Note: Because the FRB/US database is updated more frequently than the model and other material, the database is stored separately in the FRB/US data package. To run the model with the latest data, please download the FRB/US data package, create a directory in frbus_package named data and copy the files from eviews_database to the new directory. When updates are available only for the dataset, it is not necessary to re-download the FRB/US EViews package.
The eviews chunk automatically returns the outputs ofeach equation object as a dataframe, accessible viachunkLabel$pageName_equationName. For example, TheR2 of the ols equation object is0.044951, which can be accessed using`r EviewsR$eviewsrpage_ols$r2`. We can obtain the tableobject by chunkLabel$pageName_tableName. Therefore,EviewsR$eviewsrpage_olstable will give us theOLSTable object as dataframe. Note the underscore(_) between the pageName andequationName, and between the pageName andtableName.
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The purpose of the pyeviews package is to make it easier for EViews and Python to talk to each other, so Python programmers can use the econometric engine of EViews directly from Python. This package uses COM to transfer data between Python and EViews. (For more information on COM and EViews, take a look at our whitepaper on the subject.)
Install the pyeviews package using your method of choice. For example, head over to the pyeviews package at the Python Package Index and at a Windows command prompt:$ pip install pyeviewsOr, download the package, navigate to your installation directory, and use:
If you want, plot everything to see how the interpolated series follows the indicator series:>>> # load the matplotlib package to plot import matplotlib.pyplot as plt>>> # reindex the benchmarked series to the end of the quarter so the dates match those of the indicator series benchmarked_reindexed = pa.Series(benchmarked.values.flatten(), index = benchmarked.index + pa.DateOffset(months = 3, days = -1))>>> # plot fig, ax1 = plt.subplots() plt.xticks(rotation=70) ax1.plot(benchmarked_reindexed, 'b-', label='benchmarked') # multiply the indicator series by 10 to put it on the same axis as the benchmarked series ax1.plot(indicator*10, 'b--', label='indicator*10') ax1.set_xlabel('dates') ax1.set_ylabel('indicator & interpolated values', color='b') ax1.xaxis.grid(True) for tl in ax1.get_yticklabels(): tl.set_color('b') plt.legend(loc='lower right') ax2 = ax1.twinx() ax2.set_ylim([3975, 4180]) ax2.plot(benchmark, 'ro', label='benchmark') ax2.set_ylabel('benchmark', color='r') for tl in ax2.get_yticklabels(): tl.set_color('r') plt.legend(loc='upper left') plt.title("Chow-Lin interpolation: \nannual sum of benchmarked = benchmark", fontsize=14) plt.show()For more information on the pyeviews package, including a list of functions, please take a look at our whitepaper on the subject. be457b7860
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