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"use strict";(self.webpackChunkdocs=self.webpackChunkdocs||[]).push([[56659],{3905:(e,n,t)=>{t.d(n,{Zo:()=>p,kt:()=>d});var r=t(67294);function a(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function o(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);n&&(r=r.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,r)}return t}function i(e){for(var n=1;n<arguments.length;n++){var t=null!=arguments[n]?arguments[n]:{};n%2?o(Object(t),!0).forEach((function(n){a(e,n,t[n])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(t)):o(Object(t)).forEach((function(n){Object.defineProperty(e,n,Object.getOwnPropertyDescriptor(t,n))}))}return e}function l(e,n){if(null==e)return{};var t,r,a=function(e,n){if(null==e)return{};var t,r,a={},o=Object.keys(e);for(r=0;r<o.length;r++)t=o[r],n.indexOf(t)>=0||(a[t]=e[t]);return a}(e,n);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(r=0;r<o.length;r++)t=o[r],n.indexOf(t)>=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(a[t]=e[t])}return a}var c=r.createContext({}),s=function(e){var n=r.useContext(c),t=n;return e&&(t="function"==typeof e?e(n):i(i({},n),e)),t},p=function(e){var n=s(e.components);return r.createElement(c.Provider,{value:n},e.children)},u={inlineCode:"code",wrapper:function(e){var n=e.children;return r.createElement(r.Fragment,{},n)}},m=r.forwardRef((function(e,n){var t=e.components,a=e.mdxType,o=e.originalType,c=e.parentName,p=l(e,["components","mdxType","originalType","parentName"]),m=s(t),d=a,f=m["".concat(c,".").concat(d)]||m[d]||u[d]||o;return t?r.createElement(f,i(i({ref:n},p),{},{components:t})):r.createElement(f,i({ref:n},p))}));function d(e,n){var t=arguments,a=n&&n.mdxType;if("string"==typeof e||a){var o=t.length,i=new Array(o);i[0]=m;var l={};for(var c in n)hasOwnProperty.call(n,c)&&(l[c]=n[c]);l.originalType=e,l.mdxType="string"==typeof e?e:a,i[1]=l;for(var s=2;s<o;s++)i[s]=t[s];return r.createElement.apply(null,i)}return r.createElement.apply(null,t)}m.displayName="MDXCreateElement"},34073:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>c,contentTitle:()=>i,default:()=>u,frontMatter:()=>o,metadata:()=>l,toc:()=>s});var r=t(87462),a=(t(67294),t(3905));const o={title:"bpag",description:"OpenBB Terminal Function"},i="bpag",l={unversionedId:"reference/econometrics/bpag",id:"reference/econometrics/bpag",title:"bpag",description:"OpenBB Terminal Function",source:"@site/content/terminal/reference/econometrics/bpag.md",sourceDirName:"reference/econometrics",slug:"/reference/econometrics/bpag",permalink:"/terminal/reference/econometrics/bpag",draft:!1,editUrl:"https://github.com/OpenBB-finance/OpenBBTerminal/edit/main/website/content/terminal/reference/econometrics/bpag.md",tags:[],version:"current",frontMatter:{title:"bpag",description:"OpenBB Terminal Function"},sidebar:"tutorialSidebar",previous:{title:"bgod",permalink:"/terminal/reference/econometrics/bgod"},next:{title:"clean",permalink:"/terminal/reference/econometrics/clean"}},c={},s=[{value:"Usage",id:"usage",level:3},{value:"Parameters",id:"parameters",level:2},{value:"Examples",id:"examples",level:2}],p={toc:s};function u(e){let{components:n,...t}=e;return(0,a.kt)("wrapper",(0,r.Z)({},p,t,{components:n,mdxType:"MDXLayout"}),(0,a.kt)("h1",{id:"bpag"},"bpag"),(0,a.kt)("p",null,"Show Breusch-Pagan heteroscedasticity test results. Needs OLS to be run in advance with independent and dependent variables"),(0,a.kt)("h3",{id:"usage"},"Usage"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python",metastring:"wordwrap",wordwrap:!0},"bpag\n")),(0,a.kt)("hr",null),(0,a.kt)("h2",{id:"parameters"},"Parameters"),(0,a.kt)("p",null,"This command has no parameters"),(0,a.kt)("hr",null),(0,a.kt)("h2",{id:"examples"},"Examples"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"2022 Jun 01, 06:29 (\ud83e\udd8b) /econometrics/ $ load longley -a ll\n\n2022 Jun 01, 06:29 (\ud83e\udd8b) /econometrics/ $ ols -d ll.totemp -i ll.gnpdefl,ll.gnp,ll.unemp,ll.armed,ll.pop,ll.year\n OLS Regression Results \n=======================================================================================\nDep. Variable: ll.totemp R-squared (uncentered): 1.000\nModel: OLS Adj. R-squared (uncentered): 1.000\nMethod: Least Squares F-statistic: 5.052e+04\nDate: Wed, 01 Jun 2022 Prob (F-statistic): 8.20e-22\nTime: 12:29:44 Log-Likelihood: -117.56\nNo. Observations: 16 AIC: 247.1\nDf Residuals: 10 BIC: 251.8\nDf Model: 6 \nCovariance Type: nonrobust \n==============================================================================\n coef std err t P|t| [0.025 0.975]\n------------------------------------------------------------------------------\nll.gnpdefl -52.9936 129.545 -0.409 0.691 -341.638 235.650\nll.gnp 0.0711 0.030 2.356 0.040 0.004 0.138\nll.unemp -0.4235 0.418 -1.014 0.335 -1.354 0.507\nll.armed -0.5726 0.279 -2.052 0.067 -1.194 0.049\nll.pop -0.4142 0.321 -1.289 0.226 -1.130 0.302\nll.year 48.4179 17.689 2.737 0.021 9.003 87.832\n==============================================================================\nOmnibus: 1.443 Durbin-Watson: 1.277\nProb(Omnibus): 0.486 Jarque-Bera (JB): 0.605\nSkew: 0.476 Prob(JB): 0.739\nKurtosis: 3.031 Cond. No. 4.56e+05\n==============================================================================\n\nNotes:\n[1] R\xb2 is computed without centering (uncentered) since the model does not contain a constant.\n[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n[3] The condition number is large, 4.56e+05. This might indicate that there are\nstrong multicollinearity or other numerical problems.\n\nWarnings:\nkurtosistest only valid for n=20 ... continuing anyway, n=16\n\n2022 Jun 01, 06:30 (\ud83e\udd8b) /econometrics/ $ bpag\n\nBreusch-Pagan heteroscedasticity test\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 \u2503 Breusch-Pagan \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 lm-stat \u2502 7.90 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 p-value \u2502 0.16 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 f-stat \u2502 1.63 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 fp-value \u2502 0.24 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nThe result 0.16 indicates the existence of heteroscedasticity. Consider taking the log or a rate for the dependent variable.\n")),(0,a.kt)("hr",null))}u.isMDXComponent=!0}}]);
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