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Modeling and forecasting a firm’s financial statements with a VAR – VECM model

  • Otavio Ribeiro de Medeiros

    (University of Brasília)

  • Bernardus Ferdinandus Nazar Van Doornik

    (Central Bank of Brazil)

  • Gustavo Rezende de Oliveira

    (Central Bank of Brazil)

This paper reports the development and estimation of a Vector Autoregressive (VAR) econometric model representing the financial statements of a firm. Although the model can be generalized to represent the financial statements of any firm, this work was carried out as a case study, where the chosen company is Petrobras S/A. The methodology comprises correlation analysis, unit root tests, cointegration analysis, VAR modeling, Granger causality tests, in addition to impulse response and variance decomposition methods. Besides the endogenous financial statement variables, an exogenous variable vector was utilized including the Brazilian GDP, domestic and foreign interest rates, the international oil price, the exchange rate, and country risk. The model’s final version is a Vector Error Correction Model (VECM), which takes into account the cointegrating relationships among the endogenous variables. After estimation and validation, the model is used to forecast the firm’s financial statements. Estimates for the exogenous variables and dividend forecasts were also used to estimate the firm’s market value. The results are apparently robust and might contribute to the field of financial planning and forecasting.

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Article provided by Fucape Business School in its journal Brazilian Business Review.

Volume (Year): 8 (2011)
Issue (Month): 3 (July)
Pages: 20-39

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Handle: RePEc:bbz:fcpbbr:v:8:y:2011:i:3:p:20-39
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  1. Kazuo Ogawa, 2002. "Monetary Transmission and Inventory: Evidence from Japanese Balance-Sheet Data by Firm Size," The Japanese Economic Review, Japanese Economic Association, vol. 53(4), pages 425-443.
  2. Campbell, John & Shiller, Robert, 1987. "Cointegration and Tests of Present Value Models," Scholarly Articles 3122490, Harvard University Department of Economics.
  3. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
  4. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
  5. Karen Mumford, 1996. "Strikes and profits: considering an asymmetric information model," Applied Economics Letters, Taylor & Francis Journals, vol. 3(8), pages 545-548.
  6. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
  7. Otavio De Medeiros, 2005. "An Econometric Model of a Firm’s Financial Statements," Finance 0503020, EconWPA.
  8. Oxelheim, Lars, 2002. "The Impact of Macroeconomic Variables on Corporate Performance - What Shareholders Ought to Know?," Working Paper Series 571, Research Institute of Industrial Economics, revised 22 Aug 2007.
  9. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
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