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EViews Training

Data Objects: Data Functions

Note: Information for examples in this tutorial can be found in these files. ➢ Data: Data.xlsx ➢ Results: Results.wf 1 ➢ Practice Workfile: Data.wf 1

Data and Workfile Documentation

• Data.wf 1 and Data.xlsx have the following data:
• Workfile Page: Timeseries (Data.xlsx tab Timeseries ): quarterly, Q1 1980 – Q1 2012 ✓ GDP – real GDP data (billions of dollars) from the Bureau of Economic Analysis. ✓ PCE – real consumption data (billions of dollars) from the Bureau of Economic Analysis. ✓ INV – real private sector investments (billions of dollars) from the Bureau of Economic Analysis. ✓ G – real government spending (billions of dollars) from the Bureau of Economic Analysis. ✓ Y – a series that grows over time (trend series).

Generating Random Numbers

Generating Random Numbers (Series):

Example 1

• You can generate a series of (pseudo) random numbers drawn from a variety

of distributions.

• There are a number of ways to generate a random series. Generating a random series: Example 1
  1. Open EViews workfile Data.wf 1.
  2. Select Quick → Generate Series from the main menu.
  3. Type y 1 = @nrnd in the dialog box and press Enter.

Generating Random Numbers (Series):

Example 2

• Alternatively, you can create a random series by typing in the command window. Generating a random series: Example 2
  1. Type in the command window: series z=@nrnd
  2. Press Enter.
  • This creates a new series z , which

is normally distributed with mean 0

and standard deviation equal to 1.

Generating Random Numbers (Series):

Example 3

• Suppose you want to simulate a random walk process with distribution

properties similar to the observed distribution of an existing series (for

example, gdp ):

Generating a random series: Example 3

1. Type in the command window: smpl @all series newdata = 0 smpl 1980 q 2 @last newdata=newdata(- 1 )+@mean(d(gdp))+@stdev(d(gdp))*@nrnd
2. Press Enter after each command line.

• Note: for an explanation of statistical functions @mean, @stdev , or d(gdp ), see Descriptive Statistics in this tutorial.

Generating Random Numbers (Series):

Common Functions/Commands

Common Commands/Functions

Function/Command Description series y=@nrnd Normal distribution (mean 0, st. dev. 1) series y=3+@sqr(4)nrnd* Normal distribution (mean 3, variance 4) series y=@rlognorm(1,4) Lognormal distribution (mean=1, st. dev 4) series y=@runif(1,3) Uniform distribution on (1,3) series y=@rnd Uniform distribution on (0,1) series y=1+(3-1)@rnd* Uniform distribution on (1,3) (same as @runif(1,3))

• EViews has a number of functions/commands that allow you to draw from a

variety of distributions.

• The most commonly used functions/commands are summarized below.

Generating Random Numbers (Series):

PDF and CDF

Function Description series d1 = @runif(0,2) show d1 @dunif(d1,0,2) @cunif(d1,0,2 ) Creates d1 as uniform distribution on (0,2) Displays series d1 created above, pdf of d1 and cdf of d

• It is also very easy to generate the pdf and cdf of a random variable. Generating a random series together with its pdf and cdf:
  1. Type in the command window: smpl @all series d 1 = @runif(0,2) show d 1 @dunif(d1,0,2) @cunif(d1,0,2)
  2. Press Enter after each command line.

Functions for Descriptive Statistics

• EViews has extensive built-in descriptive statistical functions.
• These descriptive statistical functions take an optional sample as an argument.
• The default sample is the current workfile range.

Descriptive Statistics

Descriptive Statistics Functions:

Example 1

• Suppose you want to create a series x 1 , which is equal to the mean of the

“ gdp ” series.

Descriptive Statistics Functions: Example 1

1. Type in the command window: series x 1 = @mean(gdp)
2. Press Enter.

• This creates a new series x 1 which has all elements equal to the mean of GDP.

Descriptive Statistics Functions:

Example 2

• As a next example, consider creating another series x2 , which is equal to the

mean of the “gdp” series, defined over several sub-samples:

Descriptive Statistics Functions: Example 2

1. Type in the command window: smpl 1980 q 1 1989 q 4 series x 2 =@mean(gdp, " 1980 q 1 1989 q 4 ") smpl 1990 q 1 1999 q 4 series x 2 =@mean(gdp, " 1990 q 1 1999 q 4 ") smpl 2000 q 1 @last series x 2 =@mean(gdp, " 2000 q 1 @last") smpl @all
2. Press Enter after each command line.

Descriptive Statistics Functions:

Example 3

• Suppose you want to create a variable which is the average (or sum) of

multiple series.

Descriptive Statistics Functions: Example 3 1a. One way to do this is to type the following in the command window : series new=(gdp+pce+inv)/ 3 1b. Another way, would be to type in the command window group row functions: group groupdata gdp pce inv This creates a new group (named “groupdata”) with containing the three series. 2b. Now type the following command in the command window: series new=@rmean(groupdata) This creates a series which is computed by taking the mean of all the three series for each row.

3. Press Enter after each command line.

Descriptive Statistics Functions:

Example 4

• Suppose you want to collect descriptive statistics in a vector (or matrix). Descriptive Statistics Functions: Example 4
  1. Let’s first define the sample over which descriptive stats are computed. Type in the command window: smpl 1980m01 1990m
  2. Next, let’s create a vector v by typing in the command window: vector(3) v
  3. Next, define the vector elements to gather the desired statistics, by typing in the command window: v(1)=@mean(gdp) v(2)= @varp(gdp) v(3)= @covs(gdp,pce)
  4. Press Enter after each command line.