...Time Series Analysis Summary Tokelo Khalema 2008060978 BSc. Actuarial Science University of the Free State Bloemfontein November 1, 2012 Time Series Analysis A time-series is a stochastic process {Xt : t = 1, . . . , T } with a continous state space and discrete time domain. It arises naturally as an ordered series of values observed over time. Examples include daily closing prices of a stock index recorded over several years, say, the flow rate of the River Nile, road casualties in Great Britain over the years 1969-84, etc. Stationary time-series are particularly easy to analyse. A series is stationary if its mean and variance are constant over time. Special aids are available to help determine whether or not a series is stationary. Particularly notable in this regard are the autocorrelation function (ACF) and the partial autocorrelation function (PACF). These are plots of the sample autocorrelation and partial autocorrelation coefficients at various time lags, respectively. If the ACF decays gradually to zero, then the series is non-stationary. If on the other hand the ACF and PACF decay rapidly to zero, then the series is stationary. A series being non-stationary can be brought about by, among others, a trend, irregular fluctuations, or seasonal variation. Non-constant variance, or as commonly called, heteroscedasticity can be eliminated by using a variance-stabilising transformation. A number of ways exist that eliminate a trend. Two of which are, to subtract a regression line...
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...Prediction and Analysis of the Gap of Per Capita Annual Income between Rural and City Households based on ARMA model Abstract This paper applies ARMA model into the analysis of the gap of per capita annual income between rural and city households from 1978 to 2011. Firstly, it builds up several ARMA models based on 1978-2009 data; and then predicts the income gaps of 2010 and 2011. Compared to the real income gaps of 2010 and 2011, this paper shows that ARMA model works well in this time series prediction. Keyword B-J model, ARMA model, per capita annual income of rural and city households 1 Introduction With the rapid development of the China economy, people’s life levels are rising year after year. However, there’re many social problems in the booming economy, such as the income gap between rural and city households. The increasing gap of per capita annual income between rural and city households has been a heat topic in the society, since it may result in the inequality in the social welfare distributions. Also, it will discourage the rural households’ working efficiency and reduce their happiness. In order to deal with this social problem in a proper way, many researchers have been thinking about it and offering advice to the policy makers. LU (2004) says that the phenomenon of urbanization has an important effect on the income gap between rural and city households [1]. YAO (2005) analyzes the relationship between the unbalanced financial development and the income gap...
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...For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn't perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of people 5'5'' is less than the average weight of people 5'6'', and their average weight is less than that of people 5'7'', etc. Correlation can tell you just how much of the variation in peoples' weights is related to their heights. Although this correlation is fairly obvious your data may contain unsuspected correlations. You may also suspect there are correlations, but don't know which are the strongest. An intelligent correlation analysis can lead to a greater understanding of your data. Techniques in Determining Correlation There are several different correlation techniques. The Survey System's optional Statistics Moduleincludes the most common type, called the Pearson or product-moment correlation. The module also includes a variation on this type called partial correlation. The latter is useful when you want to look at the relationship between two variables while removing the effect of one or two other variables. Like all statistical techniques, correlation is only appropriate for certain kinds of data. Correlation works for quantifiable data in which numbers are meaningful, usually quantities of some sort. It cannot be used for purely categorical data, such...
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...c t Behavioral economics tells us that emotions can profoundly affect individual behavior and decisionmaking. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from largescale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public’s response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured...
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...page 57 Printer: Opaque this 3 Time Series Concepts 3.1 Introduction This chapter provides background material on time series concepts that are used throughout the book. These concepts are presented in 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 univariate long memory time series. Section 3.5 covers concepts for stationary and ergodic multivariate time series, introduces the class of vector autoregression models, and discusses long-run variance estimation. Rigorous treatments of the time series concepts presented in this chapter can be found in Fuller (1996) and Hamilton (1994). Applications of these concepts to financial time series are provided by Campbell, Lo and MacKinlay (1997), Mills (1999), Gourieroux and Jasiak (2001), Tsay (2001), Alexander (2001) and Chan (2002). 58 3. Time Series Concepts 3.2 Univariate Time Series 3.2.1 Stationary and Ergodic Time Series Let {yt } = {. . . yt−1 , yt , yt+1 , . . .} denote a sequence of random variables indexed by some time subscript t. Call such a sequence...
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...slump, government excise taxes and other factors such as decreased numbers in both tourist arrivals to the Caribbean island and beer exports to the U.S. As purchasing manager, Benson’s prime responsibility was maintaining adequate inventory levels for all goods and materials used in the company’s production processes, including the purchase of new bottles and the scheduling of deliveries (Erskine, 2004). State the Assignment Question As purchasing manager, Benson was responsible for all goods and materials used in the company’s production process, inclusive of new bottles purchase. Benson had to be cognizant of the fact that orders for new bottles had to be ordered four months in advance to allow for supplier transportation and lead times....
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...Research Approach and Scope - 3 - 1.4. Layout of the Report - 4 - 1. Introduction 2.1. Research Background Volatility is an important concept in finance. Volatility modelling and forecasting finds usage in several core financial operations, for instance – many asset-pricing models use volatility as an estimation parameter for simple risk; several famous option pricing formulas such as Black-Scholes use volatility; volatility estimates and forecasts are crucial for portfolio management and also in hedging risk. Because of the importance of volatility, as can be seen from the examples above, the interest in modelling and 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 of constant variance i.e. homoscedastic e.g. AMA models, or non-constant variance i.e. heteroscedastic or time-varying e.g. GARCH models. The GARCH i.e. Generalized ARCH models introduced past conditional volatility as an explanatory variable of the forecast volatility in addition to volatility new that was already a part of the original ARCH model...
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...QMT 3001 BUSINESS FORECASTING TERM PROJECT Sales Revenue Forecasting of Tat Company 20.05.2014 DOKUZ EYLUL UNIVERSITY 2009432015 – MERT ALİ BOZKURT 2009432021 – BURAK CANBAY 1. INTRODUCTION Firstly, we analyzed the quarterly financial reports of Tat Company that shows us sales revenue of quarterly from 2008 to the first quarter of 2014. And then we created our data tables by using annual reports. Our aim is to forecast last three quarters of 2014 based on the sales revenues of the past years. There are many types of variables that affect the sales of companies like demand, cost, economic and political conditions etc. however we handled macro factors in the economy as inflation rates and gross domestic products. Most of big companies determine their sales revenue forecast for planning the budgeting using various forecasting models. Companies that do not implement these forecasting models may have some problems about the financial situation in the future. When we focus on the food sector in Turkey, we see that Turkey has started to be an effective player in the world food and beverage market every passing day. At the same time Turkey is ranked at 7th biggest agriculture country with the 62 billion dollars of agricultural revenue. Consumers have become more conscious about well-balanced and healthy nutrition. Along with this developments and changes leads to improvement of the food and beverage industry. Food and Drink Industry Associations of Turkey determine...
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...Foreign direct investment And Economic Growth in Bangladesh Internship program at Brac Bank Ltd. Internship Report On “Foreign direct investment And Economic Growth in Bangladesh and Internship program at Brac Bank Ltd.” The Internship report is submitted to the Department of Finance, University of Dhaka for the partial fulfillment of the requirement of BBA program. Submitted to: Department of Finance University of Dhaka Supervised by: Mohammad Jahangir Alam Chowdhury Professor Department of Finance University of Dhaka Submitted by: Zarin Tasnim ID: 17-009 Section: A Department of Finance University of Dhaka Signature of the Supervisor Date of Submission: 7th May, 2015 Letter of Transmittal 7th May, 2015 Mohammad Jahangir Alam Chowdhury Professor Department of Finance University of Dhaka Subject: Submission of Internship Report on Foreign direct investment and Economic Growth in Bangladesh. Dear Sir, It is an absolute pleasure for me to submit the Internship Report titled “Foreign direct investment and Economic Growth in Bangladesh” as a significant part of the BBA program. While making this report, I have experienced a fair knowledge about Foreign direct investment and economy of Bangladesh and its impact on the growth of Bangladesh. I have tried my best to follow your guidelines in every aspect of preparing this report. I have collected what I believed...
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...Executive Summary Dumitri Mironescu is the owner of a limousine company in Las Vegas which currently consists of 17 vehicles. During the year of 2012, Dumitru decided that it was time to replace three of the company’s 17 vehicles. In addition, Dumitru wanted to add two new vehicles to his fleet of limousines. Dumitru submitted a business plan to the bank to finance his purchases. After reviewing his business plan, the bank was not comfortable with the company’s revenue forecast and needed further convincing. Dumutri got help from his son to prepare a forecast for visitors to Las Vegas in 2013. Revenue for the limousine service is driven by the amount of visitors to the area. Therefore, in order to forecast the company’s revenue, Denis will have to prepare a forecast for visitors to Las Vegas using different forecasting methods. The best forecast method will be chosen and will help determine the growth of revenue; and ultimately decide whether Dumitru should replace three vehicles as well as add two additional vehicles to his fleet. Background In 1983, Dumitru Mironescu and his family fled Romania and settled in Las Vegas. In order to help his family survive, Dumitru had given up ideas of furthering his education and took a job at a major hotel as a parking attendant until he learned to speak English. His incredible attention to detail and friendly interaction with everyone was noticed. Eventually his hard worked paid off and he was offered the position as the driver for...
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...gas-powered vehicles for their demand, and there are now around 120,000 on U.S. roads (American Petroleum Institute). With the ever-increasing presence of natural gas in our lives, it is important to know how it affects us on the consumer level. More importantly, it is essential to understand how it affects us monetarily. When we can understand trends and forecast the pricing of natural gas, we can improve our financial planning, resource allocation, etc. II. Data Set Description and Methods Used This paper will be conducting a time series analysis on U.S. Natural Gas prices from January 2002 through December 2012. Prices were collected by the Energy Information Administration on a monthly basis, and the prices are measured in dollars per thousand cubic feet. The raw data set is much more extensive, and measures data such as wellhead price, import and export price, etc. I chose to neglect this in my data set for simplicity sake and because it is more applicable to look at prices directly affecting everyday individual consumers. The methods used in the time series analysis of U.S. Natural Gas Prices include non-seasonal differencing, exploring autocorrelation and partial autocorrelation functions, ARIMA modeling, conducting diagnostics on modeling by looking at residual normality [QQ plot, Shapiro Test], Box-Pierce...
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...for the firm’s long-term growth. Forecasting helps make decisions by using macroforecasts of the general economic activity as inputs for their microforecasts of the industry’s and firm’s demand and sales. Forecasting helps decide a firm’s marketing strategy, production needs, sales forecast, and helps predict financial needs such as cash flow, profits, and outside financing. Furthermore, it helps make personal based decisions, as well as assist for the long-term future of the firm (Salvatore, 2012). b) Forcasting types range 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 forecast and the benefit that results from its use 2. the lead time in decision making 3. the time period of the forecast (short or long term) 4. the level of accuracy required 5. the quality and availability of the data...
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...Discussion Question #2 Week 5 Lynnlee Palmer RES/342 July 2012 Biman Ghosh Discussion Question #2 Week 5 Components of a Time Series: (1) Secular trend The term “trend” is commonly used in day-to-day conversation. We often complain about the rising trend of population, prices etc. “Trend”, also called “Secular” or “long-term” trend is the basic tendency of production, sales, income, employment etc. to grow or decline over a period. It includes steady movements over a long time and excludes short-range oscillations. Secular trend is attributable to factors such as population change, technological progress, or large-scale shifts in consumer tastes. More populations call for more food, more clothing, and more housing. Technological changes, discovery, or depletion of resources, improvements in business organization and Government intervention in the economy is other major causes for the growth or decline of many economic time series. Secular trends may be linear or nonlinear. (2) Seasonal variation Seasonal variations are those periodic movements in business activity, which occur regularly every year and have their origin in the nature of the year itself. Since they repeat over a period of 12 months, they can be predicted accurately. Almost any type of business activity is susceptible to a seasonal influence to a greater or lesser degree and as such, these variations are regarded as normal phenomena during every year. Although the word...
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...BSOP330 QUIZ: QUIZ 1: 1. Question : (TCO 7 & 8) Gradual, long-term movement in time series data is called ____________. Student Answer: seasonal variation cycles trends exponential variation random variation Instructor Explanation: Trend is the gradual movement of data up or down over time. Reference Heizer 10th edition page 108 and 119-120. Points Received: 0 of 4 Comments: 2. Question : (TCO 7 & 8) Time series data may exhibit which of the following behaviors? Student Answer: Trend Random variations Seasonality Cycles They may exhibit all of the above. Instructor Explanation: Because time series data contains past data, predicting the future with 100% accuracy is impossible. Sales trends, random variations, seasonality and purchasing cycles will always be present. Therefore all four selections are correct. Reference Heizer 10th. edition page 108. Points Received: 4 of 4 Comments: 3. Question : (TCO 6 & 8) Given an actual demand of 61, a previous forecast of 58, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing? Student Answer: 45.5 57.1 58.9 61.0 65.5 Instructor Explanation: Exponential Smoothing = Old Forecast + (Actual Demand - Old Forecast) = 58.0 + .3(61 - 58) = 58.0 + .9 = 58.9 Points Received: 0 of 4 Comments: 4. Question :...
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...ISCOM 471 December 24, 2012 Planning and Controlling the Supply Chain Within the many different organizations in todays business world there are some key factors that will establish the profitability, growth, and longevity of a company. Some of the things we will discuss will be that of how my chosen corporation American Express applies some of the forecasting techniques to better develop the company. There will also be the analysis of production plans, master production schedules, and carrying inventory and how it relates to the overall American Express budget. Along with all the above we will also compare and contrast how planning usage differentiates between a service organization such as American Express and a manufacturing organization. Lastly we will also compare and contrast the use of material requirements planning system concepts. When it comes to forecasting it is first important to determine the different types of forecasting and how they are classified. In forecasting there are four basic types which are qualitative, time series analysis, casual relationship, and simulation. The first forecasting type qualitative is "subjective or judgmental and are based on estimates and opinions", (Chase, Jacobs, & Aquilano, 2006). Some of the main characteristics of qualitative forecasting are market research which is encompassed by collecting data by surveys and interviews which help determine market hypothesis. This research is most commonly used for long range and new...
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