【正文】 第一章1. Econometrics（計(jì)量經(jīng)濟(jì)學(xué)）：the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena.the result of a certain outlook on the role of economics, consists of the application of mathematical statistics to economic data to lend empirical support to the models constructed by mathematical economics and to obtain numerical results.2. Econometric analysis proceeds along the following lines計(jì)量經(jīng)濟(jì)學(xué)分析步驟1) Creating a statement of theory or 2) Collecting 3) Specifying the mathematical model of 4) Specifying the statistical, or econometric, model of 5) Estimating the parameters of the chosen econometric 6) Checking for model adequacy : Model specification ：模型設(shè)定檢驗(yàn)7) Testing the hypothesis derived from the 8) Using the model for prediction or l Step2：收集數(shù)據(jù)216。 Three types of data三類可用于分析的數(shù)據(jù)1) Time series(時(shí)間序列數(shù)據(jù)):Collected over a period of time, are collected at regular 2) Crosssectional截面數(shù)據(jù):Collected over a period of time, are collected at regular 3) Pooled data合并數(shù)據(jù)（上兩種的結(jié)合）l Step3：設(shè)定數(shù)學(xué)模型1. plot scatter diagram or scattergram2. write the mathematical modell Step4：設(shè)立統(tǒng)計(jì)或經(jīng)濟(jì)計(jì)量模型216。 CLFPR is dependent variable應(yīng)變量216。 CUNR is independent or explanatory variable獨(dú)立或解釋變量（自變量）216。 We give a catchall variable U to stand for all these neglected factors216。 In linear regression analysis our primary objective is to explain the behavior of the dependent variable in relation to the behavior of one or more other variables, allowing for the data that the relationship between them is （應(yīng)變量）與其他一個(gè)或多個(gè)變量（自變量）只見的行為關(guān)系，當(dāng)然這種關(guān)系并非完全正確l Step5：估計(jì)經(jīng)濟(jì)計(jì)量模型參數(shù)216。 In short, the estimated regression line gives the relationship between average CLFPR and CUNR 簡(jiǎn)言之，估計(jì)的回歸直線給出了平均應(yīng)變量和自變量之間的關(guān)系216。 That is, on average, how the dependent variable responds to a unit change in the independent 。l Step6：核查模型的適用性：模型設(shè)定檢驗(yàn)The purpose of developing an econometric model is not to capture total reality, but just its salient features.l Step7：檢驗(yàn)自模型的假設(shè)Why do we perform hypothesis testing?We want to find our whether the estimated model makes economic sense and whether the results obtains conform with the underlying economic theory.第二章1. The meaning of regression（回歸）Regression analysis is concerned with the study of the relationship between one variable called the dependent or explained variable, and one or more other variables called independent or explanatory variables.2. Objectives of regression1) Estimate the mean, or average, and the dependent values given the independent values2) Test hypotheses about the nature of the dependence hypotheses suggested by the underlying economic theory3) Predict or forecast the mean value of the dependent variable given the values of the independents4) One or more of the preceding objectives bined3. Population Regression Line（PRL）In short, the PRL tells us how the mean, or average, value of Y is related to each value of X in the whole population4. The dependence of Y on X, technically called the regression of Y on X.5. How do we explain it?A student’s . score, say, the ith individual, corresponding to a specific family ine can be expressed as the sum of two ponents1) The ponent can be called the systematic, or deterministic, ponent.2) May be called the nonsystematic or random ponent6. What is the nature of U(stochastic error) term？1) The error term may represent the influence of those variables that are not explicitly included in the 2) Some intrinsic randomness in the math score is bound to occur that can not be explained even we include all relevant ，內(nèi)在隨機(jī)性也不可避免，這是做任何努力都無法解釋的。3) U may also represent errors of measurement. U還代表了度量誤差4) The principle of Ockham’s razor the description be kept as simple as possible until proved inadequate would suggest that we keep our regression model as simple as possible.“奧卡姆剃刀原則”，描述應(yīng)該盡可能簡(jiǎn)單，只要不遺漏重要信息。這表明回歸模型應(yīng)盡可能簡(jiǎn)單。7. How do we estimate the PRF（population regression function）?Unfortunately, in practice, We rarely have the entire population in our disposal, often we have only a sample from this population.8. Granted that the SRF is only an approximation of PRF. Can we find a method or a procedure that will make this approximation as close as possible? SRF僅僅是PRF的近似，那么能不能找到一種方法使這種近似盡可能接近真實(shí)呢？9. Special meaning of “l(fā)inear”1) Linearity in the variables變量線性The conditional mean value of the dependent variable is a linear function of the independent variables2) Linearity in the Parameters參數(shù)線性The conditional mean of the dependent variable is a linear function of the parameters, the B’s。 it may or may not be linear in the variables.第三章1. Unless we are willing to assume how the stochastic U terms are generated, we will not be able to tell how good an SRF is as an estimate of the true ，才能判定SRF對(duì)PRF擬合的是好是壞。2. Classical Linear Regression Model1) Assumption 1: The regression model is linear in the parameters. It may or may not be linear in the ，但不一定是變量線性的。2) Assumption 2: The explanatory variables X is uncorrelated with the disturbance term U. X’s are nonstochastic, U is stochastic. 解釋變量X與擾動(dòng)誤差項(xiàng)u不相關(guān). X是非隨機(jī)的，U是隨機(jī)的。3) Assumption 3: Given the value of Xi, the expected, or mean value of the disturbance term U is ，擾動(dòng)項(xiàng)的期望或均值為零。 Disturbance U represent all those factors that are not specifically introduced in the model干擾項(xiàng)U代表了所有未納入模型的影響因素。4) Assumption 4:The variance of each Ui is constant, or homoscedastic. U的方差為常數(shù)，或同方差。l Homoscedasticity（同方差）:a. This assumption simply means that the conditional distribution of each Y population corresponding to the given value of X has the same variance. 該假定表明，與給定的X相對(duì)應(yīng)的每個(gè)Y的條件分布具有同方差。b. The individual Y values are spread around their mean values with the same 。5) Assumption 5:There is no correlation between two error terms, this is the assumption of ，即兩個(gè)誤差項(xiàng)之間不相關(guān)。6) Assumption 6:The regression model is correctly 。There is no specification bias or specifi