which we can rewrite as a log-linear model: Y = \exp(\beta_0 + \beta_1 X + \epsilon) $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. HC0_se HC1_se HC2_se HC3_se aic bic bse centered_tss compare_f_test compare_lm_test compare_lr_test condition_number conf_int conf_int_el cov_HC0 cov_HC1 cov_HC2 cov_HC3 cov_kwds cov_params cov_type df_model df_resid eigenvals el_test ess f_pvalue f_test fittedvalues fvalue get_influence get_prediction get_robustcov_results initialize k_constant llf load model mse_model … In the time series context, prediction intervals are known as forecast intervals. fitted) values again: # Prediction intervals for the predicted Y: #from statsmodels.stats.outliers_influence import summary_table, #dt = summary_table(lm_fit, alpha = 0.05)[1], #yprd_ci_lower, yprd_ci_upper = dt[:, 6:8].T, $$\mathbb{E} (\boldsymbol{Y}|\boldsymbol{X})$$, $$\widehat{\mathbf{Y}} = \mathbb{E} (\boldsymbol{Y}|\boldsymbol{X})$$, $$\widetilde{\mathbf{X}} \widehat{\boldsymbol{\beta}}$$, \[ \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]$, We want to predict the value $$\widetilde{Y}$$, for this given value $$\widetilde{X}$$. \begin{aligned} 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. Let our univariate regression be defined by the linear model: Then sample one more value from the population. 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, ; transform (bool, optional) – If the model was fit via a formula, do you want to pass exog through the formula.Default is True. Parámetros: params: array-like . 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: \end{aligned} 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. get_prediction (X_test) #print out the predictions: ), government policies (prediction of growth rates for income, inflation, tax revenue, etc.) $\mathbf{Y} = \mathbb{E}\left(\mathbf{Y} | \mathbf{X} \right) Y = \beta_0 + \beta_1 X + \epsilon &= 0 From the distribution of the dependent variable: Có tương đương với get_prediction() khi mô hình được đào tạo với …$. \]. \begin{aligned} \] On the other hand, in smaller samples $$\widehat{Y}$$ performs better than $$\widehat{Y}_{c}$$. Therefore we can use the properties of the log-normal distribution to derive an alternative corrected prediction of the log-linear model: Furthermore, this correction assumes that the errors have a normal distribution (i.e.Â that (UR.4) holds). \widehat{Y}_i \pm t_{(1 - \alpha/2, N-2)} \cdot \text{se}(\widetilde{e}_i) 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). \], $$\mathbb{E} \left[ (Y - g(\mathbf{X}))^2 \right]$$, Unemployment RatePlease note that you will have to validate that several assumptions are met before you apply linear regression models. Prediction interval is the confidence interval for an observation and includes the estimate of the error. Having estimated the log-linear model we are interested in the predicted value $$\widehat{Y}$$. iv_l and iv_u give you the limits of the prediction interval for each point. The key point is that the confidence interval tells you about the likely location of the true population parameter. Multi-Step Out-of-Sample Forecast &= \mathbb{E}\left[ \mathbb{V}{\rm ar} (Y | X) \right] + \mathbb{E} \left[ (\mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))^2\right]. \begin{aligned} &= \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] \\ Negative Binomial Regression using the GLM class of statsmodels - negative_binomial_regression.py. We know that the true observation $$\widetilde{\mathbf{Y}}$$ will vary with mean $$\widetilde{\mathbf{X}} \boldsymbol{\beta}$$ and variance $$\sigma^2 \mathbf{I}$$. Confidence intervals are there for OLS but the access is a bit clumsy. This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. 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. \[ \mathbb{V}{\rm ar}\left( \widetilde{\mathbf{Y}} - \widehat{\mathbf{Y}} \right) \\ This tutorial is broken down into the following 5 steps: 1. This is also known as the standard error of the forecast. In our case: There is a slight difference between the corrected and the natural predictor when the variance of the sample, $$Y$$, increases. The examples are taken from "Facts from Figures" by M. J. Moroney, a Pelican book from before the days of computers., $(Actually, the confidence interval for the fitted values is hiding inside the summary_table of influence_outlier, but I need to verify this.).$, $$\mathbb{E}\left(\widetilde{Y} | \widetilde{X} \right) = \beta_0 + \beta_1 \widetilde{X}$$, $&= \mathbb{E}(Y|X)\cdot \exp(\epsilon) Because, if $$\epsilon \sim \mathcal{N}(\mu, \sigma^2)$$, then $$\mathbb{E}(\exp(\epsilon)) = \exp(\mu + \sigma^2/2)$$ and $$\mathbb{V}{\rm ar}(\epsilon) = \left[ \exp(\sigma^2) - 1 \right] \exp(2 \mu + \sigma^2)$$.$, We can defined the forecast error as \end{aligned} Proper prediction methods for statsmodels are on the TODO list. Implementation. &=\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] \\ So, a prediction interval is always wider than a confidence interval. Dataset Description 2. Tôi đang sử dụng statsmodels.tsa.SARIMAX() để đào tạo một mô hình có các biến ngoại sinh. Simple ANOVA Examples¶ Introduction¶. 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. \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}})\\ Assume that the best predictor of $$Y$$ (a single value), given $$\mathbf{X}$$ is some function $$g(\cdot)$$, which minimizes the expected squared error: \], $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). &= \sigma^2 \left( \mathbf{I} + \widetilde{\mathbf{X}} \left( \mathbf{X}^\top \mathbf{X}\right)^{-1} \widetilde{\mathbf{X}}^\top\right) \mathbb{E} \left[ (Y - \mathbb{E} [Y|\mathbf{X}])^2 \right] = \mathbb{E}\left[ \mathbb{V}{\rm ar} (Y | X) \right].$, &= \mathbb{E}\left[ \mathbb{V}{\rm ar} (Y | X) \right] + \mathbb{E} \left[ (\mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))^2\right]. 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. \end{aligned} \mathbf{Y} = \mathbb{E}\left(\mathbf{Y} | \mathbf{X} \right), \widehat{Y}_i \pm t_{(1 - \alpha/2, N-2)} \cdot \text{se}(\widetilde{e}_i) All Rights Reserved. Split Dataset 3. One-Step Out-of-Sample Forecast 5. Use the α found in step 2 to fit an NB2 regression model to the counts data set. Y = \beta_0 + \beta_1 X + \epsilon The basic idea is straightforward: For the lower prediction, use GradientBoostingRegressor(loss= "quantile", alpha=lower_quantile) with lower_quantile representing the lower bound, say 0.1 for the 10th percentile 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. \widetilde{\boldsymbol{e}} = \widetilde{\mathbf{Y}} - \widehat{\mathbf{Y}} = \widetilde{\mathbf{X}} \boldsymbol{\beta} + \widetilde{\boldsymbol{\varepsilon}} - \widetilde{\mathbf{X}} \widehat{\boldsymbol{\beta}} A confidence interval gives a range for $$\mathbb{E} (\boldsymbol{Y}|\boldsymbol{X})$$, whereas a prediction interval gives a range for $$\boldsymbol{Y}$$ itself. ... nb2_predictions = nb2_training_results. \[ \[ \end{aligned} \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] Copyright © 2020 SemicolonWorld. Specifically a data set of daily average temperatures recorded in the city of Boston, Massachusetts from 1978 to 2019. In order to do so, we apply the same technique that we did for the point predictor - we estimate the prediction intervals for $$\widehat{\log(Y)}$$ and take their exponent. I think, confidence interval for the mean prediction is not yet available in statsmodels. orden: tipo array . I try to import matplotlib.pyplt in Pycharm console import matplotlib.pyplot as plt Then in return I get: Traceback (most recent call last): File "D:\Program Files\Anaconda2\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_ Thus, $$g(\mathbf{X}) = \mathbb{E} [Y|\mathbf{X}]$$ is the best predictor of $$Y$$. Interest Rate 2. 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. 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. \[ Using the conditional moment properties, we can rewrite $$\mathbb{E} \left[ (Y - g(\mathbf{X}))^2 \right]$$ as: In fact, the statsmodels.genmod.families.family package has a whole class devoted to the NB2 model: class statsmodels.genmod.families.family.NegativeBinomial(link=None, alpha=1.0) and so on. Y &= \exp(\beta_0 + \beta_1 X + \epsilon) \\ \[ We Will Contact Soon, http://jpktd.blogspot.ca/2012/01/nice-thing-about-seeing-zeros.html, confidence and prediction intervals with StatsModels. \widehat{Y}_{c} = \widehat{\mathbb{E}}(Y|X) \cdot \exp(\widehat{\sigma}^2/2) = \widehat{Y}\cdot \exp(\widehat{\sigma}^2/2) Assume that the data really are randomly sampled from a Gaussian distribution. Note that our prediction interval is affected not only by the variance of the true $$\widetilde{\mathbf{Y}}$$ (due to random shocks), but also by the variance of $$\widehat{\mathbf{Y}}$$ (since coefficient estimates, $$\widehat{\boldsymbol{\beta}}$$, are generally imprecise and have a non-zero variance), i.e.Â it combines the uncertainty coming from the parameter estimates and the uncertainty coming from the randomness in a new observation. $\widehat{Y} = \exp \left(\widehat{\log(Y)} \right) = \exp \left(\widehat{\beta}_0 + \widehat{\beta}_1 X\right)$ \mathbb{E} \left[ (Y - g(\mathbf{X}))^2 \right] &= \mathbb{E} \left[ (Y + \mathbb{E} [Y|\mathbf{X}] - \mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))^2 \right] \\ Furthermore, since $$\widetilde{\boldsymbol{\varepsilon}}$$ are independent of $$\mathbf{Y}$$, it holds that: I do this linear regression with StatsModels: My questions are, iv_l and iv_u are the upper and lower confidence intervals or prediction intervals? &= \exp(\beta_0 + \beta_1 X) \cdot \exp(\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 # Let's calculate the mean resposne (i.e. \log(Y) = \beta_0 + \beta_1 X + \epsilon \]. Develop Model 4. \], $$g(\mathbf{X}) = \mathbb{E} [Y|\mathbf{X}]$$, \[ By, \begin{aligned} &= \mathbb{C}{\rm ov} (\widetilde{\boldsymbol{\varepsilon}}, \widetilde{\mathbf{X}} \left( \mathbf{X}^\top \mathbf{X}\right)^{-1} \mathbf{X}^\top \mathbf{Y})\\ &= \mathbb{V}{\rm ar}\left( \widetilde{\mathbf{Y}} \right) + \mathbb{V}{\rm ar}\left( \widehat{\mathbf{Y}} \right)\\ \widetilde{\boldsymbol{e}} = \widetilde{\mathbf{Y}} - \widehat{\mathbf{Y}} = \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. Results class for for an OLS model., $$\left[ \exp\left(\widehat{\log(Y)} \pm t_c \cdot \text{se}(\widetilde{e}_i) \right)\right]$$, Then, a $$100 \cdot (1 - \alpha)\%$$ prediction interval for $$Y$$ is: \end{aligned} Most notably, you have to make sure that a linear relationship exists between the dependent v… 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). The special methods that are only available for OLS are: 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 $$X$$. We can estimate the systematic component using the OLS estimated parameters: \widetilde{\mathbf{Y}}= \mathbb{E}\left(\widetilde{\mathbf{Y}} | \widetilde{\mathbf{X}} \right) + \widetilde{\boldsymbol{\varepsilon}} update see the second answer which is more recent. \], $$\widetilde{\mathbf{X}} \boldsymbol{\beta}$$, \begin{aligned} where: The expected value of the random component is zero. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. &= \exp(\beta_0 + \beta_1 X) \cdot \exp(\epsilon)\\ Taking $$g(\mathbf{X}) = \mathbb{E} [Y|\mathbf{X}]$$ minimizes the above equality to the expectation of the conditional variance of $$Y$$ given $$\mathbf{X}$$: Thanks to Josef Perktold at StatsModels for assistance with the quantile regression code, ... OLS Regression Results ... (quantiles, res_all): # get prediction for the model and plot # here we use a dict which works the same way as the df in ols plt. The same ideas apply when we examine a log-log model. and let assumptions (UR.1)-(UR.4) hold. \end{aligned} This algorithm’s calculation of the MLE (Maximum-Likelihood Estimate) means one value for each parameter estimated, i.e. I need the confidence and prediction intervals for all points, to do a plot. 3 elementos iterables, con el número de parámetros AR, MA y exógenos, incluida la tendencia The difference from the mean response is that when we are talking about the prediction, our regression outcome is composed of two parts: 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. &= \mathbb{E}(Y|X)\cdot \exp(\epsilon) We will show that, in general, the conditional expectation is the best predictor of $$\mathbf{Y}$$. \widehat{Y}_{c} = \widehat{\mathbb{E}}(Y|X) \cdot \exp(\widehat{\sigma}^2/2) = \widehat{Y}\cdot \exp(\widehat{\sigma}^2/2) Looking at the elements of gs.index, we see that DatetimeIndexes are made up of pandas.Timestamps:Looking at the elements of gs.index, we see that DatetimeIndexes are made up of pandas.Timestamps:A Timestamp is mostly compatible with the datetime.datetime class, but much amenable to storage in arrays.Working with Timestamps can be awkward, so Series and DataFrames with DatetimeIndexes have some special slicing rules.The first special case is partial-string indexing. $For larger samples sizes $$\widehat{Y}_{c}$$ is closer to the true mean than $$\widehat{Y}$$. \[ &= \sigma^2 \mathbf{I} + \widetilde{\mathbf{X}} \sigma^2 \left( \mathbf{X}^\top \mathbf{X}\right)^{-1} \widetilde{\mathbf{X}}^\top \\ Prediction intervals must account for both: (i) the uncertainty of the population mean; (ii) the randomness (i.e.Â scatter) of the data. &= 0$ OLS Regression Results; Dep. \mathbf{Y} | \mathbf{X} \sim \mathcal{N} \left(\mathbf{X} \boldsymbol{\beta},\ \sigma^2 \mathbf{I} \right) the prediction is comprised of the systematic and the random components, but they are multiplicative, rather than additive. \widehat{Y} = \exp \left(\widehat{\log(Y)} \right) = \exp \left(\widehat{\beta}_0 + \widehat{\beta}_1 X\right) Since our best guess for predicting $$\boldsymbol{Y}$$ is $$\widehat{\mathbf{Y}} = \mathbb{E} (\boldsymbol{Y}|\boldsymbol{X})$$ - both the confidence interval and the prediction interval will be centered around $$\widetilde{\mathbf{X}} \widehat{\boldsymbol{\beta}}$$ but the prediction interval will be wider than the confidence interval. \]. Because $$\exp(0) = 1 \leq \exp(\widehat{\sigma}^2/2)$$, the corrected predictor will always be larger than the natural predictor: $$\widehat{Y}_c \geq \widehat{Y}$$. What formula does this function use after computing a simple linear regression ... but I cannot find them in the index/module page. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. ... #add a derived column called 'AUX_OLS_DEP' to the pandas Data Frame. Variable: brozek: R-squared: 0.749: Model: OLS: Adj. For the time series data set, we’ll use weather data downloaded from NOAA‘s website. Python statsmodels get_prediction function formula. Parameters: exog (array-like, optional) – The values for which you want to predict. We will examine the following exponential model: \] 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}}= … Collect a sample of data and calculate a prediction interval. 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, … Y = \exp(\beta_0 + \beta_1 X + \epsilon) Prediction intervals tell you where you can expect to see the next data point sampled. \[$, Let $$\text{se}(\widetilde{e}_i) = \sqrt{\widehat{\mathbb{V}{\rm ar}} (\widetilde{e}_i)}$$ be the square root of the corresponding $$i$$-th diagonal element of $$\widehat{\mathbb{V}{\rm ar}} (\widetilde{\boldsymbol{e}})$$. Then, the $$100 \cdot (1 - \alpha) \%$$ prediction interval can be calculated as: \[ We have examined model specification, parameter estimation and interpretation techniques. Assume that the data really are randomly sampled from a Gaussian distribution. \end{aligned} $$\widehat{\mathbf{Y}}$$ is called the prediction. &= \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] \\ \mathbb{V}{\rm ar}\left( \widetilde{\boldsymbol{e}} \right) &= If you do this many times, youâd expect that next value to lie within that prediction interval in $$95\%$$ of the samples.The key point is that the prediction interval tells you about the distribution of values, not the uncertainty in determining the population mean. Say w… 3.7.1 OLS Prediction. Most of the methods and attributes are inherited from RegressionResults. statsmodels v0.13.0.dev0 (+127) Prediction (out of sample) Type to start searching statsmodels Examples; statsmodels v0.13.0.dev0 (+127) ... OLS Adj. R-squared: 0.735: Method: Least Squares: F-statistic: 54.63 \begin{aligned} &=\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] \\ To generate prediction intervals in Scikit-Learn, we’ll use the Gradient Boosting Regressor, working from this example in the docs., $$\mathbb{E}\left[ \mathbb{E}\left(h(Y) | X \right) \right] = \mathbb{E}\left[h(Y)\right]$$, $$\mathbb{V}{\rm ar} ( Y | X ) := \mathbb{E}\left( (Y - \mathbb{E}\left[ Y | X \right])^2| X\right) = \mathbb{E}( Y^2 | X) - \left(\mathbb{E}\left[ Y | X \right]\right)^2$$, $$\mathbb{V}{\rm ar} (\mathbb{E}\left[ Y | X \right]) = \mathbb{E}\left[(\mathbb{E}\left[ Y | X \right])^2\right] - (\mathbb{E}\left[\mathbb{E}\left[ Y | X \right]\right])^2 = \mathbb{E}\left[(\mathbb{E}\left[ Y | X \right])^2\right] - (\mathbb{E}\left[Y\right])^2$$, $$\mathbb{E}\left[ \mathbb{V}{\rm ar} (Y | X) \right] = \mathbb{E}\left[ (Y - \mathbb{E}\left[ Y | X \right])^2 \right] = \mathbb{E}\left[\mathbb{E}\left[ Y^2 | X \right]\right] - \mathbb{E}\left[(\mathbb{E}\left[ Y | X \right])^2\right] = \mathbb{E}\left[ Y^2 \right] - \mathbb{E}\left[(\mathbb{E}\left[ Y | X \right])^2\right]$$, $$\mathbb{V}{\rm ar}(Y) = \mathbb{E}\left[ Y^2 \right] - (\mathbb{E}\left[ Y \right])^2 = \mathbb{V}{\rm ar} (\mathbb{E}\left[ Y | X \right]) + \mathbb{E}\left[ \mathbb{V}{\rm ar} (Y | X) \right]$$, $Let’s now do all the proofs again to make things clear and easy for us to understand. Having obtained the point predictor $$\widehat{Y}$$, we may be further interested in calculating the prediction (or, forecast) intervals of $$\widehat{Y}$$.$ \widehat{\mathbf{Y}} = \widehat{\mathbb{E}}\left(\widetilde{\mathbf{Y}} | \widetilde{\mathbf{X}} \right)= \widetilde{\mathbf{X}} \widehat{\boldsymbol{\beta}} \mathbf{Y} | \mathbf{X} \sim \mathcal{N} \left(\mathbf{X} \boldsymbol{\beta},\ \sigma^2 \mathbf{I} \right) &= \mathbb{V}{\rm ar}\left( \widetilde{\mathbf{Y}} \right) - \mathbb{C}{\rm ov} (\widetilde{\mathbf{Y}}, \widehat{\mathbf{Y}}) - \mathbb{C}{\rm ov} ( \widehat{\mathbf{Y}}, \widetilde{\mathbf{Y}})+ \mathbb{V}{\rm ar}\left( \widehat{\mathbf{Y}} \right) \\ 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. \text{argmin}_{g(\mathbf{X})} \mathbb{E} \left[ (Y - g(\mathbf{X}))^2 \right]. \], , \begin{aligned} We’ll see how to perform this regression using the Python statsmodels library. (2) Proof of OLS estimator β0-hat and β1-hat. $\[ Y &= \exp(\beta_0 + \beta_1 X + \epsilon) \\ Unfortunately, our specification allows us to calculate the prediction of the log of $$Y$$, $$\widehat{\log(Y)}$$. 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: By using our site, you acknowledge that you have read and understand our, Your Paid Service Request Sent Successfully! In order to do that we assume that the true DGP process remains the same for $$\widetilde{Y}$$. &= \mathbb{C}{\rm ov} (\widetilde{\boldsymbol{\varepsilon}}, \widetilde{\mathbf{X}} \left( \mathbf{X}^\top \mathbf{X}\right)^{-1} \mathbf{X}^\top \mathbf{Y})\\ Let $$\widetilde{X}$$ be a given value of the explanatory variable. 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. \[ Adding the third and fourth properties together gives us. \mathbb{E} \left[ (Y - \mathbb{E} [Y|\mathbf{X}])^2 \right] = \mathbb{E}\left[ \mathbb{V}{\rm ar} (Y | X) \right]. \mathbb{E} \left[ (Y - g(\mathbf{X}))^2 \right] &= \mathbb{E} \left[ (Y + \mathbb{E} [Y|\mathbf{X}] - \mathbb{E} [Y|\mathbf{X}] - g(\mathbf{X}))^2 \right] \\$ 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] ¶. Prediction intervals are conceptually related to confidence intervals, but they are not the same. \text{argmin}_{g(\mathbf{X})} \mathbb{E} \left[ (Y - g(\mathbf{X}))^2 \right]. \widetilde{\mathbf{Y}}= \mathbb{E}\left(\widetilde{\mathbf{Y}} | \widetilde{\mathbf{X}} \right) + \widetilde{\boldsymbol{\varepsilon}} Finally, it also depends on the scale of $$X$$. Most of the methods and attributes are inherited from RegressionResults. \], $$\epsilon \sim \mathcal{N}(\mu, \sigma^2)$$, $$\mathbb{E}(\exp(\epsilon)) = \exp(\mu + \sigma^2/2)$$, $$\mathbb{V}{\rm ar}(\epsilon) = \left[ \exp(\sigma^2) - 1 \right] \exp(2 \mu + \sigma^2)$$, $$\exp(0) = 1 \leq \exp(\widehat{\sigma}^2/2)$$. \] 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. Prediction plays an important role in financial analysis (forecasting sales, revenue, etc. ... Confidence intervals are there for OLS …
2020 statsmodels ols get_prediction