Quantitative Methods II
Final Exam
Wednesday March 20
8 am

The final exam will be comprehensive.  You should review the material we covered for exam 1 and exam 2 .  You will need a calculator for this exam


I. Regression (Con't)

A. Variations on OLS
    1. dummy variables
    2. transforming non-linear relations to use OLS
        a. quadratic terms
        b. natural logs

B. R-squared
    1. coefficient of determination
    2. low R-squared-- lots of unexplained variation in Y

II. Regression Problems
A. regression does not show causality

B. Data collection must be methodologically sound
    1. random sample, representative of population
    2. avoid bias in survey questions
    3. results are sensitive to extreme values (outliers)

C. Statistical Issues
    1. model must be correctly specified
        a. missing variables
        b. spurious relations
    2. multicollinearity
    3. heteroskedasticity
    4. autocorrelation

III. Communicating Statistics

A. Graphing
    1. use of scale, units
    2. sample, time period
    3. graphs can be a surprisingly powerful analytic tool

B. Problems with Statistical Reporting
    1. publication bias
        a. "positive" results
        b. sponsor
    2. conclusions may not reflect statistical results
    3. results may not be statistically significant
        - is a confidence level or margin of error reported?
    4. results may not have real world significance
    5. biased samples or biased surveys will give meaningless results
        - is methodology reported?

C. Experimental Design
    1. study and control groups
        a. random
        b. blind
    2. measurement needs to be double-blind
    3. results from poorly designed experiments are not meaningful
        a. placebo effect
        b. unconscious bias
 

IV. Forecasting
A. Use estimated coefficients to forecast Y, given new X variables

B. Conditional forecasts

C. Systems of Equations

D. Forecasting issues & problems
    a. linear or non-linear equation
    b. predicting outside range of sample
    c. predicting turning points is difficult
    d. forecast does not account for unexpected shocks

V. Time series
A. Trends
    1. growth over time
    2. contraction

B. Fluctuation
    1. seasonal
    2. cyclical
    3. random
    4. smoothing techniques
          a. moving average
          b. exponential smoothing
          c. seasonal adjustment

C. Time Series Regression Analysis
    1. estimating trend lines
            Y = a + bT
    2. univariate procedures
        a. autoregressive process
            -use lagged values of Y as explanatory variables
        b. ARIMA
            -autoregressive integrated moving average
    3. multivariate procedures
        a. variables that trend together
        b. compare change in Y to change in X
        c. cointegration



Review sheets for exam 1 and exam 2 .

Quant II class page

Chuck Stull's homepage

Department of economics

Kalamazoo College Homepage