ECON90015 Managerial Economics
Managerial Economics, Custom Book Edition, Pearson, 2012. This is available from the main bookshop.

Assessment Task

Individual or Group

Hard copy or Electroni c Hardcopy in lecture

Due

Assessment Weighting

Mid-semester exam

Individual

In the week commencing Monday April 15 (Week 6) 4pm on Monday May 6 (Week 9)

10%

Assignment

Group

Electronic

20%

End-of semester exam

Individual

Hardcopy

70%

FNCE90060 Financial Management
Assessment Task Assignment 1 (group of 2 max) Assignment assignment) 2 (group Due Lecture time of week beginning 25 March Lecture time of week beginning 13 May Week 7 – Friday 19 April from 3.30-4.30pm in Wilson Hall exam Assessment period Weighting 10%

10%

Mid-semester exam

30%

End-of-semester (2-hour)

50%

MGMT90031 Project Management
Meredith, J.R., & Mantel Jr, S.J. (2012). Project Management: A Managerial Approach (8th ed.) New York: Wiley. Assessment Task Individual or Group Hard copy or Electronic Poster presentation in class Electronic Due Assessmen t Weighting

Assignment 1

Goup

In class, weeks 5 to 7

5%

Assignment 2

Group

Friday,17May,1 2 midday(end of week 10)

35%

End-of semester exam

Individual

Hardcopy

60%

Assessment Task Individual or

MGMT90144 managing for value creation
Assessment Task Assignment 1 Individual or Group Goup 10mins presentation in class,5min Q&A, 1000 words summary Electronic Due Assessment Weighting 20%

In class, week 6 Summary due by 5:00 PM on Friday May 17

Assignment 2

Group

Week 10, submitted by 5:00 pm on Friday May 17.

30%

End-of semester exam

50%

Week1 Week2 Week3 Week4 Week5 Week6
FM: Mar 29th Friday group assignment 1,10%

PM: Tue Poster presentation in class, 5%

ME: Apr17th Wed in class mid-semester exam, 10% PM: Tue Poster presentation in class, 5% VC: Tue in class presentation 10mins, 20% FM: Apr 19th Fri from 3.30-4.30 in...

...This paper is a report on the time-series analysis of continuously compounded returns for Ford and GM for the periods January 2002 till April 2007 using monthly stock prices. This analysis is aimed at estimating the ARIMA model that provides the best forecast for the series. This paper will be divided into 2 sections; the first section showing the Ford analysis and the second the GM analysis.
Section 1: Ford
Figure 1:Time series plot for raw Ford data.
Figure 1 shows a time series plot of the raw Ford stock prices against time. From this plot, a gradual but continuous upward trend can be observed. This trend was disrupted in 2005 when the stock prices experienced a huge rise moving from below 5 to above 25. This rise in stock price by Ford was not sustained as can be seen from the plot; the prices which reached a peak of above 25 fell to a about 10 by the end of 2005 and fell further in 2006 to a level below 5 fluctuations in the stock price existed and in 2007 the prices began to level out.
Raw data is likely to be affected by non-stationarity and this can result in bias in the analysis. For the purpose of this analysis, it is required that the returns be continuously compounded. To achieve this I have taken the log and first difference of the raw data: this also achieves stationarity in the time...

...Time series analysis
(Session – I)
Commands and syntax for data analysis using STATA
1. Open and Run the STATA application
• Click on the Data on the task bar and open Data editor
• Copy the data from Excel sheet and paste it on the data editor
• Preserve the data
• Close Data Editor
2. Type “describe” in the command space- Software will show the description of the
data set.
3. Graphs
i) To Draw a scatter plot of variables yvar (y-axis) against xvar (x-axis) type the following in the command box:
scatter yvar xvar
ii) Draw a line graph, i.e. scatter with connected points:
line yvar xvar
iii) Draw a correlogram (graphical representation of autocorrelation coefficients):
ac yvar
4. Autocorrelation function:
tsset time variable (set the time variable)
corrgram yvar
Time series analysis
(Session –II)
Commands and syntax for data analysis using STATA
5. Open and Run the STATA application – copy the data to the Data editor
6. Declare the dataset to be time series data, type the following
tsset time variable (set the time variable)
7. Moving average of...

...Time Series Analysis: The Multiplicative Decomposition Method
Table of Contents
Page
Abstract………………………………………………………………………………………………………………………………………….3
Introduction………………………………………………………………………………………………………………………...…4-5
Methodology: Multiplicative Decomposition……………………………………………….…5-7
Advantages/Disadvantages of Multiplicative Method………………………………7-8
Conclusion…………………………………………………………………………………………………………………………………..8
Abstract
One of the most essential pieces of information useful to compute sales, and the ability to forecast them is strategically important. Forecasts can provide useful information to cut costs, increase efficient use of resources, and improve the capability to compete in a frequently changing environment. This study tests complicated, yet simple to use time series models to forecast sales. The results will show that, with minor rearrangement of past sales data, easy-to-use time series models can accurately forecast gross sales .Forecasters often need to guesstimate doubtful quantities, but with restricted time and resources. Decomposition is a method for dealing with such problems by breaking down the estimation task down into a set of components that can be more readily estimated, and then combining the component estimates to generate an objective estimate. Estimators can effectively apply...

...SECTION A (You should attempt all 10 questions)
A1. Regression analysis is ____________________________________.
A) describes the strength of this linear relationship.
B) describes the mathematical relationship between two variables.
C) describes the pattern of the data.
D) describes the characteristic of independent variable.
A2. __________________ is used to illustrate any relationship between two variables.
A) Histogram
B) Pie chart
C) Scatter diagram
D) Frequency polygon
Questions A3 to A5 relate to the following information.
Suppose a firm fed the values of turnover, y, and advertising expenditure, x, (both in $000) for the past eight years, into a computer and obtained the regression relationship y = 26.7 + 8.5x.
A3. What is the dependent variable?
A) Number of computers
B) Size of the firm
C) Turnover
D) Advertising expenditure
A4. What is the independent variable?
A) Number of computers
B) Size of the firm
C) Turnover
D) Advertising expenditure
A5. If the advertising expenditure is $5000 in a particular year, estimate the turnover for that year.
A) $69,200
B) $42,526.70
C) $26.7
D) $69.20
A6. Explain what the following sample correlation coefficients tell you about the relationship between the x and y values in the sample:
r = - 0.8
A) No correlation.
B)...

...153:
Introduction to Time Series
January 16, 2012
• Instructor: Aditya Guntuboyina (aditya@stat.berkeley.edu)
• Lectures: 12:30 pm to 2 pm on Tuesdays and Thursdays at 160 Dwinelle
Hall.
• Oﬃce Hours: 10 am to 11 am on Tuesdays and Thursdays at 423 Evans
Hall.
• GSI: Brianna Heggeseth (bhirst@stat.berkeley.edu)
• GSI Lab Section: 10 am to 12 pm OR 12 pm to 2 pm on Fridays at 334
Evans Hall (The ﬁrst section will include a short Introduction to R).
• GSI Oﬃce Hours: TBA.
All course materials including lecture slides and assignments will be posted on
the course site at bSpace.
Short Description: A time series is a set of numerical observations, each one
being recorded at a speciﬁc time. Such data arise everywhere. This course aims
to teach you how to analyze time series data. There exist two approaches to
time series analysis: Time Domain approach and Frequency Domain approach.
Approximately, about 60% of the course will be on time domain methods and
40% on frequency domain methods.
Tentative List of Topics: Time Domain Methods: Tackling Trend and
Seasonality, Stationarity and Stationary ARMA models, ARIMA and Seasonal ARIMA models, State space models. Frequency Domain Methods: Periodogram, Spectral Density, Spectral Estimation.
Prerequisite: This course is intended for students who have taken at least one
elementary...

...Regression with Time Series Data Week 10
Main features of Time series Data
Observations have temporal ordering
Variables may have serial correlation, trends and seasonality
Time series data are not a random sample because the observations in time series are collected from the same objects at different points in time
For time series data, because MLR2 does not hold, the inference tools are valid under a set of strong assumptions (TS1-6) for finite samples
While TS3-6 are often too restrictive, they can be relaxed for large samples. In essence, TS1, TS2, (z10), (h10) and (u10) are sufficient for large sample inference
Serial correlation of a time series variable is the correlation between the variable at one point in time with the same variable at another point in time
(z10), (h10), (u10)
z10 = E(ut | xt) = 0
When (z10) holds then the regressors are contemporaneously exogenous and OLS is consistent but is not sufficient for OLS to be unbiased
When TS3 holds, which implies (z10), then the regressors are strictly exogenous and OLS is unbiased
h10 = Var(ut | xt) = 2 and is known as contemporaneous homoskedasticity and is a weaker assumption than TS4
u10 = E(utus | xt,xs) = 0 and is a weaker assumption than TS5
FDL model and LRP
A FDL model allows the lags of one or more variables to affect the dependent variable
The LRP is the impact on...

...Time Series Models for Forecasting New One-Family Houses Sold in the United States
Introduction
The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers, as they held over-leveraged positions in the mortgage backed securities market. Credit became widely available to unqualified borrowers during the nineties and the early part of the next decade which caused bankers to act predatorily in their lending practices, as they could easily sell and package subprime mortgage loans on leverage. This act caused a bubble that would later burst when unqualified homebuyers began defaulting on their loans causing a tremendous downfall in the U.S housing market. Understanding which direction key market factors, such as the housing market, are going can help re-establish stability in the market, which is at an all-time premium. This paper is designed to help better predict the direction of the housing market in the future via the use of time series models, in an effort to re-establish a sense of stability in the housing market.
The Data Pattern of New One-Family Houses Sold in the U.S.
The following chart (Figure 1) represents the time series data for non-seasonally adjusted home sales in the U.S. (NHS) from January 1975...