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Gianni Amisano
- 10 May 2019
- WORKING PAPER SERIES - No. 2279Details
- Abstract
- We study the relationship between monetary policy and long-term rates in a structural, general equilibrium model estimated on both macro and yields data from the United States. Regime shifts in the conditional variance of productivity shocks, or "uncertainty shocks", are an important model ingredient. First, they account for countercyclical movements in risk premia. Second, they induce changes in the demand for precautionary saving, which affects expected future real rates. Through changes in both risk-premia and expected future real rates, uncertainty shocks account for about 1/2 of the variance of long-term nominal yields over long horizons. The remaining driver of long-term yields are changes in inflation expectations induced by conventional, autoregressive shocks. Long-term inflation expectations implied by our model are in line with those based on survey data over the 1980s and 1990s, but less strongly anchored in the 2000s.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C34 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Truncated and Censored Models, Switching Regression Models
E40 : Macroeconomics and Monetary Economics→Money and Interest Rates→General
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 23 April 2013
- WORKING PAPER SERIES - No. 1537Details
- Abstract
- Prediction of macroeconomic aggregates is one of the primary functions of macroeconometric models, including dynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressions. This study establishes methods that improve the predictions of these models, using a representative model from each class and a canonical 7-variable postwar US data set. It focuses on prediction over the period 1966 through 2011. It measures the quality of prediction by the probability densities assigned to the actual values of these variables, one quarter ahead, by the predictive distributions of the models in real time. Two steps lead to substantial improvement. The first is to use full Bayesian predictive distributions rather than substitute a "plug-in" posterior mode for parameters. Across models and quarters, this leads to a mean improvement in probability of 50.4%. The second is to use an equally-weighted pool of predictive densities from the three models, which leads to a mean improvement in probability of 41.9% over the full Bayesian predictive distributions of the individual models. This improvement is much better than that a
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
- 20 December 2011
- WORKING PAPER SERIES - No. 1409Details
- Abstract
- This paper develops a multi-way analysis of variance for non-Gaussian multivariate distributions and provides a practical simulation algorithm to estimate the corresponding components of variance. It specifically addresses variance in Bayesian predictive distributions, showing that it may be decomposed into the sum of extrinsic variance, arising from posterior uncertainty about parameters, and intrinsic variance, which would exist even if parameters were known. Depending on the application at hand, further decomposition of extrinsic or intrinsic variance (or both) may be useful. The paper shows how to produce simulation-consistent estimates of all of these components, and the method demands little additional effort or computing time beyond that already invested in the posterior simulator. It illustrates the methods using a dynamic stochastic general equilibrium model of the US economy, both before and during the global financial crisis.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
- 27 May 2011
- WORKING PAPER SERIES - No. 1341Details
- Abstract
- Phenomena such as the Great Moderation have increased the attention of macro-economists towards models where shock processes are not (log-)normal. This paper studies a class of discrete-time rational expectations models where the variance of exogenous innovations is subject to stochastic regime shifts. We first show that, up to a second-order approximation using perturbation methods, regime switching in the variances has an impact only on the intercept coefficients of the decision rules. We then demonstrate how to derive the exact model likelihood for the second-order approximation of the solution when there are as many shocks as observable variables. We illustrate the applicability of the proposed solution and estimation methods in the case of a small DSGE model.
- JEL Code
- E0 : Macroeconomics and Monetary Economics→General
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
- 10 June 2010
- WORKING PAPER SERIES - No. 1207Details
- Abstract
- We develop a time-varying transition probabilities Markov Switching model in which inflation is characterised by two regimes (high and low inflation). Using Bayesian techniques, we apply the model to the euro area, Germany, the US, the UK and Canada for data from the 1960s up to the present. Our estimates suggest that a smoothed measure of broad money growth, corrected for real-time estimates of trend velocity and potential output growth, has important leading indicator properties for switches between inflation regimes. Thus money growth provides an important early warning indicator for risks to price stability.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
- 17 December 2009
- WORKING PAPER SERIES - No. 1128Details
- Abstract
- In this paper we address the question on whether EMU has amplified or dampened intra euro area divergencies, by looking at a time-varying VAR model of Italy
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E42 : Macroeconomics and Monetary Economics→Money and Interest Rates→Monetary Systems, Standards, Regimes, Government and the Monetary System, Payment Systems
- 3 March 2009
- WORKING PAPER SERIES - No. 1017Details
- Abstract
- A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave function of the weights and, in general, an optimal linear combination will include several models with positive weights despite the fact that exactly one model has limiting posterior probability one. The paper derives several interesting formal results: for example, a prediction model with positive weight in a pool may have zero weight if some other models are deleted from that pool. The results are illustrated using S&P 500 returns with prediction models from the ARCH, stochastic volatility and Markov mixture families. In this example models that are clearly inferior by the usual scoring criteria have positive weights in optimal linear pools, and these pools substantially outperform their best components.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
- 26 November 2008
- WORKING PAPER SERIES - No. 969Details
- Abstract
- Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative models of asset returns applied to daily S&P 500 returns from 1976 through 2005. The comparison exercise uses predictive likelihoods and is inherently Bayesian. The evaluation exercise uses the probability integral transform and is inherently frequentist. The illustration shows that the two approaches can be complementary, each identifying strengths and weaknesses in models that are not evident using the other.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
- 20 March 2008
- WORKING PAPER SERIES - No. 881Details
- Abstract
- Suppose a fund manager uses predictors in changing port-folio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by observing how the benchmark returns are related to some set of imperfect predictors, and in part on the basis of their own information set. In this portfolio allocation process, managers concern themselves with the potential benefits arising from the market timing generated by benchmark predictors and by private information. In doing this, we impose a structure on fund returns, betas, and bench-mark returns that help to analyse how managers really use predictors in changing investments over time. The main findings of our empirical work are that beta dynamics are significantly affected by economic variables, even though managers do not care about bench-mark sensitivities towards the predictors in choosing their instrument exposure, and that persistence and leverage effects play a key role as well. Conditional market timing is virtually absent, if not negative, over the period 1990-2005. However such anomalous negative timing ability is offset by the leverage effect, which in turn leads to an increase in mutual fund extra performance.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G13 : Financial Economics→General Financial Markets→Contingent Pricing, Futures Pricing
- 30 November 2007
- WORKING PAPER SERIES - No. 831Details
- Abstract
- With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in terms of the number of components in the mixture and the roots of the Markov process. We use the model prior predictive distribution to study its implications for some interesting functions of returns. We apply the model to construct predictive distributions of daily S&P500; returns, dollarpound returns, and one- and ten-year bonds. We compare the performance of the model with ARCH and stochastic volatility models using predictive likelihoods. The model's performance is about the same as its competitors for the bond returns, better than its competitors for the S&P 500 returns, and much better for the dollar-pound returns. Validation exercises identify some potential improvements.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C14 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Semiparametric and Nonparametric Methods: General
- 23 May 2007
- WORKING PAPER SERIES - No. 754Details
- Abstract
- We estimate the approximate nonlinear solution of a small DSGE model on euro area data, using the conditional particle filter to compute the model likelihood. Our results are consistent with previous findings, based on simulated data, suggesting that this approach delivers sharper inference compared to the estimation of the linearised model. We also show that the nonlinear model can account for richer economic dynamics: the impulse responses to structural shocks vary depending on initial conditions selected within our estimation sample.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C15 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Statistical Simulation Methods: General
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy