...Forecasting Project House Sales in USA Presented by: Michelle Deets Ravin Seju Ankan Sinha March 7, 2016 Professor Dawit Zerom ISDS 526 Executive Summary The data in the report “Monthly total houses sold in the United States for the period January 1978 through July 2007” is time series data representing sales in thousands of units. The data has not been seasonally adjusted. Our project was to produce forecasts of housing sales by creating a model using Forecast Pro’s Expert Selection Method. The model was generated by withholding 2 years of data and creating a forecast based upon the data from January 1978 to July 2005. We provided fit measures based upon MAPE and RMSE and evaluated the model’s accuracy MAPE, RMSE, and GMRAE from Forecast Pro’s out-of-sample statistic evaluation table. The MAPE numbers show that the forecast expands from a 7.95% error at the beginning of the holdout period and quickly grows to 32% error within 24 months. The acceptability of this error depends upon which managers are using it. The housing industry touches many fields, from moving to painting to construction to land purchases. This large of an error might could be unacceptable given the amount of risk and resources involved in construction of new single family homes; a manager might prefer to only use the first 6 months of forecasted data to stay within a 10% error range. Looking at the results of the forecast’s graph, the actual data (Exhibit A, represented by the...
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...(IHEC), University of Sfax, Tunisia. A B S T R A C T This paper empirically tests whether the low contemporaneous returns-earnings association set by previous empirical researches may be explained by lack of timeliness of accounting numbers. It hypothesises that if the criteria for accounting recognition yield a multi-period lag in earnings recognitions of economic events and if these events affect an informed market immediately when they occur, then future periods’ earnings would have explanatory for current returns as well as current earnings. To assess the significance of future earnings as an explanatory variable for stock returns we regress at first step annual returns on current earnings and at second step, annual returns on current earnings and successively next period and next two periods’ earnings. Results show that future earnings continue to explain current returns. The evidence is characteristic of a substantial recognition lag in earnings that extends to the immediate next period. However, over one year, earnings do not seem reflecting any relevant economic event impounded in security prices at previous period. The earnings recognition lag seems to decrease after one year. Keywords: Value-relevance, Accounting earnings, Lack of timeliness. INTRODUCTION Accounting recognition is “the process of formally recording or incorporating an item in the accounts and financial statements of an entity” (FASB 1980, SFAC N°. 3 par. 83)1. Most accounting standards have strict...
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...Applied Science and Engineering Technology Centre for the Built Environment PROJECT MANAGEMENT- ENVIRONMENT GRADUATE CERTIFICATE 1 Today’s Agenda • • • • • Review and case assignments for Tutorial 1 due today Textbook status Cover material for Tutorial 2 Begin to work on review and case assignments for Tutorial 2 Next week: complete review and case assignments for Tutorial 2 • Tutorial 2 assignments are due next Monday, January 28 2 Tutorial 2: Creating a Project Schedule Microsoft Project 2010 ® In Tutorial Section 2.1 you will: • • • • • • Start a new project Examine scheduling defaults Change a project and task calendar Enter and edit tasks and durations Enter and edit recurring tasks and milestones Enter lag and lead times New Perspectives on Microsoft Project 2010 4 In Tutorial Section 2.2 you will: • • • • • • Enter and edit task dependencies View project statistics Show the project summary Review project statistics Create and manipulate summary tasks Develop a work breakdown structure New Perspectives on Microsoft Project 2010 5 Scheduling • By default, the new project file is scheduled from a project Start date • All tasks are scheduled to begin as soon as possible • Project 2010 calculates the project’s Finish date based on the: – Tasks – Durations – Dependencies New Perspectives on Microsoft Project 2010 6 Project Information Dialog Box New Perspectives on Microsoft Project 2010 7 Scheduling a Project...
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...the period 1965 to 2004 is selected and the hold-out 2005 to 2009 is used to examine the out-of-sample forecasting performance. To begin with, the GDP –China graph is plotted by using the STATA command in order to check overall the trend. Next, it is necessary to tell the STATA that this dataset is the time series format by using “tsset time” command. Then, the autocorrelations and partial autocorrelation are used to examine that whether the series is non-stationary or not. As can be seen the autocorrelation and partial autocorrelation graph below, they can be suggested that the series is non-stationary because the AC graph The series is non-stationary as the autocorrelations decrease slowly as the number of time lags increases and the partial autocorrelations show a large spike close to one at lag 1. Autocorrelation (AC) Partial autocorrelations (PAC) To estimate the fitted model, there are four possible trends that are chosen to compare as follows: Linear trend model In order to forecast the trend, we start to fit a linear trend model to the data by regressing the GDP on a constant and a linear time trend. The p-value of the t- statistic on the time trend is zero and the regression’s R2 is high so it can be implied that the trend appears highly significant. Moreover, as can be seen in the residual graph, it can be concluded that the linear trend is inadequate due to the fact that the actual trend is nonlinear. Residual Additionally, the linear...
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...Journal of Abnormal Psychology 2003, Vol. 112, No. 4, 558 –577 Copyright 2003 by the American Psychological Association, Inc. 0021-843X/03/$12.00 DOI: 10.1037/0021-843X.112.4.558 Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling David A. Cole Vanderbilt University Scott E. Maxwell University of Notre Dame R. M. Baron and D. A. Kenny (1986) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data. Graduating from cross-sectional to longitudinal designs enables researchers to make more rigorous inferences about the causal relations implied by such models. In this transition, misconceptions and erroneous assumptions are the norm. First, we describe some of the questions that arise (and misconceptions that sometimes emerge) in longitudinal tests of mediational models. We also provide a collection of tips for structural equation modeling (SEM) of mediational processes. Finally, we suggest a series of 5 steps when using SEM to test mediational processes in longitudinal designs: testing the measurement model, testing for added components, testing for omitted paths, testing the stationarity assumption, and estimating the mediational effects. Tests of mediational models have been an integral component of research in the behavioral sciences for decades. Perhaps the prototypical example of mediation was Woodsworth’s (1928) S-O-R model, which suggested...
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...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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...Continuing the example .................................................................................................. 9 Reading Stata Output ...................................................................................................... 9 2 STATA LANGUAGE ................................................................................................... 12 Basic Rules of Stata ...................................................................................................... 12 Stata functions ............................................................................................................... 14 Missing values .......................................................................................................... 16 Time series operators. ............................................................................................... 16 Setting panel data. ..................................................................................................... 17 Variable labels. ......................................................................................................... 18 System variables ....................................................................................................... 18 3 DATA MANAGEMENT............................................................................................... 19 Getting your data into Stata .......................................................................................... 19 Using the data editor...
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...first success on the way of setting-up SEC in Vietnam. VN-Index of the HSEC, however, has experienced a truly ups and downs movement and changed considerable during almost last two years. In the first section on July 28, 2000, VN-Index was 100 points and increased to a peak of 571.04 points in June 25, 2001 before sliding to lower 150 points in the first months of the year 2003. Therefore, the problem is: Why they fluctuated so much, lack of orientation, and whether or not they reflected to some extent the real health of the related stock companies? From a common sense, some experts from the brokerage firms said that the stock prices at the HSEC has been fluctuated by “sheep flock effect” psychology of “naive speculators” (short time but...
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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 in the activity and the method of measurement. Approaches to time Series Forecasting: There are two basic approaches to forecasting time series: the self-projecting time series and the cause-and-effect approach. Cause and effect methods attempt to forecast based on underlying series that are believed to cause the behavior of the original series. The self-projecting time series uses only the time series data of the activity to be forecast to generate forecasts. This latter approach is typically less expensive to apply and requires far less data and is useful for short to medium-term forecasting. Box-Jenkins Forecasting Method: The univariate version of this methodology is a self- projecting time series forecasting method. The underlying goal is to find an appropriate formula so that the residuals are as small as possible and exhibit no pattern. The model- building process involves four steps. Repeated as necessary, to end up with a specific formula that...
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...the same time, but on the other hand, those same files will soon be out of date and remain un-updated until the next group updated, creating a time lag (Chaffey, 2004). The time lag occurs between the time of the actual event and when it is recorded and imputed into the system. For example, batch processing is often used with pay-roll systems because the information only needs to be updated at the set intervals of the pay cycles, and does not need to be opened up other than that time. Real-time systems are a little bit different, because they do not process information together in groups but they process transaction as they occurs, keeping files updated constantly, eliminating the time lag. Real time systems are used when information may need to be accessed and updated at any given time (Chaffey, 2004). For example, a sales order processing system would work better with real-time processing. This would enable information to be accessed at any given time, allowing customer’s questions to be answered immediately, and not have to wait until the next time the information is updated for an accurate answer (Chaffey, 2004). Resource use is one characteristic used to distinguish between batch and real-time systems. Batch processing is usually preferred when looking at it from this aspect because it requires the use of fewer resources than real time systems (Chaffey, 2004). It requires fewer resources such as programmer time, computer time, hardware, and user training. Real-time systems...
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...ABSTRACT Reaction time is one of the important methods to study a person’s central information processing speed and coordinated peripheral movement response. Simple reaction time is usually defined as the time required for an observer to detect the presence of a stimulus. The main purpose of this study is to determine whether there is a significant relationship between distance and the reaction time of an individual to light whereas distance is the independent variable and RT is the dependent variable. There were two participants involved in this study; the subject and the experimenter. The students were instructed to pick a laboratory partner and test how fast the participant will tap the table or chair as soon as he/she sees the light flashes while the experimenter holds it and the stopwatch to record the reaction time within the distance of 20 and 30 feet with 10 trials each. Based on the results made by an 18 year old female subject, there were minimal differences on 20 ft and 30 ft but it was not enough to conclude that there is a relationship between distance and reaction time. Therefore, the hypothesis was proven to be precise by the data that was collected. However, there are some indications that affect the delay in reaction time of an individual such as: age, gender, predictability, preparation, and other external factors. INTRODUCTION Reaction is a purposeful voluntary response to an external stimulus. From the start of the experiment, the experimenter hypothesized...
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...does not work, use File.xls Choice of sample period: Sample / @all @first @last 1990 2010 1981Q3 2005Q1 1960M1 2000M11 in command line e.g.: smpl @first 1990 Univariate statistics: Click series / View / Spreadsheet Graph Descriptive Statistics&Tests Correlogram data as numbers Graphics z.B. histogram, mean, etc. autocorrelationen Generation/Transformation of series: Generate / x = 0 generates a series with zeros Generate / pi = (pc – pc(-1))/pc(-1)*100 Generates the inflation rate in % based on prices pc Generate / x = log(y) taking logs Generate / dlx = dlog(x) dlx = log(x) – log(x(-1)) Growth rate in continuous time Generate / y = exp(x) exp(x) as command: series x=0 Trend variable (linear): Generate / t = @trend Standard normal distributed realizations: Generate / x = nrnd Lags, lagged variables, taking differences: Generate / x1 = x(-1) x1(t) = x(t-1), Lag 1 of x Generate / dx = d(x) dx(t) = x(t) – x(t-1) = (1-B)x(t) first difference Generate / d2x = d(x,2) d2x(t) = dx(t) – dx(t-1) = (1-B)^(2)x(t) taking first differences twice Generate / d12x = d(x,0,12) d12x(t) = x(t) - x(t-12) = [1-B^(12)]x(t) seasonal difference for monthly data Generate d12_1x = d(x,1,12) d12_1x(t) = (1-B)[1-B^(12)]x(t) Geneartion of dummy variables: seasonal dummies: s=1,2,3,... Generate / ds = @seas(s) as command: series ds = @seas(s) Generate / d1 = 0 and manually in View/Spreadsheet use Edit+/p-value for x of a test statistic as command: (N-, t-, scalar p scalar p scalar p scalar p scalar p ...
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...(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 the underlying trend. It typically takes a sustained move of at least 30K in claims to signal a meaningful change in job growth. Goal: The goal of this project is to model seasonally adjusted initial claims and then predict the initial claims for the future because it is used by financial institutions, especially in currency trading, to get a better idea about the direction of the United States economy. Note: The data is acquired from the Federal Reserve Economic Data website: http://research.stlouisfed.org/fred2/series/ICSA II. Exploratory Data Analysis The plot of the time series...
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...How well does the River Tillingbourne match the Bradshaw Model? As you travel further downstream a river, the rivers discharge increases. The water discharge increases as it flows downstream it meets more streams and also collects rain from the increased catchment area. As you travel further downstream a river, the rivers discharge increases. The water discharge increases as it flows downstream it meets more streams and also collects rain from the increased catchment area. Channel depth increases downstream. As there is an increased discharge, you have much more energy for erosion, also a larger body of water. Channel depth increases downstream. As there is an increased discharge, you have much more energy for erosion, also a larger body of water. The Bradshaw describes how a river's characteristics vary between the upper course and lower course of a river. Load particle size decreases as you travel downstream. The load the river carries is broken down into smaller material via processes such as attrition. Load particle size decreases as you travel downstream. The load the river carries is broken down into smaller material via processes such as attrition. Although we haven’t studied the River Tillingbourne specifically, we have studied the River Severn, which has similar characteristics such as meanders. We should still expect similar features in aspects such as velocity and discharge. It is important to study rivers as it means you can research characteristics...
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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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