

Exhibition:
Causality analysis and model identification 

Exhibition, Part 1: Causality analysis An excellent Danish reference with an exact description of
the method behind causality analysis is Andersen [1989, Chapter 2]. For an English
reference, see Kogiku [1968, Chapter 2]. 

Exhibition, Part 2: Model identification This table shows the calculation of the necessary condition for model
identification as discussed in Chapter 4, Section 2.3. To repeat, this
condition is _{}, where W is a
matrix of qualifying instruments for the entire model as well as outside the
model, k_{j} is the number of explanatory variables (endogenous as
well as predetermined) in equation j, and r(*) is
notation for the rank of a matrix. The calculation is applied on the primary
model in Exhibition 1 with data as described in Table 2 below. The calculations
are made explicit for reasons of understandability and the following
variables represent the numbers shown: (118 = # of DIndust1_{i,t}),
(4 = # of DExchange_{i,t}), (6 = # of DIncorp_{i,t}), (10 = #
of DIndust2_{i,t}), (15 = 14 exogenous variables in EQ1 + 1 exogenous
variables in EQ2), and ([*]; sum of other explanatory variables in the
equation in question). The calculation is made under the assumption that all
exogenous variables are indeed qualified instruments.
