講義講稿大三下ch

1、Steps in Empiricalysis 實(shí)證分析-Economicysis using econometric mand dataFormulate the question oferest(2) construct economic mand choose itsvariables (factors)-mathematical equationt reflects economic relationship among variables.-causal relationship (causality):effect y: the variable on the lefnd sidec

2、ause(s) x: the variable(s) on the righnd side61.1.3 Data數(shù)據(jù)Mainly nonexperimental data (orobservational data 觀測數(shù)據(jù))- passively collected in real world- appear in sol science not experimental data- controlled and collected in labs- appear in natural science5Methods in Econometricssistical methods-mainl

3、y multiple regresysis(多元回歸分析)(2) spel techniques in Econometrics4What is Econometrics (計(jì)量經(jīng)濟(jì)學(xué))?Econometrics:a branch of economics,a mixture of mathematics, sistics and economics,using mathematical mand dao study economic or non-economic i es.3MaopicsWhat is Econometrics?Steps in Empiricalysis 實(shí)證分析 Th

4、e Types of Economic Data Causality and Ceteris Paribus2Ch.1 The Nature of Econometrics andEconomic Data1xs: causes, educ, exper, training(6) Applicationestimate economic relationshipstest economic theoriesforecastevaluate and implementernment andbusiness policy- and/ or12(4) Collect data for y and e

5、ach x-he final m, All y and xs musveavailable data.(5) Sistical inference and econometric tests Sistical inferenceestimate she mtest hypotheses about the values and signs of s-test if economic relationship betn y andxs is OK.11General descriptiony = 0 + 1x1 + 2x2 + 3x3 + uy: effect, wages: unknown p

6、arameters (coefficient) 參數(shù)或者系數(shù)asso ted with xs-describe the directions (+,-) and strengths (absolute value) of the relationship bet n y and xsu: unobserved causes other n xs, including unknown variables, variables ing no available data, unimportant variables.Subscript: number of parameters and varia

7、bles10(3) turn economic mo econometric mSpecify function form: linear, log, etc.Incorporate error term u (error or disturbance term)（隨機(jī)）誤差項(xiàng)或者擾動項(xiàng)u contains unobserved variablehe mExample: Demand Equation-contunuedlog Q = 0 + 1 log P + 2 log I + uExample 1.2 - continuedwage = 0 + 1 educ + 2 exper + 3

8、training+ u (1.4)u: “innate ability,” quality of education, family background, etc.9b) based on reasoning,uition or common sense- less formallyExample 1.2: Wage Mwage = f (educ, exper, training)wherewage: hourly wageeduc: years of formal education, exper: years of working experience training:ks spen

9、t in job training8a) .based on theoryExample: Demand Equationutilityization Q = f (P, I)7(3) key feature:chronological 時(shí)間順序ordering ofobservations mattersobservations over time are not independent, but are related to their recent historyTrends 趨勢and seasonality 季節(jié)性波動will be importante.g., Consumptio

10、n more complicated toyze181.3.2 Time series data(1) Definition:consist of observations on a variable or several variables for each time period over time-e.g. daily stock priin 2004,he past 20 years(2) data frequency:Daily,kly, monthly, quarterly and annually17obsno: observation number assigned to ea

11、chhe sample,-showing the order of observation, but not characteristic of the individual.(2) Key feature of Cross-Sectional dataordering of data does not matterObservations 觀測值 are amed to be independent each other1615Cross-sectional dataDefinition:a sample of individuals, households, firms, cities,s

12、 es, countries, or a variety of other units, taken at a given poime.-a random sample from underlying population.-information at a given poimeTable 1.1: a cross-sectional data set on 526 workers for the year 1976141.3 The Structure of Economic DataTypes of data:Cross-Sectional data 截面數(shù)據(jù)Time Series da

13、ta 時(shí)間序列數(shù)據(jù)Pooled Cross Section data (omitted)混合數(shù)據(jù)Panel data (omitted) 綜列數(shù)據(jù)Omit p10-1313Example: wage =0+1educ+2exper+3train+ucausality: education determines wageceteris paribus: when study the causalrelationship bet n education and wage (measured by ), all other causes (exper, train and u) are a med

14、to be held fixed.wage = 1 educ + 2 exper+3 train+u if exper=0, train=0, u=0,y = 1 educ22Example 1.4: wage = 0 + 1 educ + ucausality: education determines wageceteris paribus: when study the causalrelationship bet n education and wage (measured by 1), all other causes (u) are a med to be held fixed.wage = 0 + 1educ+ udwage = 1deduc+ duapproximay, wage = 1 educ + u if u=0, y = 1 educ211.4 Causality 因果關(guān)系and Not