# 1 Pertemuan 24 Deret Berkala, Peramalan, dan Angka Indeks-2 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1.

## Presentasi berjudul: "1 Pertemuan 24 Deret Berkala, Peramalan, dan Angka Indeks-2 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1."— Transcript presentasi:

1 Pertemuan 24 Deret Berkala, Peramalan, dan Angka Indeks-2 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1

2 Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Menghubungkan beberapa deret berkala bagi penyusunan aangka indeks, peramalan dengan menggunakan metode rata-rata bergerak, dan exponential smoothing

3 Outline Materi Metode Rata-rata Bergerak Metode Exponential Smoothing Angka Indeks

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-4 Forecasting a Multiplicative Series: Example 12-3

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-5 Multiplicative Series: Review

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-6 Smoothing is used to forecast a series by first removing sharp variation, as does the moving average. Exponential smoothing is a forecasting method in which the forecast is based in a weighted average of current and past series values. The largest weight is given to the present observations, less weight to the immediately preceding observation, even less weight to the observation before that, and so on. The weights decline geometrically as we go back in time. 0-5-10-15 0.4 0.3 0.2 0.1 0.0 Lag W e i g h t Weights Decline as We Go Back in Time and Sum to 1 Weights Decline as we go back in Time -10 0 Weight Lag 12-5 Exponential Smoothing Methods

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-7 The Exponential Smoothing Model

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-8 DayZw=.4w=.8 1925925.000925.000 2940925.000925.000 3924931.000937.000 4925928.200926.600 5912926.920925.320 6908920.952914.664 7910915.771909.333 8912913.463909.867 9915912.878911.573 10924913.727914.315 11943917.836922.063 12962927.902938.813 13960941.541957.363 14958948.925959.473 15955952.555958.295 16*953.533955.659 DayZw=.4w=.8 1925925.000925.000 2940925.000925.000 3924931.000937.000 4925928.200926.600 5912926.920925.320 6908920.952914.664 7910915.771909.333 8912913.463909.867 9915912.878911.573 10924913.727914.315 11943917.836922.063 12962927.902938.813 13960941.541957.363 14958948.925959.473 15955952.555958.295 16*953.533955.659 Original data: Smoothed, w=0.4:...... Smoothed, w=0.8: ----- Day 151050 960 950 940 930 920 910 w =. 4 Exponential Smoothing: w=0.4 and w=0.8 Example 12-4

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-9 Example 12-4 – Using the Template

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-10 An index number is a number that measures the relative change in a set of measurements over time. For example: the Dow Jones Industrial Average (DJIA), the Consumer Price Index (CPI), the New York Stock Exchange (NYSE) Index. 12-6 Index Numbers

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-11 Index Index Year Price 1984-Base 1991-Base 1984121100.0 64.7 1985121100.0 64.7 1986133109.9 71.1 1987146120.7 78.1 1988162133.9 86.6 1989164135.5 87.7 1990172142.1 92.0 1991187154.5100.0 1992197162.8105.3 1993224185.1119.8 1994255210.7136.4 1995247204.1132.1 1996238196.7127.3 1997222183.5118.7 Index Index Year Price 1984-Base 1991-Base 1984121100.0 64.7 1985121100.0 64.7 1986133109.9 71.1 1987146120.7 78.1 1988162133.9 86.6 1989164135.5 87.7 1990172142.1 92.0 1991187154.5100.0 1992197162.8105.3 1993224185.1119.8 1994255210.7136.4 1995247204.1132.1 1996238196.7127.3 1997222183.5118.7 Year P r i c e Price and Index (1982=100) of Natural Gas Price 199519901985 250 150 50 Original Index (1984) Index (1991) Index Numbers: Example 12-5

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-12 Example 12-6: Adjusted YearSalarySalary 19802950011953.0 19813100011380.3 19823360011610.2 19833500011729.2 19843670011796.8 19853800011793.9 Example 12-6: Adjusted YearSalarySalary 19802950011953.0 19813100011380.3 19823360011610.2 19833500011729.2 19843670011796.8 19853800011793.9 C P I Year 450 350 250 150 50 1995199019851980197519701965196019551950 Consumer Price index (CPI): 1967=100 Consumer Price Index – Example 12-6

COMPLETE 5 t h e d i t i o n BUSINESS STATISTICS Aczel/Sounderpandian McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., 2002 12-13 Example 12-6: Using the Template

14 Penutup Deret Berkala pada dasarnya bertujuan untuk mengidentifikasi faktor-faktor atau komponen deret berkala (trend, variasi musim, perilaku siklus dan variasi lainnya) yang selanjutnya digunakan sebagai landasan untuk meramalkan nilai-nilai tersebut di masa mendatang

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