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00 Econometrics Methods

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on November 22, 2012 at 7:22:26 pm
 

 

TOPICS
Application Examples

Applied Regression Analysis

    
Linear regression model    
Generalized linear models    

Logistics regression (or probit)
  • For binary Y variables
  • Odds ratio of female voting for Obama
Poisson regression
  • For count data
  
Multinomial logistic regression (or multinomial probit)
  • For categorical data
 
Ordered probit
  • For ordinal data (ranks)
  

Factor Analysis and Scale Construction

    

Panel Data Analysis Methods

    
Fixed Effects Model    

Entity FE

(when time constant)

 If you need to control for omitted variables that differ among panels, but are constant over time

 Effect of 'experience' on 'earning'

Here, earning (wage) obviously is influenced by factors other than experience (tenure), such as personality of the person, which can be assumed to stay constant over time

Time FE

(when panel constant)

 If you need to control for omitted variables that vary across time but NOT among panels.
 Ex: national economy may impact everyone the same way but it varies across times (ex: in y1 it may be low and in y3 it may be higher)

Random Effects Model

(random variability
among time and panels)

 When some omitted variables may be constant over time but vary among panels, and others may be fixed among panels but vary over time
Same as above; but if either time or panel can be constant
Between Effects Model    
Propensity Scores Matching    
Instrumental Variables    
Difference-in-Differences    

 

 

 

Quick Reference

  1. Three Types of data
  2. Types of measurements
  3. Data Mining Process

 

Three Types of data

 

  1. Cross sectional data
    • One dimensional data: (Obs v Variables)
    • Random sample; if not random then selection bias
    • Each observation is a new individual, firm, etc, with information at a point in time
    • Ex: household survey from year 2012
  2. Panel data
    • Multi-dimensional data: (Obs v Variables v Time)
    • Can pool random cross sections, and then treat similar to normal cross-section
    • Need to account for the time difference
    • Ex: compiled household surveys from 2000 - 2012
    • In Biostats - often called Longitudinal data (here, a patient is considered a panel)
  3. Time series data
    • Each observation is from a time (Time=Obs v Variable)
    • Has a separate observation for each time period (eg. stock prices)
    • Not random sample
    • Trends and seasonality become important 

 

Types of measurements

 

  • Transaction data (price, interest rates, exchange rates)
  • Aggregate data (GDP, employment, unemployment, price index, money, etc.)
  • Survey data (qualitative, quantitative)
  • Data revisions (real time v historical measurements)

 

Data Mining Process
  1. Anomaly detection
    • (Outlier/change/deviation detection)
    • The identification of unusual data records, that might be interesting or data errors and require further investigation.
  2. Association rule learning
    • (Dependency modeling) –
    • Searches for relationships between variables.
    • Ex: Market basket analysis: For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes.
  3. Clustering
    • Method of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
  4. Classification
    • is the task of generalizing known structure to apply to new data
    • For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam"
  5. Regression
    • Attempts to find a function which models the data with the least error.
  6. Summarization
    • Providing a more compact representation of the data set, including visualization and report generation.
 

 

 

 

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