from expensive to inexpensive, as well as simple to complex. Forecasting techniques can be qualitative, and others can be quantitative. Salvatore focuses on qualitative forecasts. These forecasts include: time-series, smoothing techniques (moving averages), barometric forecasts with leading indicators, econometric forecasts, and input-output forecasts. c) A firm determines the most suitable forecasting method to use by using the following criterion: 1. the cost of preparing the
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APPLICATION OF ARIMA MODEL FOR TESTING “SERIAL INDEPENDENCE” OF STOCK PRICES AT THE HSEC Cao Hao Thi – Pham Phu – Pham Ngoc Thuy School of Industrial Management HoChiMinh City University of Technology ABSTRACT The paper is an attempt to test the “serial independence” of stock prices at HoChiMinh City Stock Exchange Center (HSEC) in Vietnam by applying the ARIMA model for preliminary assessment in terms of its market efficiency. From findings derived, it appears to be
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economic changes, so four-week moving averages are typically used for the initial claims metric. Initial jobless claims measure the number of filings for state jobless benefits. This report provides a timely, but often misleading, indicator of the direction of the economy, with increases (decreases) in claims potential signaling slowing (accelerating) job growth. On a week-to-week basis, claims are quite volatile, and many analysts therefore track a four week moving average to get a better sense of
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a. Calculate the forecast for periods 7 through 11 using moving average models with n=2, n=4, and n=6 b. Calculate the bias and MAD for each set forecasts. Which moving average model is best? Table for n=2 Period Demand Moving Average for n=2 MFE MAD 1 40 2 33 3 56 4 43 5 23 6 45 7 38 34 4 4 8 40 41.5 -1.5 1.50 9 29 39 -10 10.00 10 40 34.5 5.5 5.50 11 34.5 -2 21.00 Period Demand Moving Average for n=4 MFE MAD 1 40 2 33 3 56 4 43 5 23
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Box-Jenkins Methodology Introduction Forecasting Basics: The basic idea behind self-projecting time series forecasting models is to find a mathematical formula that will approximately generate the historical patterns in a time series. Time Series: A time series is a set of numbers that measures the status of some activity over time. It is the historical record of some activity, with measurements taken at equally spaced intervals (exception: monthly) with a consistency
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Crisis Period Forecast Evaluation of the DCC-GARCH Model Yang Ding Andrew Schwert Dr. Emma Rasiel & Professor Aino Levonmaa, Faculty Advisors Honors thesis submitted in partial fulfillment of the requirements for Graduation with Distinction in Economics in Trinity College of Duke University Duke University Durham, North Carolina 2010 Acknowledgements We would like to thank Dr. Emma Rasiel and Professor Aino Levonmaa for their invaluable direction, patience, and guidance throughout
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an informal way, and extensive examples using S-PLUS are used to build intuition. Section 3.2 discusses time series concepts for stationary and ergodic univariate time series. Topics include testing for white noise, linear and autoregressive moving average (ARMA) process, estimation and forecasting from ARMA models, and long-run variance estimation. Section 3.3 introduces univariate nonstationary time series and defines the important concepts of I(0) and I(1) time series. Section 3.4 explains
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research CHAPTER 1 INTRODUCTION 1.1 BACKGROUND OF INDUSTRIAL TRAINING All final year students of Bachelor of Sciences (Hons) (Statistics), Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM) are required to undergo the industrial training. The students will be placed in the government or private organizations of their choice for a period of three months, during which they are also required to design a research
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. . . . . . . . . . . . . . . . 1 Models for time series 1.1 Time series data . . . . . . . . . . . . . 1.2 Trend, seasonality, cycles and residuals 1.3 Stationary processes . . . . . . . . . . 1.4 Autoregressive processes . . . . . . . . 1.5 Moving average processes . . . . . . . . 1.6 White noise . . . . . . . . . . . . . . . 1.7 The turning point test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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forecasting volatility has increased many-fold in recent times, with a special emphasis on forecasting. There are several types of techniques available for forecasting volatility, with extraordinary diversity of procedure such as the Autoregressive Moving Average (ARMA) models, Autoregressive Conditional Heteroscedasticity (ARCH) models, Stochastic Volatility (SV) models, regime switching and threshold models. (Xiao and Aydemir, 2007:1) A broad division between the techniques is based on primary assumptions
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