Final Review Econ 240A

Outline The Big Picture Processes to remember ( and habits to form) for your quantitative career (FYQC) Concepts to remember FYQC Discrete Distributions Continuous distributions Central Limit Theorem Regression

The Classical Statistical Trail Rates & Proportions Inferential Statistics Application Descriptive Statistics Discrete Random Variables Binomial Probability Discrete Probability Distributions; Moments

Where Do We Go From Here? Regression Contingency Tables Properties Assumptions Violations Diagnostics Modeling Count ANOVA Probability

Processes to Remember Exploratory Data Analysis Distribution of the random variable Histogram Lab 1 Stem and leaf diagram Lab 1 Box plot Lab 1 Time Series plot: plot of random variable y(t) Vs. time index t X-y plots: Y Vs. x1, y Vs. x2 etc. Diagnostic Plots Actual, fitted and residual

Concepts to Remember Random Variable: takes on values with some probability Flipping a coin Repeated Independent Bernoulli Trials Flipping a coin twice Random Sample Likelihood of a random sample Prob(e1^e2 …^en) = Prob(e1)*Prob(e2)…*Prob(en)

Discrete Distributions Discrete Random Variables Probability density function: Prob(x=x*) Cumulative distribution function, CDF Equi-Probable or Uniform E.g x = 1, 2, 3 Prob(x=1) =1/3 = Prob(x=2) =Prob(x=3)

Discrete Distributions Binomial: Prob(k) = [n!/k!*(n-k)!]* pk (1-p)n-k E(k) = n*p, Var(k) = n*p*(1-p) Simulated sample binomial random variable Lab 2 Rates and proportions Poisson

Continuous Distributions Continuous random variables Density function, f(x) Cumulative distribution function Survivor function S(x*) = 1 – F(x*) Hazard function h(t) =f(t)/S(t) Cumulative hazard functin, H(t)

Continuous Distributions Simple moments E(x) = mean = expected value E(x2) Central Moments E[x - E(x)] = 0 E[x – E(x)]2 =Var x E[x – E(x)]3 , a measure of skewness E[x – E(x)]4 , a measure of kurtosis

Continuous Distributions Normal Distribution Simulated sample random normal variable Lab 3 Approximation to the binomial, n*p>=5, n*(1-p)>=5 Standardized normal variate: z = (x-)/ Exponential Distribution Weibull Distribution Cumulative hazard function: H(t) = (1/) t Logarithmic transform ln H(t) = ln (1/) +  lnt

Central Limit Theorem Sample mean,

Population Random variable x Distribution f(m, s2) f ? Pop. Sample Sample Statistic Sample Statistic:

The Sample Variance, s2 Is distributed chi square with n-1 degrees of freedom (text, 12.2 “inference about a population variance) (text, pp. 266-270, Chi-Squared distribution)

Regression Models Statistical distributions and tests Assumptions Student’s t F Chi Square Assumptions Pathologies

Regression Models Time Series Linear trend model: y(t) =a + b*t +e(t) Lab 4 Exponential trend model: y(t) =exp[a+b*t+e(t)] Natural logarithmic transformation ln Ln y(t) = a + b*t + e(t) Lab 4 Linear rates of change: yi = a + b*xi + ei dy/dx = b Returns generating process: [ri(t) – rf0] =  + *[rM(t) – rf0] + ei(t) Lab 6

Regression Models Percentage rates of change, elasticities Cross-section Ln assetsi =a + b*ln revenuei + ei Lab 5 dln assets/dlnrevenue = b = [dassets/drevenue]/[assets/revenue] = marginal/average

Linear Trend Model Linear trend model: y(t) =a + b*t +e(t) Lab 4

Lab 4

Lab Four F-test: F1,36 = [R2/1]/{[1-R2]/36} = 196 = Explained Mean Square/Unexplained mean square t-test: H0: b=0 HA: b≠0 t =[ -0.000915 – 0]/0.0000653 = -14

Lab 4

Lab 4

Lab 4 2.5% -14 -2.03

Lab Four 5% 4.12 196

Exponential Trend Model Exponential trend model: y(t) =exp[a+b*t+e(t)] Natural logarithmic transformation ln Ln y(t) = a + b*t + e(t) Lab 4

Lab Four

Lab Four

Percentage Rates of Change, Elasticities Cross-section Ln assetsi =a + b*ln revenuei + ei Lab 5 dln assets/dlnrevenue = b = [dassets/drevenue]/[assets/revenue] = marginal/average

Lab Five Elasticity b = 0.778 H0: b=1 HA: b<1 t-crit(5%) = -1.71

Linear Rates of Change Linear rates of change: yi = a + b*xi + ei dy/dx = b Returns generating process: [ri(t) – rf0] =  + *[rM(t) – rf0] + ei(t) Lab 6

Watch Excel on xy plots! True x axis: UC Net

Lab Six rGE = a + b*rSP500 + e

Lab Six

Lab Six

View/Residual tests/Histogram-Normality Test

Linear Multivariate Regression House Price, # of bedrooms, house size, lot size Pi = a + b*bedroomsi + c*house_sizei + d*lot_sizei + ei

Lab Six price bedrooms House_size Lot_size

Price = a*dummy2 +b*dummy34 +c*dummy5 +d*house_size01 +e

C captures three and four bedroom houses Lab Six C captures three and four bedroom houses

See Project I PowerPoint application to lottery with Bern variable Regression Models How to handle zeros? Labs Six and Seven: Lottery data-file Linear probability model: dependent variable: zero-one Logit: dependent variable: zero-one Probit: dependent variable: zero-one Tobit: dependent variable: lottery See Project I PowerPoint application to lottery with Bern variable

Regression Models Failure time models Exponential Weibull Survivor: S(t) = exp[-*t], ln S(t) = -*t Hazard rate, h(t) =  Cumulative hazard function, H(t) = *t Weibull Hazard rate, h(t) = f(t)/S(t) = (/)(t/)-1 Cumulative hazard function: H(t) = (1/) t Logarithmic transform ln H(t) = ln (1/) +  lnt

Applications: Discrete Distributions Binomial Equi-probable or uniform Poisson Rates & proportions, small samples, ex. Voting polls If I asked a question every day, without replacement, what is the chance I will ask you a question today? Approximate the binomial where p→0

Aplications: Discrete Distributions Multinomial More than two outcomes, ex each face of the die or 6 outcomes

Applications: Continuous Distributions Normal Equi-probable or uniform Students t Rates & proportions, np>5, n(1-p)>5; tests about population means given 2 Tests about population means, 2 not known; test regression parameter = 0

Applications: Continuous Distributions F Ch-Square, 2 Regression: ratio of explained mean square to unexplained mean square, i.e. R2/k÷(1-R2)/(n-k); test dropping 2 or more variables (Wald test) Contingency Table analysis; Likelihood ratio tests (Wald test)

Applications: Continuous Distributions Exponential Weibull Failure (survival) time with constant hazard rate Failure time analysis, test whether hazard rate is constant or increasing or decreasing

Labs 7, 8, 9 Lab 7 Failure Time Analysis Lab 8 Contingency Table Analysis Lab 9 One-Way and Two-Way ANOVA