SPSS Assignment Help

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Posted bycheapnisha Posted in,
Posted on Dec 02, 2016

SPSS Assignment Help
IMPORTING AN EXCEL FILE INTO SPSS AND SETTING UP

  1. Create the data file the in Excel format.
  2. To download this file from Excel into SPSS start SPSS and with the Data Editor open on the screen click on File, Open, Data, from the menu at the top of the screen.
  3. In the section labelled Files of Type, choose Excel. Excel files have a .xls extension. Find the file that contains your data. Click on it so it appears in the File name
  4. Click on the Open button. A screen will appear labelled Opening Excel Data Source. Make sure there is a tick in the box Read variable names from the row of data. Click on OK.
  5. To save this as a SPSS file choose File, and then Save As from the menu at the top of the screen. Type in a file name you want. Make sure that the Save as Type is set at SPSS (*.sav). Click on Save.

The data saved in a .sav file (The saved file example is given below. Remember that in your case, you need to upload the excel file that has dates and returns time-series only for ACF estimation)

IMPORTING AN EXCEL FILE INTO SPSS AND SETTING UP

ACF Estimation:I. IMPORTING AN EXCEL FILE INTO SPSS AND SETTING UP

Once you uploaded the returns time-series onto SPSS then the following screenshots would help you to get ACF.

Note that in the screenshot below I am selecting Returns and Adj Close time-series. But for your coursework, you need not consider Adj Close. Just estimate ACF for the 8 stocks.

 ACF Estimation

The Output: 

ACF

Notes
Output Created 20-AUG-2015 21:02:54
Comments
Input Data C:\Users\Manas\Desktop\FOS_2015-16\AAL.L_Data.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 523
Date <none>
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used For a given time series variable, cases with missing values are not used in the analysis. Also, cases with negative or zero values are not used, if the log transform is requested.
Syntax ACF VARIABLES=Returns AdjClose

/NOLOG

/MXAUTO 16

/SERROR=IND

/PACF.

Resources Processor Time 00:00:03.23
Elapsed Time 00:00:01.10
Use From First observation
To Last observation
Time Series Settings (TSET) Amount of Output PRINT = DEFAULT
Saving New Variables NEWVAR = CURRENT
Maximum Number of Lags in Autocorrelation or Partial Autocorrelation Plots MXAUTO = 16
Maximum Number of Lags Per Cross-Correlation Plots MXCROSS = 7
Maximum Number of New Variables Generated Per Procedure MXNEWVAR = 60
Maximum Number of New Cases Per Procedure MXPREDICT = 1000
Treatment of User-Missing Values MISSING = EXCLUDE
Confidence Interval Percentage Value CIN = 95
Tolerance for Entering Variables in Regression Equations TOLER = .0001
Maximum Iterative Parameter Change CNVERGE = .001
Method of Calculating Std. Errors for Autocorrelations ACFSE = IND
Length of Seasonal Period Unspecified
Variable Whose Values Label Observations in Plots Unspecified
Equations Include CONSTANT
Model Description
Model Name MOD_2
Series Name 1 Returns
2 Adj Close
Transformation None
Non-Seasonal Differencing 0
Seasonal Differencing 0
Length of Seasonal Period No periodicity
Maximum Number of Lags 16
Process Assumed for Calculating the Standard Errors of the Autocorrelations Independence(white noise)a
Display and Plot All lags
Applying the model specifications from MOD_2
a. Not applicable for calculating the standard errors of the partial autocorrelations.
Case Processing Summary
Returns Adj Close
Series Length 523 523
Number of Missing Values User-Missing 0 0
System-Missing 0 0
Number of Valid Values 523 523
Number of Computable First Lags 522 522

Returns

Autocorrelations
Series:   Returns
Lag Autocorrelation Std. Errora Box-Ljung Statistic
Value df Sig.b
1 -.039 .044 .814 1 .367
2 -.014 .044 .921 2 .631
3 -.008 .044 .952 3 .813
4 -.022 .043 1.220 4 .875
5 -.040 .043 2.050 5 .842
6 .009 .043 2.093 6 .911
7 .017 .043 2.238 7 .945
8 .003 .043 2.243 8 .973
9 .022 .043 2.493 9 .981
10 -.065 .043 4.730 10 .908
11 .045 .043 5.836 11 .884
12 -.046 .043 6.977 12 .859
13 .020 .043 7.182 13 .893
14 .005 .043 7.194 14 .927
15 .010 .043 7.254 15 .950
16 -.020 .043 7.479 16 .963
a. The underlying process assumed is independence (white noise).
b. Based on the asymptotic chi-square approximation.

 

SPSS Assignment Help

Partial Autocorrelations
Series:   Returns
Lag Partial Autocorrelation Std. Error
1 -.039 .044
2 -.016 .044
3 -.009 .044
4 -.023 .044
5 -.042 .044
6 .005 .044
7 .015 .044
8 .003 .044
9 .021 .044
10 -.064 .044
11 .043 .044
12 -.043 .044
13 .018 .044
14 .004 .044
15 .007 .044
16 -.018 .044

SPSS Assignment Help

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