Ch. 5 Hypothesis Testing The current framework of hypothesis testing is largely due to the work of Neyman and Pearson in the late 1920s, early 30s, complementing Fisher's work on estimation. As in estimation, we begin by postulating a statistical model but instead of seeking an estimator of 6 in e we consider the question whether

Ch. 6 The Linear model under ideal conditions The(multiple) linear model is used to study the relationship between a dependent variable(Y) and several independent variables(X1, X2, ,Xk). That is ∫(X1,X2,…,Xk)+ E assume linear function 1X1+B2X2+…+6kXk+E xB+ where Y is the dependent or explained

Ch. 7 Violations of the ideal conditions 1 ST pecification 1.1 Selection of variables Consider a initial model. which we assume that Y=x1/1+E, It is not unusual to begin with some formulation and then contemplate adding more variable(regressors) to the model

Ch.8 Nonspherical Disturbance This chapter will assume that the full ideal conditions hold except that the covari- ance matrix of the disturbance, i.e. E(EE)=02Q2, where Q is not the identity matrix. In particular, Q may be nondiagonal and / or have unequal diagonal ele- ments Two cases we shall consider in details are heteroscedasticity and auto-

Ch. 4 Asymptotic Theory From the discussion of last Chapter it is obvious that determining the dis- tribution of h(X1, X2, . . Xr) is by no means a trival exercise. It turns out that more often than not we cannot determine the distribution exactly. Because of the importance of the problem, however, we are forced to develop approximations the subject of this Chapter

Ch. 23 Cointegration 1 Introduction An important property of (1) variables is that there can be linear combinations of theses variables that are I(O). If this is so then these variables are said to be cointegrated. Suppose that we consider two variables Yt and Xt that are I(1) (For example, Yt= Yt-1+ St and Xt= Xi-1+nt.)Then, Yt and Xt are said to be cointegrated if there exists a B such

where a subscribed element of a matrix is always read as arou, column. Here we confine the element to be real number a vector is a matrix with one row or one column. Therefore a row vector is Alxk and a column vector is AixI and commonly denoted as ak and ai,respec- tively. In the followings of this course, we follow conventional custom to say that a vector is a columnvector except for

Ch. 16 Stochastic Model Building Unlike linear regression model which usually has an economic theoretic model built somewhere in economic literature, the time series analysis of a stochastic process needs the ability to relating a stationary ARMA model to real data. It is usually best achieved by a three-stage

判断模型好坏的标准(AC. Harvey): 1.简约性(Parsimony) 2.可识别性(Identifiability)参数的估计唯一 3. Goodness of fit越高越好; 4.理论一致性(Theoretical consistency)与理论或常识要一致。如在消费函数中,可支配收入的系数一般为正; 5. Predictive power

第一节 金融资产概述 第二节 交易性金融资产 第三节 持有至到期投资 一、持有至到期投资及其特征 二、持有至到期投资的确认和计量 三、持有至到期投资的会计处理 第四节 可供出售金融资产