x 1 = x i1 x i2 y = x 1 β x K β K + ε, x i =
|
|
- Moses Ellis
- 6 years ago
- Views:
Transcription
1
2
3 x k T x k k = 1,, K T K X X x 1 = 1 β 1 y T y 1 y T ε T T 1 x i1 x i2 y = x 1 β x K β K + ε, x i = y T 1 = X T K β K 1 + ε T 1. x it T 1 y x 1 x K y = Xβ + ε X T K K E[ε i x j1, x j2,, x jk ] = 0 i ε i E[ε X] = 0. ε i σ 2 ε j E[εε X] = σ 2 I. (x j1, x j2,, x jk ) X ε X N(0, σ 2 I). β 1 β 2 β K ( ) 2 T T K RSS = ˆε ˆε = ˆε 2 t = y t x it β i t=1 t=1 i=1
4 ˆβ 1 ˆβ = ˆβ 2 = (X X) 1 X y. ˆβ k σ 2 s 2 = RSS T T K = t=1 ˆε2 t T K K K K T K ( ˆβ) = s 2 (X X) 1. ˆβ = (X X) 1 X y = β + (X X) 1 X ε s 2 T t=1 = ˆε2 t T K ( ˆβ) = s 2 (X X) 1. t ˆβ i SE( ˆβ t(t K) i ) SE( ˆβ i ) = ( ˆβ) ii F F β F F RRSS URSS URSS T K m F (m, T K) URSS RRSS m T K
5 RRSS URSS t F t 2 (T K) F (1, T K). (X X) x k x j r kj = s kj s k s j. t 1 V IF k = 1 Rk 2 R 2 k x k
6 t R 2 y
7 E[ε 2 i X] T T 1 T 2 s 2 1 = ˆε 1 ˆε 1/(T 1 K), s 2 2 = ˆε 2 ˆε 2/(T 2 K) GQ GQ = s2 1 s 2 2 s 2 1 > s 2 2 F (T 1 K, T 2 K) GQ y t = β 1 + β 2 x 2t + β 3 x 3t + ε t. (ε t ) = σ 2 ˆε t ˆε 2 t = α 1 + α 2 x 2t + α 3 x 3t + α 4 x 2 2t + α 5 x 2 3t + α 6 x 2t x 3t + ν t. (ε t ) = E[ε 2 t ] E[ε t ] = 0 ˆε 2 t F ˆε 2 t F R 2 R 2 R 2 R 2 T T R 2 χ 2 (m) m F
8 α 2 = α 3 = α 4 = α 5 = α 6 = 0 χ 2 x k σε 2 t = σεx 2 α kt y t = β 1 + β 2 x 2t + + β K x Kt + ε t ˆε 2 t ˆε 2 t = γ + α x kt + ν t α t t = ˆαˆσ ˆα α σ 2 σ 2 z t (ε t ) = σ 2 z 2 t. z t y t 1 x 2t x 3t = β 1 + β 2 + β 3 + ν t z t z t z t z t ν t = εt z t
9 se( ˆβ T 1 ) HC = t=1 (x t x) 2ˆε 2 t ( T ) 2. t=1 (x t x) 2 se( ˆβ T k ) HC = t=1 ˆω2 tk ˆε2 t ) 2 ( T t=1 ˆω2 tk ˆω tk 2 x k y t = β 1 +β 2 x 2t + +β K x Kt +ε t ˆε 2 t K 1 T (K 1) (ˆω tpk 2 ) se( ˆβ T k ) HC = t=1 ˆω2 tk ˆε2 t ) 2. ( T t=1 ˆω2 tk ˆε t ˆε t 1 ˆε t (ˆε t 1, ˆε t ) (ˆε t 1, ˆε t ) ˆε t
10 µ r ± 1.96σ r µ r σ r µ r = 2T 1T 2 2T 1 T 2 (2T 1 T 2 T 1 T 2 ) + 1, σ r = T 1 + T 2 (T 1 + T 2 ) 2 (T 1 + T 2 1) r T 1 T 2 T t ε t = ρε t 1 + ν t ν t N(0, σ 2 nu) H 0 : ρ = 0, H 1 : ρ 0. T t=2 DW = (ˆε t ˆε t 1 ) 2 T t=2 ˆε2 t 2(1 ˆρ) ˆρ t 1 t d U d L AR(1)
11 r ε t = ρ 1 ε t 1 + ρ 2 ε t ρ r ε t r + ν t, ν t N(0, σ 2 ν). H 0 : ρ 1 = ρ 2 = = ρ r = 0, H 1 : ρ 1 0 ρ 2 0 ρ r 0. ˆε t R 2 ˆε t = γ 1 + K γ i x it + i=2 r ρ j ˆε t j + ν t, ν t N(0, σν). 2 T j=1 (T r)r 2 χ 2 r. (T r) R 2 T r r (T r) r r R 2 K y t = β 1 + β i x it + ε t, ε t = ρε t 1 + ν t. i=2 1 < ρ < 1 ν t E[ν t ε t 1 ] = 0 (ν t ε t 1 ) = σ 2 ν (ν t, ν s ) = 0 t s ε t = ν t + ρν t 1 + ρ 2 ν t 2 + ρ 3 ν t 3 +.
12 E[ε t ] = 0, (ε t ) = σ 2 ν + ρ 2 σ 2 ν + = σ2 ν 1 ρ 2. ρ < 1 ρ = 0 σε 2 = σ2 ν 1 ρ 2 CO y t = β 1 + K β i x it + ε t i=2 ε t = ρε t 1 + ν t. ˆε t = ρˆε t 1 + ν t. ˆρ yt = y t ˆρy t 1 β1 = (1 ˆρ)β 1 x 2t = (x 2t ˆρx 2(t 1) K yt = β1 + β i x it + ν t y t = β 1 + i=2 K β i x it + ν t i=2 ˆρ AR(1) AR(q) P W
13 y t = β 1 + K i=2 β ix it + ε t ˆε t x 2t = α 1 + K i=3 α ix it + r t ˆr t ˆα t = ˆr tˆε t 4(T /100) 2/9 ˆv = T t=1 ˆα2 t + 2 [ ] ( g T h=1 1 h g+1 t=h+1 ˆα t ˆα t h ) g x 2 se( ˆβ 2 ) HAC = ( se( ˆβ ) 2 2 ) ˆv. ˆσ ε x 3 x K y
14 y t W = T [ b (b 2 3) 2 ] 24 T b 1 b 2 b 1 = E[ε3 ] (σ 2 ) 3/2, b 2 = E[ε4 ] (σ 2 ) 2. W χ 2 (2) b 1 b 2 ˆε
15 ŷ 2 t ŷ 3 t ŷ 4 t y y t = β 1 + β 2 x 1t + + β K x Kt + ε t y t = α 1 + α 2 ŷ 2 t + + α p ŷ p t + K β i x it + ν t. y t F T R 2 χ 2 (p 1) R 2 A B RSS r T 2K A RSS ur,a T A K B RSS ur,b T B K F F = i=1 RSS r (RSS ur,a +RSS ur,b ) K RSS ur,a +RSS ur,b. T 2K F F F (K, T 2K) F F
16 y y RSS RSS RSS 1 T 1 RSS RSS 1 RSS T 1 K T 2 T 2 F (T 2, T 1 K) F F F F F ±2 ±2
17 y = Xβ + ε E[ε X] = 0 E[εε X] = σ 2 Ω = Σ, Ω n T ˆβ = (X X) 1 X y = β + (X X) 1 X ε F t ˆβ X [ ˆβ X] = E[( ˆβ β)( ˆβ β) X] = E[(X X) 1 X εε X(X X) 1 X] = (X X) 1 X (σ 2 Ω)X(X X) 1 ( ) 1 ( ) ( ) 1 = σ n n X X n X ΩX n X X ( ) 1 ( ) ( ) = n X X n Φ n X X. ( ) Φ = σ2 n X 1 ΩX = n X (y Xβ) E X [[b X]] ˆβ ε ε ˆβ X N(β, σ 2 (X X) 1 (X ΩX)(X X) 1 ) [ ˆβ X] ˆβ (X X/n) 1 (σ 2 /n)(x ΩX/n) Q = p (X X/n) p (X ΩX/n) ˆβ β p ˆβ = β.
18 X Ω ˆβ Ω [ ˆβ] = σ2 n Q 1 p ( 1 n X ΩX ) Q 1. Ω Ω Ω Ω Ω ˆβ σ 2 Ω ˆβ V OLS = 1 n ( ) 1 ( ) ( ) n X X n X [σ 2 Ω]X n X X. σ 2 Ω = E[εε X] (Ω) = n σ 2 Ω = σ 2 I Σ = (σ ij ) i,j = σ 2 Ω = σ 2 (ω ij ) i,j K(K + 1)/2 Q = 1 n X ΣX = 1 n σ ij x i x n j. x i i X Q i = 1,, K i,j=1 x i1 x i2 X = [x 1,, x K ], x i = x i = [x 1i, x 2i,, x Ki ] x j = [x 1j, x 2j,, x Kj ], 1 i, j n. x in x 1 x 2 X =, X = [ x 1, x 2,..., x n ], X ΣX = x n n σ ij x i x j i,j=1 ˆβ β ˆε i ε i X ˆε Q
19 S 0 = 1 n ˆε 2 i n x i x i i=1 p S 0 = p Q. [ ˆβ] = 1 ( ) ( ) n ( ) 1 1 n n X X ˆε 2 i n x i x i n X X = n (X X) 1 S 0 (X X) 1. i=1 ˆβ Q = 1 n σ ij x i x j n ˆQ = 1 n i,j=1 n i,j=1 ˆε iˆε j x i x j ˆQ 1/n n 2 ˆQ X ˆQ ˆQ = S n L n l=1 t=l+1 w lˆε tˆε t l ( x t x t l + x t l x t), w l = 1 l L + 1. L L T 1/4
20
(X i X) 2. n 1 X X. s X. s 2 F (n 1),(m 1)
X X X 10 n 5 X n X N(µ X, σx ) n s X = (X i X). n 1 (n 1)s X σ X n = (X i X) σ X χ n 1. t t χ t (X µ X )/ σ X n s X σx = X µ X σ X n σx s X = X µ X n s X t n 1. F F χ F F n (X i X) /(n 1) m (Y i Y ) /(m
More information7. GENERALIZED LEAST SQUARES (GLS)
7. GENERALIZED LEAST SQUARES (GLS) [1] ASSUMPTIONS: Assume SIC except that Cov(ε) = E(εε ) = σ Ω where Ω I T. Assume that E(ε) = 0 T 1, and that X Ω -1 X and X ΩX are all positive definite. Examples: Autocorrelation:
More informationQuestions and Answers on Heteroskedasticity, Autocorrelation and Generalized Least Squares
Questions and Answers on Heteroskedasticity, Autocorrelation and Generalized Least Squares L Magee Fall, 2008 1 Consider a regression model y = Xβ +ɛ, where it is assumed that E(ɛ X) = 0 and E(ɛɛ X) =
More informationMa 3/103: Lecture 24 Linear Regression I: Estimation
Ma 3/103: Lecture 24 Linear Regression I: Estimation March 3, 2017 KC Border Linear Regression I March 3, 2017 1 / 32 Regression analysis Regression analysis Estimate and test E(Y X) = f (X). f is the
More informationHeteroskedasticity and Autocorrelation
Lesson 7 Heteroskedasticity and Autocorrelation Pilar González and Susan Orbe Dpt. Applied Economics III (Econometrics and Statistics) Pilar González and Susan Orbe OCW 2014 Lesson 7. Heteroskedasticity
More informationIntroduction to Econometrics Midterm Examination Fall 2005 Answer Key
Introduction to Econometrics Midterm Examination Fall 2005 Answer Key Please answer all of the questions and show your work Clearly indicate your final answer to each question If you think a question is
More informationEconometrics of Panel Data
Econometrcs of Panel Data Jakub Mućk Meetng # 8 Jakub Mućk Econometrcs of Panel Data Meetng # 8 1 / 17 Outlne 1 Heterogenety n the slope coeffcents 2 Seemngly Unrelated Regresson (SUR) 3 Swamy s random
More informationEC3062 ECONOMETRICS. THE MULTIPLE REGRESSION MODEL Consider T realisations of the regression equation. (1) y = β 0 + β 1 x β k x k + ε,
THE MULTIPLE REGRESSION MODEL Consider T realisations of the regression equation (1) y = β 0 + β 1 x 1 + + β k x k + ε, which can be written in the following form: (2) y 1 y 2.. y T = 1 x 11... x 1k 1
More informationFormulary Applied Econometrics
Department of Economics Formulary Applied Econometrics c c Seminar of Statistics University of Fribourg Formulary Applied Econometrics 1 Rescaling With y = cy we have: ˆβ = cˆβ With x = Cx we have: ˆβ
More informationFENG CHIA UNIVERSITY ECONOMETRICS I: HOMEWORK 4. Prof. Mei-Yuan Chen Spring 2008
FENG CHIA UNIVERSITY ECONOMETRICS I: HOMEWORK 4 Prof. Mei-Yuan Chen Spring 008. Partition and rearrange the matrix X as [x i X i ]. That is, X i is the matrix X excluding the column x i. Let u i denote
More informationSection 6: Heteroskedasticity and Serial Correlation
From the SelectedWorks of Econ 240B Section February, 2007 Section 6: Heteroskedasticity and Serial Correlation Jeffrey Greenbaum, University of California, Berkeley Available at: https://works.bepress.com/econ_240b_econometrics/14/
More informationEcon 620. Matrix Differentiation. Let a and x are (k 1) vectors and A is an (k k) matrix. ) x. (a x) = a. x = a (x Ax) =(A + A (x Ax) x x =(A + A )
Econ 60 Matrix Differentiation Let a and x are k vectors and A is an k k matrix. a x a x = a = a x Ax =A + A x Ax x =A + A x Ax = xx A We don t want to prove the claim rigorously. But a x = k a i x i i=
More informationGLS and related issues
GLS and related issues Bernt Arne Ødegaard 27 April 208 Contents Problems in multivariate regressions 2. Problems with assumed i.i.d. errors...................................... 2 2 NON-iid errors 2 2.
More informationTopic 6: Non-Spherical Disturbances
Topic 6: Non-Spherical Disturbances Our basic linear regression model is y = Xβ + ε ; ε ~ N[0, σ 2 I n ] Now we ll generalize the specification of the error term in the model: E[ε] = 0 ; E[εε ] = Σ = σ
More informationEconometrics of Panel Data
Econometrics of Panel Data Jakub Mućk Meeting # 2 Jakub Mućk Econometrics of Panel Data Meeting # 2 1 / 26 Outline 1 Fixed effects model The Least Squares Dummy Variable Estimator The Fixed Effect (Within
More informationWell-developed and understood properties
1 INTRODUCTION TO LINEAR MODELS 1 THE CLASSICAL LINEAR MODEL Most commonly used statistical models Flexible models Well-developed and understood properties Ease of interpretation Building block for more
More informationCh.10 Autocorrelated Disturbances (June 15, 2016)
Ch10 Autocorrelated Disturbances (June 15, 2016) In a time-series linear regression model setting, Y t = x tβ + u t, t = 1, 2,, T, (10-1) a common problem is autocorrelation, or serial correlation of the
More informationEconometrics. Week 4. Fall Institute of Economic Studies Faculty of Social Sciences Charles University in Prague
Econometrics Week 4 Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Fall 2012 1 / 23 Recommended Reading For the today Serial correlation and heteroskedasticity in
More informationProblems. Suppose both models are fitted to the same data. Show that SS Res, A SS Res, B
Simple Linear Regression 35 Problems 1 Consider a set of data (x i, y i ), i =1, 2,,n, and the following two regression models: y i = β 0 + β 1 x i + ε, (i =1, 2,,n), Model A y i = γ 0 + γ 1 x i + γ 2
More informationEconometrics of Panel Data
Econometrics of Panel Data Jakub Mućk Meeting # 4 Jakub Mućk Econometrics of Panel Data Meeting # 4 1 / 30 Outline 1 Two-way Error Component Model Fixed effects model Random effects model 2 Non-spherical
More informationGeneral Linear Test of a General Linear Hypothesis. Copyright c 2012 Dan Nettleton (Iowa State University) Statistics / 35
General Linear Test of a General Linear Hypothesis Copyright c 2012 Dan Nettleton (Iowa State University) Statistics 611 1 / 35 Suppose the NTGMM holds so that y = Xβ + ε, where ε N(0, σ 2 I). opyright
More information3. For a given dataset and linear model, what do you think is true about least squares estimates? Is Ŷ always unique? Yes. Is ˆβ always unique? No.
7. LEAST SQUARES ESTIMATION 1 EXERCISE: Least-Squares Estimation and Uniqueness of Estimates 1. For n real numbers a 1,...,a n, what value of a minimizes the sum of squared distances from a to each of
More informationAdvanced Quantitative Methods: Regression diagnostics
Advanced Quantitative Methods: Regression diagnostics Johan A. Elkink University College Dublin 9 February 2018 1, leverage, influence 2 3 Heteroscedasticity 4 1, leverage, influence 2 3 Heteroscedasticity
More information9. AUTOCORRELATION. [1] Definition of Autocorrelation (AUTO) 1) Model: y t = x t β + ε t. We say that AUTO exists if cov(ε t,ε s ) 0, t s.
9. AUTOCORRELATION [1] Definition of Autocorrelation (AUTO) 1) Model: y t = x t β + ε t. We say that AUTO exists if cov(ε t,ε s ) 0, t s. ) Assumptions: All of SIC except SIC.3 (the random sample assumption).
More informationClustering as a Design Problem
Clustering as a Design Problem Alberto Abadie, Susan Athey, Guido Imbens, & Jeffrey Wooldridge Harvard-MIT Econometrics Seminar Cambridge, February 4, 2016 Adjusting standard errors for clustering is common
More informationWorking Paper Series. An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts. Michael W. McCracken and Joseph McGillicuddy
RESEARCH DIVISION Working Paper Series An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts Michael W. McCracken and Joseph McGillicuddy Working Paper 2017-040A https://doi.org/10.20955/wp.2017.040
More informationNon-Spherical Errors
Non-Spherical Errors Krishna Pendakur February 15, 2016 1 Efficient OLS 1. Consider the model Y = Xβ + ε E [X ε = 0 K E [εε = Ω = σ 2 I N. 2. Consider the estimated OLS parameter vector ˆβ OLS = (X X)
More informationTotal Least Squares Approach in Regression Methods
WDS'08 Proceedings of Contributed Papers, Part I, 88 93, 2008. ISBN 978-80-7378-065-4 MATFYZPRESS Total Least Squares Approach in Regression Methods M. Pešta Charles University, Faculty of Mathematics
More informationMathematical and Information Technologies, MIT-2016 Mathematical modeling
473 474 ρ u,t = p,x q,y, ρ v,t = q,x p,y, ω,t = 2 q + µ x,x + µ y,y, φ,t = ω, p,t = k u,x + v,y + β T,t, q,t = α v,x u,y 2 α ω + q/η, µ x,t = γ ω,x, µ y,t = γ ω,y, c T,t = 11 T,x + 12 T,y,x + 12 T,x +
More informationWeighted Least Squares
Weighted Least Squares The standard linear model assumes that Var(ε i ) = σ 2 for i = 1,..., n. As we have seen, however, there are instances where Var(Y X = x i ) = Var(ε i ) = σ2 w i. Here w 1,..., w
More informationLinear Regression. In this problem sheet, we consider the problem of linear regression with p predictors and one intercept,
Linear Regression In this problem sheet, we consider the problem of linear regression with p predictors and one intercept, y = Xβ + ɛ, where y t = (y 1,..., y n ) is the column vector of target values,
More informationEcon 583 Final Exam Fall 2008
Econ 583 Final Exam Fall 2008 Eric Zivot December 11, 2008 Exam is due at 9:00 am in my office on Friday, December 12. 1 Maximum Likelihood Estimation and Asymptotic Theory Let X 1,...,X n be iid random
More information. a m1 a mn. a 1 a 2 a = a n
Biostat 140655, 2008: Matrix Algebra Review 1 Definition: An m n matrix, A m n, is a rectangular array of real numbers with m rows and n columns Element in the i th row and the j th column is denoted by
More informationK e sub x e sub n s i sub o o f K.. w ich i sub s.. u ra to the power of m i sub fi ed.. a sub t to the power of a
- ; ; ˆ ; q x ; j [ ; ; ˆ ˆ [ ˆ ˆ ˆ - x - - ; x j - - - - - ˆ x j ˆ ˆ ; x ; j κ ˆ - - - ; - - - ; ˆ σ x j ; ˆ [ ; ] q x σ; x - ˆ - ; J -- F - - ; x - -x - - x - - ; ; 9 S j P R S 3 q 47 q F x j x ; [ ]
More informationDifferent types of regression: Linear, Lasso, Ridge, Elastic net, Ro
Different types of regression: Linear, Lasso, Ridge, Elastic net, Robust and K-neighbors Faculty of Mathematics, Informatics and Mechanics, University of Warsaw 04.10.2009 Introduction We are given a linear
More informationLecture 4: Heteroskedasticity
Lecture 4: Heteroskedasticity Econometric Methods Warsaw School of Economics (4) Heteroskedasticity 1 / 24 Outline 1 What is heteroskedasticity? 2 Testing for heteroskedasticity White Goldfeld-Quandt Breusch-Pagan
More informationQuick Review on Linear Multiple Regression
Quick Review on Linear Multiple Regression Mei-Yuan Chen Department of Finance National Chung Hsing University March 6, 2007 Introduction for Conditional Mean Modeling Suppose random variables Y, X 1,
More informationLecture Notes on Different Aspects of Regression Analysis
Andreas Groll WS 2012/2013 Lecture Notes on Different Aspects of Regression Analysis Department of Mathematics, Workgroup Financial Mathematics, Ludwig-Maximilians-University Munich, Theresienstr. 39,
More informationLecture 14 Simple Linear Regression
Lecture 4 Simple Linear Regression Ordinary Least Squares (OLS) Consider the following simple linear regression model where, for each unit i, Y i is the dependent variable (response). X i is the independent
More informationA COMPARISON OF HETEROSCEDASTICITY ROBUST STANDARD ERRORS AND NONPARAMETRIC GENERALIZED LEAST SQUARES
A COMPARISON OF HETEROSCEDASTICITY ROBUST STANDARD ERRORS AND NONPARAMETRIC GENERALIZED LEAST SQUARES MICHAEL O HARA AND CHRISTOPHER F. PARMETER Abstract. This paper presents a Monte Carlo comparison of
More informationLecture 1: OLS derivations and inference
Lecture 1: OLS derivations and inference Econometric Methods Warsaw School of Economics (1) OLS 1 / 43 Outline 1 Introduction Course information Econometrics: a reminder Preliminary data exploration 2
More informationG023: Econometrics.
G023: Econometrics Jérôme Adda j.adda@ucl.ac.uk Office # 203 I am grateful to Andrew Chesher for giving me access to his G023 course notes on which most of these slides are based. G023. I Syllabus Course
More informationLecture 15. Hypothesis testing in the linear model
14. Lecture 15. Hypothesis testing in the linear model Lecture 15. Hypothesis testing in the linear model 1 (1 1) Preliminary lemma 15. Hypothesis testing in the linear model 15.1. Preliminary lemma Lemma
More informationNonparametric Regression. Badr Missaoui
Badr Missaoui Outline Kernel and local polynomial regression. Penalized regression. We are given n pairs of observations (X 1, Y 1 ),...,(X n, Y n ) where Y i = r(x i ) + ε i, i = 1,..., n and r(x) = E(Y
More informationNonstationary Panels
Nonstationary Panels Based on chapters 12.4, 12.5, and 12.6 of Baltagi, B. (2005): Econometric Analysis of Panel Data, 3rd edition. Chichester, John Wiley & Sons. June 3, 2009 Agenda 1 Spurious Regressions
More informationMassachusetts Institute of Technology Department of Economics Time Series Lecture 6: Additional Results for VAR s
Massachusetts Institute of Technology Department of Economics Time Series 14.384 Guido Kuersteiner Lecture 6: Additional Results for VAR s 6.1. Confidence Intervals for Impulse Response Functions There
More informationMultivariate Regression Analysis
Matrices and vectors The model from the sample is: Y = Xβ +u with n individuals, l response variable, k regressors Y is a n 1 vector or a n l matrix with the notation Y T = (y 1,y 2,...,y n ) 1 x 11 x
More informationFactor Model Risk Analysis
Factor Model Risk Analysis Eric Zivot University of Washington BlackRock Alternative Advisors April 29, 2011 Outline Factor Model Specification Risk measures Factor Risk Budgeting Portfolio Risk Budgeting
More informationImproved Inference for First Order Autocorrelation using Likelihood Analysis
Improved Inference for First Order Autocorrelation using Likelihood Analysis M. Rekkas Y. Sun A. Wong Abstract Testing for first-order autocorrelation in small samples using the standard asymptotic test
More informationIntroductory Econometrics
Based on the textbook by Wooldridge: : A Modern Approach Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna December 11, 2012 Outline Heteroskedasticity
More informationMa 3/103: Lecture 25 Linear Regression II: Hypothesis Testing and ANOVA
Ma 3/103: Lecture 25 Linear Regression II: Hypothesis Testing and ANOVA March 6, 2017 KC Border Linear Regression II March 6, 2017 1 / 44 1 OLS estimator 2 Restricted regression 3 Errors in variables 4
More informationHeteroskedasticity in Panel Data
Essex Summer School in Social Science Data Analysis Panel Data Analysis for Comparative Research Heteroskedasticity in Panel Data Christopher Adolph Department of Political Science and Center for Statistics
More informationAdvanced Econometrics I
Lecture Notes Autumn 2010 Dr. Getinet Haile, University of Mannheim 1. Introduction Introduction & CLRM, Autumn Term 2010 1 What is econometrics? Econometrics = economic statistics economic theory mathematics
More informationHeteroskedasticity in Panel Data
Essex Summer School in Social Science Data Analysis Panel Data Analysis for Comparative Research Heteroskedasticity in Panel Data Christopher Adolph Department of Political Science and Center for Statistics
More informationLinear Regression Model. Badr Missaoui
Linear Regression Model Badr Missaoui Introduction What is this course about? It is a course on applied statistics. It comprises 2 hours lectures each week and 1 hour lab sessions/tutorials. We will focus
More informationTime Series Analysis
Time Series Analysis hm@imm.dtu.dk Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby 1 Outline of the lecture Regression based methods, 1st part: Introduction (Sec.
More informationSTAT 213 Interactions in Two-Way ANOVA
STAT 213 Interactions in Two-Way ANOVA Colin Reimer Dawson Oberlin College 14 April 2016 Outline Last Time: Two-Way ANOVA Interaction Terms Reading Quiz (Multiple Choice) If there is no interaction present,
More informationRegression. ECO 312 Fall 2013 Chris Sims. January 12, 2014
ECO 312 Fall 2013 Chris Sims Regression January 12, 2014 c 2014 by Christopher A. Sims. This document is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License What
More informationSingle Equation Linear GMM with Serially Correlated Moment Conditions
Single Equation Linear GMM with Serially Correlated Moment Conditions Eric Zivot November 2, 2011 Univariate Time Series Let {y t } be an ergodic-stationary time series with E[y t ]=μ and var(y t )
More informationGeneralized Least Squares
Generalized Leas Squares Augus 006 1 Modified Model Original assumpions: 1 Specificaion: y = Xβ + ε (1) Eε =0 3 EX 0 ε =0 4 Eεε 0 = σ I In his secion, we consider relaxing assumpion (4) Insead, assume
More informationAuto-correlation of Error Terms
Auo-correlaio of Error Terms Pogsa Porchaiwiseskul Faculy of Ecoomics Chulalogkor Uiversiy (c) Pogsa Porchaiwiseskul, Faculy of Ecoomics, Chulalogkor Uiversiy Geeral Auo-correlaio () YXβ + ν E(ν)0 V(ν)
More information20.1. Balanced One-Way Classification Cell means parametrization: ε 1. ε I. + ˆɛ 2 ij =
20. ONE-WAY ANALYSIS OF VARIANCE 1 20.1. Balanced One-Way Classification Cell means parametrization: Y ij = µ i + ε ij, i = 1,..., I; j = 1,..., J, ε ij N(0, σ 2 ), In matrix form, Y = Xβ + ε, or 1 Y J
More informationECON 3150/4150, Spring term Lecture 7
ECON 3150/4150, Spring term 2014. Lecture 7 The multivariate regression model (I) Ragnar Nymoen University of Oslo 4 February 2014 1 / 23 References to Lecture 7 and 8 SW Ch. 6 BN Kap 7.1-7.8 2 / 23 Omitted
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON3150/ECON4150 Introductory Econometrics Date of exam: Wednesday, May 15, 013 Grades are given: June 6, 013 Time for exam: :30 p.m. 5:30 p.m. The problem
More informationSpatial Models in Econometrics: Section 13 1
Spatial Models in Econometrics: Section 13 1 1 Single Equation Models 1.1 An over view of basic elements Space is important: Some Illustrations (a) Gas tax issues (b) Police expenditures (c) Infrastructure
More informationEconomics 672 Fall 2017 Tauchen. Jump Regression
Economics 672 Fall 2017 Tauchen 1 Main Model In the jump regression setting we have Jump Regression X = ( Z Y where Z is the log of the market index and Y is the log of an asset price. The dynamics are
More informationGENERALISED LEAST SQUARES AND RELATED TOPICS
GENERALISED LEAST SQUARES AND RELATED TOPICS Haris Psaradakis Birkbeck, University of London Nonspherical Errors Consider the model y = Xβ + u, E(u) =0, E(uu 0 )=σ 2 Ω, where Ω is a symmetric and positive
More informationFinancial Time Series Analysis Week 5
Financial Time Series Analysis Week 5 25 Estimation in AR moels Central Limit Theorem for µ in AR() Moel Recall : If X N(µ, σ 2 ), normal istribute ranom variable with mean µ an variance σ 2, then X µ
More informationSingle Equation Linear GMM with Serially Correlated Moment Conditions
Single Equation Linear GMM with Serially Correlated Moment Conditions Eric Zivot October 28, 2009 Univariate Time Series Let {y t } be an ergodic-stationary time series with E[y t ]=μ and var(y t )
More informationPOL 572 Multivariate Political Analysis
POL 572 Multivariate Political Analysis Gregory Wawro Associate Professor Department of Political Science Columbia University 420 W. 118th St. New York, NY 10027 phone: (212) 854-8540 fax: (212) 222-0598
More informationEconometrics. Andrés M. Alonso. Unit 1: Introduction: The regression model. Unit 2: Estimation principles. Unit 3: Hypothesis testing principles.
Andrés M. Alonso andres.alonso@uc3m.es Unit 1: Introduction: The regression model. Unit 2: Estimation principles. Unit 3: Hypothesis testing principles. Unit 4: Heteroscedasticity in the regression model.
More informationApplied Econometrics (MSc.) Lecture 3 Instrumental Variables
Applied Econometrics (MSc.) Lecture 3 Instrumental Variables Estimation - Theory Department of Economics University of Gothenburg December 4, 2014 1/28 Why IV estimation? So far, in OLS, we assumed independence.
More informationSTAT Regression Methods
STAT 501 - Regression Methods Unit 9 Examples Example 1: Quake Data Let y t = the annual number of worldwide earthquakes with magnitude greater than 7 on the Richter scale for n = 99 years. Figure 1 gives
More informationVector Auto-Regressive Models
Vector Auto-Regressive Models Laurent Ferrara 1 1 University of Paris Nanterre M2 Oct. 2018 Overview of the presentation 1. Vector Auto-Regressions Definition Estimation Testing 2. Impulse responses functions
More informationBasic Distributional Assumptions of the Linear Model: 1. The errors are unbiased: E[ε] = The errors are uncorrelated with common variance:
8. PROPERTIES OF LEAST SQUARES ESTIMATES 1 Basic Distributional Assumptions of the Linear Model: 1. The errors are unbiased: E[ε] = 0. 2. The errors are uncorrelated with common variance: These assumptions
More informationIntermediate Econometrics
Intermediate Econometrics Heteroskedasticity Text: Wooldridge, 8 July 17, 2011 Heteroskedasticity Assumption of homoskedasticity, Var(u i x i1,..., x ik ) = E(u 2 i x i1,..., x ik ) = σ 2. That is, the
More informationCh 2: Simple Linear Regression
Ch 2: Simple Linear Regression 1. Simple Linear Regression Model A simple regression model with a single regressor x is y = β 0 + β 1 x + ɛ, where we assume that the error ɛ is independent random component
More informationEconomics 582 Random Effects Estimation
Economics 582 Random Effects Estimation Eric Zivot May 29, 2013 Random Effects Model Hence, the model can be re-written as = x 0 β + + [x ] = 0 (no endogeneity) [ x ] = = + x 0 β + + [x ] = 0 [ x ] = 0
More informationVAR Models and Applications
VAR Models and Applications Laurent Ferrara 1 1 University of Paris West M2 EIPMC Oct. 2016 Overview of the presentation 1. Vector Auto-Regressions Definition Estimation Testing 2. Impulse responses functions
More informationPeter Hoff Linear and multilinear models April 3, GLS for multivariate regression 5. 3 Covariance estimation for the GLM 8
Contents 1 Linear model 1 2 GLS for multivariate regression 5 3 Covariance estimation for the GLM 8 4 Testing the GLH 11 A reference for some of this material can be found somewhere. 1 Linear model Recall
More informationUnderstanding Regressions with Observations Collected at High Frequency over Long Span
Understanding Regressions with Observations Collected at High Frequency over Long Span Yoosoon Chang Department of Economics, Indiana University Joon Y. Park Department of Economics, Indiana University
More informationAdvanced Quantitative Methods: ordinary least squares
Advanced Quantitative Methods: Ordinary Least Squares University College Dublin 31 January 2012 1 2 3 4 5 Terminology y is the dependent variable referred to also (by Greene) as a regressand X are the
More informationNBER WORKING PAPER SERIES ROBUST STANDARD ERRORS IN SMALL SAMPLES: SOME PRACTICAL ADVICE. Guido W. Imbens Michal Kolesar
NBER WORKING PAPER SERIES ROBUST STANDARD ERRORS IN SMALL SAMPLES: SOME PRACTICAL ADVICE Guido W. Imbens Michal Kolesar Working Paper 18478 http://www.nber.org/papers/w18478 NATIONAL BUREAU OF ECONOMIC
More informationEmpirical Economic Research, Part II
Based on the text book by Ramanathan: Introductory Econometrics Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna December 7, 2011 Outline Introduction
More informationLecture 19 Multiple (Linear) Regression
Lecture 19 Multiple (Linear) Regression Thais Paiva STA 111 - Summer 2013 Term II August 1, 2013 1 / 30 Thais Paiva STA 111 - Summer 2013 Term II Lecture 19, 08/01/2013 Lecture Plan 1 Multiple regression
More informationHeteroskedasticity. y i = β 0 + β 1 x 1i + β 2 x 2i β k x ki + e i. where E(e i. ) σ 2, non-constant variance.
Heteroskedasticity y i = β + β x i + β x i +... + β k x ki + e i where E(e i ) σ, non-constant variance. Common problem with samples over individuals. ê i e ˆi x k x k AREC-ECON 535 Lec F Suppose y i =
More informationEconometrics of Panel Data
Econometrics of Panel Data Jakub Mućk Meeting # 6 Jakub Mućk Econometrics of Panel Data Meeting # 6 1 / 36 Outline 1 The First-Difference (FD) estimator 2 Dynamic panel data models 3 The Anderson and Hsiao
More informationIntroduction to Econometrics Final Examination Fall 2006 Answer Sheet
Introduction to Econometrics Final Examination Fall 2006 Answer Sheet Please answer all of the questions and show your work. If you think a question is ambiguous, clearly state how you interpret it before
More informationSpecification errors in linear regression models
Specification errors in linear regression models Jean-Marie Dufour McGill University First version: February 2002 Revised: December 2011 This version: December 2011 Compiled: December 9, 2011, 22:34 This
More informationØkonomisk Kandidateksamen 2004 (I) Econometrics 2. Rettevejledning
Økonomisk Kandidateksamen 2004 (I) Econometrics 2 Rettevejledning This is a closed-book exam (uden hjælpemidler). Answer all questions! The group of questions 1 to 4 have equal weight. Within each group,
More informationSimple Linear Regression
Simple Linear Regression September 24, 2008 Reading HH 8, GIll 4 Simple Linear Regression p.1/20 Problem Data: Observe pairs (Y i,x i ),i = 1,...n Response or dependent variable Y Predictor or independent
More informationBiostatistics 533 Classical Theory of Linear Models Spring 2007 Final Exam. Please choose ONE of the following options.
1 Biostatistics 533 Classical Theory of Linear Models Spring 2007 Final Exam Name: KEY Problems do not have equal value and some problems will take more time than others. Spend your time wisely. You do
More information7 Day 3: Time Varying Parameter Models
7 Day 3: Time Varying Parameter Models References: 1. Durbin, J. and S.-J. Koopman (2001). Time Series Analysis by State Space Methods. Oxford University Press, Oxford 2. Koopman, S.-J., N. Shephard, and
More informationBootstrapping Heteroskedasticity Consistent Covariance Matrix Estimator
Bootstrapping Heteroskedasticity Consistent Covariance Matrix Estimator by Emmanuel Flachaire Eurequa, University Paris I Panthéon-Sorbonne December 2001 Abstract Recent results of Cribari-Neto and Zarkos
More informationChapter 6. Panel Data. Joan Llull. Quantitative Statistical Methods II Barcelona GSE
Chapter 6. Panel Data Joan Llull Quantitative Statistical Methods II Barcelona GSE Introduction Chapter 6. Panel Data 2 Panel data The term panel data refers to data sets with repeated observations over
More informationSummer School in Statistics for Astronomers V June 1 - June 6, Regression. Mosuk Chow Statistics Department Penn State University.
Summer School in Statistics for Astronomers V June 1 - June 6, 2009 Regression Mosuk Chow Statistics Department Penn State University. Adapted from notes prepared by RL Karandikar Mean and variance Recall
More information11 Hypothesis Testing
28 11 Hypothesis Testing 111 Introduction Suppose we want to test the hypothesis: H : A q p β p 1 q 1 In terms of the rows of A this can be written as a 1 a q β, ie a i β for each row of A (here a i denotes
More informationRobust Standard Errors in Small Samples: Some Practical Advice
Robust Standard Errors in Small Samples: Some Practical Advice Guido W. Imbens Michal Kolesár First Draft: October 2012 This Draft: December 2014 Abstract In this paper we discuss the properties of confidence
More informationIn the bivariate regression model, the original parameterization is. Y i = β 1 + β 2 X2 + β 2 X2. + β 2 (X 2i X 2 ) + ε i (2)
RNy, econ460 autumn 04 Lecture note Orthogonalization and re-parameterization 5..3 and 7.. in HN Orthogonalization of variables, for example X i and X means that variables that are correlated are made
More informationStationary Stochastic Time Series Models
Stationary Stochastic Time Series Models When modeling time series it is useful to regard an observed time series, (x 1,x,..., x n ), as the realisation of a stochastic process. In general a stochastic
More information