Forecasting for Economics and Business
eBook - ePub

Forecasting for Economics and Business

Gloria González-Rivera

Share book
512 pages
ePUB (mobile friendly)
Available on iOS & Android
eBook - ePub

Forecasting for Economics and Business

Gloria González-Rivera

Book details
Book preview
Table of contents

About This Book

For junior/senior undergraduates in a variety of fields such as economics, business administration, applied mathematics and statistics, and for graduate students in quantitative masters programs such as MBA and MA/MS in economics.

A student-friendly approach to understanding forecasting.

Knowledge of forecasting methods is among the most demanded qualifications for professional economists, and business people working in either the private or public sectors of the economy. The general aim of this textbook is to carefully develop sophisticated professionals, who are able to critically analyze time series data and forecasting reports because they have experienced the merits and shortcomings of forecasting practice.

Frequently asked questions
How do I cancel my subscription?
Simply head over to the account section in settings and click on “Cancel Subscription” - it’s as simple as that. After you cancel, your membership will stay active for the remainder of the time you’ve paid for. Learn more here.
Can/how do I download books?
At the moment all of our mobile-responsive ePub books are available to download via the app. Most of our PDFs are also available to download and we're working on making the final remaining ones downloadable now. Learn more here.
What is the difference between the pricing plans?
Both plans give you full access to the library and all of Perlego’s features. The only differences are the price and subscription period: With the annual plan you’ll save around 30% compared to 12 months on the monthly plan.
What is Perlego?
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, we’ve got you covered! Learn more here.
Do you support text-to-speech?
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Is Forecasting for Economics and Business an online PDF/ePUB?
Yes, you can access Forecasting for Economics and Business by Gloria González-Rivera in PDF and/or ePUB format, as well as other popular books in Economics & Economic Theory. We have over one million books available in our catalogue for you to explore.




Tools of the Forecaster

In Chapter 1, we have learned that our objective is to construct forecasts based on time series models. These are representations (equations) that link past information with the present, and by doing so, they summarize the time dependence in the data. Time dependence is the key to predicting future values of the variable of interest. However, before constructing a time series model, the forecaster needs to consider three basic elements that will guide the production of the forecast. These are:
1. The information set.
2. The forecast horizon.
3. XThe loss function.
These elements are a priori choices that the forecaster must make. In this sense, we call them tools. If you are ready to build a dining table, you need to choose nails, hammers, wood, glues, machine saws, and so on. In the same fashion, if you are ready to build a time series model, you need to choose at least the three basic elements just listed. For instance, suppose that you wish to forecast the number of new homes in Riverside County. You will need to collect information related to the construction sector in the area, the state of the local economy, the population inflows, the actual supply of houses, and so on. You are constructing the information set by gathering relevant and up-to-date information for the problem at hand. This information will be fed into the time series models. Because different models will process information differently, it may happen that some information is more important than others or that some information may be irrelevant for the forecast of interest.
The forecaster needs to choose how far into the future she wishes to predict. Do we want a 1-month-ahead, a 1-day-ahead, or a 1-year-ahead prediction? It depends on the use of the forecast. For instance, think about policy makers who plan to design or revamp the transportation services of the area or any other infrastructure. It is likely that they will be more interested in long-term predictions of new housing (i.e., forecasts for 1 year, 2 years, 5 years) than in short-term predictions (i.e., forecasts for 1 day, 1 month, 1 quarter). The forecast horizon influences the choice of the frequency of the time series data. If our interest is a 1-month-ahead prediction, we may wish to collect monthly data, or if our interest is a 1-day-ahead forecast, we may collect daily data. Of course, it is possible to forecast 1 month ahead with daily observations but, in some instances, this may not be desirable.
The forecaster must deal with uncertainty, which is inherent in any exercise involving the future. Only when time passes and the future becomes a reality does the forecaster know whether her prediction was right or wrong, and if it is wrong, by how much. In other words, forecast errors will happen but more importantly, they will be costly. The loss function is a representation of the penalties associated with forecast errors. Suppose that based on the forecast of new housing construction, some policy makers decide to invest in a system of new highways. If the forecast happens to overestimate the construction of new housing and, as a result, new construction is less than expected, the highways likely will be underutilized. On the contrary, if the forecast underestimates the construction of new housing and construction is more than expected, the highways will be overcrowded and congested. Either case has a cost. In the first case, more was invested than needed; thus, resources were wasted. In the second case, there were costs associated with traffic congestion, air pollution, longer commuting times, and so on. The costs of underestimation and of overestimation may be of different magnitude. It is sensible to assume that the forecaster may want to avoid forecast errors that are costly and to choose a forecast that minimizes the forecaster’s losses. This is deeme...

Table of contents

Citation styles for Forecasting for Economics and Business
APA 6 Citation
González-Rivera, G. (2016). Forecasting for Economics and Business (1st ed.). Taylor and Francis. Retrieved from (Original work published 2016)
Chicago Citation
González-Rivera, Gloria. (2016) 2016. Forecasting for Economics and Business. 1st ed. Taylor and Francis.
Harvard Citation
González-Rivera, G. (2016) Forecasting for Economics and Business. 1st edn. Taylor and Francis. Available at: (Accessed: 15 October 2022).
MLA 7 Citation
González-Rivera, Gloria. Forecasting for Economics and Business. 1st ed. Taylor and Francis, 2016. Web. 15 Oct. 2022.