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The only reason I chose to read this book on a weekend was to verify the null hypothesis of the author, ” an intuitive guide” ๐Ÿ™‚ Well, intuition and econometrics are kind of animals that don’t sleep together that well in the SAME book. Either econometrics books are very math/probability oriented OR they are “stats for dummies” types. I had tons of other things to do on this weekend, but took a peek in to this ~100 page double line spacing book,just out of curiosity.

Points that grabbed my attention, not becoz of the math , but becoz of the nice angrezi that was used ๐Ÿ™‚

  • Model building means moving from a unconditional expectation to conditional expectation.
  • Expected value of a Unbiased estimator equals the parameter to be estimated, irrespective of the number of trials
  • While unbiased is difficult to verify in most situations, Consistent estimator is what is generally needed
  • Examining the asymptotic behavior of the estimate is a clue to its consistency
  • OLS regression method essentially minimizes the variance of the error term by choosing the appropriate parameters for the linear model
  • Don’t ask whether random walk fits the data or not ? Instead ask , “How can I beat the random walk ?”
  • “Are mid-sized cars equally expensive in Japan and in the US, after correction for the exchange rate between these two countries ?” IS FAR BETTER A QUESTION than “Does the purchasing power parity hold good for Japan and USA? “
  • Economic theories deal with aggregated quantities..Empirical work needs fine grained data
  • 7 case studies to illustrate various models
    1. convergence of countries attributes – a Convergence model with no stochastic behavior
    2. Direct mailing problem – probit model for selecting customers for direct mail (a truncated regression model )
    3. Does automated trading improve trading efficiency – use multiple equation regression model for futures and spot
    4. Unemployment and recession linkage — auto regressive model with latent variable…. With all this jazz could anyone have predicted the 10% unemployment rate that the US is facing now !! Sometimes it makes me feel that all macroeconomic variable prediction is just some jazz and has no practical value beyond keeping a few economists/econometricians in a job!
    5. Brand loyalty of the customers — Multinomial probit
    6. Do people make up their minds before elections — artificial neural network problem ..Basically I look at it as a non-linear regression with fancy functions!
    7. Temperature forecast uncertainty — Use GARCH to judge the correlation structure of the forecast errors

My takeaway : Considering my interest is modeling variables in finance, I think that it would be naive to depend on some linear parameters of an equation with gaussian assumptions in it. Even though a flavor of models are presented in this book, I don’t think models of such type are going to be useful anymore. THE GAME OF GAUSSIAN ERROR TERM is over.

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