Large dynamic factor models, forecasting, and nowcasting in Statsmodels

statespace_large_dynamic_factor_models.ipynb

"Hello @ChadFulton, I hope you're doing well. I want to express my gratitude to you for providing this high-quality content for those interested. I'm learning a lot from this material.

I'm trying to run the model codes and encountered a stumbling block that I'm having difficulty resolving.

It refers to the replacement of names and the mapping of variables. It seems that the names are not being assigned properly and this is causing problems further on, with variables not being found.

I've reviewed the data, variables, names, etc., and apparently everything is correct, but when I run the code, problems occur further on.

If there have been any important changes to the data or how to execute it, or any updates to this part of the code, I believe I might have missed them. If you could take a look at this part and guide me on how to proceed, I would be very grateful.

*Definitions from the Appendix for FRED-MD variables
defn_m = pd.read_csv('C:\Users\Cliente\Documents\fredmd_definitions.csv', encoding='windows-1252')
defn_m.index = defn_m.fred

*Definitions from the Appendix for FRED-QD variables
defn_q = pd.read_csv('C:\Users\Cliente\Documents\fredqd_definitions.csv', encoding='windows-1252')
defn_q.index = defn_q['FRED MNEMONIC']

*Example of the information in these files:
print(defn_m.head())
print(defn_q.head())

*Replace the names of the columns in each monthly and quarterly dataset
map_m = defn_m['description'].to_dict()
map_q = defn_q['DESCRIPTION'].to_dict()
for date, value in dta.items():
value.orig_m.columns = value.orig_m.columns.map(map_m)
value.dta_m.columns = value.dta_m.columns.map(map_m)
value.orig_q.columns = value.orig_q.columns.map(map_q)
value.dta_q.columns = value.dta_q.columns.map(map_q)

*Get the mapping of variable id to group name, for monthly variables
groups = defn_m[['description', 'group']].copy()

*Re-order the variables according to the definition CSV file
*(which is ordered by group)
columns = [name for name in defn_m['description']
if name in dta['2024-10'].dta_m.columns]
for date in dta.keys():
dta[date].dta_m = dta[date].dta_m.reindex(columns, axis=1)

*Add real GDP (our quarterly variable) into the "Output and Income" group
gdp_description = defn_q.loc['GDPC1', 'DESCRIPTION']
new_row = pd.DataFrame([{'description': gdp_description, 'group': 'Output and Income'}])
groups = pd.concat([groups, new_row], ignore_index=True)

*Display the number of variables in each group
(groups.groupby('group', sort=False)
.count()
.rename({'description': '# series in group'}, axis=1))"