课程名称︰计量经济理论一B 课程性质︰经研所必修 课程教师︰郭汉豪 Hon Ho Kwok 开课学院：社科院 开课系所︰经济系 考试日期（年月日）︰111.12.22 考试时限（分钟）：180 试题 : 注：部分数学式以LaTeX语法撰写 1. Constraint Least Squares (20 points) This question is about the statistical properties of constraint least square (C LS) estimators. Suppose we have the following linear model: y_i = x'_i\beta + e_i with E(x_ie_i)=0. y_i is the endogenous variable. x_i is the k*1 column of exo- genous variables. \beta is the k*1 column of parameters of interest. The CLS e- stimator is the solution of minimizing SSE(\beta) = \sum_{i=1}^n (y_i-x'_i\beta)^2 subject to the constraint R'\beta = c. R is a k*q matrix. c is a q*1 column. Define X as the n*k matrix whose i-th row is x'_i. Define y as the n*1 column whose i-th entry is y_i. First, derive the CLS estimator, \tilde{\beta}_{CLS}, by solving the constrain- ed minimization problem. Recall that the ordinary lest squaire (OLS) estimator, \hat{\beta}_{OLS}, is (X'X)^{-1}X'y. Show that \tilde{\beta}_{CLS} = \hat{\beta}_{OLS} - (X'X)^{-1}R[R'(X'X)^{-1}R]^{-1}(R'\hat{\beta}_{OLS}-c). Second, verify that the CLS estimator satisfies the constraint R'\beta=c/ Third, prove that the CLS estimator is consistent and asymptotically normal. 2. M-estimators (40 points) The data consists of a sequence of observed vectors w_i = (y_i,x'_i)' where i= 1,...,n. The scalar y_i denotes the dependent (endogenous) variable. The column x_i is the k*1 vector of dependent (exogenous) variables. The scalar m_i=m(w_i;\theta) is a function of w_i, where \theta is the p*1 vector of para- meters. The true parameter vector is denoted by \theta_0. The function m(w_i;\theta) is twice continuously differentiable with respect to \theta for all w_i. An extremum estimator is an M-estimator if the criterion function is a sample average: Q_n = \frac{1}{n}\sum_{i=1}^n m(w_i;\theta). We have the following notation for first and second derivatives: s_i = s(w_i;\theta) = \frac{\partial m(w_i;\theta)}{\partial\theta} and H_i = H(w_i;\theta) = \frac{\partial^2 m(w_i;\theta)}{\partial\theta\partial\theta'}. 2.1 M-Estimator Asymptotics (20 points) The M-estimator \hat{\theta} is the maximizer of Q_n(\theta) (assume it is uni- que). We have the following two assumptions. First, the M-estimator \hat{\theta} is a consistent estimator of \theta_0. Second, \frac{1}{\sqrt{n}}\sum_{i=1}^n s(w_i;\theta_0) converges to Normal(0,\Sigma) in distribution where \Sigma is p*p and positive definite. Show that the M-estimator is asymptotically normal and write down the asymptotic variance of the M-estimator. 2.2 Maximum Likelihood (20 points) Let f = f(y_i|x_i;\theta) be the conditional density of y_i given x_i and \theta. Then the maximum likelihood estimator of \theta_0 is a special case of the M-estimator where m_i = \log f_i. It is assumed that f(y_i|x_i;\theta)>0 for all (y_i,x_i) and \theta, so it is legitimate to take logs of the density function. We consider the following linear regression model: y_i = x'_i\beta + e_i where the scalar e_i is normally distributed with zero mean and variance \sigma^2. The log conditional density for observation i is \log f(y_t|x_t;\beta,\sigma^2) = -\frac{1}{2}\log(2\pi) - \frac{1}{2}\log(\sigma^2) - \frac{(y_t-x'_t\beta)^2}{2\sigma^2}. Derive the conditional ML estimator of the parameters and write down the asymp- totic variance of the estimator. Is the conditional ML estimator of \sigma^2 unbiased? 3. Generalized Method of Moments (GMM) (20 points) The data consists of a sequence of observed vectors w_i=(y_i,x'_i,z'_i)' where i=1,...,n. The scalar y_i denotes the dependent (endogenous) variable. The col- umn x_i is the k*1 vector of independent variables (which are potentially endo- genous). The column z_i denotes the l*1 vector of instruments (which are exoge- nous). The l*1 vector g_i=g(w_i;\theta) is a function of w_i, where \theta is the p*1 vector of parameters. The true parameter vector is denoted by \theta_0. We have the following l moment conditions E(g_i) = 0. The GMM criterion function is Q_n(\theta) = \frac{1}{2} g_n(\theta)'\hat{W}g_n(\theta) where g_n(\theta) = \frac{1}{n}\sum_{i=1}^n g(w_i;\theta), and \hat{W} is a l*l matrix which is symmetric and positive definite. The weig- ht matrix \hat{W} converges in probability to W, which is also l*l and positive definite. The covariance matrix of g_i is denoted by S = E(g_ig'_i). In this question, we consider the following linear model: y_i = x'_i\beta + e_i, and the moment conditions arer E(g_i) = E(z_ie_i) = 0 where the scalar e_i is the unobserved exogenous variable (or the regression e- rror) and i=1,...,n. You may use the following notation. Y is the n*1 columns whose i-th entry is y_i. X is the n*k matrix whose i-th row is x'_i. Z is the n*l matrix whose i-th row is z'_i. E is the n*1 column whose i-th entry is e_i. The population and sample covariance matrices are denoted as follows: \sigma_{xy} = E(x_iy_i) \Sigma_{xz} = E(x_iz'_i) s_{xy} = \frac{1}{n}\sum_{i=1}^n x_iy_i, and S_{xz} = \frac{1}{n}\sum_{i=1}^n x_iz'_i. First, please derive the GMM sample error \hat{\theta} - \theta. Second, prove that the GMM estimator is consistent. Third, prove that the GMM estimator is asymptotically normal and derive the asymptotic covariance matrix of the effic- ient GMM estimator. Please writhe your arguments clearly. 4. Minimum Distance Estimators (20 points) Suppose \theta is the p*1 vector of the parameters of interest. \theta_0 denot- es the true parameter column. The s*1 vector \pi is a column of the reduced fo- rm parameters and we know that \pi=h(\theta), where h is a known continuously differentiable function. h is not a function of sample size n. \pi_0=h(\theta_0) denotes the true value of \pi. \hat{\pi} is a consistent est- imator of \pi_0. And we know that \sqrt{n}(\hat{\pi}-\pi_0) converges in distr- ibution to N(0,\Xi_0). \hat{\Xi} is a consistent estimator of \Xi. The criterion function for the minimum distance estimator is Q(\theta,\hat{W}) = (\hat{\pi}-h(\theta))'\hat{W}(\hat{\pi}-h(\theta)), where \hat{W} is a s*s positive definite matrix. \hat{W} converges in probabil- ity to W, which is also positive definite. The minimum distance estimator is the \theta which minimizes Q. First, write down the first order conditions for the minimum distance estimator and state the identification condition. Is it necessary that s>=p? Explain your answer. Second, prove that minimum distance estimator is asymptotically normal. Write down the asymptotic covariance matrix for a general weight matrix W. What is the optimal choice of the weight matrix W? What is the probabilistic limit of the minimized objective function Q? What is the asymptotic distribution fo the minimized nQ(\theta,\hat{W}) with the optim- al weight matrix? --

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