How can I transform this model to get the original one before the transformation?

- Thread starter jontessier
- Start date

How can I transform this model to get the original one before the transformation?

Can you provide more information about the origins and context of your question.

Thanks.

I have questions based on this scenario. There is no data series, only the model.

I think that I need to resolve the initial model to answer the questions.

Question 9.

Let an annual series Yt be stationary. However, the series transformed and differentiated Dt = ln(Yt) - ln(Yt-1) is stationary. Moreover, we suppose that it obeys the following theoretical model: Dt = -0.12 + 0.75 Dt-1 + et, in which the error term and is a white noise of variance σ2 = 0.012.

a) It is an ARIMA model (p, d, q) with which values for p, d and q?

b) Is this model stationary? Justify.

c) If the most recent observations for 2017 and 2018 are Yt-2 = 207 and Yt-1 = 231, calculate a one-time forecast Ŷt for the year 2019.

d) Build a 95% confidence interval on the Yt value that will be observed in 2019. Suggestion: first calculate an IC on Dt using the normal law and then do the transformations required.

a). what terms in the model and was differencing used?

b.) is the Dt-1 a unit root term?

c + d) plug data into calculations.

What does IC stand for?

This might answer your question I am not sure.

0

Let "xd" denote the differenced data and "x" denote the original data. Then xd[n]=x[n+1]-x[n]. Therefore, x[n+1]=x[n]+xd[n]. If you add the first element of first difference forecast to the real data with the same indice, then you will get the next real data forecast.

This is about forecasting. I am not sure if the formula works with past data.

https://stackoverflow.com/questions/47793868/get-back-to-un-differenced-data-after-forecast