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Albert Marcet

25 April 2005
WORKING PAPER SERIES - No. 469
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Abstract
This paper studies the short run correlation of inflation and money growth. We study whether a model of learning does better or worse than a model of rational expectations, and we focus our study on countries of high inflation. We take the money process as an exogenous variable, estimated from the data through a switching regime process. We find that the rational expectations model and the model of learning both offer very good explanations for the joint behavior of money and prices.
JEL Code
D83 : Microeconomics→Information, Knowledge, and Uncertainty→Search, Learning, Information and Knowledge, Communication, Belief
E17 : Macroeconomics and Monetary Economics→General Aggregative Models→Forecasting and Simulation: Models and Applications
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
23 February 2008
WORKING PAPER SERIES - No. 862
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Abstract
Introducing bounded rationality into a standard consumption based asset pricing model with a representative agent and time separable preferences strongly improves empirical performance. Learning causes momentum and mean reversion of returns and thereby excess volatility, persistence of price-dividend ratios, long-horizon return predictability and a risk premium, as in the habit model of Campbell and Cochrane (1999), but for lower risk aversion. This is obtained, even though we restrict consideration to learning schemes that imply only small deviations from full rationality. The findings are robust to the particular learning rule used and the value chosen for the single free parameter introduced by learning, provided agents forecast future stock prices using past information on prices.
JEL Code
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
3 November 2010
WORKING PAPER SERIES - No. 1263
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Abstract
We propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modeled series. We first show that the Bayesian vs frequentist small sample bias controversy is driven by different default initial conditions. These initial conditions are usually arbitrary and our prior serves to replace them in an intuitive way. To implement this prior we develop a technique for translating priors about observables into priors about parameters. We find that our prior makes a big difference for the estimated persistence of output responses to monetary policy shocks in the United States.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes