Asymptotic normality Sketch of proof: I follows by arguments similar to our derivation of the delta method. Proof: We shall derive it as a special case when doing GMM testing . endobj I thank Mattia Mariantoni for pointing out a typo in Equation 202020. Assumption 222, strict exogeneity, is that the expectation of the error term is zero: E[nX]=0,n{1,,N}. /Length 23 G-M implies neither consistency nor asymptotic normality. \begin{aligned} \\ \tag{2} (22), The Fisher information is the negative expected value of this second derivative or, IN(p)=E[n=1N[Xnp2+Xn1(1p)2]]=n=1N[E[Xn]p2E[Xn]1(1p)2]=n=1N[1p+11p]=Np(1p).
OLS Asymptotics | Alex Stephenson \tag{27} 0000004483 00000 n
\mathbb{E}\left[ \mathbf{e}^{\top} \mathbf{e} \mid \mathbf{X} \right] &= (N-P) \sigma^2. /Matrix[1 0 0 1 0 0] Asymptotic Theory for Consistency Consider the limit behavior of asequence of random variables bNas N.This is a stochastic extension of a sequence of real numbers, such as aN=2+(3/N). Using basic properties of the normal distribution, we can immediately derive the distribution of the OLS estimator: ^N(,2(XX)1). fb @7QDE~(1B0hBJ! \\ Tm
I Ordinary least squares (OLS) I Maximum likelihood estimation (MLE) I Condence sets 5/40. If we dene = Ex3 i =Q, then our result can be rewritten as n1=2(^ n )! \mathbb{E}[s^2 \mid \mathbf{X}] &= \sigma^2, \tag{24} In MLE case, a variance of ^ is in distribution as 1 I ( ), but in OLS case 2 Q x x 1 n is not a variance of ^. Theorem 11 (On asymptotic normality)Suppose the assumptionsH1-H5hold, and in addition it is assumed that: 1 the fourth moment is finite, i.e., 2 the matrix is stable14. I if m is twice differentiable, . \log f_X(X; p) \mathbb{E}[\hat{\boldsymbol{\beta}} - \boldsymbol{\beta} \mid \mathbf{X}] = \mathbf{0}. xb``e``=R @1630t`X9U"k#Cd;wn_ox2-EV!eh q+M" Eic Sfgcy?Aeauc C%c|7#O9,X(xq` $kMX002h0lXpo:CA&1 < j0 y_1%H3-10D2 \boldsymbol{\varepsilon} \mid \mathbf{X} \sim \mathcal{N}(\mathbf{0}, \sigma^2 \mathbf{I}_N), \tag{25} V[logfX(X1;0)]=E[(logfX(X1;0))2]=0E[logfX(X1;0)]2=I(0).(11). /FormType 1 Nonetheless, it is relatively easy to analyze the asymptotic performance of the OLS estimator and construct large-sample tests. Then for some point c=~(^N,0)c = \tilde{\theta} \in (\hat{\theta}_N, \theta_0)c=~(^N,0), we have, LN(^N)=LN(0)+LN(~)(^N0). \middle| \mathbf{X} \right] 0000008281 00000 n
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We derive the asymptotic normality and the asymptotic variance-covariance matrix of this two- stage quantile regression estimator. ^=(XX)1X.(27). My in-class lecture notes for Matias Cattaneos. ^N=argmaxlogfX(x;)LN(^N)=0.(4).
(21), In other words, the MLE of the Bernoulli bias is just the average of the observations, which makes sense. 0000003541 00000 n
Thus, we can write Equation 212121 as, E[M]=iNMii2=trace(M)2. Theorem 5.2 (Asymptotic Normality of OLS) Under the Gauss-Markov Assumptions MLR.1 through MLR.5, (i) __ n ( j j) ~ Normal(0, 2 / a j 2), where 2 / a j 2 0 is the asymptotic variance of __ n ( j j); for the slope coefficients, a j 2 plim n 1 i 1 n r ij 2, where the r ij are the residuals from regressing x j on the other . L^{\prime\prime}_N(\tilde{\theta}) \rightarrow^p - \mathcal{I}(\theta_0). \\ 0000011843 00000 n
/Length 23 &= (\mathbf{X}^{\top} \mathbf{X})^{-1} (\mathbf{X}^{\top} \mathbf{X}) \boldsymbol{\beta} + (\mathbf{X}^{\top} \mathbf{X})^{-1} \mathbf{X}^{\top} \boldsymbol{\varepsilon}) - \boldsymbol{\beta} (19) }=+UaHD2 L_N^{\prime\prime}(\tilde{\theta}) \rightarrow^p \mathbb{E}\left[ \frac{\partial^2}{\partial \theta^2} \log f_X(X_1; \theta_0) \right] = - \mathcal{I}(\theta_0). \tag{3} 0000029635 00000 n
The proof of Theorem 1, as well as the proof of the sufficiency in Theorem 2, is elementary and straight forward. 0000017450 00000 n
b+G \mathbb{E}[\boldsymbol{\varepsilon}^{\top} \mathbf{M} \boldsymbol{\varepsilon} \mid \mathbf{X}] Asymptotic Efficiency of OLS * The issue of this chapter is deeply based on Appendix C.3. \hat{\theta}_N = \arg\!\max_{\theta \in \Theta} \log f_X(x; \theta) \quad \implies \quad L^{\prime}_N(\hat{\theta}_N) = 0. \\ &= \frac{N}{p(1-p)}. (16), As discussed in the introduction, asymptotic normality immediately implies, ^NdN(0,IN(0)1). \tag{17} (16) \mathbf{M}\mathbf{X} &= (\mathbf{I}_N - \mathbf{H}) \mathbf{X} &= \sqrt{N} \left( \frac{1}{N} \left[ \frac{\partial}{\partial \theta} \log \prod_{n=1}^N f_X(X_n; \theta_0) \right] \right) \\\\ \end{aligned} \tag{12} where I(0)\mathcal{I}(\theta_0)I(0) is the Fisher information. With these properties in mind, lets prove some important facts about the OLS estimator ^\hat{\boldsymbol{\beta}}^. Consider the sequence of random variables ; Z1, Z2, Zn, We say that this sequence converges in probability to a constant c if for any small This is not bad. \mathbb{E}[\varepsilon_n \mid \mathbf{X}] = 0, \quad n \in \{1, \dots, N\}. For the numerator, by the linearity of differentiation and the log of products we have, NLN(0)=N(1N[logfX(X;0)])=N(1N[logn=1NfX(Xn;0)])=N(1Nn=1N[logfX(Xn;0)])=N(1Nn=1N[logfX(Xn;0)]E[logfX(X1;0)]). If we assume normality in the errors, then clearly, XN(0,2IN),(25) (21), E[ji|X]={2ifi=j,0otherwise. (9), See my previous post on properties of the Fisher information for a proof. Finally, Ill show how if we assume our error terms are normally distributed, we can pin down the distribution of ^\hat{\boldsymbol{\beta}}^ exactly.
PDF Chapter 05 Ch.5 Multiple Regression: Asymptotic Multiple Regression Dr. Henry Asymptotic normality of OLS Kankwamba. \mathbb{V}[\hat{\boldsymbol{\beta}} \mid \mathbf{X}] (6) &\stackrel{*}{=} \mathbf{A} (\sigma^2 \mathbf{I}_N) \mathbf{A}^{\top} E[jiX]={20ifi=j,otherwise.(22), This is the assumption 444, spherical errors. \begin{aligned} &\stackrel{\star}{=} \mathbf{y}^{\top} \mathbf{M} \mathbf{y} + \cancel{\boldsymbol{\beta}^{\top} \mathbf{X}^{\top} \mathbf{M} \mathbf{X} \boldsymbol{\beta}} - \cancel{\mathbf{y}^{\top}\mathbf{M}\mathbf{X}\boldsymbol{\beta}} - \cancel{\mathbf{X}^{\top} \boldsymbol{\beta}^{\top} \mathbf{M} \mathbf{y}} We show how we can use Central Limit Therems (CLT) to establish the asymptotic normality of OLS parameter estimators. Ill start this post by working through the standard OLS assumptions. Therefore, a low-variance estimator ^N\hat{\theta}_N^N estimates the true parameter 0\theta_00 more precisely. 0
\end{cases} \tag{22} /Subtype/Form To prove asymptotic normality of MLEs, define the normalized log-likelihood function and its first and second derivatives with respect to \theta as, LN()=1NlogfX(x;),LN()=(1NlogfX(x;)),LN()=22(1NlogfX(x;)). hw6TH/*23Q0 B]0
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PDF Topic 27. Asymptotic normality of the MLE - Pennsylvania State University b. they are approximately normally distributed in large enough sample sizes.
Asymptotic Normality - an overview | ScienceDirect Topics \\ &= \mathbb{E}\left[ 925 0 obj
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(24) \mathbb{E}[\hat{\boldsymbol{\beta}} \mid \mathbf{X}] = \boldsymbol{\beta}. hTMo0 Now by definition LN(^N)=0L^{\prime}_N(\hat{\theta}_N) = 0LN(^N)=0, and we can write, ^N0=LN(0)LN(~)N(^N0)=NLN(0)LN(~)(7) 0000029385 00000 n
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Convergence in Probability the Law of Large Numbers. \\ endstream NLN(0)dN(0,I(0))(14), LN(~)pI(0). \begin{aligned} \\ 0) 0 E( = Definition of unbiasedness: The coefficient estimator is unbiased if and only if ; i.e., its mean or expectation is equal to the true coefficient trace(M)=trace(INH)=trace(IN)trace(H)=Ntrace(X(XX)1X)=Ntrace(XX(XX)1)=Ntrace(IP)=NP.(24), If we make assumption 555, that the error terms are normally distributed, then ^\hat{\boldsymbol{\beta}}^ is also normally distributed. MLE is popular for a number of theoretical reasons, one such reason being that MLE is asymtoptically efficient: in the limit, a maximum likelihood estimator achieves minimum possible variance or the CramrRao lower bound. We know the mean of ^\hat{\boldsymbol{\beta}} - \boldsymbol{\beta}^ from Equation 999, and we know the variance from Equation 101010. (12) Asymptotic Normality Large Sample Inference t, F tests based on normality of the errors (MLR.6) if drawn from other distributions j will not be normal t, F statistics will not have t, F distributions solutionuse CLT: OLS estimators are approximately normally distributed (at least for large sample sizes) Proof of Lemma 2: With OLS rst-stage estimators, we have 20 (q) = aq 2 + 2bq + c . trailer
PDF Lecture 14 | Consistency and asymptotic normality of the MLE 14.1 /Resources 12 0 R \\ ^N(0,2(XX)1).(28). \hat{\boldsymbol{\beta}} \sim \mathcal{N}(\boldsymbol{\beta}, \sigma^2 (\mathbf{X}^{\top} \mathbf{X})^{-1}). Least squares estimator for [ edit] Using matrix notation, the sum of squared residuals is given by. \begin{cases} %%EOF
\mathbb{E}\left[\frac{\partial}{\partial \theta} \log f_X(X_1; \theta_0)\right] = 0. ~] x$tuK~"g0/5Yn8Xs&rYR X^yl# xAGX+*mGLF))g5v5n5/IZ.Q}j>2E827Z$AFCg5c"skgDepf\O?~k`f}w- (27) N(0;Q2P): The value for could also be obtained along a dierent route. Now lets set it equal to zero and solve for ppp: 0=n=1N[Xnp+Xn1p]N1pN1p=n=1NXn[1p+11p]p(1p)1p=1Nn=1NXn. \tag{16} \tag{10} One of them is called asymptotic normality, which basically states the MLE estimator is asymptotically distributed with Gaussian behavior as the data sample size goes up, in particular [ 112 ]: (6.17) where J is the Fisher information matrix computed from all samples, 0 and are the true value and the MLE of the parameter , respectively. - We come to this approximation by the CLT because the OLS estimators involve the use of sample averages (mathematically, this can get complicated). Another property that we are interested in is whether an estimator is consistent. 0000003375 00000 n
xS0PpW0PHWP( 7@hRNaZ!i(CZ@c ~9mgpSgA*h:3-Dx>^ mlcv]U=oAlIl9l|U1! \tag{13} Econometrics 3 5.1 Consistency Under the Gauss-Markov assumptions OLSE . (2) Stationary fractional cointegration The stochastic process {xt,t=1,2,}generated by (2)has spectral density(4)f()g-2das0+,where gis a constant and the symbol "" means that the ratio of the left- and right-hand sides tends to one in the limit. An exogenous variable is a variable that is not determined by other variables or parameters in the model. >> &= IN(p)=E[n=1N[p2Xn+(1p)2Xn1]]=n=1N[p2E[Xn](1p)2E[Xn]1]=n=1N[p1+1p1]=p(1p)N.(23), Thus, by the asymptotic normality of the MLE of the Bernoullli distributionto be completely rigorous, we should show that the Bernoulli distribution meets the required regularity conditionswe know that, p^NdN(p,p(1p)N). c. they are approximately normally distributed in samples with less than 10 observations. According to the asymptotic properties of the OLS estimator: OLS is consistent, The estimator converges in distribution to standard normal, Inference can be performed based on the asymptotic convergence to the standard normal, and OLS is the most efficient among many consistent estimators of . usual t-statistic is asymptotically normal (8) \end{aligned} \tag{3} E[^X]=(XX)1XE[X]=0.(9). Difference between asymptotic normalities of OLS and MLE. Then, we apply our variance reduction method by choosing optimally the combination weight in the redened dependent variable. /Resources 14 0 R 0000009736 00000 n
&= \mathbf{0}. \tag{6} \end{aligned} \tag{18} 0000001857 00000 n
(28) \\ \tag{21} \begin{aligned} \mathbf{E}[\boldsymbol{\varepsilon}^{\top} \mathbf{M} \boldsymbol{\varepsilon}] = \sum_{i}^N M_{ii} \sigma^2 = \text{trace}(\mathbf{M}) \sigma^2. That implies, yMX=XMy=Xe=0. trace(M)=NP.(16). Theorem - Asymptotic normality of OLS: with the asymptotic normality of OLS theorem, the ^2 is a consistent estimator of 2 - under the unbiased estimation of 2 theorem we know that ^2 is unbiased for 2 under the Guass markov assumptions - consistency implies what? endstream
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&= \boldsymbol{\beta} + (\mathbf{X}^{\top} \mathbf{X})^{-1} \mathbf{X}^{\top} \boldsymbol{\varepsilon} - \boldsymbol{\beta} /Filter/FlateDecode 0000029880 00000 n
(6) Since the random variable \boldsymbol{\varepsilon} does not depend on X\mathbf{X}X, clearly the marginal distribution is also normal, N(0,2IN). &= \sum_{n=1}^N \left[ \frac{1}{p} + \frac{1}{1 - p} \right] y_n = \beta_0 + \beta_1 x_{n,1} + \dots + \beta_P x_{n,P} + \varepsilon_n. \\ Now lets apply the mean value theorem, Mean value theorem: Let fff be a continuous function on the closed interval [a,b][a, b][a,b] and differentiable on the open interval. \\ &\stackrel{\ddagger}{=} \mathbf{A} \mathbb{V}[\boldsymbol{\varepsilon} \mid \mathbf{X}] \mathbf{A}^{\top} N(^N0)dN(I(0)1). This follows immediately from MSE [o; X /n] = E [X X/n2 ] = 2 X X/n2 , which converges towards O. 0000029003 00000 n
&= \mathbf{y}^{\top} \mathbf{M} \mathbf{y} xref
First, lets prove that ^\hat{\boldsymbol{\beta}}^ is unbiased, i.e. &= \sum_{n=1}^N \left[ \frac{X_n}{p} + \frac{X_n - 1}{1 - p} \right]. ") K8fuA,%dN4*AyPQ[}F)8[&4O@=& hFT$)2ji~2vf$cdzbn$|qHd,V7=[8#*d\dZHwIP&"j} (3) The terms (1p)(1 - p)(1p) cancel, leaving us with the MLE: p^N=1Nn=1NXn. \tag{29} xS0PpW0PHWP( 51 54
&= \sigma^2 \mathbf{A A}^{\top} If we assume normally distributed errors, then ^\hat{\boldsymbol{\beta}}^ is itself normally distributed. 0000007234 00000 n
fZ=Rr&P*EDqL%)P&b!o2HWF6ABDbVJh Motivation. \end{aligned} \tag{18} 39 0 obj s^2 = \frac{\mathbf{e}^{\top} \mathbf{e}}{N - P}, \tag{11} \begin{bmatrix} \varepsilon_1 \\ \vdots \\ \varepsilon_N \end{bmatrix} \sqrt{N} L^{\prime}_N(\theta_0) \rightarrow^d \mathcal{N}(0, \mathcal{I}(\theta_0)) \tag{14} \begin{bmatrix} \varepsilon_1 & \dots & \varepsilon_N \end{bmatrix} XN(0,2IN),(25). 0 & \text{otherwise.} Our theorem on asymptotic normality also implies that, regardless of the error's distribution, the OLS estimators, when properly standardized, have approximate standard normal distributions. / *zl}w
m`%Lbb{ wD= (17) We can empirically test this by drawing the probability density function of the above normal distribution, as well as a histogram of p^N\hat{p}_Np^N for many iterations (Figure 111). 919 0 obj
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lec05-regressionAsymptotics | PDF | Ordinary Least Squares | Regression (10) 0000023921 00000 n
Step \star is because the true value \boldsymbol{\beta} is non-random; step \dagger is just applying Equation 555 from above; step \ddagger is because A\mathbf{A}A is non-random; and step * is assumption 444 or spherical errors. (18) (b6=!lI~u^ ?)>]W B-3)%dH dG%[)c_JL%r3!Z9KfzDH3YDEM955)w4#tN sZg~{1gCto
.L?| \middle| \mathbf{X} \right] \\ \\ an exact rst-order Taylor series expansion. Examples include: (1) bN is an estimator, say b;(2)bN is a component of an estimator, such as N1 P ixiui;(3)bNis a test statistic. \end{aligned} \tag{12} This assumption is not required for OLS theory, but some sort of distributional assumption about the noise is required for hypothesis testing in OLS. &= N - P. &= \sum_{n=1}^N \left[ X_n \log p + (1 - X_n) \log (1 - p) \right]. >>
PDF Multiple Regression Analysis: Asymptotics 0
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&= \mathbb{E}\left[ \\ Our claim of asymptotic normality is the following: Asymptotic normality: Assume ^Np0\hat{\theta}_N \rightarrow^p \theta_0^Np0 with 0\theta_0 \in \Theta0 and that other regularity conditions hold. &= \sum_{n=1}^N \log \left[p^{X_n} (1-p)^{1 - X_n} \right] Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site LN(~)pE[22logfX(X1;0)]=I(0).(13). HWj@}GvHikAB.JK%x53gZ{v-4@ stream This subsection relies on facts about the residual maker M\mathbf{M}M, which I discussed in my first post on OLS. ^NdN(0,IN(0)1).(2). /FormType 1 0000048978 00000 n
\\ Knowing this distribution is useful in analyzing the results of linear models, such as when performing hypothesis testing for a given estimated parameter ^p\hat{\beta}_p^p. 0000024472 00000 n
&= (\mathbf{y} - \mathbf{X}\boldsymbol{\beta})^{\top} \mathbf{M} (\mathbf{y} - \mathbf{X}\boldsymbol{\beta})
normality assumption - Difference between asymptotic normalities of OLS (Note that other proofs might apply the more general Taylors theorem and show that the higher-order terms are bounded in probability.) There are no real details on the CLT, j. &= \sum_{n=1}^N \left[ \frac{\mathbb{E}[X_n]}{p^2} - \frac{\mathbb{E}[X_n] - 1}{(1 - p)^2} \right] LN(^N)=LN(0)+LN(~)(^N0). Assumption 1 is just Equation 1; it means that we have correctly specified our model. samples from a Bernoulli distribution with true parameter ppp. One of the main uses of the idea of an asymptotic distribution is in providing approximations to the cumulative distribution functions of statistical . M=X(XX)1X,(14), which I discussed in my first post on OLS. without assuming A.MLR6 and so without assuming normal errors: #1 If i = 0; then b i =se b i is asymptotically N(0;1): Recalling that a Student-t approaches a normal as the number of degree of freedom approaches in-nity, it is true that for n large . \frac{N}{1 - p} &= \sum_{n=1}^N X_n \left[ \frac{1}{p} + \frac{1}{1 - p} \right] Finally, a set of simulations illustrate the asymptotic behavior of the OLS. xS0PpW0PHWP( % Then we can invoke Slutskys theorem. 910 0 obj
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\middle| \mathbf{X} \right] 2.1. We focus on the behavior of b (and the test statistics) when T -i.e., large samples. Therefore, IN()=NI()\mathcal{I}_N(\theta) = N \mathcal{I}(\theta)IN()=NI() provided the data are i.i.d.
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