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Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

Author

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  • Guillen, Osmani Teixeira Carvalho
  • Hecq, Alain
  • Issler, João Victor
  • Saraiva, Diogo Vinícius Menezes
Abstract
This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

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  • Guillen, Osmani Teixeira Carvalho & Hecq, Alain & Issler, João Victor & Saraiva, Diogo Vinícius Menezes, 2014. "Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 753, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    • Handle: RePEc:fgv:epgewp:753
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    2. Castro, Andressa Monteiro de & Issler, João Victor, 2016. "Consumption-Wealth Ratio and Expected Stock Returns: Evidence from Panel Data on G7 Countries," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 70(4), December.
    3. Gianluca Cubadda & Alain Hecq & Sean Telg, 2019. "Detecting Co‐Movements in Non‐Causal Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(3), pages 697-715, June.
    4. Machado, Vicente da Gama & Portugal, Marcelo Savino, 2014. "Measuring inflation persistence in Brazil using a multivariate model," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(2), June.

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