ChadFulton
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| # Parameters | |
| nobs = 100 | |
| mean = 2.0 | |
| phi = 0.5 | |
| sigma2 = 2.4 | |
| # Simulate | |
| tmp = [0] | |
| mod = sm.tsa.arima.ARIMA(tmp, order=(1, 0, 0), trend='c') | |
| mod.simulate([mean, phi, sigma2], nobs) |
ChadFulton
/ statespace_large_dynamic_factor_models.ipynb
Last active
May 22, 2024 13:55
Large dynamic factor models, forecasting, and nowcasting in Statsmodels
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ChadFulton
/ statespace_news.ipynb
Created
May 31, 2020 16:32
Statsmodels: forecasting, updating datasets, and the "news"
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ChadFulton
/ state_space_obs_autocov.ipynb
Last active
April 25, 2020 00:39
State space models - unconditional autocovariance matrix for the observation vector
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ChadFulton
/ tvregression.ipynb
Created
June 24, 2019 08:24
Statsmodels state space: time-varying regression
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ChadFulton
/ ss_fixed_params.ipynb
Created
May 16, 2019 21:19
Estimating and specifying parameters in state space models
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ChadFulton
/ sarimax_fcast_weekly.ipynb
Created
February 9, 2019 01:41
SARIMAX - Forecasting weekly data
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ChadFulton
/ sm_ss_forecast_sim.ipynb
Created
January 25, 2019 04:49
Statsmodels state space: forecasting using `simulate`
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ChadFulton
/ arma_cov_params.ipynb
Last active
January 25, 2022 14:56
AR, MA models parameters covariance matrix
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