Ex 4f SEASONAL ADJUSTMENT.

Slides:

Advertisements

Similar presentations

Unit 9 It's warm 清丰县第一实验小学唐利娟.

Advertisements

Contributed by National Academy of Statistical Administration

A.S. 3.8 INTERNAL 4 CREDITS Time Series. Time Series Overview Investigate Time Series Data A.S. 3.8 AS91580 Achieve Students need to tell the story of.

FITTING TREND LINES AND FORECASTING.  FITTING BY EYE: inadequate technique. We will look at the following two:  3-MEDIAN METHOD AND  LEAST-SQUARES.

Time-Series Analysis and Forecasting – Part III

19- 1 Chapter Nineteen McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.

Trends and Seasonality Using Multiple Regression with Time Series Data Many time series data have a common tendency of growing over time, and therefore.

1 BIS APPLICATION MANAGEMENT INFORMATION SYSTEM Advance forecasting Forecasting by identifying patterns in the past data Chapter outline: 1.Extrapolation.

Ka-fu Wong © 2003 Chap Dr. Ka-fu Wong ECON1003 Analysis of Economic Data.

Seasonal Variations Why do we have seasons ? (continued…)

Chapter 5 Time Series Analysis

1 Pertemuan 23 Deret Berkala, Peramalan, dan Angka Indeks-1 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1.

ForecastingModelsWith Trend and Seasonal Effects.

Part II – TIME SERIES ANALYSIS C2 Simple Time Series Methods & Moving Averages © Angel A. Juan & Carles Serrat - UPC 2007/2008.

Unemployment. Definition A person is considered unemployed if: ◦They are without work ◦They are able to begin work ◦They are actively seeking employment.

7Lc A plan for all seasons 2 Explain what a hemisphere is.

Copyright © Cengage Learning. All rights reserved. 14 Further Integration Techniques and Applications of the Integral.

SMOOTHING TECHNIQUES TIME SERIES. COMPONENTS OF A TIME SERIES Components of a time series Seasonal effect Long term trend Cyclical effect Irregularity,

Business Cycle Measurement

Winter’s Exponential smoothing

Datta Meghe Institute of Management Studies Quantitative Techniques Unit No.:04 Unit Name: Time Series Analysis and Forecasting 1.

Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved.

Chapter 5 Demand Forecasting. Qualitative Forecasts Survey Techniques Planned Plant and Equipment Spending Expected Sales and Inventory Changes Consumers’

Time Series Analysis Lecture#29 MGT 601. Time Series Analysis Introduction: A time series consists of numerical data collected, observed or recorded at.

Chapter 5 Demand Forecasting.

McGraw-Hill/Irwin Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. 3-2 Business Forecasting with Accompanying Excel-Based ForecastX™

Definition of Time Series: An ordered sequence of values of a variable at equally spaced time intervals. The variable shall be time dependent.

Analyse time series data to make a forecast.  Forecast will be based on:  estimates of the trend for the smoothed data  estimates of seasonal effects.

Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 15-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter.

Chapter 6 Business and Economic Forecasting Root-mean-squared Forecast Error zUsed to determine how reliable a forecasting technique is. zE = (Y i -

INTERNAL ACHIEVEMENT STANDARD 3 CREDITS Time Series 1.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

Quantitative Decision Making 7 th ed By Lapin and Whisler.

MEDIAN SMOOTHING.  Smoothing involves replacing the original time series with another one where most of the variation has been removed, in order to see.

Time Series A collection of measurements recorded at specific time intervals.

PowerPoint Slides by Robert F. BrookerCopyright (c) 2001 by Harcourt, Inc. All rights reserved. Managerial Economics in a Global Economy Chapter 5 Demand.

C opyright  2007 by Oxford University Press, Inc. PowerPoint Slides Prepared by Robert F. Brooker, Ph.D.Slide 1.

Economics 173 Business Statistics Lecture 23 © Fall 2001, Professor J. Petry

ECNE610 Managerial Economics Week 4 MARCH Dr. Mazharul Islam Chapter-5.

Economics 173 Business Statistics Lecture 26 © Fall 2001, Professor J. Petry

COMPLETE BUSINESS STATISTICS

Time Series and Trend Analysis

Time Series - A collection of measurements recorded at specific intervals of time. 1. Short term features Noise: Spike/Outlier: Minor variation about.

Forecasting Chapter 6 7E M ANAGERIAL E CONOMICS F U N D A M E N T A L S O F © 2003 South-Western/Thomson Learning M A R K H I R S C H E Y PowerPoint Presentation.

Time Series and Forecasting

QM Spring 2002 Business Statistics Analysis of Time Series Data: an Introduction.

TIME SERIES ‘Time series’ data is a bivariate data, where the independent variable is time. We use scatterplot to display the relationship between the.

Time Series - A collection of measurements recorded at specific intervals of time. 1. Short term features Noise: Spike/Outlier: Minor variation about.

©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Time Series and Forecasting Chapter 16.

Chapter 20 Time Series Analysis and Forecasting. Introduction Any variable that is measured over time in sequential order is called a time series. We.

©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Time Series and Forecasting Chapter 16.

Yandell – Econ 216 Chap 16-1 Chapter 16 Time-Series Forecasting.

Analysis of Time Series

Moving average method A quantitative method of forecasting or smoothing a time series by averaging each successive group (no. of observations) of data.

TOPIC 15 Time Series.

Chapter Nineteen McGraw-Hill/Irwin

A season is a time of the year.

Techniques for Seasonality

Spring Summer Autumn Winter

What is Correlation Analysis?

Moving Averages OCR Stage 8.

Moving Averages.

Forecasting Models With Trend and Seasonal Effects

12 Further mathematics Seasonal Indicies.

Time Series Moving Averages

BUSINESS MATHEMATICS & STATISTICS.

Chapter Nineteen McGraw-Hill/Irwin

Today we are going back in time to help us predict the future!!!

Moving Averages.

Presentation transcript:

Ex 4f SEASONAL ADJUSTMENT

SEASONAL AND CYCLICAL TRENDS A seasonal trend is similar to a cyclical trend where there are defined peaks and troughs in the time- series data, except for one notable difference. Seasonal trends have a fixed and regular period of time between one peak and the next peak in the data values. Conversely, there is a fixed and regular period of time between one trough and the next trough.

WHAT CAN BE DONE TO SEASONAL TRENDS??? Seasonal trends can not be adjusted using average smoothing or moving-average. We may just have to accept that the data vary from season to season and treat record individually. We can effectively remove the season on our time series using the technique of seasonally adjusting or ‘deseasonalising’ to remove the seasonal variation and exposing any other trends (secular, cyclic or random) that may be hidden by seasonal variation.

DESEASONALISING TIME SERIES Unemployment figures have been collected over a 5-year period and presented in this table. It is difficult to see any trends, other than seasonal ones. a. Calculate the seasonal indices. b. Deseasonalise the data using the seasonal indices. c. Plot the original and deseasonalised data. d. Comment on your results, supporting your statements with mathematical evidence. Season 2005 2006 2007 2008 2009 Summer 6.2 6.5 6.4 6.7 6.9 Autumn 8.1 7.9 8.3 8.5 Winter 8.0 8.2 Spring 7.2 7.7 7.5 7.6

CHECK YOUR ANSWERS.. Year 2005 2006 2007 2008 2009 Average 7.3750 7.5750 7.5250 7.7750 7.7250 Season 2005 2006 2007 2008 2009 Summer 0.8407 0.8581 0.8505 0.8617 0.8932 Autumn 1.0983 1.0429 1.1030 1.0932 1.0485 Winter 1.0847 1.0825 1.0498 1.0547 1.0744 Spring 0.9763 1.0165 0.9967 0.9904 0.9838 Season Summer Autumn Winter Spring Seasonal index 0.8608 1.0772 1.0692 0.9927 2005 2006 2007 2008 2009 Summer 7.202 7.551 7.435 7.783 8.015 Autumn 7.520 7.334 7.705 7.891 Winter 7.482 7.669 7.388 7.763 Spring 7.253 7.756 7.555 7.656

CLASSWORK/HOMEWORK Exercise 4F pg 187 Q’s 1-5