Publications by Steven Sabol
Forecasting in R: Starting From Square One
Okay in the past few posts I jumped the gun a little bit. Errors I made include rushing everything, not explaining anything and not giving my blog readers the love and respect they deserve. What am I talking about? Well before we do anything with a series we have to fit it to some sort of trend (which I did very haphazardly) and p...
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Forecasting in R: Modeling GDP and dealing with trend.
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 don’t have to worry about seasonality which leaves us with only three worries. If we ...
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Forecasting In R: The Greatest Shortcut That Failed The Ljung-Box
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 best arima model to your time series. In a word- it is “brilliant”. Lets take a look at...
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Story of the Ljung-Box Blues: Progress Not Perfection
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 series at hand, because it means that at least one of the autocorrelation functions is ...
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Forecasting In R: A New Hope with AR(10)
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 when it should have been much closer to zero. Today we discover that an AR(10) of the de-...
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Assessing the Forecasting Ability of Our Model
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=(1:2...
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Ladies and Gents: GDP has finally gotten its long awaited forecast
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:trendyx= 892.656210 + -30.365580*x + 0.335586*x2where x2=x^2and the vector length we wi...
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Monetary Policy and Credit Easing
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 economics research. Recently, I wrote a paper on how the Federal Reserves actions have...
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Monetary Policy and Credit Easing pt. 6: Empirical Estimation and Methodology
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...
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Monetary Policy & Credit Easing pt. 7: R Econometrics Tests
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 t...
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