x) .
@freakonometrics 34

35. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
From kernels to k-nearest neighbours
An alternative is to consider
˜mk(x) =
1
n
n
i=1
ωi,k(x)yi
where ωi,k(x) =
n
k
if i ∈ Ik
x with
Ik
x = {i : xi one of the k nearest observations to x}
Lai (1977) Large sample properties of K-nearest neighbor procedures if k → ∞ and
k/n → 0 as n → ∞, then
E[ ˜mk(x)] ∼ m(x) +
1
24f(x)3
(m f + 2m f )(x)
k
n
2
while Var[ ˜mk(x)] ∼
σ2
(x)
k
@freakonometrics 35
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36. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
From kernels to k-nearest neighbours
Remark: Brent & John (1985) Finding the median requires 2n comparisons
considered some median smoothing algorithm, where we consider the median
over the k nearest neighbours (see section #4).
@freakonometrics 36

37. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
k-Nearest Neighbors and Curse of Dimensionality
The higher the dimension, the larger the distance to the closest neigbbor
min
i∈{1,··· ,n}
{d(a, xi)}, xi ∈ Rd
.
q
dim1 dim2 dim3 dim4 dim5
0.00.20.40.60.81.0
dim1 dim2 dim3 dim4 dim5
0.00.20.40.60.81.0
n = 10 n = 100
@freakonometrics 37

38. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Bandwidth selection : MISE for Density
MSE[ ˜f(y)] = bias[ ˜f(y)]2
+ Var[ ˜f(y)]
MSE[ ˜f(y)] = f(y)
1
nh
k(u)2
du + h4 f (y)
2
k(u)u2
du
2
+ o h4
+
1
nh
Bandwidth choice is based on minimization of the asymptotic integrated MSE
(over y)
MISE( ˜f) = MSE[ ˜f(y)]dy ∼
1
nh
k(u)2
du + h4 f (y)
2
k(u)u2
du
2
@freakonometrics 38

39. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Bandwidth selection : MISE for Density
Thus, the first-order condition yields
−
C1
nh2
+ h3
f (y)2
dyC2 = 0
with C1 = k2
(u)du and C2 = k(u)u2
du
2
, and
h = n− 1
5
C1
C2 f (y)dy
1
5
h = 1.06n− 1
5 Var[Y ] from Silverman (1986) Density Estimation
1 > bw.nrd0(cars$speed)
2 [1] 2.150016
3 > bw.nrd(cars$speed)
4 [1] 2.532241
with Scott correction, see Scott (1992) Multivariate Density Estimation
@freakonometrics 39

40. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Bandwidth selection : MISE for Regression Model
One can prove that
MISE[mh] ∼
bias2
h4
4
x2
k(x)dx
2
m (x) + 2m (x)
f (x)
f(x)
2
dx
+
σ2
nh
k2
(x)dx ·
dx
f(x)
variance
as n → 0 and nh → ∞.
The bias is sensitive to the position of the xi’s.
h = n− 1
5


C1
dx
f(x)
C2 m (x) + 2m (x)f (x)
f(x) dx


1
5
Problem: depends on unknown f(x) and m(x).
@freakonometrics 40

41. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Bandwidth Selection : Cross Validation
Let R(h) = E (Y − mh(X))2
.
Natural idea R(h) =
1
n
n
i=1
(yi − mh(xi))
2
Instead use leave-one-out cross validation,
R(h) =
1
n
n
i=1
yi − m
(i)
h (xi)
2
where m
(i)
h is the estimator obtained by omitting the ith
pair (yi, xi) or k-fold cross validation,
R(h) =
1
n
k
j=1 i∈Ij
yi − m
(j)
h (xi)
2
where m
(j)
h is the estimator obtained by omitting pairs
(yi, xi) with i ∈ Ij.
@freakonometrics 41
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42. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Bandwidth Selection : Cross Validation
Then find (numerically)
h = argmin R(h)
In the context of density estimation, see Chiu
(1991) Bandwidth Selection for Kernel Density Es-
timation 2 4 6 8 10
1416182022
bandwidth
Usual bias-variance tradeoff, or Goldilock principle:
h should be neither too small, nor too large
• undersmoothed: bias too large, variance too small
• oversmoothed: variance too large, bias too small
@freakonometrics 42

43. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Local Linear Regression
Consider ˆm(x) defined as ˆm(x) = β0 where (β0, β) is the solution of
min
(β0,β)
n
i=1
ω
(x)
i yi − [β0 + (x − xi)T
β]
2
where ω
(x)
i = kh(x − xi), e.g.
i.e. we seek the constant term in a weighted least squares regression of yi’s on
x − xi’s. If Xx is the matrix [1 (x − X)T
], and if W x is a matrix
diag[kh(x − x1), · · · , kh(x − xn)]
then ˆm(x) = 1T
(XT
xW xXx)−1
XT
xW xy
This estimator is also a linear predictor :
ˆm(x) =
n
i=1
ai(x)
ai(x)
yi
@freakonometrics 43

44. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
where
ai(x) =
1
n
kh(x − xi) 1 − s1(x)T
s2(x)−1 x − xi
h
with
s1(x) =
1
n
n
i=1
kh(x−xi)
x − xi
h
and s2(x) =
1
n
n
i=1
kh(x−xi)
x − xi
h
x − xi
h
Note that Nadaraya-Watson estimator was simply the solution of
min
β0
n
i=1
ω
(x)
i (yi − β0)
2
where ω
(x)
i = kh(x − xi)
E[ ˆm(x)] ∼ m(x) +
h2
2
m (x)µ2 where µ2 = k(u)u2
du.
Var[ ˆm(x)] ∼
1
nh
νσ2
x
f(x)
@freakonometrics 44

45. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
where ν = k(u)2
du
Thus, kernel regression MSE is
h2
4
g (x) + 2g (x)
f (x)
f(x)
2
µ2
2 +
1
nh
νσ2
x
f(x)
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020406080100120
Vitesse du véhciule
Distancedefreinage
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Vitesse du véhciule
Distancedefreinage
@freakonometrics 45

46. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
1 > loess(dist ~ speed , cars ,span =0.75 , degree =1)
2 > predict(REG , data.frame(speed = seq(5, 25, 0.25)), se = TRUE)
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020406080100120
Vitesse du véhciule
Distancedefreinage
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Distancedefreinage
@freakonometrics 46

47. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Local polynomials
One might assume that, locally, m(x) ∼ µx(u) as u ∼ 0, with
µx(u) = β
(x)
0 + β
(x)
1 + [u − x] + β
(x)
2 +
[u − x]2
2
+ β
(x)
3 +
[u − x]3
2
+ · · ·
and we estimate β(x)
by minimizing
n
i=1
ω
(x)
i yi − µx(xi)
2
.
If Xx is the design matrix 1 xi − x
[xi − x]2
2
[xi − x]3
3
· · · , then
β
(x)
= XT
xW xXx
−1
XT
xW xy
(weighted least squares estimators).
1 > library(locfit)
2 > locfit(dist~speed ,data=cars)
@freakonometrics 47

48. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Series Regression
Recall that E[Y |X = x] = m(x).
Why not approximate m by a linear combination of approx-
imating functions h1(x), · · · , hk(x).
Set h(x) = (h1(x), · · · , hk(x)), and consider the regression
of yi’s on h(xi)’s,
yi = h(xi)T
β + εi
Then β = (HT
H)−1
HT
y
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@freakonometrics 48

49. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Series Regression : polynomials
Even if m(x) = E(Y |X = x) is not a polynomial function,
a polynomial can still be a good approximation.
From Stone-Weierstrass theorem, if m(·) is continuous on
some interval, then there is a uniform approximation of
m(·) by polynomial functions.
1 > reg <- lm(y~x,data=db)
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@freakonometrics 49

50. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Series Regression : polynomials
Assume that m(x) = E(Y |X = x) =
k
i=0
αixi
, where pa-
rameters α0, · · · , αk will be estimated (but not k).
1 > reg <- lm(y~poly(x ,5) ,data=db)
2 > reg <- lm(y~poly(x ,25) ,data=db)
@freakonometrics 50
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51. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Series Regression : (Linear) Splines
Consider m + 1 knots on X, min{xi} ≤ t0 ≤ t1 ≤ · · · ≤ tm ≤ max{xn}, then
define linear (degree = 1) splines positive function,
bj,1(x) = (x − tj)+ =



x − tj if x > tj
0 otherwise
for linear splines, consider
Yi = β0 + β1Xi + β2(Xi − s)+ + εi
1 > positive_part <- function(x) ifelse(x>0,x ,0)
2 > reg <- lm(Y~X+positive_part(X-s), data=db)
@freakonometrics 51
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52. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Series Regression : (Linear) Splines
for linear splines, consider
Yi = β0 + β1Xi + β2(Xi − s1)+ + β3(Xi − s2)+ + εi
1 > reg <- lm(Y~X+positive_part(X-s1)+
2 positive_part(X-s2), data=db)
3 > library(bsplines)
A spline is a function defined by piecewise polynomials.
b-splines are defined recursively
@freakonometrics 52
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53. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
b-Splines (in Practice)
1 > reg1 <- lm(dist~speed+positive_part(speed -15) ,
data=cars)
2 > reg2 <- lm(dist~bs(speed ,df=2, degree =1) , data=
cars)
Consider m+1 knots on [0, 1], 0 ≤ t0 ≤ t1 ≤ · · · ≤ tm ≤ 1,
then define recursively b-splines as
bj,0(t) =



1 if tj ≤ t < tj+1
0 otherwise, and
bj,n(t) =
t − tj
tj+n − tj
bj,n−1(t)
+
tj+n+1 − t
tj+n+1 − tj+1
bj+1,n−1(t)
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@freakonometrics 53

54. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
b-Splines (in Practice)
1 > summary(reg1)
2
3 Coefficients :
4 Estimate Std Error t value Pr(>|t|)
5 (Intercept) -7.6519 10.6254 -0.720 0.475
6 speed 3.0186 0.8627 3.499 0.001 **
7 (speed -15) 1.7562 1.4551 1.207 0.233
8
9 > summary(reg2)
10
11 Coefficients :
12 Estimate Std Error t value Pr(>|t|)
13 (Intercept) 4.423 7.343 0.602 0.5493
14 bs(speed)1 33.205 9.489 3.499 0.0012 **
15 bs(speed)2 80.954 8.788 9.211 4.2e -12 ***
@freakonometrics 54
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55. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
b and p-Splines
Note that those spline function define an orthonormal ba-
sis.
O’Sullivan (1986) A statistical perspective on ill-posed in-
verse problems suggested a penalty on the second deriva-
tive of the fitted curve (see #3).
m(x) = argmin
n
i=1
yi − b(xi)T
β
2
+ λ
R
b (xi)T
β
@freakonometrics 55
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56. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Adding Constraints: Convex Regression
Assume that yi = m(xi) + εi where m : Rd
→ ∞R is some convex function.
m is convex if and only if ∀x1, x2 ∈ Rd
, ∀t ∈ [0, 1],
m(tx1 + [1 − t]x2) ≤ tm(x1) + [1 − t]m(x2)
Proposition (Hidreth (1954) Point Estimates of Ordinates of Concave Functions)
m = argmin
m convex
n
i=1
yi − m(xi)
2
Then θ = (m (x1), · · · , m (xn)) is unique.
Let y = θ + ε, then
θ = argmin
θ∈K
n
i=1
yi − θi)
2
where K = {θ ∈ Rn
: ∃m convex , m(xi) = θi}. I.e. θ is the projection of y onto
the (closed) convex cone K. The projection theorem gives existence and unicity.
@freakonometrics 56

57. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Adding Constraints: Convex Regression
In dimension 1: yi = m(xi) + εi. Assume that observations are ordered
x1 < x2 < · · · < xn.
Here
K = θ ∈ Rn
:
θ2 − θ1
x2 − x1
≤
θ3 − θ2
x3 − x2
≤ · · · ≤
θn − θn−1
xn − xn−1
Hence, quadratic program with n − 2 linear con-
straints.
m is a piecewise linear function (interpolation of
consecutive pairs (xi, θi )).
If m is differentiable, m is convex if
m(x) + m(x) · [y − x] ≤ m(y)
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@freakonometrics 57

58. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Adding Constraints: Convex Regression
More generally: if m is convex, then there exists ξx ∈ Rn
such that
m(x) + ξx · [y − x] ≤ m(y)
ξx is a subgradient of m at x. And then
∂m(x) = m(x) + ξ · [y − x] ≤ m(y), ∀y ∈ Rn
Hence, θ is solution of
argmin y − θ 2
subject to θi + ξi[xj − xi] ≤ θj, ∀i, j
and ξ1, · · · , ξn ∈ Rn
.
@freakonometrics 58
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59. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Spatial Smoothing
One can also consider some spatial smoothing, if we want to predict E[Y |X = x]
for some coordinate x.
1 > library(rgeos)
2 > library(rgdal)
3 > library(maptools)
4 > library( cartography )
5 > download.file("http://bit.ly/2G3KIUG","zonier.RData")
6 > load("zonier.RData")
7 > cols=rev(carto.pal(pal1="red.pal",n1=10, pal2="green.pal",n2 =10))
8 > download.file("http://bit.ly/2GSvzGW","FRA_adm0.rds")
9 > download.file("http://bit.ly/2FUZ0Lz","FRA_adm2.rds")
10 > FR=readRDS("FRA_adm2.rds")
11 > donnees_carte=data.frame(FRdata)
@freakonometrics 59

60. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Spatial Smoothing
1 > FR0=readRDS("FRA_adm0.rds")
2 > plot(FR0)
3 > bk = seq (-5,4.5, length =21)
4 > cuty = cut(simbase$Y,breaks=bk ,labels
=1:20)
5 > points(simbase$long ,simbase$lat ,col=cols
[cuty],pch =19, cex =.5)
One can consider a choropleth map (spatial version of the histogram).
@freakonometrics 60

61. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
1 > A=aggregate(x = simbase$Y,by=list(
simbase$dpt),mean)
2 > names(A)=c("dpt","y")
3 > d=donnees_carte$CCA_2
4 > d[d=="2A"]="201"
5 > d[d=="2B"]="202"
6 > donnees_carte$dpt=as.numeric(as.
character(d))
7 > donnees_carte=merge(donnees_carte ,A,all.
x=TRUE)
8 > donnees_carte=donnees_carte[order(
donnees_carte$OBJECTID) ,]
9 > bk=seq ( -2.75 ,2.75 , length =21)
10 > donnees_carte$cuty=cut(donnees_carte$y,
breaks=bk ,labels =1:20)
11 > plot(FR , col=cols[donnees_carte$cuty],
xlim=c( -5.2 ,12))
Spatial Smoothing
@freakonometrics 61

62. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Spatial Smoothing
Instead of a "continuous" gradient of colors, one can consider only 4 colors (4
levels) for the prediction.
1 > bk=seq ( -2.75 ,2.75 , length =5)
2 > donnees_carte$cuty=cut(donnees_carte$y,
breaks=bk ,labels =1:4)
3 > plot(FR , col=cols[c(3 ,8 ,12 ,17)][ donnees_
carte$cuty],xlim=c( -5.2 ,12))
@freakonometrics 62

63. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Spatial Smoothing
1 > P1 = FR0@polygons [[1]] @Polygons [[355]]
@coords
2 > P2 = FR0@polygons [[1]] @Polygons [[27]]
@coords
3 > plot(FR0 ,border=NA)
4 > polygon(P1)
5 > polygon(P2)
6 > grille <-expand.grid(seq(min(simbase$long
),max(simbase$long),length =101) ,seq(min
(simbase$lat),max(simbase$lat),length
=101))
7 > paslong =(max(simbase$long)-min(simbase$
long))/100
8 > paslat =(max(simbase$lat)-min(simbase$lat
))/100
@freakonometrics 63

64. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Spatial Smoothing
We need to create a grid (i.e. X) on which we approximate E[Y |X = x]
1 > f=function(i){ (point.in.polygon (grille
[i, 1]+ paslong/2 , grille[i, 2]+ paslat/
2 , P1[,1],P1[ ,2]) >0)+( point.in.polygon
(grille[i, 1]+ paslong/2 , grille[i,
2]+ paslat/2 , P2[,1],P2[ ,2]) >0) }
2 > indic=unlist(lapply (1: nrow(grille),f))
3 > grille=grille[which(indic ==1) ,]
4 > points(grille [ ,1]+ paslong/2,grille [ ,2]+
paslat/2)
@freakonometrics 64

65. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Spatial Smoothing
Consider here some k-NN, with k = 20
1 > library(geosphere)
2 > knn=function(i,k=20){
3 + d= distHaversine (grille[i,1:2] , simbase[,c
("long","lat")], r=6378.137)
4 + r=rank(d)
5 + ind=which(r<=k)
6 + mean(simbase[ind ,"Y"])
7 + }
8 > grille$y=Vectorize(knn)(1: nrow(grille))
9 > bk=seq ( -2.75 ,2.75 , length =21)
10 > grille$cuty=cut(grille$y,breaks=bk ,
labels =1:20)
11 > points(grille [ ,1]+ paslong/2,grille [ ,2]+
paslat/2,col=cols[grille$cuty],pch =19)
@freakonometrics 65

66. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Spatial Smoothing
Again, instead of a "continuous" gradient, we can use 4 levels,
1 > bk=seq ( -2.75 ,2.75 , length =5)
2 > grille$cuty=cut(grille$y,breaks=bk ,
labels =1:4)
3 > plot(FR0 ,border=NA)
4 > polygon(P1)
5 > polygon(P2)
6 > points(grille [ ,1]+ paslong/2,grille [ ,2]+
paslat/2,col=cols[c(3 ,8 ,12 ,17)][ grille$
cuty],pch =19)
@freakonometrics 66

67. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing (Non-)Linearities
In the linear model,
y = Xβ = X[XT
X]−1
XT
H
y
Hi,i is the leverage of the ith element of this hat matrix.
Write
yi =
n
j=1
[XT
i [XT
X]−1
XT
]jyj =
n
j=1
[H(Xi)]jyj
where
H(x) = xT
[XT
X]−1
XT
The prediction is
m(x) = E(Y |X = x) =
n
j=1
[H(x)]jyj
@freakonometrics 67

68. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing (Non-)Linearities
More generally, a predictor m is said to be linear if for all x if there is
S(·) : Rn
→ Rn
such that
m(x) =
n
j=1
S(x)jyj
Conversely, given y1, · · · , yn, there is a matrix S n × n such that
y = Sy
For the linear model, S = H.
trace(H) = dim(β): degrees of freedom
Hi,i
1 − Hi,i
is related to Cook’s distance, from Cook (1977), Detection of Influential
Observations in Linear Regression.
@freakonometrics 68

69. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing (Non-)Linearities
For a kernel regression model, with kernel k and bandwidth h
S
(k,h)
i,j =
kh(xi − xj)
n
k=1
kh(xk − xj)
where kh(·) = k(·/h), while S(k,h)
(x)j =
Kh(x − xj)
n
k=1
kh(x − xk)
For a k-nearest neighbor, S
(k)
i,j =
1
k
1(j ∈ Ixi
) where Ixi
are the k nearest
observations to xi, while S(k)
(x)j =
1
k
1(j ∈ Ix).
@freakonometrics 69

70. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing (Non-)Linearities
Observe that trace(S) is usually seen as a degree of smoothness.
Do we have to smooth? Isn’t linear model sufficent?
Define
T =
Sy − Hy
trace([S − H]T[S − H])
If the model is linear, then T has a Fisher distribution.
Remark: In the case of a linear predictor, with smoothing matrix Sh
R(h) =
1
n
n
i=1
(yi − m
(−i)
h (xi))2
=
1
n
n
i=1
Yi − mh(xi)
1 − [Sh]i,i
2
We do not need to estimate n models. One can also minimize
GCV (h) =
n2
n2 − trace(S)2
·
1
n
n
i=1
(Yi − mh(xi))
2
∼ Mallow’s Cp
@freakonometrics 70

71. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Intervals
If y = mh(x) = Sh(x)y, let σ2
=
1
n
n
i=1
(yi − mh(xi))
2
and a confidence interval
is, at x mh(y) ± t1−α/2σ Sh(x)Sh(x)T .
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5 10 15 20 25
020406080100120
vitesse du véhicule
distancedefreinage
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@freakonometrics 71

72. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
0
50
100
150
5
10
15
20
25
dist
speed 0
50
100
150
5
10
15
20
25
dist
speed
@freakonometrics 72

73. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
Also called variability bands for functions in Härdle (1990) Applied Nonparametric
Regresion.
From Collomb (1979) Condition nécessaires et suffisantes de convergence uniforme
d’un estimateur de la régression, with Kernel regression (Nadarayah-Watson)
sup |m(x) − mh(x)| ∼ C1h2
+ C2
log n
nh
sup |m(x) − mh(x)| ∼ C1h2
+ C2
log n
nhdim(x)
@freakonometrics 73

74. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
So far, we have mainly discussed pointwise convergence with
√
nh (mh(x) − m(x))
L
→ N(µx, σ2
x).
This asymptotic normality can be used to derive (pointwise) confidence intervals
P(IC−
(x) ≤ m(x) ≤ IC+
(x)) = 1 − α ∀x ∈ X.
But we can also seek uniform convergence properties. We want to derive
functions IC±
such that
P(IC−
(x) ≤ m(x) ≤ IC+
(x) ∀x ∈ X) = 1 − α.
@freakonometrics 74

75. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
• Bonferroni’s correction
Use a standard Gaussian (pointwise) confidence interval
IC±
(x) = m(x) ±
√
nhσt1−α/2.
and take also into accound the regularity of m. Set
V (η) =
1
2
2η + 1
n
+
1
n
m ∞,x, for some 0 < η < 1
where ϕ ∞,x is on a neighborhood of x. Then consider
IC±
(x) = IC±
(x) ± V (η).
@freakonometrics 75

76. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
• Use of Gaussian processes
Observe that
√
nh (mh(x) − m(x))
D
→ Gx for some Gaussian process (Gx).
Confidence bands are derived from quantiles of sup{Gx, x ∈ X}.
If we use kernel k for smoothing, Johnston (1982) Probabilities of Maximal
Deviations for Nonparametric Regression Function Estimates proved that
Gx = k(x − t)dWt, for some standard (Wt) Wiener process
is then a Gaussian process with variance k(x)k(t − x)dt. And
IC±
(x) = ϕ(x) ±
qα
2 log(1/h)
+ dn
5
7
σ2
√
nh
with dn = 2 log h−1 +
1
2 log h−1
log
3
4π2
, where exp(−2 exp(−qα)) = 1 − α.
@freakonometrics 76

77. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
• Bootstrap (see #2)
Finally, McDonald (1986) Smoothing with Split Linear Fits suggested a bootstrap
algorithm to approximate the distribution of Zn = sup{|ϕ(x) − ϕ(x)|, x ∈ X}.
@freakonometrics 77

78. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
Depending on the smoothing parameter h, we get different corrections
@freakonometrics 78

79. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Confidence Bands
Depending on the smoothing parameter h, we get different corrections
@freakonometrics 79

80. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Boosting to Capture NonLinear Effects
We want to solve
m = argmin E (Y − m(X))2
The heuristics is simple: we consider an iterative process where we keep modeling
the errors.
Fit model for y, h1(·) from y and X, and compute the error, ε1 = y − h1(X).
Fit model for ε1, h2(·) from ε1 and X, and compute the error, ε2 = ε1 − h2(X),
etc. Then set
mk(·) = h1(·)
∼y
+ h2(·)
∼ε1
+ h3(·)
∼ε2
+ · · · + hk(·)
∼εk−1
Hence, we consider an iterative procedure, mk(·) = mk−1(·) + hk(·).
@freakonometrics 80

81. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Boosting
h(x) = y − mk(x), which can be interpreted as a residual. Note that this residual
is the gradient of
1
2
[y − mk(x)]2
A gradient descent is based on Taylor expansion
f(xk)
f,xk
∼ f(xk−1)
f,xk−1
+ (xk − xk−1)
α
f(xk−1)
f,xk−1
But here, it is different. We claim we can write
fk(x)
fk,x
∼ fk−1(x)
fk−1,x
+ (fk − fk−1)
β
?
fk−1, x
where ? is interpreted as a ‘gradient’.
@freakonometrics 81

82. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Boosting
Construct iteratively
mk(·) = mk−1(·) + argmin
h∈H
n
i=1
(yi − [mk−1(xi) + h(xi)])2
mk(·) = mk−1(·) + argmin
h∈H
n
i=1
([yi − mk−1(xi)] − h(xi)])2
where h ∈ H means that we seek in a class of weak learner functions.
If learner are two strong, the first loop leads to some fixed point, and there is no
learning procedure, see linear regression y = xT
β + ε. Since ε ⊥ x we cannot
learn from the residuals.
In order to make sure that we learn weakly, we can use some shrinkage
parameter ν (or collection of parameters νj).
@freakonometrics 82

83. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Boosting with Piecewise Linear Spline & Stump Functions
Instead of εk = εk−1 − hk(x), set εk = εk−1 − ν·hk(x)
Remark : bumps are related to regression trees (see 2015 course).
@freakonometrics 83
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84. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Ruptures
One can use Chow test to test for a rupture. Note that it is simply Fisher test,
with two parts,
β =



β1 for i = 1, · · · , i0
β2 for i = i0 + 1, · · · , n
and test



H0 : β1 = β2
H1 : β1 = β2
i0 is a point between k and n − k (we need enough observations). Chow (1960)
Tests of Equality Between Sets of Coefficients in Two Linear Regressions suggested
Fi0
=
ηT
η − εT
ε
εT
ε/(n − 2k)
where εi = yi − xT
i β, and ηi =



Yi − xT
i β1 for i = k, · · · , i0
Yi − xT
i β2 for i = i0 + 1, · · · , n − k
@freakonometrics 84

85. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Ruptures
1 > Fstats(dist ~ speed ,data=cars ,from =7/50)
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020406080100120
Vitesse du véhicule
Distancedefeinage
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Fstatistics
0 10 20 30 40 50
024681012
q
@freakonometrics 85

86. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Ruptures
1 > Fstats(dist ~ speed ,data=cars ,from =2/50)
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5 10 15 20 25
020406080100120
Vitesse du véhicule
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@freakonometrics 86

87. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Ruptures
If i0 is unknown, use CUSUM types of tests, see Ploberger & Krämer (1992) The
Cusum Test with OLS Residuals. For all t ∈ [0, 1], set
Wt =
1
σ
√
n
nt
i=1
εi.
If α is the confidence level, bounds are generally ±α, even if theoretical bounds
should be ±α t(1 − t).
1 > cusum <- efp(dist ~ speed , type = "OLS -CUSUM",data=cars)
2 > plot(cusum ,ylim=c(-2,2))
3 > plot(cusum , alpha = 0.05 , alt.boundary = TRUE ,ylim=c(-2,2))
@freakonometrics 87

88. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Ruptures
OLS−based CUSUM test
Time
Empiricalfluctuationprocess
0.0 0.2 0.4 0.6 0.8 1.0
−2−1012
OLS−based CUSUM test with alternative boundaries
Time
Empiricalfluctuationprocess
0.0 0.2 0.4 0.6 0.8 1.0
−2−1012
@freakonometrics 88

89. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
From a Rupture to a Discontinuity
See Imbens & Lemieux (2008) Regression Discontinuity Designs.
@freakonometrics 89

90. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
From a Rupture to a Discontinuity
Consider the dataset from Lee (2008) Randomized experiments from non-random
selection in U.S. House elections.
1 > library(RDDtools)
2 > data(Lee2008)
We want to test if there is a discontinuity in
0.
• with parametric tools
• with nonparametric tools
@freakonometrics 90
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−1.0 −0.5 0.0 0.5 1.0
0.00.20.40.60.81.0
x
y

91. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing for a rupture
Use some 4th order polynomial, on each part
1 > idx1 = (Lee2008$x >0)
2 > reg1 = lm(y~poly(x ,4) ,data=Lee2008[
idx1 ,])
3 > idx2 = (Lee2008$x <0)
4 > reg2 = lm(y~poly(x ,4) ,data=Lee2008[
idx2 ,])
5 > s1=predict(reg1 ,newdata=data.frame(x
=0))
6 > s2=predict(reg2 ,newdata=data.frame(x
=0))
7 > abs(s1 -s2)
8 1
9 0.07659014
@freakonometrics 91
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−1.0 −0.5 0.0 0.5 1.0
0.00.20.40.60.81.0
x
y

92. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing for a rupture
1 > reg_para <- RDDreg_lm(RDDdata(y =
Lee2008$y, x = Lee2008$x, cutpoint
= 0), order = 4)
2 > reg_para
3 ### RDD regression: parametric ###
4 Polynomial order: 4
5 Slopes: separate
6 Number of obs: 6558 (left: 2740 ,
right: 3818)
7
8 Coefficient :
9 Estimate Std. Error t value Pr(>|t|)
10 D 0.076590 0.013239 5.7851 7.582e -09
***
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−1.0 −0.5 0.0 0.5 1.0
0.00.20.40.60.81.0
x
y
@freakonometrics 92

93. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing for a rupture
or use a simple local regression, see Imbens &
Kalyanaraman (2012).
1 > reg1 = ksmooth(Lee2008$x[idx1],
Lee2008$y[idx1], kernel = "normal",
bandwidth = 0.1)
2 > reg2 = ksmooth(Lee2008$x[idx2],
Lee2008$y[idx2], kernel = "normal",
bandwidth = 0.1)
3 > s1 = reg1$y[1]
4 > s2 = reg2$y[length(reg2$y)]
5 > abs(s1 -s2)
6 [1] 0.09883813
@freakonometrics 93
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−1.0 −0.5 0.0 0.5 1.0
0.00.20.40.60.81.0
x
y

94. Arthur CHARPENTIER, Advanced Econometrics Graduate Course, Winter 2018, Université de Rennes 1
Testing for a rupture
1 > reg_nonpara <- RDDreg_np(RDDobject = Lee2008_
rdd , bw = .1)
2 > print(reg_nonpara)
3 ### RDD regression: nonparametric local linear
4 Bandwidth: 0.1
5 Number of obs: 1209 (left: 577, right: 632)
6
7 Coefficient :
8 Estimate Std. Error z value Pr(>|z|)
9 D 0.059397 0.014119 4.207 2.588e-05 ***
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−1.0 −0.5 0.0 0.5 1.0
0.00.20.40.60.81.0
x
y
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0.0 0.2 0.4 0.6 0.8 1.0
0.000.020.040.060.080.10
bandwidth
@freakonometrics 94