Central bank research hub - Papers by Gianni Amisano
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Research hub papers by author Gianni AmisanoenUncertainty shocks, monetary policy and long-term interest rates
https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2279~ef5686736f.en.pdf
European Central Bank Working Papers by Gianni Amisano and Oreste TristaniUncertainty shocks, monetary policy and long-term interest rates2019-05-01T00:00:09ZWe 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.Uncertainty shocks, monetary policy and long-term interest ratesECBFull texthttps://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2279~ef5686736f.en.pdfOreste TristaniGianni AmisanoGianni Amisano and Oreste Tristani2019-05European Central Bank Working PapersC11C34E40E43E52Uncertainty Shocks, Monetary Policy and Long-Term Interest Rates
https://www.federalreserve.gov/econres/feds/files/2019024pap.pdf
Board of Governors of the Federal Reserve System Finance and Economics Discussion Series by Gianni Amisano and Oreste TristaniUncertainty Shocks, Monetary Policy and Long-Term Interest Rates2019-04-11T00:00:24ZWe 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 in ation expectations induced by conventional, autoregressive shocks. Long-term in ation expectations implied by our model are in line with those based on survey data over the 1980s and 1990s, but less dogmatically anchored in the 2000s.Uncertainty Shocks, Monetary Policy and Long-Term Interest RatesFull texthttps://www.federalreserve.gov/econres/feds/files/2019024pap.pdfOreste TristaniGianni AmisanoGianni Amisano and Oreste Tristani2019-04-11Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC11C34E40E43E52Prediction using several macroeconomic models
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1537.pdf
European Central Bank Working papers by Gianni Amisano, John GewekePrediction using several macroeconomic models2013-04-23T12:35:00ZPrediction 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¤orded by Bayesian model averaging. The study uses several analytical tools, including pooling, analysis of predictive variance, and probability integral transform tests, to understand and interpret the improvements.Prediction using several macroeconomic modelsECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1537.pdfJohn GewekeGianni AmisanoGianni Amisano, John Geweke2013-04-23European Central Bank Working PapersC11C51C53Analysis of variance for bayesian inference,
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1409.pdf
European Central Bank Working papers by John Geweke, Gianni AmisanoAnalysis of variance for bayesian inference,2011-12-21T12:37:00ZThis 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.Analysis of variance for bayesian inference,ECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1409.pdfJohn GewekeGianni AmisanoJohn Geweke, Gianni Amisano2011-12-20European Central Bank Working PapersExact likelihood computation for nonlinear DSGE models with heteroskedastic innovations,
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1341.pdf
European Central Bank Working papers by Gianni Amisano, Oreste TristaniExact likelihood computation for nonlinear DSGE models with heteroskedastic innovations,2011-05-27T12:35:59ZPhenomena 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.Exact likelihood computation for nonlinear DSGE models with heteroskedastic innovations,ECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1341.pdfGianni AmisanoOreste TristaniGianni Amisano, Oreste Tristani2011-05-27European Central Bank Working PapersMoney growth and inflation: a regime switching approach,
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1207.pdf
European Central Bank Working papers by Gianni Amisano, Gabriel FaganMoney growth and inflation: a regime switching approach,2010-06-25T12:43:59ZWe 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.Money growth and inflation: a regime switching approach,ECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1207.pdfGabriel FaganGianni AmisanoGianni Amisano, Gabriel Fagan2010-06-10European Central Bank Working PapersC11C53E31EMU and the adjustment to asymmetric shocks: the case of Italy,
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1128.pdf
European Central Bank Working papers by Gianni Amisano, Nicola Giammarioli, Livio Stracca,EMU and the adjustment to asymmetric shocks: the case of Italy,2009-12-18T17:38:00Z(JEL: E31, E32, E42.) 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¿s relative performance compared with the rest of the euro area, spanning from 1976 to 2009. Our main result is that EMU does not appear to have materially changed the transmission mechanism of idiosyncratic demand and cost push shocks, but has removed an importance source of relative performance variability given by idiosyncratic monetary shocks. The net effect of EMU, therefore, has been to reduce the relative performance variability. The conclusions that we reach could be usefully tested on other countries.EMU and the adjustment to asymmetric shocks: the case of Italy,ECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1128.pdfLivio StraccaNicola GiammarioliGianni AmisanoGianni Amisano, Nicola Giammarioli, Livio Stracca,2009-12-17European Central Bank Working PapersOptimal Prediction Pools
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1017.pdf
European Central Bank Working papers by John Geweke, Gianni AmisanoOptimal Prediction Pools2009-03-03T17:38:59Z(JEL: ) 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.Optimal Prediction PoolsECBFull texthttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1017.pdfJohn GewekeGianni AmisanoJohn Geweke, Gianni Amisano2009-03-03European Central Bank Working PapersC11C53Comparing and evaluating Bayesian predictive distributions of assets returns
http://www.ecb.int/pub/pdf/scpwps/ecbwp969.pdf
European Central Bank Working papers by John Geweke and Gianni AmisanoComparing and evaluating Bayesian predictive distributions of assets returns2008-11-27T07:12:00ZComparing and evaluating Bayesian predictive distributions of assets returnsECBFull texthttp://www.ecb.int/pub/pdf/scpwps/ecbwp969.pdfJohn GewekeGianni AmisanoJohn Geweke and Gianni Amisano2008-11European Central Bank Working PapersC11C53Imperfect predictability and mutual fund dynamics. How managers use predictors in changing systematic risk.
http://www.ecb.int/pub/pdf/scpwps/ecbwp881.pdf
European Central Bank Working papers by Gianni Amisano and Roberto SavonaImperfect predictability and mutual fund dynamics. How managers use predictors in changing systematic risk.2008-03-20T17:36:00ZImperfect predictability and mutual fund dynamics. How managers use predictors in changing systematic risk.ECBFull texthttp://www.ecb.int/pub/pdf/scpwps/ecbwp881.pdfRoberto SavonaGianni AmisanoGianni Amisano and Roberto Savona2008-03European Central Bank Working PapersC11C13G12G13Hierarchical Markov normal mixture models with applications to financial asset returns
http://www.ecb.int/pub/pdf/scpwps/ecbwp831.pdf
European Central Bank Working papers by John Geweke and Gianni AmisanoHierarchical Markov normal mixture models with applications to financial asset returns2007-12-01T07:12:00ZHierarchical Markov normal mixture models with applications to financial asset returnsECBFull texthttp://www.ecb.int/pub/pdf/scpwps/ecbwp831.pdfJohn GewekeGianni AmisanoJohn Geweke and Gianni Amisano2007-11European Central Bank Working PapersC11C14C53G12Euro area inflation persistence in an estimated nonlinear DSGE model
http://www.ecb.int/pub/pdf/scpwps/ecbwp754.pdf
European Central Bank Working papers by Gianni Amisano and Oreste TristaniEuro area inflation persistence in an estimated nonlinear DSGE model2007-05-23T17:38:00ZEuro area inflation persistence in an estimated nonlinear DSGE modelECBFull texthttp://www.ecb.int/pub/pdf/scpwps/ecbwp754.pdfOreste TristaniGianni AmisanoGianni Amisano and Oreste Tristani2007-05European Central Bank Working PapersC11C15E31E32E52