![nrnd eviews nrnd eviews](https://image4.slideserve.com/1154640/generating-random-numbers-series-common-functions-commands-l.jpg)
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Perhaps this different calculation time happened because the lm () function that I used in R creates a large object of type lm which contains a lot of information about the estimated model and for 100,000 observations already weighs about 23 Mb, which, again, is stored in random access memory. In the end, I want to say that you should not immediately write this in R as a minus.
#Nrnd eviews free#
It should be noted that Eviews practically does not consume RAM, while R cumulatively increases the amount of memory consumed for its needs and does not free it until the program is closed.Īgain, the residuals in the models are not normal, you need to add more variables. There is heteroskedasticity in errors, which testifies to the inclusion of a variable - the processor load level in the model. The graphs show a significant amount of outliers, which most likely indicates a much more significant effect of processor load on the calculation time in Eviews. In the logarithmic model, the elasticity is 0.306 (3.3 times less than in R). The linear model predicts that an additional million observations will increase the model evaluation time by 0.018 seconds (75.8 times less than in R). The models obtained at Eviews do not describe the dependence of the construction time on the number of observations in such a high quality. Nevertheless, the hypothesis of normality can be accepted in the logarithmic model (since the critical value of the Harkey-Behr test statistics is 8.9% and exceeds the standard critical significance level of 5%).Įviews Results (Linear and Logarithmic Models) Visually, histograms of the remainder of the models are not similar to the normal distribution, which means that the estimates obtained in the models are biased, because, most likely, we do not take into account a significant variable - the processor load level. if the number of observations increases by 1%, then the regression calculation time will increase by 1.014%). According to the linear model, we get that each additional million observations increases the construction time by 1.39 seconds, and the model in the logarithms shows the elasticity of the number of observations over time 1.014 (i.e. There is even a hint of the normality of the residuals in the regression. The multiplicative model gives more beautiful results. As expected, the number of observations significantly affects the construction time of the regression. I added the dum variable - dummy for one of the observations (you can see the outlier on the chart, at that moment I needed to open the browser). Results R (Linear and Logarithmic Models) We will change the number of observations N in the regression and compare the estimation time for each. In this post, I compare the linear regression estimation time in R and Eviews depending on the number of observations.įor this test we will use a simple linear regression: But if the number of observations is large? Regression is not always evaluated in an instant.
#Nrnd eviews software#
If you need to evaluate an econometric model with a small number of observations, then the software in which this can be done is determined solely by your preferences and financial capabilities.