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Chapter 12 Time Series Analysis 12.1 Stochastic processes A stochastic process is a family of random variables {Xt,t ET}. Example{St,t 0, 1,2,...} where St i=o X; and iid(0,2). St has a different distribution at each point t
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Chapter 2 The Classical Multiple Linear Regression Model 2.1 Linear Regression Model Notation dependent variable, regressand
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Chapter 4 Finite-Sample properties of the LSE Finnite-sample the n is assumed to be fixed normal dist n assumed Large-sample theory n is sent to oo, general distn assumed
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Chapter 6 Large Sample Inference and Prediction 6.1 Large sample inference Consider the null hypothesis
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Review Problems for Midterm 1 Least Squares 1. In the linear regression model
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Expectations and Conditional Expectations Definition 1 Discrete Random Variable random wariable is discrete f the set of outcomes is either finite in number or countably
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LAW OF ITERATED EXPECTATIONS Law of Iterated Expectations Theorem 1 Law of iterated expectation.s The notation Er[ indicates the expectation over the value of a Example
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●联立方程模型及其偏倚 ●联立方程模型的识别 ●联立方程模型的估计
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●设定误差 ●设定误差的检验 ●测量误差
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●基本概念 ●虚拟变量设置规则
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