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Truncation means that complete observations beyond a cutpoint are missing. OLS estimates with censored or truncated data are biased. In (a) data are censored at a. One knows that there true value is a or less. The regression line would be less steep (dashed line). Truncation means that cases below a are completely missing. Truncation also biases OLS estimates. (b) is the case of incidential truncation or sample selection. Due to a non-random selection mechanism information on Y is missing for some cases.

Functional form Use scattergram with lowess (see above). Influential data We investigate not single cases but X-patterns. There are K patterns, m k is the number of cases with pattern k. P k is the predicted PY  1 and Y k is the number of ones. Pearson residuals are defined by rk  Yk − mkPk . m k P k 1 − P k  The Pearson  2 statistic is K 2  ∑ r 2k . k1 This measures the deviation from the saturated model (this is a model that contains a parameter for every X-pattern). The saturated model fits the data perfectly (see example 1).

For instance, education effects are positive for three parties (Grüne, FDP, PDS), and negative for the rest. Especially strong is the negative effect on other. This produces the positive odds effects. Note that the age effect on SPD in the West is non monotonic! Note: We specified a model without interactions. This is true for the logit effects. But the probability effects show interactions: Look at the effect of education in West and East on the probability for PDS! This is a general point for logit models: though you specify no interactions for logits there might be some in probabilities.

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Applied Regression Analysis Using Stata by Bruderl


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