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In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The test is named after Carlos Jarque and Anil K. Bera. The test statistic JB is defined as

JB=n6(S2+14(K3)2)

where n is the number of observations (or degrees of freedom in general); S is the sample skewness, and K is the sample kurtosis:

S=μ^3σ^3=1ni=1n(xix¯)3(1ni=1n(xix¯)2)3/2K=μ^4σ^4=1ni=1n(xix¯)4(1ni=1n(xix¯)2)2,

where μ^3 and μ^4 are the estimates of third and fourth central moments, respectively, x¯ is the sample mean, and σ^2 is the estimate of the second central moment, the variance.

If the data comes from a normal distribution, the JB statistic asymptotically has a chi-squared distribution with two degrees of freedom, so the statistic can be used to test the hypothesis that the data are from a normal distribution. The null hypothesis is a joint hypothesis of the skewness being zero and the excess kurtosis being zero. Samples from a normal distribution have an expected skewness of 0 and an expected excess kurtosis of 0 (which is the same as a kurtosis of 3). As the definition of JB shows, any deviation from this increases the JB statistic.

For small samples the chi-squared approximation is overly sensitive, often rejecting the null hypothesis when it is in fact true. Furthermore, the distribution of p-values departs from a uniform distribution and becomes a right-skewed uni-modal distribution, especially for small p-values. This leads to a large Type I error rate. The table below shows some p-values approximated by a chi-squared distribution that differ from their true alpha levels for small samples.

Calculated p-value equivalents to true alpha levels at given sample sizes
True α level 20 30 50 70 100
0.1 0.307 0.252 0.201 0.183 0.1560
0.05 0.1461 0.109 0.079 0.067 0.062
0.025 0.051 0.0303 0.020 0.016 0.0168
0.01 0.0064 0.0033 0.0015 0.0012 0.002

(These values have been approximated by using Monte Carlo simulation in Matlab)

In MATLAB's implementation, the chi-squared approximation for the JB statistic's distribution is only used for large sample sizes (> 2000). For smaller samples, it uses a table derived from Monte Carlo simulations in order to interpolate p-values.[1]

History

Considering normal sampling, and √β1 and β2 contours, Template:Harvtxt noticed that the statistic JB will be asymptotically χ2(2)-distributed; however they also noted that “large sample sizes would doubtless be required for the χ2 approximation to hold”. Bowman and Shelton did not study the properties any further, preferring D’Agostino’s K-squared test.

Around 1979, Anil Bera and Carlos Jarque while working on their dissertations on regression analysis, have applied the Lagrange multiplier principle to the Pearson family of distributions to test the normality of unobserved regression residuals and found that the JB test was asymptotically optimal (although the sample size needed to “reach” the asymptotic level was quite large). In 1980 the authors published a paper (Template:Harvnb), which treated a more advanced case of simultaneously testing the normality, homoscedasticity and absence of autocorrelation in the residuals from the linear regression model. The JB test was mentioned there as a simpler case. A complete paper about the JB Test was published in the International Statistical Review in 1987 dealing with both testing the normality of observations and the normality of unobserved regression residuals, and giving finite sample significance points.

Jarque–Bera test in regression analysis

According to Robert Hall, David Lilien, et al. (1995) when using this test along with multiple regression analysis the right estimate is:

JB=nk6(S2+14(K3)2)

where n is the number of observations and k is the number of regressors when examining residuals to an equation.

References

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    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

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    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

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Template:Refend

Implementations

  • ALGLIB includes implementation of the Jarque–Bera test in C++, C#, Delphi, Visual Basic, etc.
  • gretl includes an implementation of the Jarque–Bera test
  • R includes implementations of the Jarque–Bera test: jarque.bera.test in package tseries, for example, and jarque.test in package moments.
  • MATLAB includes implementation of the Jarque–Bera test, the function "jbtest".
  • Python statsmodels includes implementation of the Jarque–Bera test, "statsmodels.stats.stattools.py".

Template:Statistics