Reference
Classes
Main classes provided by the package
- Ambric
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Augmented Mixed-frequency Bayesian Regional Inference with Constraints.
Ambric Methods
Methods for the Ambric class
- Ambric.__repr__()
- Ambric.fit()
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Fit the ambric model.
- Ambric.save_trace()
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Saves the model trace to a NetCDF file.
- Ambric.populate_results()
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Returns results from model estimation, and original data, in format:
- Ambric.plot_national_quarterly_vs_implied()
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Plot national quarterly growth rates vs implied estimates from the model.
- Ambric.plot_regional_annual_estimate()
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Plot regional annual growth rates vs estimated from the model.
- Ambric.plot_single_region_annual_estimate()
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Plot a single region’s annual growth rates vs estimated from the model.
- Ambric.plot_estimated_regional_quarterly()
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Plot estimated regional quarterly growth rates from the model.
- Ambric.plot_current_nowcast()
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Plot the latest nowcast (ie the period for which no annual regional observations are available.)
- Ambric.assemble_loadings_data()
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Assemble estimated loadings from the model posterior.
- Ambric.plot_loadings_by_region()
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Plot estimated loadings for each region, coloured by broad type.
- Ambric.plot_loadings_aggregate()
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Plot loading distributions across regions, grouped by broad type.
- Ambric.bands_indicator()
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Produce a table indicating bands
- Ambric.point_estimates_q_on_4q()
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Produce a table of nowcast point estimates by region.
- Ambric.point_estimates_q_on_q()
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Produce a table of nowcast point estimates by region.
- Ambric.to_index_q_on_q()
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Produce a table of nowcast index.
- Ambric.seasonally_adjusted_index_and_growth_by_region()
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Produce seasonally adjusted index and q-on-q growth rates
Functions
Utility functions
- bands_indicator_out_of_sample_results()
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Bands classification of out-of-sample trend q-on-q nowcasts.
- build_ambric_model()
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Build the AMBRIC Bayesian state-space model.
- diagnostics.oos_q_on_4q_performance_table()
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Compute up/down classification accuracy by region and horizon.
- diagnostics.plot_out_of_sample_nowcasts()
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Plot out-of-sample nowcasts vs outturns for each region.
- diagnostics.plot_out_of_sample_rmse()
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Plot out-of-sample RMSEs by quarters-to-publication for each region.
- plot_seasonally_adjusted_q_on_q_growth()
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summary
- prep_data_for_model_run()
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Expects a data frame in following format:
- run_out_of_sample_exercise()
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Run out-of-sample exercise to evaluate model performance.
- trace_to_series()
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Convert a trace to the relevant estimated series coming out of the model.
- trend_adjust_out_of_sample_results()
- utilities.generate_realistic_simulated_data()
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Generate simulated mixed-frequency regional data in long format.
- utilities.simulate_data()
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Simulate mixed-frequency data with regional panels and macro indicators.
- utilities.simulate_real_time_data()
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Simulate mixed-frequency data with a realistic publication lag.
Constants
Module-level constants and data