By P. Arestis, G. Zezza
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Additional resources for Advances in Monetary Policy and Macroeconomics
Schiantarelli and A. Sembenelli (1998) ‘Firms’ Financial and Real Responses to Monetary Tightening: Evidence for Small and Large Italian Companies’, Giornale degli Economisti e Annali di Economia, 57, 35–64. Rudebusch, D. ’, International Economic Review, 39, 907–31. O. (1981) ‘Real Money Balances as a Productive Input. Further Evidence’, Journal of Monetary Economics, 7, 207–25. Townsend, R. (1979) ‘Optimal Contracts and Competitive Markets with Costly State Verification’, Journal of Economic Theory, 20, 265–93.
15) taken as an equality for the economy as a whole. : log (Lt Rt ) = β0 + β1 log (Pt+12 Q t+12 ) + ut . 4 1987 1988 1989 1990 1991 1992 A proxy for firms’ default risk variables in the model. Whenever these errors tend to depart from the common trend of the model, they are pulled towards it by some adjustment coefficients estimated together with the long-run coefficients β ( Johansen, 1995: 41). 14), where the vector xt consists of the indexed and log-transformed variables log(Lt Rt ) and log(Pt+12 Qt+12 ), the constant in the model is restricted to the cointegration space (that is a constant is allowed in the cointegrating relation), and the term β xt−1 − β0 measures the deviations of the stationary relation β xt−1 from its expected value β0 , that is the disequilibrium errors ut .
To make our comparison complete, we also consider the old hypothesis of naïve expectations. Agents use only the last-known value on inflation to predict future inflation. In this case, inflation expectations follow a random walk. In a first step, we undertake a statistical analysis of these different assumptions. We study the predictive properties of five expectation schemes: the random walk (RW), the AR process, and VAR with respectively 2, 3 and 6 variables. We show that only three models lead to significantly different forecasts: RW, AR and VAR-6.