statsmodels

Say I want to put a custom prior on two variables a and b in PyMC, e.g.: p(a,b)∝(a+b)^(−5/2) (for the motivation behind this choice of prior, see this answer ) Can this be done in PyMC? If so how? As an example, I would like to define such prior on a and b in the model below. import pymc as pm # ... # Code that defines the prior: p(a,b)∝(a+b)^(−5/2) # ... theta = pm.Beta("prior", alpha=a, beta=b) # Binomials that share a common prior bins = dict() for i in xrange(N_cities): bins[i] = pm.Binomial('bin_{}'.format(i), p=theta,n=N_trials[i], value=N_yes[i], observed=True) mcmc = pm.MCMC([bins, ps]

I just began working on time series analysis using statsmodels. I have a dataset with dates and values (for about 3 months). I am facing some issues with providing the right order to the ARIMA model. I am looking to adjust for trends and seasonality and then compute outliers. My 'values' are not stationary and statsmodel says that I have to either induce stationarity or provide some differencing to make it work. I played around with different ordering (without understanding deeply about the consequences of changing p,q and d). When I introduce 1 for differencing, I get this error: ValueError:

I want to use a logit model and trying to import statsmodels library. My Version: Python 3.6.8 The best suggestion I got is to downgrade scipy but unclear how to and to what version should I downgrade. Please help how to resolve. https://github.com/statsmodels/statsmodels/issues/5747 import statsmodels.formula.api as smf ImportError Traceback (most recent call last) <ipython-input-52-f897a2d817de> in <module> ----> 1 import statsmodels.formula.api as smf ~/anaconda3/envs/py36/lib/python3.6/site-packages/statsmodels/formula/api.py in <module> 13 from statsmodels.robust.robust_linear_model

I am trying to use Python for statistical analysis. In Stata I can define local macros and expand them as necessary: program define reg2 syntax varlist(min=1 max=1), indepvars(string) results(string) if "`results'" == "y" { reg `varlist' `indepvars' } if "`results'" == "n" { qui reg `varlist' `indepvars' } end sysuse auto, clear So instead of: reg2 mpg, indepvars("weight foreign price") results("y") I could do: local options , indepvars(weight foreign price) results(y) reg2 mpg `options' Or even: local vars weight foreign price local options , indepvars(`vars') results(y) reg2 mpg `options'

I've been digging into the API of statsmodels.regression.linear_model.RegressionResults and have found how to retrieve different flavors of heteroskedasticity corrected standard errors (via properties like HC0_se , etc.) However, I can't quite figure out how to get the t -tests on the coefficients to use these corrected standard errors. Is there a way to do this in the API, or do I have to do it manually? If the latter, can you suggest any guidance on how to do this with statsmodels results? The fit method of the linear models, discrete models and GLM, take a cov_type and a cov_kwds argument

I have a DataFrame with a few time series: divida movav12 var varmovav12 Date 2004-01 0 NaN NaN NaN 2004-02 0 NaN NaN NaN 2004-03 0 NaN NaN NaN 2004-04 34 NaN inf NaN 2004-05 30 NaN -0.117647 NaN 2004-06 44 NaN 0.466667 NaN 2004-07 35 NaN -0.204545 NaN 2004-08 31 NaN -0.114286 NaN 2004-09 30 NaN -0.032258 NaN 2004-10 24 NaN -0.200000 NaN 2004-11 41 NaN 0.708333 NaN 2004-12 29 24.833333 -0.292683 NaN 2005-01 31 27.416667 0.068966 0.104027 2005-02 28 29.750000 -0.096774 0.085106 2005-03 27 32.000000 -0.035714 0.075630 2005-04 30 31.666667 0.111111 -0.010417 2005-05 31 31.750000 0.033333 0.002632

I have the following linear regression: import statsmodels.formula.api as sm model = sm.ols(formula = 'a ~ b + c', data = data).fit() I want to add a quadratic term for b in this model. Is there a simple way to do this with statsmodels.ols? Is there a better package I should be using to achieve this? Although the solution by Alexander is working, in some situations it is not very convenient. For example, each time you want to predict the outcome of the model for new values, you need to remember to pass both b**2 and b values which is cumbersome and should not be necessary. Although patsy does