majestickhan / nowcasting-python Goto Github PK

thanks for your code! I find freqs with [day, week, month,quarter, year] in your code, but the example code just use month and quarter data. Is there any way that I can use your code with yearly data?

Hey,

I dont know if this is an issue with how I am formatting data or something else, but I am trying to run the code on dataframes which have different release dates.

As a result I have formatted the data in a weekly format with NAs in between.

When I run this code i then either get the error:

ValueError: expected square matrix

or

ValueError: Internal work array size computation failed: -5

Ideally I would also be able to run with actual weekly data as an input.

Have attached the relevant files and would love any thoughts you have?
Spec_US_example.xlsx

2016-06-29.xlsx

Unit testing

• Create unit test to ensure python model is working properly

Graphs and formatting

• Plotting graphs: finished plotting graphs for example_dfm.py in plot
• Plot loglik in Plotly
• Branch plot has been merged to the master. Should be able to see the graphs that are show in the Matlab code.

Comments:

• Next set of tasks is working on creating unit tests

Thanks for sharing your code.
Recently I'm learning about nowcasting models, your code is just what I need.
But I got something wrong when running your update_Nowcast.py，so I tried to fix it.

When I change the vintage, I may got error on line 407 ~ line 422，so I rewrite like this

for jk in range(1, lag + 1):

    Plag.append(np.zeros(Plag[0].shape))

    for jt in range(Plag[0].shape[0], lag, -1):

        As = np.matmul(np.matmul(Vf[jt-jk-1], A.T), np.linalg.pinv(np.matmul(np.matmul(A, Vf[jt-jk-1]), A.T) + Q))

        Plag[jk][jt-1] = np.matmul(As, Plag[jk-1][jt-1]) 

Also you mention that weight seems suspicious.
I find a mistake on line 281，it should be
if t_miss[j] > t_miss[i]:

I'm not sure whether line 245 should like this
m = max(t_miss[i],t_fcst[0])

Finally, I run the code with vintage_old = '2016-11-30' and vintage_new = '2016-12-23'.
I can get these：

          2016-11-30 nowcast:              2.80471
  Impact from data revisions:      0.10666
   Impact from data releases:      -0.49701
                                 +_________
                Total impact:      -0.39035
          2016-12-23 nowcast:              2.41436