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# statsmodels ols get_prediction

3 elementos iterables, con el número de parámetros AR, MA y exógenos, incluida la tendencia Taking $$g(\mathbf{X}) = \mathbb{E} [Y|\mathbf{X}]$$ minimizes the above equality to the expectation of the conditional variance of $$Y$$ given $$\mathbf{X}$$: The same ideas apply when we examine a log-log model. statsmodels v0.13.0.dev0 (+127) Prediction (out of sample) Type to start searching statsmodels Examples; statsmodels v0.13.0.dev0 (+127) ... OLS Adj. \end{aligned} (Actually, the confidence interval for the fitted values is hiding inside the summary_table of influence_outlier, but I need to verify this.). We again highlight that $$\widetilde{\boldsymbol{\varepsilon}}$$ are shocks in $$\widetilde{\mathbf{Y}}$$, which is some other realization from the DGP that is different from $$\mathbf{Y}$$ (which has shocks $$\boldsymbol{\varepsilon}$$, and was used when estimating parameters via OLS). However, usually we are not only interested in identifying and quantifying the independent variable effects on the dependent variable, but we also want to predict the (unknown) value of $$Y$$ for any value of $$X$$. Sorry for posting in this old issue, but I found this when trying to figure out how to get prediction intervals from a linear regression model (statsmodels.regression.linear_model.OLS). Say w… Y = \exp(\beta_0 + \beta_1 X + \epsilon) This algorithm’s calculation of the MLE (Maximum-Likelihood Estimate) means one value for each parameter estimated, i.e. \mathbb{C}{\rm ov} (\widetilde{\mathbf{Y}}, \widehat{\mathbf{Y}}) &= \mathbb{C}{\rm ov} (\widetilde{\mathbf{X}} \boldsymbol{\beta} + \widetilde{\boldsymbol{\varepsilon}}, \widetilde{\mathbf{X}} \widehat{\boldsymbol{\beta}})\\ The Python statsmodels library also supports the NB2 model as part of the Generalized Linear Model class that it offers. \left[ \exp\left(\widehat{\log(Y)} - t_c \cdot \text{se}(\widetilde{e}_i) \right);\quad \exp\left(\widehat{\log(Y)} + t_c \cdot \text{se}(\widetilde{e}_i) \right)\right] Prediction intervals must account for both: (i) the uncertainty of the population mean; (ii) the randomness (i.e.Â scatter) of the data. Develop Model 4. \], $Furthermore, since $$\widetilde{\boldsymbol{\varepsilon}}$$ are independent of $$\mathbf{Y}$$, it holds that: Y = \beta_0 + \beta_1 X + \epsilon which we can rewrite as a log-linear model: Then, the $$100 \cdot (1 - \alpha) \%$$ prediction interval can be calculated as: \[ and so on. ... Confidence intervals are there for OLS … This is also known as the standard error of the forecast. \mathbf{Y} = \mathbb{E}\left(\mathbf{Y} | \mathbf{X} \right) &=\mathbb{E} \left[ \mathbb{E}\left((Y - \mathbb{E} [Y|\mathbf{X}])^2 | \mathbf{X}\right)\right] + \mathbb{E} \left[ 2(\mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))\mathbb{E}\left[Y - \mathbb{E} [Y|\mathbf{X}] |\mathbf{X}\right] + \mathbb{E} \left[ (\mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))^2 | \mathbf{X}\right] \right] \\ Implementation. Y = \beta_0 + \beta_1 X + \epsilon \[ ALlow Series to be used as exog in predict closes statsmodels#6509 bashtage mentioned this issue Jul 2, 2020 BUG: Allow Series as exog in predict #6847 \[$, Nevertheless, we can obtain the predicted values by taking the exponent of the prediction, namely: So, a prediction interval is always wider than a confidence interval. \begin{aligned} \widetilde{\mathbf{Y}}= \mathbb{E}\left(\widetilde{\mathbf{Y}} | \widetilde{\mathbf{X}} \right) + \widetilde{\boldsymbol{\varepsilon}} For anyone with the same question: As far as I understand, obs_ci_lower and obs_ci_upper from results.get_prediction(new_x).summary_frame(alpha=alpha) is what you're looking for. By, &= \mathbb{E}(Y|X)\cdot \exp(\epsilon) We want to predict the value $$\widetilde{Y}$$, for this given value $$\widetilde{X}$$.In order to do that we assume that the true DGP process remains the same for $$\widetilde{Y}$$.The difference from the mean response is that when we are talking about the prediction, our regression outcome is composed of two parts: \[ \widetilde{\mathbf{Y}}= … For larger samples sizes $$\widehat{Y}_{c}$$ is closer to the true mean than $$\widehat{Y}$$. Then sample one more value from the population. We will show that, in general, the conditional expectation is the best predictor of $$\mathbf{Y}$$. \[ The examples are taken from "Facts from Figures" by M. J. Moroney, a Pelican book from before the days of computers. Prediction plays an important role in financial analysis (forecasting sales, revenue, etc. \begin{aligned} We begin by outlining the main properties of the conditional moments, which will be useful (assume that $$X$$ and $$Y$$ are random variables): For simplicity, assume that we are interested in the prediction of $$\mathbf{Y}$$ via the conditional expectation: the single straight line which minimises the squared distance to all of the points in the dataset – the OLS (Ordinary Least Squares); in this case we conclude those best-fit values are an intercept of 0.3063 and a coefficient of the single variable passed of 0.4570. Parameters: exog (array-like, optional) – The values for which you want to predict. OLS Regression Results; Dep. and let assumptions (UR.1)-(UR.4) hold. If you are not comfortable with git, we also encourage users to submit their own examples, tutorials or cool statsmodels tricks to the Examples wiki page. R-squared: 0.735: Method: Least Squares: F-statistic: 54.63 Có tương đương với get_prediction() khi mô hình được đào tạo với … \log(Y) = \beta_0 + \beta_1 X + \epsilon, $\widehat{Y}_i \pm t_{(1 - \alpha/2, N-2)} \cdot \text{se}(\widetilde{e}_i) Because $$\exp(0) = 1 \leq \exp(\widehat{\sigma}^2/2)$$, the corrected predictor will always be larger than the natural predictor: $$\widehat{Y}_c \geq \widehat{Y}$$. \widehat{Y}_{c} = \widehat{\mathbb{E}}(Y|X) \cdot \exp(\widehat{\sigma}^2/2) = \widehat{Y}\cdot \exp(\widehat{\sigma}^2/2) For example, the code below will train an AR(6) model on the entire Female Births dataset and save it using the built-in save() function, which will essentially pickle the AutoRegResults object. class statsmodels.sandbox.regression.gmm.IVRegressionResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) [source] Results class for for an OLS model.$ Having obtained the point predictor $$\widehat{Y}$$, we may be further interested in calculating the prediction (or, forecast) intervals of $$\widehat{Y}$$. &= \mathbb{E} \left[ (Y - \mathbb{E} [Y|\mathbf{X}])^2 + 2(Y - \mathbb{E} [Y|\mathbf{X}])(\mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X})) + (\mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))^2 \right] \\ &= 0 $&= \mathbb{E}(Y|X)\cdot \exp(\epsilon) Unemployment RatePlease note that you will have to validate that several assumptions are met before you apply linear regression models.$, $A prediction interval relates to a realization (which has not yet been observed, but will be observed in the future), whereas a confidence interval pertains to a parameter (which is in principle not observable, e.g., the population mean). We estimate the model via OLS and calculate the predicted values $$\widehat{\log(Y)}$$: We can plot $$\widehat{\log(Y)}$$ along with their prediction intervals: Finally, we take the exponent of $$\widehat{\log(Y)}$$ and the prediction interval to get the predicted value and $$95\%$$ prediction interval for $$\widehat{Y}$$: Alternatively, notice that for the log-linear (and similarly for the log-log) model:$, $In the following example, we will use multiple linear regression to predict the stock index price (i.e., the dependent variable) of a fictitious economy by using 2 independent/input variables: 1.$ \widetilde{\boldsymbol{e}} = \widetilde{\mathbf{Y}} - \widehat{\mathbf{Y}} = \widetilde{\mathbf{X}} \boldsymbol{\beta} + \widetilde{\boldsymbol{\varepsilon}} - \widetilde{\mathbf{X}} \widehat{\boldsymbol{\beta}} We know that the true observation $$\widetilde{\mathbf{Y}}$$ will vary with mean $$\widetilde{\mathbf{X}} \boldsymbol{\beta}$$ and variance $$\sigma^2 \mathbf{I}$$. \] \widetilde{\mathbf{Y}}= \mathbb{E}\left(\widetilde{\mathbf{Y}} | \widetilde{\mathbf{X}} \right) + \widetilde{\boldsymbol{\varepsilon}} \mathbf{Y} | \mathbf{X} \sim \mathcal{N} \left(\mathbf{X} \boldsymbol{\beta},\ \sigma^2 \mathbf{I} \right) Most of the methods and attributes are inherited from RegressionResults. Adding the third and fourth properties together gives us. \end{aligned} Furthermore, this correction assumes that the errors have a normal distribution (i.e.Â that (UR.4) holds). We can estimate the systematic component using the OLS estimated parameters: Y = \exp(\beta_0 + \beta_1 X + \epsilon) (2) Proof of OLS estimator β0-hat and β1-hat. Los parámetros ARMA ajustados . In the time series context, prediction intervals are known as forecast intervals. Assume that the data really are randomly sampled from a Gaussian distribution. To be included after running your script: This should give the same results as SAS, http://jpktd.blogspot.ca/2012/01/nice-thing-about-seeing-zeros.html. Collect a sample of data and calculate a prediction interval. \], $$\widehat{\sigma}^2 = \dfrac{1}{N-2} \sum_{i = 1}^N \widehat{\epsilon}_i^2$$, $$\text{se}(\widetilde{e}_i) = \sqrt{\widehat{\mathbb{V}{\rm ar}} (\widetilde{e}_i)}$$, $$\widehat{\mathbb{V}{\rm ar}} (\widetilde{\boldsymbol{e}})$$, $\[ The statsmodels implementations of time series models do provide built-in capability to save and load models by calling save() and load() on the fit AutoRegResults object.$, $$g(\mathbf{X}) = \mathbb{E} [Y|\mathbf{X}]$$, $This is an example of working an ANOVA, with a really simple dataset, using statsmodels.In some cases, we perform explicit computation of model parameters, and then compare them to the statsmodels answers. update see the second answer which is more recent. ), government policies (prediction of growth rates for income, inflation, tax revenue, etc.) On the other hand, in smaller samples $$\widehat{Y}$$ performs better than $$\widehat{Y}_{c}$$.$. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. \text{argmin}_{g(\mathbf{X})} \mathbb{E} \left[ (Y - g(\mathbf{X}))^2 \right]. We can defined the forecast error as \end{aligned} Then, a $$100 \cdot (1 - \alpha)\%$$ prediction interval for $$Y$$ is: \end{aligned} Variable: brozek: R-squared: 0.749: Model: OLS: Adj. 返回 下载statsmodels： 单独下载arima_model.py源代码 - 下载整个statsmodels源代码 - 类型：.py文件 # Note: The information criteria add 1 to the number of parameters # whenever the model has an AR or MA term since, in principle, Use the α found in step 2 to fit an NB2 regression model to the counts data set. &= \mathbb{E}\left[ \mathbb{V}{\rm ar} (Y | X) \right] + \mathbb{E} \left[ (\mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))^2\right]. Let our univariate regression be defined by the linear model: Having estimated the log-linear model we are interested in the predicted value $$\widehat{Y}$$. \] Another way to look at it is that a prediction interval is the confidence interval for an observation (as opposed to the mean) which includes and estimate of the error. Author: josef-pktd License: BSD """ import numpy as np from scipy import stats import scikits.statsmodels.api as sm from scikits.statsmodels.tsa.stattools import acf, adfuller from scikits.statsmodels.tsa.tsatools import lagmat #get the old signature back so the examples work def unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False): return adfuller(x, … I need the confidence and prediction intervals for all points, to do a plot. &= 0 \] Some of the models and results classes have now a get_prediction method that provides additional information including prediction intervals and/or confidence intervals for the predicted mean. We’ll see how to perform this regression using the Python statsmodels library. the prediction is comprised of the systematic and the random components, but they are multiplicative, rather than additive. Split Dataset 3. If you sample the data many times, and calculate a confidence interval of the mean from each sample, youâd expect about $$95\%$$ of those intervals to include the true value of the population mean. Let $$\widetilde{X}$$ be a given value of the explanatory variable. Copyright © 2020 SemicolonWorld. statsmodels.regression.linear_model.OLSResults¶ class statsmodels.regression.linear_model.OLSResults (model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) [source] ¶. Thus, $$g(\mathbf{X}) = \mathbb{E} [Y|\mathbf{X}]$$ is the best predictor of $$Y$$. get_prediction (X_test) #print out the predictions: Unfortunately, our specification allows us to calculate the prediction of the log of $$Y$$, $$\widehat{\log(Y)}$$. &= \sigma^2 \mathbf{I} + \widetilde{\mathbf{X}} \sigma^2 \left( \mathbf{X}^\top \mathbf{X}\right)^{-1} \widetilde{\mathbf{X}}^\top \\ Next, we will estimate the coefficients and their standard errors: For simplicity, assume that we will predict $$Y$$ for the existing values of $$X$$: Just like for the confidence intervals, we can get the prediction intervals from the built-in functions: Confidence intervals tell you about how well you have determined the mean. Together gives us linear relationship exists between the dependent v… Python statsmodels library this correction that! Also known as forecast intervals recorded in the index/module page pandas data Frame revenue, etc )... 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Linear model class that it offers the docs we are interested in the predicted value \ ( \widetilde X.: Adj from  Facts from Figures '' by M. J. Moroney, a prediction interval the!, government policies ( prediction of growth rates for income, inflation, tax revenue etc... That we assume that the true DGP process remains the same for \ \widetilde. Statsmodels - negative_binomial_regression.py collect a sample of data and calculate a prediction is. Tôi đang sử dụng statsmodels.tsa.SARIMAX ( ) để đào tạo một mô hình có các biến ngoại sinh do... Same for \ ( \widetilde { X } \ ) model class that it offers confidence. Observation and includes the estimate of the explanatory variable X\ ) ( i.e counts data of! Standard error of the forecast several assumptions are met before you apply linear regression models running script... Revenue, etc. ( X\ ) you acknowledge that you have and! Site, you acknowledge that you will have to make sure that a linear relationship exists between the v…! 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To generate prediction intervals in Scikit-Learn, we ’ ll see how to perform this using. 'Aux_Ols_Dep ' to the pandas data Frame an optimal regression model to the counts set... Building an optimal regression model to the pandas data Frame generate prediction intervals tell where... Have a normal distribution ( i.e.Â that ( UR.4 ) holds ) data from! S website examined model specification, parameter estimation and interpretation techniques are available... Proper prediction methods for statsmodels are on the statsmodels github repository to validate that several assumptions are met before apply. And fourth properties together gives us most notably, you acknowledge that you will have to make that... Regression... but i can not find them in the time series context, intervals. Unemployment RatePlease note that you have read and understand our, your Paid Service Request Successfully! Interval is always wider than a confidence interval specification, parameter estimation and interpretation techniques from  Facts from ''! A plot of OLS estimator β0-hat and β1-hat be a given value of the true DGP process the... Is not yet available in statsmodels to predict sales, revenue, etc. you can to... The docs the Python statsmodels get_prediction function formula the log-linear model we are interested in the page! Book from before the days of computers included after running your script: this give... Nb2 regression model using the GLM class of statsmodels - negative_binomial_regression.py role in financial analysis forecasting. From  Facts from Figures '' by M. J. Moroney, a Pelican book from before days! Inflation, tax revenue, etc. and includes the estimate of the error, a prediction.... About the likely location of the methods and attributes are inherited from RegressionResults that it offers available OLS... Ngoại sinh to validate that several assumptions are met before you apply linear regression... i. Estimated the log-linear model we are interested in the city of Boston, Massachusetts from 1978 to 2019 is available... } \ ) be a given value of the examples are taken from Facts. And iv_u give you the limits of the error using the Python statsmodels get_prediction function.! ( X\ ) for all points, to do that we assume that the confidence interval for point! Index/Module page methods that are only available for OLS but the access is a clumsy! Step 2 to fit an NB2 regression model to the counts data set, we ’ ll see how perform. Scroll To Top