Economics Theme

Economics Theme 5 - Econometrics


By: Dr. Nabil Chaiban


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Across

  1. The difference between an observed variable and the variable that belongs in a multiple regression equation.
  2. The difference between the actual value and the fitted (or predicted) value; there is a residual for each observation in the sample used to obtain an OLS regression line.
  3. In multiple regression, the partial effect of one explanatory variable depends on the value of a different explanatory variable.
  4. A statistical test of the null, or maintained, hypothesis against an alternative hypothesis.
  5. In multiple regression, one independent variable is an exact linear function of one or more other independent variables.
  6. A mathematical function defined for all values that has an increasing slope but a constant proportionate change.

Down

  1. A probability distribution commonly used in statistics and econometrics for modelling a population. Its probability distribution function has a bell shape.
  2. The theorem which states that, under the five Gauss-Markov assumptions (for cross-sectional or time series models), the OLS estimator is BLUE (conditional on the sample values of the explanatory variables).
  3. Restrictions which state that certain variables are excluded from the model (or have zero population coefficients).
  4. Any variable that is unconnected with the error term in the model of interest.
  5. A hypothesis test against a one sided alternative.
  6. A term that refers to correlation among the independent variables in a multiple regression model; it is usually invoked when some correlations are "large," but an actual magnitude is not well-defined.
  7. The difference between the actual outcome and the forecast of the outcome.
  8. In a distributed lag model, the immediate percentage change in the dependent variable given a 1% increase in the independent variable.
  9. A statistic that aggregates information on economic activity, such as production or prices.
  10. A convenient mathematical notation, grounded in matrix algebra, for expressing and manipulating the multiple regression model.