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By Girosi F.,King G.

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Methods Without Covariates models to another, and it may or may not reflect some a priori knowledge. Once a particular f has been chosen, then most methods proceed in three stages: 1. 2, using nonlinear least squares. 2. Treat the estimates of γ1 , . . , γT as data in a multivariate time series (remember that γt can be a vector of parameters), or as a collection of independent univariate time series, depending on the particular model. Use standard autoregressive methods to forecast these time series.

1998) estimate the lung cancer mortality rates in South Korea by a model with a constant and indicators for sex, age, cohort, and an interaction of sex and age. They then assume that predicted mortality rates from this model will remain constant over time. In applying these methods, the rate of decline in mortality is often adjusted for expert opinions in various areas. For example, Goss et al. , Mexico, and Canada, and all three use similar extrapolative methods, with rates of mortality decline in various crosssections a function of expert judgement.

The problem is that existing methods require the same variables to be available for all the units. This is easy to see when trying to pool coefficients, since omitting a variable from the time series in one cross-sectional unit will make all the coefficients take on a different meaning and so they become impossible to pool either partially or completely (at least without adding untenable assumptions). The result is that in order to use existing methods, scholars routinely make what would otherwise seem like bizarre data analysis decisions.

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