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  1. Nov 14, 2019
    • Sébastien Villemot's avatar
      Improvements to the test for balanced growth path · 5a5817b4
      Sébastien Villemot authored
      — Raise the default tolerance for cross-derivatives to 1e-6, to reduce the
        number of false positives
      
      — New option “balanced_growth_test_tol” to the “model” block for changing that
        tolerance
      
      — Turn back test failures into errors. Since there is now an option for
        controlling the tolerance, the user always has the possibility of making the
        test pass.
      
      Closes: dynare#1389
      5a5817b4
  2. Nov 07, 2019
  3. Oct 30, 2019
  4. Oct 29, 2019
  5. Oct 28, 2019
  6. Oct 24, 2019
  7. Oct 22, 2019
    • Sébastien Villemot's avatar
      VAR/TCM models: use lag-equivalence relationship to compute maximum lags · 2a127b1f
      Sébastien Villemot authored
      This is made possible by the getLagEquivalenceClass() method introduced in the
      previous commit.
      
      Previously, the static version of the LHS expressions was used.
      
      As a consequence, drop ModFile::diff_static_model, now useless.
      2a127b1f
    • Sébastien Villemot's avatar
      Fix bug with diff or unary ops that have same static representation · 8a83e08e
      Sébastien Villemot authored
      Previously, for testing whether two diff() expressions or two unary ops were
      the lead/lag of each other, the preprocessor would test whether they have the
      same static representation. This is ok for simple expressions (e.g.
      diff(x(-1))), but not for more complex ones (e.g. diff(x-y) and diff(x(-1)-y)
      should not be given the same auxiliary variable).
      
      This commit fixes this by properly constructing the equivalence relationship
      and choosing a representative within each equivalence class. See the comments
      above lag_equivalence_table_t in ExprNode.hh for more details.
      
      Closes #27
      8a83e08e
    • Sébastien Villemot's avatar
      Fix semantics of ExprNode::maxLag(), maxLead() and maxLagWithDiffsExpanded() with constants · c5d223a7
      Sébastien Villemot authored
      Those methods can return a negative value in some cases. For example,
      maxLead(x₋₁) = −1.
      
      But constants were always returning a value of zero, which means that we had
      inconsistent behaviour like maxLead(x₋₁ + 2) = 0.
      
      This commits fixes the behaviour by making these methods return the smallest
      possible integer when called on constants.
      c5d223a7
  8. Oct 17, 2019
  9. Oct 15, 2019
  10. Oct 11, 2019
  11. Oct 10, 2019
  12. Oct 09, 2019
  13. Oct 08, 2019
  14. Oct 07, 2019
  15. Oct 04, 2019
  16. Oct 03, 2019
  17. Oct 02, 2019
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