1. Financial Analysis, Planning and Forecasting Theory and Application
Chapter 26
Econometric Approach to Financial Analysis,
Planning, and Forecasting By
Cheng F. Lee
Rutgers University, USA
John Lee
Center for PBBEF Research, USA

2. Outline
26.1 Introduction
26.2 Simultaneous nature of financial analysis, planning,
and forecasting
26.3 The simultaneity and dynamics of corporate-budgeting
decisions
26.4 Applications of SUR estimation method in financial analysis
and planning
26.5 Applications of structural econometric models in financial
analysis and planning
26.6 Programming vs. simultaneous vs. econometric financial
models
26.7 Financial analysis and business policy decisions
26.8 Summary
Appendix 26A. Instrumental variables and two-stages least squares
Appendix 26B. Johnson & Johnson as a case study

3. 26.1 Introduction

4. 26.2 Simultaneous nature of financial analysis, planning, and forecasting
Basic concepts of simultaneous econometric models
Interrelationship of accounting information
Interrelationship of financial policies

5. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
Definitions of endogenous and exogenous variables
Model specification and applications

6. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
TABLE Endogenous and exogenous variables
1. The endogenous variables are:
a) X1,t = DIVt = Cash dividends paid in period t;
b) X2,t = ISTt = Net investment in short-term assets
during period t;
c) X3,t = ILTt = Gross investment in long-term assets
d) X4,t = -DFt = Minus the net proceeds from the
new debt issues during period t;
e) X5,t = -EQFt = Minus the net proceeds from new
equity issues during period t.
2. The exogenous variables are:
a) Yt = Σ Xi,t = Σ X*i,t, where Y = net profits +
i= i= depreciation allowance;
a reformulation of the sources = uses identity.

7. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
TABLE Endogenous and exogenous variables (Cont.)
b) RCB = Corporate Bond Rate (which corresponds to the
weighted-average cost of long-term debt in the FR
Model [Eqs. (20), (23), and (24) in Table 23.10],
and the parameter for average interest rate in the
WS Model [Eq. (7) in Table 23.1].
c) RDPt = Average Dividend-Price Ratio (or dividend yield,
related to the P/E ratio used by WS as well as the
Gordon cost-of-capital model, discussed in Chapter 8).
The dividend-price ratio represents the yield expected by
investors in a no-growth, no-dividend firm.
d) DELt = Debt Equity Ratio (parameter used by WS in Eq. (18)
of Table 23.1).
e) Rt = The rates-of-return the corporation could expect to
earn on its future long-term investment (or the
internal rate-of-return discussed in Chapter 12).
f) CUt = Rates of Capacity Utilization (used by FR to lag
capital requirements behind changes in percent sales;
used here to define the Rt expected).

8. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
Σ Xi,t = Σ X*i,1 = Yt, (26.1)
i= i=1
where X1,t, X2,t, X3,t, X4,t, X5,t, X*1,t and Yt are identical to those defined in Table 25.1.
Expanding Eq. (25.1) we obtain
X1,t + X2,t + X3,t + X4,t + X5,t = X*1,t + X*2,t + X*3,t + X*4,t + X*5,t = Yt (26.1')

9. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
X*t = AZt, (26.2)
where
X*' = (DIV*IST*ILT* - DF* - EQF*),
Z' = (1 Q1 Q2 Q3 Y RCB RDP DEL R CU),
┌ a10 a a19 ┐
│ │
A = │ │ .
│ a50 a a59 │
└ ┘

10. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
DIV*t = a10 + a11 Q1 + a12 Q2 + a13 Q3 + a14 Yt
+ a5 RCBt + a16 RDPt + a17 DELt
+ a18 Rt + a19 CUt,
IST*t = a20 + a21 Q1 + a22 Q2 + a23 Q3 + a24 Yt
+ a25 RCBt + a26 RDPt + a27 DELt
+ a28 Rt + a29 CUt,

11. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
ILT*t = a30 + a31 Q1 + a32 Q2 + a33 Q3 + a34 Yt
+ a35 RCBt + a36 RDPt + a37 DELt
+ a38 Rt + a39 CUt,
-DF*t = a40 + a41 Q1 + a42 Q2 + a43 Q3 + a44 Yt
+ a45 RCBt + a46 RDPt + a47 DELt
+ a48 Rt + a49 CUt,
-EQF*t = a50 + a51 Q1 + a52 Q2 + a53 Q3 + a54 Yt
+ a55 RCBt + a56 RDPt + a57 DELt
+ a58 Rt + a59 CUt.

12. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
Xi,t = Xi,t-1 + δi(X*i,t - Xi,t-1) (26.3) or
(a) X1,t = X1,t-1 + δ1(X*1,t - X1,t-1),
(b) X2,t = X2,t-1 + δ2(X*2,t - X2,t-1),
(c) X3,t = X3,t-1 + δ3(X*3,t - X3,t-1),
(d) X4,t = X4,t-1 + δ(X*4,t - X4,t-1),
(e) X5,t = X5,t-1 + δ5(X*5,t - X5,t-1).

13. 25.3 The simultaneity and dynamics of corporate-budgeting decisions
Σ X*i,t = Σ Xi,t = Yt.
i= i=1
X2,t = X2,t-1 + (1 - δ1)(X*1,t - X1,t-1). 5
Xi,t = Xi,t-1 + Σ δij(X*j,t - Xj,t-1) (i = 1, 2, 3, 4, 5), (26.4)
j=1
Σ δij = 1.
i=1

14. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
Xt = Xt-1 + D(X*t - Xt-1)
= Xt-1 + D(AZt - Xt-1) (26.5)
= Xt-1 + DAZt - DXt-1
= (I - D)Xt-1 + DAZt,
┌ δ11 δ δ15 ┐
│ │
│ │ .
D = │ │
│ δ51 δ δ55 │
└ ┘

15. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
TABLE An expanded version of Eq. (26.5)
X = BXt-1 + CZt + Ut, (26.6)

16. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
TABLE An expanded form of Eq. (26.6)

17. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
D = i - B, (26.7)
A = D-1 C (26.8) 5
Σ Xit = Yt for every period t.
i=1
Σ bij = Σ ĉik = 0 for all j and all k≠4,
i= i=1
and that
Σĉ = 1.

18. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
TABLE Adjustment coefficients of

19. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
TABLE Adjustment coefficients of (Cont.)

20. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
TABLE Summary of results

21. 26.3 The simultaneity and dynamics of corporate-budgeting decisions
Fig (From Spies, R. R., “The dynamics of corporate capital budging,” Journal of Finance 29 (September 1974): Fig. 1. Reprinted by permission.)

22. The role of firm-related variables in capital-asset pricing
26.4 Applications of SUR estimation method in financial analysis and planning
The role of firm-related variables in capital-asset pricing
The role of capital structure in corporate-financing decisions

23. R1t = α1 + ß1Rmt + γ11X11 + γ12X12 + γ13X13 + E1t,
26.4 Applications of SUR estimation method in financial analysis and planning
R1t = α1 + ß1Rmt + γ11X11 + γ12X12 + γ13X13 + E1t,
R2t = α2 + ß2Rmt + γ21X21 + γ22X22 + γ23X23 + E2t, .
Rnt = αn + ßnRmt + γn1Xn1 + γn2Xn2 + γn3Xn3 + Ent,
(26.9)

24. 26.4 Applications of SUR estimation method in financial analysis and planning
where
Rjt = Return on the jth security over time interval t
(j = 1, 2, ..., n),
Rmt = Return on a market index over time interval t,
Xj1t = Profitability index of jth firm over time interval
t (j = 1, 2, ..., n),
Xj2t = Leverage index of jth firm over time period t
Xj3t = Dividend policy index of jth firm over time
period t (j = 1, 2, ..., n),
γjk = Coefficient of the kth firm-related variable in
the jth equation (k = 1, 2, 3),
ßj = Coefficient of market rate-of-return in the jth equation
Ejt = Disturbance term for the jth equation, and
aj's are intercepts ( j = 1, 2, ..., n).
Rjt = α′ + ß ′ + Ejt (26.10)

25. 26.4 Applications of SUR estimation method in financial analysis and planning
TABLE OLS and SUR estimates of oil industry

26. 26.4 Applications of SUR estimation method in financial analysis and planning
TABLE OLS and SUR estimates of oil industry (Cont.)
*t-values appear in parentheses beneath the corresponding coefficients.
†Denotes significant at 0.10 level of significant or better for two-tailed test.
‡Denotes significant at 0.05 level of significant or better for two-tailed test.
From Lee, C. F., and J. D. Vinso, “Single vs. simultaneous-equation models in capital-asset pricing: The role of firm-related variables,” Journal of Business Research (1980): Table 3. Copyright 1980 by Elsevier Science Publishing Co., Inc. Reprinted by permission of the publisher.

27. 26.4 Applications of SUR estimation method in financial analysis and planning
TABLE OLS parameter estimates of oil industry-Sharpe Model*
* t-values appear in parenthesis beneath the corresponding coefficients
From Lee, C.F., and J.D. Vinso, “Single vs. simultaneous-equation models in capital-asset pricing: The role of firm-related variables.” Journal of Business Research (1980): Table 2. Copyright 1980 by Elsevire Science Publishing Co., Inc. Reprinted by permission of the publisher.

28. 26.4 Applications of SUR estimation method in financial analysis and planning
TABLE Residual correlation coefficient matrix after OLS estimate

29. 26.4 Applications of SUR estimation method in financial analysis and planning
ΔLDBT = α1(LDBT* - LDBTt-1) + α2(PCB* - PCBt-1 - RE)
+ α3 STOCKT + α4RT + ε1, (26.11)
ΔGSTK = ß1(LDBT* - LDBTt-1) + ß2(PCB* - PCBt-1 - RE)
+ ß3 STOCKT + ß4RT + ε2, (26.12)
STRET = η1(LDBT* - LDBTt-1) + η2(PCB* - PCBt-1 - RE)
+ η4RT + ε3, (26.13)
ΔLIQ = LIQ* + γ2(TC* - TCt-1) + γ3(ΔA - RE) + γ4RT
+ ε4, (26.14)

30. 26.4 Applications of SUR estimation method in financial analysis and planning
ΔSDBT = ΔLIQ* + λ2(TC* - TCt-1) + λ3 (ΔA - RE) + λ4RT + ε5,
(26.15)
where
LDBT* = bSTOCK (i/i) = A target for the book value of long-term debt,
STOCK = Market value of equity,
b = LDM/STOCK = Desired debt-equity ratio,
LDM = Market value of debt = (LDBT)(i/i),
i/i = Ratio between the average contractual interest rate on long-term debt outstanding and the current new-issue rate on long-term debt,
LDBTt-1 = Book value of long-term debt in previous period,
PCB* = Permanent capital (book value) = net capital stock (NK) + the permanent portion of working assets (NWA),
PCBt-1 = Permanent capital in the previous period,
RE = Stock retirements,
STOCKT = Stock-market timing variable = average short-term market value of equity divided by average long-term market value of equity,
RT = Interest timing variable, weighted average (with weight 0.67 and 0.33) of two most recent quarters' changes in the commercial paper rate,
TC* = Target short-term capital = short-term asset-liquid assets,
TCt-1 = Short-term debt in the previous period,
ΔA = Changes in total assets,
ΔLIQ* = Change of target liquidity assets.

31. AT&T’s econometric planning model
26.5 Applications of structural econometric models in financial analysis and planning
A brief review
AT&T’s econometric planning model

32. 26.3 Applications of structural econometric models in financial analysis and planning
Fig Tripartite structure of FORECYT.

33. 26.6 Programming vs. simultaneous vs. econometric financial models

34. 26.2 Applications of structural econometric models in financial analysis and planning
Fig. 26.3
Flow chart of FORECYT.
(From Davis, B. E., G. C. Caccappolo, and M. A. Chaudry, “An econometric planning model for American Telephone and Telegraph Company,” The Bell Journal of Economics and Management Science 4 (Spring 1973): Fig. 2. Copyright © 1973, The American Telephone and Telegraph Company. Reprinted with permission.

35. 26.7 Financial analysis and business policy decisions

36. 26.8 Summary
Based upon the information, theory, and methods discussed in previous chapters, we discussed how the econometrics approach can be used as alternative to both the programming approach and simultaneous-equation approach to financial planning and forecasting. Both the SUR method and the structural simultaneous-equation method were used to show how the interrelationships among different financial-policy variables can be more effectively taken into account. In addition, it is also shown that financial planning and forecasting models can also be incorporated with the environment model and the management model to perform business-policy decisions.

37. Appendix 26A. Instrumental variables and two-stages least squares

38. Appendix 26A. Instrumental variables and two-stages least squares

39. Appendix 26A. Instrumental variables and two-stages least squares

40. Appendix 26B. Johnson & Johnson as a case study
TABLE 25.A.1 Sales in Different Segment
2003
2004
2005
2006
Division
Sales
Profits %
Consumer
18%
13%
11%
12%
10%
Pharmaceuticals
47%
56%
58%
44%
48%
Medical Devices
and Diagnostics
36%
31%
38%
40%
43%
Total
100%
Domestic
60%
59%
International
41%

41. Appendix 26B. Johnson & Johnson as a case study
Table 26.B.2 Balance sheet

42. Appendix 26B. Johnson & Johnson as a case study

43. Appendix 26B. Johnson & Johnson as a case study

44. Appendix 26B. Johnson & Johnson as a case study

45. Appendix 26B. Johnson & Johnson as a case study

46. Appendix 26B. Johnson & Johnson as a case study

47. Appendix 26B. Johnson & Johnson as a case study

48. Appendix 26B. Johnson & Johnson as a case study

49. Appendix 26B. Johnson & Johnson as a case study
Xt = BXt-1 + CZ + Ut (26.B.1)

50. Appendix 26B. Johnson & Johnson as a case study

51. Appendix 26B. Johnson & Johnson as a case study
Xt-1 = BXt + CZt + Ut
= B2Xt-1 + BCZt + CZt+1 + BUt + Ut (26.B.2)