words‚ either quoted directly or paraphrased. We also certify that this paper was prepared by us specifically for this course. Student’s Signature: BASS Instructor’s Grade on Assignment: Instructor’s Comments: TITLE OF RUBRIC: Case Analysis (Page 1 of 2) Course: QNT 5040 LEARNING OUTCOME/S: (see syllabus) Date: PURPOSE: To facilitate effective decision making under uncertain conditions by quantifying risk. Name of Student: VALIDITY: Best practices in Monte Carlo simulation.
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smoothing methods allow a smoothing parameter to change over time‚ in order to adapt to changes in the characteristics of the time series. However‚ these methods have tended to produce unstable forecasts and have performed poorly in empirical studies. This paper presents a new adaptive method‚ which enables a smoothing parameter to be modelled as a logistic function of a user-specified variable. The approach is analogous to that used to model the time-varying parameter in smooth transition models. Using
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methods: moving averages‚ exponential smoothing‚ and trend projection. Moving Averages CD file Gasoline To show how Minitab can be used to develop forecasts using the moving averages method‚ we will develop a forecast for the gasoline sales time series in Table 18.1 and Figure 18.5. The sales data for
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Study Guide for the Second Exam Aggregate Production Planning (APP) 1. What are the major inputs‚ constraints‚ and outputs of the aggregate production plan (APP)? Inputs - Strategic objectives of the corporation‚ policies‚ demand. Constraints - financial constraints (cash) and capacity constraints (machining capacity‚ limited labor in certain skill category‚ a critical component and/or raw material.) Outputs - is to determine the gross levels of inventory‚ overtime‚ subcontracting‚ backordering
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returns in India. The study by taking into consideration the domestic gold prices and stock market returns based on BSE 100 index‚ investigates the Granger causality in the Vector Error Correction Model for the period January 1991 to December 2009. The analysis provides the evidence of feedback causality between the variables. It infers that the Gold prices Granger-causes stock market returns and stock market returns also Granger-causes the gold prices in India during the sample period. Thus‚ both the variables
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Bank‚ and use an ARMA-GARCH design to model the heteroskedasticity in both of these series. The input from a range of some important auxiliary tests is also taken into account. The report has the following structure. Section two describes the data. On the other hand‚ the empirical analysis and interpretation of the results is conducted in section three. Finally‚ the last section concludes. Data In our analysis‚ we employ data collected from Thompson Reuters’ Datastream on 5-year CDS spreads
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referred to as a computer application that is used for mining data‚ authoring of surveys analysis of statistics‚ carrying out analysis of texts as well as deployment and collaboration. The fundamental features of SPSS include data analysis modules which are inclusive of arithmetical descriptions like plots‚ frequencies‚ charts‚ lists as well as complex procedures in statistics the table on variance analysis (ANOVA). Recently‚ an increasing endeavor has been made in a bid to establish testing procedures
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GDP (INV) and Export as percentage of GDP (EXP) have been selected for judging the impact of public debt burden (DB) on these variables. The study period is 1980-81 to 2011-12. Augmented Dickey-Fuller test has been used to diagnose whether the time series data are non-stationary. Granger Causality test has been performed to identify whether DB can be used for prediction of GDP‚ MANF‚ INV and EXP‚ and vice-versa. Then on the basis of the result of Johansen co-integration test‚ Vector Autoregressive
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to forecast demand 3. Identify the three forecasting time horizons. State an approximate duration for each. 1. Short-range forecast: Used for planning purchasing‚ job scheduling‚ workforce levels‚ job assignments‚ and production levels. Time span is up to 1 year‚ but generally less than 3 months. 2. Medium-range forecast: Used in sales planning‚ production planning and budgeting‚ cash budgeting‚ and analysis of operating plans. Time span is from 3 months to 3 years. 3. Long-range forecast:
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1) Raw data‚ not seasonalized 2) Seasonal Adjustment used: Census II X-12 multiplicative (MASA): Used because of the presence of seasonal variations that are increasing with the level of my series. Increasing degree of variability overtime… TX non seasonalized and seasonalized 3) Combined seasonally adjusted with non-seasonally adjusted De-seasonalizing the data helped with the removal of seasonal component that creates higher volatility in model. Now‚ variations
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