# Blog Archives

## Monetary Policy & Credit Easing pt. 8: Econometrics Tests in R

January 2, 2012
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Hello, folks its time to cover some important econometrics tests you can do in R.The Akaike information criterion is a measure of the relative goodness of fit of a statistical model.  If you have 10 models and order them by AIC, the...

## Monetary Policy & Credit Easing pt. 7: R Econometrics Tests

January 1, 2012
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In post 6 we introduced some econometrics code that will help those working with time-series to gain asymptoticly efficient results.  In this post we look at the different commands and libraries necessary for testing our assumptions and such. Testing our Assumptions and Meeting the Gauss-Markov TheoremIn this section we will seek to test and verify the assumptions of the simple linear...

## Monetary Policy and Credit Easing pt. 6: Empirical Estimation and Methodology

December 30, 2011
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IT is now appropriate to lay out our two regression models in full for empirical estimation over our two separate time periods. The first estimation is from 4/1/71 to 7/1/97 and the second is from 4/1/01 to 4/1/11. The methodology employed in the estimation of these two models is a procedure using Generalized Least Squares with a Cochrane-Orcutt, style iterated...

## Monetary Policy and Credit Easing

December 26, 2011
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Here at the dancing economist, we wish to educate our followers on the finer points of economics and this includes econometrics and using R. R as mentioned previously is a free statistical software that enables regular people like us to do high end eco...

## Ladies and Gents: GDP has finally gotten its long awaited forecast

September 4, 2011
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Today we will be finally creating our long awaited GDP forecast.  In order to create this forecast we have to combine both the forecast from our deterministic trend model and the forecast from our de-trended GDP model. Our model for the trend is:t...

## Assessing the Forecasting Ability of Our Model

September 2, 2011
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Today we wish to see how our model would have faired forecasting the past 20 values of GDP. Why? Well ask yourself this: How can you know where your going, if you don't know where you've been? Once you understand please proceed on with the following post.First recall the trend portion that we have already accounted for:> t=(1:258)> t2=t^2> trendy= 892.656210 +...

## Forecasting In R: A New Hope with AR(10)

September 1, 2011
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In our last post we determined that the ARIMA(2,2,2) model was just plain not going to work for us.  Although i didn't show its residuals failed to pass the acf and pacf test for white noise and the mean of its residuals was greater than three whe...

## Story of the Ljung-Box Blues: Progress Not Perfection

August 31, 2011
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In the last post we determined that our ARIMA(2,2,2) model failed to pass the Ljung-Box test.  In todays post we seek to completely discredit the last posts claim and finally arrive at some needed closure. The Ljung-Box is first performed on the s...

## Forecasting In R: The Greatest Shortcut That Failed The Ljung-Box

August 27, 2011
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Okay so you want to forecast in R, but don't want to manually find the best model and go through the drudgery of plotting and so on.  I have recently found the perfect function for you.  Its called auto.arima and it automatically fits the bes...

## Forecasting in R: Modeling GDP and dealing with trend.

August 25, 2011
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Okay so we want to forecast GDP. How do we even begin such a burdensome ordeal?Well each time series has 4 components that we wish to deal with and those are seasonality, trend, cyclicality and error.  If we deal with seasonally adjusted data we d...