Managerial Decision-Making Introduction To Using Excel In Forecasting

May 28-31, 2012

Thomas H. Payne, Ph.D.

Dunagan Chair of Excellence in Banking Chair, Department of Accounting, Finance,

Economics and Political Science

Preparing for Forecasting: Installing the Analysis Tool Pak in Excel

If you have Windows 7 and MS 2010 software on your PC: Open Excel and click on the “File” tab on the upper left hand side of the page, choose “Options” toward the bottom of the left hand side, then select “Add-Ins”. At the bottom of the page check Manage: Excel Add-Ins (in the drop down box) and click “Go”. From the dialogue box select “Analysis Tool Pak” & “Analysis Tool-Pak – VBA”. That will do it!

The Importance of Forecasting Demand

Profitability of firms depends on the demand for the goods and services produced by the firm. In your case, it is important to have some reasonable prediction for the demand for deposits, loans, and fee generating services.

Your home study project is designed to introduce you to the process of projecting that demand in the future.

Note that this process should lead you to ask questions and better understand your data. As always, past trends are not always predictive of future results. However the better you understand those trends and the variables that affect them – the better your forecasts will be.

Demand Forecasting

Time-Series models are useful for forecasting demand.

Four core elements of a time-series model Long-Run Trends (LRT) Business Cycles (BC) Seasonal Variations (SV) Random Fluctuations (RF)

Use data from the FDIC (www.fdic.gov) to forecast the number of farm loans in each quarter of 2011.

Note that the example dataset is

formatted and available on the GSB Website at http://www.gsblsu.org/3_8.html

Demand Forecasting

Long-Run Trends

Linear Trend

-

10,000.00

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00

70,000.00

198

6Q

119

86

Q4

198

7Q

319

88

Q2

198

9Q

119

89

Q4

199

0Q

319

91Q

219

92

Q1

199

2Q

419

93

Q3

199

4Q

219

95

Q1

199

5Q

419

96

Q3

199

7Q

219

98

Q1

199

8Q

419

99

Q3

20

00

Q2

20

01Q

12

00

1Q4

20

02

Q3

20

03

Q2

20

04

Q1

20

04

Q4

20

05

Q3

20

06

Q2

20

07

Q1

20

07

Q4

20

08

Q3

20

09

Q2

20

10Q

12

010

Q4

Farm Loans ($M)

Demand Forecasting

Long-Run Trends

Let’s start by controlling for a time trend

Assuming a linear trend: y = a+bt

Remember the equation that you used for a line back in school. The line intercepted the vertical axis at “a”. And the slope of the line, how much y changed for a change in x or in our case time (t), was “b”.

STEP 1

Add a new column on your spreadsheet and label it “t”.

Then, for each line, enter the *year* of that observation.

Time Period

(As downloaded from the FDIC database)

Loan Amount

(your dependent variable – i.e. what you eventually want to forecast)

t (as provided in the

dataset)

1986Q1 34,370.65 1986

1986Q2 34,690.13 1986

1986Q3 34,203.11 1986

1986Q4 31,602.33 1986

1987Q1 29,199.55 1987

1987Q2 30,819.61 1987

1987Q3 31,041.34 1987

1987Q4 29,427.25 1987

Demand Forecasting

Seasonal Variations

But we can do better than this . . .

What might we control for next if we are attempting to “forecast” farm loans in four quarters (time periods) of 2011?

Demand Forecasting

Seasonal/Quarterly Variation

-

10,000.00

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00

70,000.00

198

6Q

119

86

Q4

198

7Q

319

88

Q2

198

9Q

119

89

Q4

199

0Q

319

91Q

219

92

Q1

199

2Q

419

93

Q3

199

4Q

219

95

Q1

199

5Q

419

96

Q3

199

7Q

219

98

Q1

199

8Q

419

99

Q3

20

00

Q2

20

01Q

12

00

1Q4

20

02

Q3

20

03

Q2

20

04

Q1

20

04

Q4

20

05

Q3

20

06

Q2

20

07

Q1

20

07

Q4

20

08

Q3

20

09

Q2

20

10Q

12

010

Q4

Farm Loans ($M)

Demand Forecasting

Seasonal Variations

But we can do better than this . . .

What might we control for next if we are attempting to forecast farm loans in four quarters of 2011?

Add a “dummy variable” for the season, quarter, or month of the year for which you have observations for your dependent variable:

y=a+bt+cQ1+dQ2+eQ3+fQ4

STEP 2

Add four more columns representing the quarter of the year.

Then, for each line, enter a “1” for the correct quarter, and a “0” otherwise.

Time Period

Loan Amount t Q1 Q2 Q3 Q4

1986Q1

34,370.65 1986 1 0 0 0

1986Q2

34,690.13 1986 0 1 0 0

1986Q3

34,203.11 1986 0 0 1 0

1986Q4

31,602.33 1986 0 0 0 1

1987Q1

29,199.55 1987 1 0 0 0

1987Q2

30,819.61 1987 0 1 0 0

1987Q3

31,041.34 1987 0 0 1 0

1987Q4

29,427.25 1987 0 0 0 1

Demand Forecasting

Observed Data Cycles

One Final Consideration: Note that loans in 2004/2005 were “off

trend” – notice that the loans dropped off of the trend line during that time. We call this a *cycle*, and we want to control for it.

For your bank project, the cycles you observe in the data may be lined up with true “business cycles” (i.e. recoveries and recessions). Here they are not – however, another cycle does appear and is accounted for in the analysis.

Note that our adjustment is “observational”

but that observations typically have an underlying cause. So, you will want to consider things that cause this when analyzing your data.

Demand Forecasting

Business Cycle or other irregular data cycle.

-

10,000.00

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00

70,000.00

198

6Q

119

86

Q4

198

7Q

319

88

Q2

198

9Q

119

89

Q4

199

0Q

319

91Q

219

92

Q1

199

2Q

419

93

Q3

199

4Q

219

95

Q1

199

5Q

419

96

Q3

199

7Q

219

98

Q1

199

8Q

419

99

Q3

20

00

Q2

20

01Q

12

00

1Q4

20

02

Q3

20

03

Q2

20

04

Q1

20

04

Q4

20

05

Q3

20

06

Q2

20

07

Q1

20

07

Q4

20

08

Q3

20

09

Q2

20

10Q

12

010

Q4

Farm Loans ($M)

STEP 3

Add one more column representing the observed cycle. Again, code this as a “1” for

years that are in the cycle, and a “0” for years that are out of it. The series below shows

part of this cycle which we estimated as lasting from the 2nd quarter of 2003 through the

4th quarter of 2005.

2005Q1

45,379.52 2005 1 0 0 0 1

2005Q2

48,273.03 2005 0 1 0 0 1

2005Q3

50,707.90 2005 0 0 1 0 1

2005Q4

51,669.39 2005 0 0 0 1 1

2006Q1

49,242.74 2006 1 0 0 0 0

2006Q2

52,706.24 2006 0 1 0 0 0

2006Q3

54,009.98 2006 0 0 1 0 0

2006Q4

54,256.92 2006 0 0 0 1 0

Time Period

Loan Amount t Q1 Q2 Q3 Q4 Cycle_1

Demand Forecasting

Now the full equation for our farm loan model looks like:

y=a+bt+cQ1+dQ2+eQ3+fQ4+ gCycle_1+ RF

STEP 4

Run the full model in Excel to calculate the values of a, b, c, d, e, f, & g.

Steps: Data -> Data Analysis -> Regression -> OK

Select Y-Range: B1-B101

Select X-Range: C1-H101

Check the “Labels” box

Pick an Output Range (New Sheet)

Note: One of your four quarters (or 12 months) will be zero. In our examples it was quarter 4 (the one we did in class) or quarter 1 (the example

above); regardless, however, the answer will be the same when you plug the appropriate data and associated coefficients into your forecast

model.

Coefficients

Intercept -2470997.225

t 1257.517677

Q1 0

Q2 2787.00352

Q3 3670.70032

Q4 3059.24012

Cycle_1 -3987.266825

Demand Forecasting

Predicting Future Values of y

Now we have a formula that tells us the relationship between y (farm loans) and my control variables.

Y = -2470997.2 + 1257.52 x t

+ 0 x Q1 + 2787.00 x Q2

+ 3670.70 x Q3 + 3059.24 x Q4

+ -3987.27 x CYCLE1

Coefficients

Intercept -2470997.225

t 1257.517677

Q1 0

Q2 2787.00352

Q3 3670.70032

Q4 3059.24012

Cycle_1 -3987.266825

Checking Our Results

Your home study project will allow you to predict loans, services, etc. through late 2012 and into 2013.

However, for example purposes, this example used data through 2010 to “predict” farm loans into 2011. The results are shown on the panel on the right.

Farm Loans Predicted in 2011

Actual Farm Loans in 2011 (Millions) Year/Quarter

Predicted (In Millions)

$61,337 2011 (Q4) $56,947

$59,802 2011 (Q3) $61,546

$57,668 2011 (Q2) $60,663

$55,033 2011 (Q1) $57,876

Error (predicted-actual) Percent Error

-$4,389 -7.16%

$1,744 2.92%

$2,995 5.19%

$2,842 5.16%

Y (Loans) = -2470997.2 + 1257.52 x t + 0 x Q1 + 2787 x Q2 + 3670.70 x Q3 + 3059.24 x Q4 + -3987.27 x CYCLE1