- •#3. Role of Econometrics.
- •#4. Main Application of Econometrics.
- •#5. Cross Section Data.
- •#8. Panel or Longitudinal Data.
- •#12. Simple Regression Model (srm)
- •# 13. Simple Linear Model (slm)
- •#18. What is rss, ess, tss?
- •#25. The Random Components of the Regression Coefficients.
- •#26. The Gauss – Markov Theorem.
- •Indeed there have to be satisfied four conditions, known as the Gauss-Markov conditions, if ordinary least squares regression analysis is to give the best possible result.
- •#27. Unbiasedness of the Regression Coefficients.
- •#28. Precision of the Regression Coefficients.
- •#29. Testing Hypotheses Relating to the Regression Coefficients.
- •It has to be estimated by the standard error of b2, given by
- •#30. Confidence Intervals.
- •#35. What Can You Do about Heteroscedasticity?
- •#38. What Can You Do about Autocorrelation?
- •#45. Тhе apt model.
#45. Тhе apt model.
An asset pricing model based on the idea that an asset's returns can be predicted using the relationship between that same asset and many common risk factors. Created in 1976 by Stephen Ross, this theory predicts a relationship between the returns of a portfolio and the returns of a single asset through a linear combination of many independent macro-economic variables.
Risky
asset returns are said to follow a factor
structure if
they can be expressed
as:
,
where
E(rj) is the jth asset's expected return,
Fk is a systematic factor (assumed to have mean zero),
bjk is the sensitivity of the jth asset to factor k, also called factor loading,
and εj is the risky asset's idiosyncratic random shock with mean zero.
