SARIMAX prediction: including 'deterministic polynomial trend' I'm wanting to use Statsmodel's SARIMAX model to predict sharemarket values into the future. This is part of a larger effort towards helping strictly non-profits and social-enterprise organisations.
Question: I'm having an issue (maybe conceptual) where when I introduce a 'deterministic polynomial trend', using Statsmodels' implementation, I can't see any effects on the subsequent prediction (I would have thought the prediction would shift upwards if I introduced a larger constant, for example). Am I missing something fundemental?
Typical code is below, but without source data (I can provide data if needed):
# SARIMA forecasting model
trendCoeffsList = [15000, 2.069764822521753, 0.0012385250036528476]
def forecast_sarima(data, order, sOrder):
    try:
        model = SARIMAX(data, trend=trendCoeffsList, order=order, seasonal_order=sOrder, 
                    enforce_stationarity=False, enforce_invertibility=False)
        print(model.trend) # coefficients exist within model
        model_fit = model.fit(disp=False)
        start = 1
        end = len(dataWhole) - dataRange
        predictData = model_fit.predict(start, end)
    except:
        predictData = False
    return predictData


Answer: I've realised that Statsmodel's documentation is not clear about the trend argument. Specifically, the iterable for differing orders of polynomials (only including the highest term) is:
[0]       # degree = 0
[1]       # degree = 1
[0, 1]    # degree = 2
[0, 0, 1] # degree = 3

