1. Open the data file called JCrew on Blackboard under the Assignments link.

2. Get a 4 point Moving Average for the data using Time Series Analysis.

3. Highlight the Revenue column and the 4MA column. Insert /Line.

4. Go back to the data. Time Series Analysis/ Exponential Smoothing. Use alpha of .7.

5. Highlight Revenue and Smoothed and Insert /Line.

6. Go back to the data. Time Series Analysis/ Trendline / pick Exp Ln. Check the Scatterplot and all boxes on the right side.

7. Finally, go back to the data and choose Time Series/ Deseasonalize.

Questions:

1. Compare the 4 point moving average chart to the exponentially smoothed one. Which one shows the SECULAR trend better? Explain. The four point moving average shows the secular trend better because its values aren’t as volatile as they are in the exponentially smoothed model.

2. What is the forecasted revenue for JCrew in Quarter I of 2010 using Exponential Smoothing? 377.388 in Q1 of 2010

Look at the Logged Model

3. What percent of the variation in Revenue is explained by Time? 84% of the variation is explained by time

4. By how much does Revenue change per quarter on average? Revenue changes by 4.6% per quarter on average

5. Are there any outliers (suspicious or definite)?
There is one outlier at time period 4, but it is only suspicious

6. Is Autocorrelation a problem?
No because the Durbin-Watson is 2.77 therefore reject fail to reject H0

H0: No residual correlation (p=0)

H1: Positive residual correlation (p>1)

7. Does the data seem to fit the plot well? Explain.
Yes it fits the plot well in general. There is one suspicious value that skews the plot.

Look at the Deseasonalized Model

8. What is the secular trendline?
y=10.15x + 139.39

...ANNEXTURE
Questionnaire
Dear respondent,
I m a student of “Bhagwan mahavir college of business administration, surat” conducting a survey for my project preparation, as the requirement of partial fulfilment of subject project in third year(semester-VI) BBA in surat city of a study on “A COMPARATIVE STUDY ON BRITANNIA AND PARLE COMPANY IN SURAT CITY (A SURVEY ON BISCUIT )” I assure that the information given by you are strictly used for academic purpose only. I request you to help me in gathering information by filling up yhe following information.
Thank you,
Abhishek sojitra
Bhagwan mahavir business administration
Top of Form
1) Do you eat biscuit?
Yes
No
2) Select your likely tastes for biscuit?
Sweet
Salty
Sweet & Salty
Cream biscuit
Others
3) What type of biscuit you normally prefer?
Branded
Bakery product
4) How often do you eat biscuit?
Once in a week
Once in a month
Once in a fortnight
Alternate days
Every day
5) When do you have biscuit?
At breakfast time
At evening
Any time
6) Which brand you normally buy?
Britannia
Parle
Both
Other:
7) From where do you buy biscuit?
Provisional store
Hawkers
Convenience store
Other:
8) Out of the...

...Regression with TimeSeries Data Week 10
Main features of Timeseries Data
Observations have temporal ordering
Variables may have serial correlation, trends and seasonality
Timeseries data are not a random sample because the observations in timeseries are collected from the same objects at different points in time
For timeseries 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 timeseries 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...

...153:
Introduction to TimeSeries
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 timeseries 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 timeseries data. There exist two approaches to
timeseries 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...

....2.3 Timeseries models
Timeseries is an ordered sequence of values of a variable at equally spaced time intervals. Timeseries occur frequently when looking at industrial data. The essential difference between modeling data via timeseries methods and the other methods is that Timeseries analysis accounts for the fact that data points taken over time may have an internal structure such as autocorrelation, trend or seasonal variation that should be accounted for. A Time-series model explains a variable with regard to its own past and a random disturbance term. Special attention is paid to exploring the historic trends and patterns (such as seasonality) of the timeseries involved, and to predict the future of this series based on the trends and patterns identified in the model. Since timeseries models only require historical observations of a variable, it is less costly in data collection and model estimation.
. Timeseries models can broadly be categorized into linear and nonlinear Models. Linea models depend linearly on previous data points. They include the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models. The general autoregressive...

...Timeseries 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
Timeseries 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 timeseries data, type the following
tsset time variable (set the time variable)
7. Moving average of xvar :...

...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: Timeseries plot for raw Ford data.
Figure 1 shows a timeseries 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 timeseries data....

...TIMESERIES ANALYSIS
Introduction
Economic and business timeseries analysis is a major field of research and application. This analysis method has been used for economic forecasting, sales forecasting, stock market analysis and company internal control. In this paper, we will talk about timeseries and review techniques that are useful for analyzing timeseries data.
Definition of TimeSeries and TimeSeries Analysis
Timeseries is an ordered sequence of values of a variable at equally spaced time intervals. Timeseries data often arise when monitoring industrial processes or tracking corporate business metrics.
The analysis of timeseries is based on two basic assumptions. One is successive values in the data file represent consecutive measurements taken at equally spaced time intervals. The other assumption is that time is the only one independent variable in timeseries function.
Applications of TimeSeries Models
• Identify the nature of the phenomenon represented by the sequence of observations, and
• Fit a model and proceed to forecasting, monitoring or even feedback and feedforward control.
Both of these...

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