797A Lecture1 Sep 6-2011

797A Lecture1 Sep 6-2011 - Res Ec 797A: Forecasting Order...

Info iconThis preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 2
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 4
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 6
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 8
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Res Ec 797A: Forecasting Order Of Topics And Readings For Lecture 1 (September 6, 2011) Abbreviations BM: Bernie Morzuch notes GK: Gayner and Kirkpatrick Source m Epic Syllabus 2 Material coverage; Text; Exams; Assignments GK 1 Time line in forecasting Anonymous 1 Picture: Retail Sales - Intended to show trend and seasonality. - Seasonality especially is a very important component for one or more of your variables in Assignment 1. Assignment 1 Data Collection: Due September 13, 2011 (Two pages) BM 1 Purpose of Res Ec 797A - Determining a Sen'es’ Data Generating Process - Forecasting Components of a Time Series - Level — Trend - Seasonality - White Noise Department of Resource Economics University of Massachusetts Fall 2011 Res Ec 797A: Special Topics in Forecasting (The name of the course will be changed to Applied Univariate and Econometric Time-Series Techniques) Instructor: Bernie Morzuch 221 Stockbridge Hall Phone: 545-5718 email: [email protected] Office Hours: As you need me or by appointment Readings: Applied Econometric Time Series by Walter Enders, Second Edition (Wiley). It is available at the Textbook Annex. (Please note: There is a third edition. I want to stick with the second edition). Prerequisites: A graduate-level course in regression, knowledge of matrix algebra, and an intennediate- level ability in statistics are necessary. Assignments: There will be weekly assignments. These must be done in a timely fashion. The purpose of each assignment is to ensure that you are mastering and digesting the topics as they are being covered. Assignments will be worth 50% of your final grade. Exams: There will be two exams: a midterm (that is truly at the mid-tenn!) and a final exam. The final exam will concentrate on material covered since the midteim exam. Each exam is worth 25% of your final grade. The midterm exam will be administered during a two-hour period outside of the normally-scheduled class pen'od. The final exam will be scheduled during finals week. Objectives: 1 will attempt to do the following: (1) Summarize important time-series methodologies and quantitative forecasting techniques in use and the issues surrounding their use; (2) Discuss in detail some of the most important (though not necessarin the most widely used) techniques; (3) Develop the theory necessary to understand the techniques used. This is not an econometric theory, statistical theory, or forecasting theory course. Forecasting is a pragmatic art. My goal is to have the course reflect this pragmatism. Course Organization: The material will be presented in a lecture format. Implementation of the appropriate computer software will be explained as we proceed through the lectures. You will not be required to make in-class presentations nor will you have to do a paper for this course. Course Outline: I will begin with background material on forecasting. This includes terminology, followed by simple and common univariate estimation procedures not found in Enders. I will provide you with readings that explain these univariate techniques in detail. I then move to several not-so-simple univariate methods. All of this paves the way for exploring the behavior of univariate time series and, in particular, testing for their stationarity. This semester we have 26 class periods. Approximately half of the course (12 lectures) will be devoted to this background material and to univariate techniques. For the remaining 14 lectures, we will be covering the topics in Enders presented below. When you pick up the text and page through these readings, you may find them a bit frightening. I will attempt to lessen the difficulty by deciphering these pages for you. Enders is close to state-of-the-art regarding time-series econometrics. During the last lecture, we address what happens to forecasts when we combine forecasts from different methods. We complete the course by comparing the forecasting performance of all of the techniques that we've studied through the semester. Enders - Difference equations: Chapterl - Stationary time—series models: Chapter 2 - Autoregressive conditional heteroscedasticity (ARCH): Chapter 3, pp. 112-145 - Testing for trends and unit roots: Chapter 4 . Unit root processes, pp. 156-181 ' Unit root tests, pp. 181-229 . Multiequation time-series models: Chapter 5 . Intervention analysis, pp. 240-247 - Transfer function models, pp. 247-264 ' Vector autoregression, pp. 264-283 - Granger causality, pp. 283-290 - Cointegration and error-correction models: Chapter 6 ~ Cointegration, pp. 319-328 - Error-correction models, pp. 328—339 Time Line In Forecasting Historical y data Y BEGINNING Y END T Sample ______________ _ _ Y1 Yt Yn Observations Within-sample , forecasts . EX post Ex ante A A Backcasts forecasts A forecasts A Yn+1 YN YN+1 YN+k Q‘Bvackcasting period é———-—— Forecasting period —--—> %— Backcasts m v0 Source: Gayner and Kirkpatrick, 1994, p.11. 60.00 5000 1 -unafi 4000 HO—flm 3000 2,000 ‘ 13 25 37 Retail Sales 49 NSAISA 61 Month I NSA 73 85 ““ " SAL/CHAS - _.._:___ 97 109 University of Massachusetts Department of Resource Economics RESEC 797A Fall 2011 Assignment 1 Data Collection Because this is a course with a practical orientation, you will use data to make quantitative forecasts. Collect time series of three variables. I encourage you to collect data related to your research interest. If you already have data that meet the criteria set down below, so much the better. Variable 1 should be a series that is reported monthly or possibly quaiterly. Its most important feature is that you expect it to display a seasonal pattern. Possible contenders are prices of many agricultural commodities, sales of some luxury consumer goods, incidences of infectious diseases, etc. Please do not collect any meteorological data. Variable 2 can be monthly, quarterly or annual data. A higher frequency (such as daily stock prices) is not desirable. This is a series that you do not expect to display a seasonal pattern. Variable 3 is related to either variable 1 or variable 2. Its most important feature is that you can provide some cause-and-effect explanation. Variable 3 should be at the same frequency as the variable it causes. To choose variable 3 rely on theory for guidance. For example, in economics, quantity produced might be related to price, quantity exported to exchange rate, employment to inflation, etc. (We are not talking high theory here!) Each series must meet the following requirement: Preferably at least 50 observations, definitely at least 40. With monthly data try to get twice that amount. If there are one or two missing observations, you could insert values for the missing data. Preferably find a seiies where this is not necessary. Some forecasting techniques require a minimum of 40 observations. You will also want to retain the last few observations for post- sample testing. That is, you will make ex post forecasts. In future exercises retain 8 years, 8 quarters or 18 months of data for post-sample testing. Consistency is important! We want to make comparisons at the end of the semester, both across forecasting methods and across forecasters (i.e. you). Some ideas for sources of data will be found on the next page. First order of business: using a spreadsheet, produce a separate plot of each series against time. Briefly describe the data, looking especially for trends, seasonal patterns and outlier observations. Just exactly what these terms mean will be discussed in future classes. For the moment, use your understanding of the everyday meaning of the terms. You must present a detailed write-up for each variable. You must explain your plots, trends, plots, and anything else in the context of your write-up. You must use proper grammar. I will deduct points for poorly written assignments. Sources of data You can find a variety of data series (economic and non-economic) by using Google. Examples of web sites for economic time-series data 1. Energy Information Administration, Department of Energy: www.cia.doe. gov 2. Census Bureau: www.census. gov 3. Economic Research Services, U. S. Department of Agriculture: www.ers.usda.gov 4. Federal Reserve System: www.federalresewe. gov 5. Bureau of Economic Analysis, US. Department of Commerce: www.bea. gov Typical examples of pairs of economic variables that may be related 1. Monthly consumption and average price of natural gas for residential use 2. Number of workers employed in the farm sector and farm wages 3. Production (output level) of any agricultural commodity and its price 4. A country's gross domestic product (gdp) and its investment 5. Monthly residential building starts and the mortgage interest rate 6. Industrial production and the unemployment rate 7. A country‘s exchange rate and its level of imports 8. A country‘s exchange rate and its level of exports 9. Inflation rate and energy prices Purpose of Res Ec 797A This course is concerned with analyzing the behavior of a time series. Issues related to behavior are: - Determining a series' DGP: Can we identify the reasons or the process that generates the series or data? We will devote considerable attention to trying to find the data generating process (DGP) for each series that we analyze. ‘ Forecasting: Once we feel that we have captured the behavior of the series, can we use this knowledge to predict or forecast where the series will go, beyond the limits of the data set used to capture its DGP? - Components of A Time Series A time series typically has one or more of the following components at time period t: level (Lt); trend (Tl); seasonal variation (8,); cycle (C1); and white noise or an irregular (It) or €1TOI‘(81) fluctuation. General definitions of these components are as follows: - level (L1): The value where the series starts or where it tends to return or gravitate as time progresses. - trend (Tl): An upward or downward movement in the series. The duration of the movement can be short or long. It does not have to be persistent, because persistence is a matter of judgment. In this context you will be exposed to two kinds of trend: deterministic and stochastic. - seasonal variation (8,): Commonly referred to as seasonality. It very often is applied in the context of a calendar year. It is the regular monthly or quarterly fluctuation within a year. It is a pattern of change that completes itself within a year. It is repeated on a yearly basis and is influenced by some seasonal factor; e. g., weather or custom, sales at Christmas, turkeys at Thanksgiving. Seasonality is not restricted to a year. There can be seasonality within a week or even within a day. Exploring seasonality depends upon the frequency of the data being analyzed, e. g., either high frequency or low frequency. ' cycle (Ct): A long term upward or downward change, the duration of which can be two to ten years or longer. It is measured from peak to peak and from trough to trough. In economics, we see that the economy goes through business cycles. A variable like gross domestic product (gdp) is used to analyze the peaks and troughs in a business cycle. I will not address cycles in this course. - white noise (at): A random disturbance. l ...
View Full Document

This note was uploaded on 12/08/2011 for the course ECON 797A taught by Professor Bernardmorzuch during the Fall '11 term at UMass (Amherst).

Page1 / 8

797A Lecture1 Sep 6-2011 - Res Ec 797A: Forecasting Order...

This preview shows document pages 1 - 8. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online