By far the main piece of work for the assignment is the Main Report.

• Use the various forecasting methods developed in the course to make forecasts on your dataset
• Evaluate those models as forecasting models for your dataset
• Identify which model makes the ‘best’ point-forecasts
• Make some density/interval and 1-year ahead forecasts
• Due the day before your new data is released; new data must be released between the 1st and 12th of November.
• Usually comes to about 9 or 10 pages $+$ code

EMET3007/8012 – Forecasting Major Project

For this project you must choose a monthly or quarterly time series, make a forecast, and evaluate your success. You will need to turn in two very short reports, and one major report.
Project Description
The Project Description report will be due on the 26th of September. This report is a brief description (1-2 paragraphs) of the variable you intend to forecast. You must include the following:

• A precise description of the variable you will be forecasting
• The original source for the time-series
• Where you obtained the series
• Which date you intend to forecast, when that data will be released, and where the data will be released.

If you have access to proprietary data and wish to use that, that’s fine ${ }^1$. Otherwise you can use any data series you wish except those used in the lectures/assignments. E.g. US data series are fine to use if you want. The Project Description must not be more than one page.

Very Important: The time-series you are forecasting must have a new realization some time between the 1st of November and the 12th of November. Realisation times outside this window require prior written approval.

Project Report

This is the main forecast report. It is due no later than Friday the 11th of November (but may be due earlier). This report will include

• A description of the data series, historical data, and time-series properties/problems of the series
• A description of various forecasting methods which you have used on this data, and the results of those models
• Use of a wide range of models, including combining model components where appropriate
• An evaluation of the efficacy of the forecasting models used
• An indication of the optimal forecasting method
• Forecasts of the series for the coming year using the optimal method
• This should include point forecasts as well as more interval and/or density forecasts ${ }^2$
• Anything else you think would be of interest
All code used must be included as an appendix. Use of appropriate graphs is strongly recommended. The report should be approximately 10 pages long, excluding appendices.

Due Date: This report is due the day before the new realization of the data series. This means it will be due no later than the 11th of November, and may be due as early as the 1st of November. This is so you can evaluate your forecast.

Forecast Evaluation

This is a short evaluation of you forecast, where you compare your one-step-ahead forecast with the actual realization. Would a decision maker have been wise to follow your forecast? This is due on the 14th of November. The evaluation should be no longer than half a page, and could be shorter.

Marking Guide

You will be graded based on a combination of the following factors:

• Quality of Analysis: The forecasting analysis should be well-developed and argued. All analysis should be free from errors. Appropriate regressors should be identified and utilised. A range of appropriate models should be attempted, and the best model identified. More difficult to implement models should be included to demonstrate econometric sophistication.
• Quality of Expression: How well the report is written and expressed. This is primarily concerned with the flow and presentation of the report.
• Topic Choice: The relevance and importance of the topic.
• Use of Appropriate References: The quality and relevance of references used.
• Referencing: References must be provided, the referencing system used must be consistent, and of sufficient detail to be able to find the references. All data used used must be sourced.
• Accuracy of Forecast: The actual accuracy of the forecast is assessed. In cases where the forecast is inaccurate, the quality of argument as to why it was inaccurate is also assessed.

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