fix pygments hightlighting

parent 9c0780d6
......@@ -362,7 +362,7 @@ JSON with older versions of MATLAB and with Octave. Using the JSONLab
distributed with Dynare allows us to access the model block specified as an AST
in just two lines:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 45
......@@ -380,7 +380,7 @@ the only one we’re interested in and sets it to the output variable
equation. Entry 23 of this cell array corresponds to the monetary policy
equation and looks like:
.. code-block:: text
.. code-block:: matlabsession
>> ast{23}
......@@ -423,7 +423,7 @@ As we only want to perform OLS on this equation, we don't need the AST
corresponding to the other equations in the ``.mod`` file. Hence, we overwrite
it with the aid of a helper function, selecting only the necessary equation:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 54
......@@ -439,7 +439,7 @@ Given the pared-down ``ast`` variable returned by ``getEquationsByTags.m``, we
then call another helper function, ``common_parsing.m`` that handles the
parsing for OLS-style estimation routines (e.g. SUR, pooled OLS, FGLS, ...).
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 118
......@@ -448,7 +448,7 @@ parsing for OLS-style estimation routines (e.g. SUR, pooled OLS, FGLS, ...).
In turn, this function calls a helper function, ``parse_ols_style_equation.m``
that handles the parsing of one OLS-style equation at a time.
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 57
......@@ -484,7 +484,7 @@ evaluating the LHS of the specified equation. Given that all equations are
represented as ``BinaryOpNode``'s (the two arguments being the LHS, ``arg1``,
and the RHS, ``arg2``), the call to evaluate the LHS of the equation is:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 69
......@@ -493,7 +493,7 @@ and the RHS, ``arg2``), the call to evaluate the LHS of the equation is:
``evalNode``, a local function in ``parse_ols_style_equation.m`` then evaluates
this node given the ``dseries`` contained in ``ds``.
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 309
......@@ -570,7 +570,7 @@ we decompose the RHS (``arg2``) of the equation into additive terms, storing
them in a cell array called ``terms``. We do this by calling the locally
defined function:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 232
......@@ -630,7 +630,7 @@ Hence, in this case, 4 nodes of ``terms`` contain ``BinaryOpNode``'s
To understand how the terms are stored, one need only look at the following
output for the first term, ``crpiMcrpiXcrr*pinf``:
.. code-block:: text
.. code-block:: matlabsession
>> terms
......@@ -700,7 +700,7 @@ parameter (the second argument of the node).
Now that we have ``terms`` set, we can construct the regressor matrix ``X`` by
entering the loop on line 75 of ``parse_ols_style_equation.m``:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 75
......@@ -823,7 +823,7 @@ Condition 4: BinaryOpNode with multiplication operator (lines 109-137)
In this case, we parse the ``node_to_parse``, by calling the local function
``parseTimesNode``:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 257
......@@ -888,7 +888,7 @@ been constructed.
After the loop, the ``Y`` vector is adjusted, subtracting any constant terms on
the RHS:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 152
......@@ -897,7 +897,7 @@ the RHS:
Following that, the first and last observed periods of the ``dseries`` are
assigned to the variables ``fp`` and ``lp``:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 154
......@@ -917,7 +917,7 @@ assigned to the variables ``fp`` and ``lp``:
Finally, the ``Y``, ``X``, and ``lhssub`` datasets are adjusted given ``fp``
and ``lp``:
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 188
......@@ -944,7 +944,7 @@ arrays corresponds to the vector/matrix of observables and regressors in each
equation to be estimated. Analogously, ``fp`` and ``lp`` are cells containing
the first and last observed period of the estimation range for each equation.
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 118
......@@ -958,7 +958,7 @@ provided by the user or created by ``dyn_ols.m``. In this case, they will be
saved to ``oo_.ols.taylor_rule``. Furthermore, the estimated parameter values
are set in ``M_``.
.. code-block:: MATLAB
.. code-block:: matlab
:linenos: inline
:linenostart: 125
......@@ -992,27 +992,27 @@ And that’s it! The rest of the code simply takes care of calculating the
various statistics and standard errors (all stored in ``oo_.ols``) and
displaying the estimated parameters in a table:
.. code::
.. code-block:: matlabsession
OLS Estimation of equation 'taylor_rule' [name = 'taylor_rule']
OLS Estimation of equation 'taylor_rule' [name = 'taylor_rule']
Dependent Variable: r
No. Independent Variables: 4
Observations: 231 from 1947Q2 to 2004Q4
Dependent Variable: r
No. Independent Variables: 4
Observations: 231 from 1947Q2 to 2004Q4
Estimates t-statistic Std. Error
________________ ________________ ________________
Estimates t-statistic Std. Error
________________ ________________ ________________
crpiMcrpiXcrr 0.062335 2.6913 0.023161
cryMcryXcrr 0.0087422 2.7559 0.0031722
crdy 0.056321 4.0914 0.013766
crr 0.95707 59.384 0.016117
crpiMcrpiXcrr 0.062335 2.6913 0.023161
cryMcryXcrr 0.0087422 2.7559 0.0031722
crdy 0.056321 4.0914 0.013766
crr 0.95707 59.384 0.016117
R^2: 0.942770
R^2 Adjusted: 0.942014
s^2: 0.043718
Durbin-Watson: 1.703493
_____________________________________________________________________________
R^2: 0.942770
R^2 Adjusted: 0.942014
s^2: 0.043718
Durbin-Watson: 1.703493
_________________________________________________________________________
Conclusion
-----------------------
......
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