- 23 Mar, 2013 1 commit
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Sébastien Villemot authored
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- 22 Mar, 2013 14 commits
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Stéphane Adjemian authored
last commits
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
This reverts commit f16a8b16.
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
- Iterative linear solvers using CUDA - interpreter.cc decomposed
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Ferhat Mihoubi authored
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Ferhat Mihoubi authored
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Sébastien Villemot authored
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Sébastien Villemot authored
Fixing of potentially serious bugs in estimation
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- 21 Mar, 2013 25 commits
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Johannes Pfeifer authored
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Johannes Pfeifer authored
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Johannes Pfeifer authored
Tests both ML and Bayesian estimation and test calibrated correlation of those shocks
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Johannes Pfeifer authored
1. The first call to set_prior overwrote the first column of estim_params_.var_endo storing the position of the variable with measurement error in M_.endo_names with the position in options_.var_obs. All subsequent calls to set_prior then lead to crashes. 2. At the same time, for correlations of ME, the first column of estim_params_.corrn still stored the position of the variable with measurement error in M_.endo_names. But subsequent calls to it were done as if it stored the position in options_.var_obs I introduced two new variables in estim_params_ storing the respective positions in var_obs so as to not necessitate changes in the preprocessors. 3. For cases of calibrated measurement error correlations, the covariance matrix was not updated. 4. Fixing a lot of smaller bugs related to measurement errors, including some copy and paste errors -
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Johannes Pfeifer authored
Due to only using the diagonal of Sigma_e and the correlation matrix having ones on the diagonal, the diagonal entries of the covariance matrix are correctly built from recent draw. Later, when using the new draw for the correlations, only the correctly updated diagonal entries of Sigma_e are used.
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Sébastien Villemot authored
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Sébastien Villemot authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
Names for the results of basic operations on dynSeries objects was '--NA--'. The name of the result variables now keep track of the operations. For instance, if we have two dynSeries objects, ts1 and ts2, respectively holding two variables 'A1' and 'B1', then: - the name of the variable in ts3=ts1+ts2 is 'plus(A1,B1)' - the name of the variable in ts3=ts1-ts2 is 'minus(A1,B1)' - the name of the variable in ts3=ts1*ts2 is 'multiply(A1,B1)' - the name of the variable in ts3=ts1/ts2 is 'divide(A1,B1)' - the tex name of the variable in ts3=ts1+ts2 is '(A1+B1)' - the tex name of the variable in ts3=ts1-ts2 is '(A1-B1)' - the tex name of the variable in ts3=ts1*ts2 is '(A1*B1)' - the tex name of the variable in ts3=ts1/ts2 is '(A1/B1)'
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Stéphane Adjemian authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
Returns zero if the input argument is a well defined dynSeries object.
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Stéphane Adjemian authored
+ Added the possibility to rename the assigned variables. + Added unitary tests.
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Stéphane Adjemian authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
Issue an error if two dynSeries objects with common variable names are concatenated. Added a unitary test.
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Stéphane Adjemian authored
Compare two one dimensional cells of strings and count the number of common strings. The optional second argument returns a formated list of common variables. *Example* If we have: A = {'A1'; 'A2'; 'A3'; 'A4'; 'A5'; 'A6'}; B = {'B1'; 'A2'; 'B3'; 'A4'; 'A1'}; then [n,message] = common_strings_in_cell_arrays(A,B); returns n=3 and message='A2, A4 and A1'.
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Stéphane Adjemian authored
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Stéphane Adjemian authored
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Stéphane Adjemian authored
Removes a variable form a dynSeries object.
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Stéphane Adjemian authored
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