The central limit theorem for Student s distribution. Problem Karim M. Abadir and Jan R. Magnus. Econometric Theory, 19, 1195 (2003)

Similar documents
This section is optional.

7.1 Convergence of sequences of random variables

Convergence of random variables. (telegram style notes) P.J.C. Spreij

7.1 Convergence of sequences of random variables

Random Variables, Sampling and Estimation

Lecture 19: Convergence

Solution to Chapter 2 Analytical Exercises

MA Advanced Econometrics: Properties of Least Squares Estimators

Lecture 20: Multivariate convergence and the Central Limit Theorem

LECTURE 8: ASYMPTOTICS I

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d

Asymptotic distribution of products of sums of independent random variables

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002

Parameter, Statistic and Random Samples

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

Regression with an Evaporating Logarithmic Trend

1 = δ2 (0, ), Y Y n nδ. , T n = Y Y n n. ( U n,k + X ) ( f U n,k + Y ) n 2n f U n,k + θ Y ) 2 E X1 2 X1

Stat 421-SP2012 Interval Estimation Section

Statistical Inference Based on Extremum Estimators

Chapter 3. Strong convergence. 3.1 Definition of almost sure convergence

STAT Homework 1 - Solutions

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1.

Kernel density estimator

ECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors

Statistical Theory; Why is the Gaussian Distribution so popular?

SDS 321: Introduction to Probability and Statistics

Statistical and Mathematical Methods DS-GA 1002 December 8, Sample Final Problems Solutions

Central limit theorem and almost sure central limit theorem for the product of some partial sums

Lecture Chapter 6: Convergence of Random Sequences

Asymptotic Results for the Linear Regression Model

Direction: This test is worth 250 points. You are required to complete this test within 50 minutes.

NANYANG TECHNOLOGICAL UNIVERSITY SYLLABUS FOR ENTRANCE EXAMINATION FOR INTERNATIONAL STUDENTS AO-LEVEL MATHEMATICS

First Year Quantitative Comp Exam Spring, Part I - 203A. f X (x) = 0 otherwise

Large Sample Theory. Convergence. Central Limit Theorems Asymptotic Distribution Delta Method. Convergence in Probability Convergence in Distribution

An Introduction to Randomized Algorithms

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 2 9/9/2013. Large Deviations for i.i.d. Random Variables

Kolmogorov-Smirnov type Tests for Local Gaussianity in High-Frequency Data

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n.

MATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4

Probability 2 - Notes 10. Lemma. If X is a random variable and g(x) 0 for all x in the support of f X, then P(g(X) 1) E[g(X)].

Notes 5 : More on the a.s. convergence of sums

2.2. Central limit theorem.

The Central Limit Theorem

1+x 1 + α+x. x = 2(α x2 ) 1+x

MBACATÓLICA. Quantitative Methods. Faculdade de Ciências Económicas e Empresariais UNIVERSIDADE CATÓLICA PORTUGUESA 9. SAMPLING DISTRIBUTIONS

Exercise 4.3 Use the Continuity Theorem to prove the Cramér-Wold Theorem, Theorem. (1) φ a X(1).

Lecture 01: the Central Limit Theorem. 1 Central Limit Theorem for i.i.d. random variables

Let us give one more example of MLE. Example 3. The uniform distribution U[0, θ] on the interval [0, θ] has p.d.f.

Properties and Hypothesis Testing

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 6 9/23/2013. Brownian motion. Introduction

Expectation and Variance of a random variable

In this section we derive some finite-sample properties of the OLS estimator. b is an estimator of β. It is a function of the random sample data.

January 25, 2017 INTRODUCTION TO MATHEMATICAL STATISTICS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013

Economics 241B Relation to Method of Moments and Maximum Likelihood OLSE as a Maximum Likelihood Estimator

Statistical Theory MT 2008 Problems 1: Solution sketches

Econ 325 Notes on Point Estimator and Confidence Interval 1 By Hiro Kasahara

Mathematical Statistics - MS

Module 1 Fundamentals in statistics

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula

Confidence Level We want to estimate the true mean of a random variable X economically and with confidence.

Statistical Theory MT 2009 Problems 1: Solution sketches

Statisticians use the word population to refer the total number of (potential) observations under consideration

Discrete Mathematics for CS Spring 2008 David Wagner Note 22

Distributions of Functions of. Normal Random Variables Version 27 Jan 2004

11 THE GMM ESTIMATION

Probability and Statistics

Section 14. Simple linear regression.

Lecture 3. Properties of Summary Statistics: Sampling Distribution

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss

Chapter 6 Principles of Data Reduction

1.010 Uncertainty in Engineering Fall 2008

Axioms of Measure Theory

of the matrix is =-85, so it is not positive definite. Thus, the first

EE 4TM4: Digital Communications II Probability Theory

The variance of a sum of independent variables is the sum of their variances, since covariances are zero. Therefore. V (xi )= n n 2 σ2 = σ2.

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

4. Basic probability theory

Geometry of LS. LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT

IIT JAM Mathematical Statistics (MS) 2006 SECTION A

Math Solutions to homework 6

Notes 19 : Martingale CLT

Lecture 15: Density estimation

Chapter 13: Tests of Hypothesis Section 13.1 Introduction

Efficient GMM LECTURE 12 GMM II

Lecture 3: August 31

Limit distributions for products of sums

Lecture 2: Concentration Bounds

This exam contains 19 pages (including this cover page) and 10 questions. A Formulae sheet is provided with the exam.

MATH/STAT 352: Lecture 15

Quick Review of Probability

EECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1

Quick Review of Probability

Machine Learning Brett Bernstein

Riesz-Fischer Sequences and Lower Frame Bounds

Chapter 6 Sampling Distributions

Notes On Median and Quantile Regression. James L. Powell Department of Economics University of California, Berkeley

Transcription:

The cetral limit theorem for Studet s distributio Problem 03.6.1 Karim M. Abadir ad Ja R. Magus Ecoometric Theory, 19, 1195 (003)

Z Ecoometric Theory, 19, 003, 1195 1198+ Prited i the Uited States of America+ DOI: 10+10170S06646660319613 PROBLEMS AND SOLUTIONS PROBLEMS 03.6.1. The Cetral Limit Theorem for Studet s Distributio Karim Abadir Uiversity of York, UK Ja Magus Tilburg Uiversity, The Netherlads Let x 1,+++,x be a radom sample from Studet s t~! distributio, where R + Ivestigate whether z : ( i 1 x i 0l is asymptotically N~0,1! for a suitable choice of l + 03.6.. Ubiasedess of the OLS Estimator with Radom Regressors Michael Jasso UC Berkeley Cosider the liear regressio model y Xb u, where X is a k matrix of radom regressors, u is a -vector of error terms, ad b is a k-vector of parameters+ Suppose X has full colum rak with probability oe+ It is a stadard textbook claim that the ordiary least squares ~OLS! estimator b ~X ' X! 1 X ' y of b is ubiased if E~u6X! a+s+ 0, where a+s+ sigifies almost sure equality+ Specifically, it is claimed that ubiasedess follows from the law of iterated expectatios ad the relatio E~ b6x Z! a+s+ b ~X ' X! 1 X ' E~u6X!+ As it turs out, this argumet is flawed+ ~a! Show by example that E~u6X! a+s+ 0 does ot imply existece of E~ b!+ Z ~b! Provide stroger coditios uder which E~ b! Z exists ~ad equals b!+ SOLUTIONS 0.6.1. Oblique Projectors 1 Solutio Götz Trekler ~the poser of the problem! Uiversity of Dortmud, Germay It is well kow that a oblique projector P ca be writte as U P I r K 0 0 U*, 003 Cambridge Uiversity Press 066-4666003 $1+00 1195

Solutio Ecoometric Theory, 0, 161 163 (004)

Ecoometric Theory, 0, 004, 161 164+ Prited i the Uited States of America+ DOI: 10+10170S06646660406089 PROBLEMS AND SOLUTIONS SOLUTIONS 03.6.1 The Cetral Limit Theorem for Studet s Distributio Solutio Karim Abadir Uiversity of York, UK Ja Magus ~the poser of the problem! Tilburg Uiversity, The Netherlads Cosider the Lideberg Feller cetral limit theorem ~CLT!, which we state as follows+ Let $x % be a sequece of idepedet radom variables with meas $m % ad ozero variaces $s % ~both existig!, ad c+d+f+s $F %+ Defie l 0byl ( i 1 s i +The, Lideberg s coditio e lim u m i r` (i 1 df i ~u! 0, e 0, 6u m i 6 l l is equivalet to z : ( i 1 ~x i m i! a N~0,1! ad lim max l Pr 6x i m i e 6 0, r` 1 i l where the latter limit is kow as the uiform asymptotic egligibility ~u+a++! coditio+ Oe ca usually iterpret l as the variace of the umerator of z + We shall see, however, that there are cases where asymptotic ormality holds i spite of $x % havig ifiite variaces+ Let $x % be a radom sample from Studet s t~!+ For, o l exists that ca lead to z : ( i 1 x i 0l a N~0,1!+ This is because the tails of the desity of t~! decay at a rate of u 1 ad the stable limit theorem tells us that a oormal stable law arises if the tails of the p+d+f+ of x i decay at a rate of u a where a 3; e+g+, see Loève ~1977, 5! or Hoffma-Jørgese ~1994, 5+5!+ For example, for 1, the average of stadard Cauchy variates is stadard Cauchy too, so that there exists o l achievig asymptotic ormality of z + For, both the mea ad the variace exist, ad the Lideberg Feller CLT applies, with l var~x i!+ The iterestig part is, where we will show that asymptotic ormality of z holds, i spite of var~x i! beig ifiite, ad we will derive the appropriate l + We will require the additioal assump- 004 Cambridge Uiversity Press 066-4666004 $1+00 161

16 PROBLEMS AND SOLUTIONS tio that l r ` as r `+ I the stadard CLT, this assumptio was uecessary, as it followed from l var~x i!+ We will see subsequetly that l ca be iterpreted i terms of trucated variaces for + To prove the asymptotic ormality of z, we eed to show that the characteristic fuctio w~t! : E~exp~itx i!! satisfies w lim log t r` l t (1) for some choice of l, with l r ` as r `+ Because the sequece $x % is i+i+d+, the uiform asymptotic egligibility coditio lim r` max Pr 6x e i 6 1 i l 0 is satisfied for all e 0, thus implyig lim r` w t l 1 0+ This allows us to take the leadig term of the logarithmic expasio of the left-had side of ~1! as w lim log t lim r` l w t r` l 1 t lim u r` 6u6 l e l df~u!, l r `, where the liear term i t drops out because the sequece $x % is cetered aroud zero+ Asymptotic stadard-ormality obtais if we ca fid the appropriate l that makes the latter limit equal to 1 for all e 0+ Notice that this limit is the complemet of Lideberg s coditio, where ( i 1 is replaced by because $x % is a i+i+d+ sequece+ From Studet s t~! desity, cm u cm c du log~m1 c c! M8 1 u M1 c 30 sih 1 ~c! c M1 c

teds to ifiity as c r `+ We eed to solve 1 lim r` l 6u6 l e sih 1 ~l u e M! df~u! lim r` l where we have dropped c M1 c r that is domiated by sih 1 ~c! r `+ By usig the logarithmic represetatio of the latter ad simplifyig, log~l! 1 lim r` l PROBLEMS AND SOLUTIONS 163 is solved by l M log~! or ay other fuctio that is asymptotically equivalet to it ~such as M log~! M!+ Therefore, z 1 ( x i a M log~! i 1 N~0,1!+ REFERENCES Hoffma-Jørgese, J+ ~1994! Probability with a View toward Statistics, vol+ I+ Chapma ad Hall+ Loève, M+ ~1977! Probability Theory I, 4th ed+ Spriger-Verlag+ 03.6.. Ubiasedess of the OLS Estimator with Radom Regressors Solutio Michael Jasso ~the poser of the problem! Uiversity of Califoria, Berkeley ~a! Suppose 1 ad let X ad u be idepedet stadard ormal variates+ The X is ozero with probability oe ad E~u6X! a+s+ 0, but E6b6 Z ` because the distributio of bz b u0x is Cauchy+ ~b! The matrix X has full colum rak with probability oe if ad oly if Pr@l mi ~X ' X! 0# 1, (1) where l mi ~{! deotes the miimal eigevalue of the argumet+ E~ b! Z exists if ~ad oly if! E6c ' ~ bz b!6 ` for ay k-vector c with c ' c 1+ I the sequel, let c be a arbitrary k-vector with uit legth+ Now, 6c ' ~ bz b!6 6c ' ~X ' X! 1 X ' u6 Mc ' ~X ' X! 1 cmu ' u Ml 1 mi ~X ' X!Mu ' u, where the first iequality uses the Cauchy Schwarz iequality ad the secod iequality uses Magus ad Neudecker ~1988!, Theorem 11+4+ If X ad u are idepedet, E~u6X! a+s+ 0, ad E @l 10 mi ~X ' X!# `, (3)