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Applied Econometrics - Coggle Diagram

- - - - Ratio of two variables - Y1/Y2
      - distance between two variables Y2-Y1
      - ordering of variables Y2>Y1
  - - - Problems with time series data
        
        autocorrelation
        
        stationary vs non-stationary
    - - Problems with cross-sectional data
        
        heterogenety
  - - - The regression model is linear in the parameters, it may or may not e linear in variables Y and Xs.
      - The regressors are assumed to be fixed or nonstochastic in the sense their values are fixed in repeated sampling. (Might not be true for all economic data)
      - Given the values of the X variables, the conditional expected value of the error (mean) term is zero.
        E(Y|X) = BX + E(u|X), since E(u|X) = 0
        E(Y|X) = BX <---- this is the population (mean) regression function)
        In the regression analysis our main objective is to estimate this function.
      - The variance of each u, given the values of X, is constant, or homoscedastic (homo=equal, scedastic=variance)
        var(u|X) = σ^2
      - There is no correlation between two error terms. That is, there is no autocorrelation.
      - There are no perfect linear relationships among the X variables. Assumption of no mutlicollinearity.
      - The regression model is correctly specified = no specification bias or specification error in the model used in empirical analysis.
      - Although not part of CLRM, it is assumed that the error term follows the normal distribution with zero mean and constant variance, σ^2
      - OLS provides estimators of the parameters of PRF that have several desirable statistical properties
        
        The estimator s are linear = they are lnear functions of the dependent variable Y
        
        The estimators are unbiased = in repeated applications of the method, on average, the estimators are equal to their true values
        
        In the class of linear unbiased estimators, OLS estimators have minimum variance. As a result, the true parameter values can be estimated with least possible uncertainty; an unbiased estimator with the least variance is called an efficient estimator.