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14
INFORMATION CRITERIA FOR IMPULSE RESPONSE FUNCTION MATCHING ESTIMATION OF DSGE MODELS
, 2007
"... Abstract: We propose a new Information Criterion for Impulse Response Function Matching estimators of the structural parameters of macroeconomic models. The main advantage of our procedure is that it allows the researcher to select the impulse responses that are most informative about the deep param ..."
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Abstract: We propose a new Information Criterion for Impulse Response Function Matching estimators of the structural parameters of macroeconomic models. The main advantage of our procedure is that it allows the researcher to select the impulse responses that are most informative about the deep parameters, therefore reducing the bias and improving the efficiency of the estimates of the model’s parameters. We show that our method substantially changes key parameter estimates of representative Dynamic Stochastic General Equilibrium models, thus reconciling their empirical results with the existing literature. Our criterion is general enough to apply to impulse responses estimated by VARs, local projections, as well as simulation methods. J.E.L. Codes: C32, E47, C52, C53. Acknowledgements. We thank Craig Burnside for sharing his codes and for many useful suggestions along the way. We also thank L. Christiano for making the Altig et al. (2004) codes available in his webpage. We
Path forecast evaluation
, 2007
"... A forecast path refers to the vector of forecasts over the next 1 to h periods into the future. These forecasts are correlated across horizons so that to properly understand the uncertainty associated with the forecast path, one requires the joint predictive density of the path rather than the colle ..."
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Cited by 14 (1 self)
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A forecast path refers to the vector of forecasts over the next 1 to h periods into the future. These forecasts are correlated across horizons so that to properly understand the uncertainty associated with the forecast path, one requires the joint predictive density of the path rather than the collection of marginal predictive densities for each horizon. This paper derives the joint predictive density for forecasts generated by VARs or from direct forecast methods (e.g. Marcellino, Stock and Watson, 2003). Given this density, we introduce the mean square forecast path metric to compare the predictive ability between competing models and appropriately modify DieboldMarianoWest and GiacominiWhite predictive ability tests. We then use Scheffé’s Smethod to construct simultaneous confidence regions for the forecast path and show how to construct path forecasts conditional on assumed paths for a subset of the system’s variables, along with their conditional predictive density and a test on the assumed path’s likelihood.
2010), “How Reliable Are Local Projection Estimators of Impulse Responses? ” forthcoming: Review of Economics and Statistics
"... We compare the finitesample performance of impulse response confidence intervals based on local projections (LPs) and vector autoregressive (VAR) models in linear stationary settings. We find that in small samples the asymptotic LP interval often is less accurate than the biasadjusted bootstrap VA ..."
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Cited by 7 (4 self)
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We compare the finitesample performance of impulse response confidence intervals based on local projections (LPs) and vector autoregressive (VAR) models in linear stationary settings. We find that in small samples the asymptotic LP interval often is less accurate than the biasadjusted bootstrap VAR interval, notwithstanding its excessive average length. Although the asymptotic LP interval has adequate coverage in sufficiently large samples, its average length still far exceeds that of biasadjusted bootstrap VAR intervals with comparable accuracy. Bootstrap LP intervals (with or without bias correction) and asymptotic VAR intervals are shorter on average, but often lack coverage accuracy in finite samples. JEL Classification Number: C32, C52, C53
The HarrodBalassaSamuelson hypothesis: real exchange rates and their longrun equilibrium
 International Economic Review
, 2012
"... Frictionless, perfectly competitive tradedgoods markets justify thinking of purchasing power parity (PPP) as the main driver of exchange rates in the longrun. But differences in the traded/nontraded sectors of economies tend to be persistent and affect movements in local price levels in ways that ..."
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Cited by 5 (0 self)
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Frictionless, perfectly competitive tradedgoods markets justify thinking of purchasing power parity (PPP) as the main driver of exchange rates in the longrun. But differences in the traded/nontraded sectors of economies tend to be persistent and affect movements in local price levels in ways that upset the PPP balance (the underpinning of the HarrodBalassaSamuelson hypothesis, HBS). This paper uses paneldata techniques on a broad collection of countries to investigate the longrun properties of the PPP/HBS equilibrium using novel local projection methods for cointegrated systems. These semiparametric methods isolate the longrun behavior of the data from contaminating factors such as frictions not explicitly modelled and thought to have effects only in the shortrun. Absent the shortrun effects, we find that the estimated speed of reversion to longrun equilibrium is much higher. In addition, the HBS effects means that the real exchange rate is converging not to a steady mean, but to a slowly to a moving target. The common failure to properly model this effect also biases the estimated speed of reversion downwards. Thus, the socalled “PPP puzzle ” is not as bad as we thought.
An efficient minimum distance estimator for DSGE models,” Bank of England working papers 439
, 2011
"... ..."
Impulse Response Matching Estimators for DSGE Models
, 2015
"... One of the leading methods of estimating the structural parameters of DSGE models is the VARbased impulse response matching estimator. The existing asympotic theory for this estimator does not cover situations in which the number of impulse response parameters exceeds the number of VAR model param ..."
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One of the leading methods of estimating the structural parameters of DSGE models is the VARbased impulse response matching estimator. The existing asympotic theory for this estimator does not cover situations in which the number of impulse response parameters exceeds the number of VAR model parameters. Situations in which this order condition is violated arise routinely in applied work. We establish the consistency of the impulse response matching estimator in this situation, we derive its asymptotic distribution, and we show how this distribution can be approximated by bootstrap methods. Our analysis sheds new light on the choice of the weighting matrix and covers both weakly and strongly identified DSGE model parameters. We also show that under our assumptions special care is needed to ensure the asymptotic validity of Bayesian methods of inference. A simulation study suggests that the interval estimators we propose are reasonably accurate in practice. We also show that using these methods may affect the substantive conclusions in empirical work.
October 2007 Path Forecast Evaluation ∗
"... A forecast path refers to the vector of forecasts over the next 1 to h periods into the future. These forecasts are correlated across horizons so that to properly understand the uncertainty associated with the forecast path, one requires the joint predictive density of the path rather than the colle ..."
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A forecast path refers to the vector of forecasts over the next 1 to h periods into the future. These forecasts are correlated across horizons so that to properly understand the uncertainty associated with the forecast path, one requires the joint predictive density of the path rather than the collection of marginal predictive densities for each horizon. This paper derives the joint predictive density for forecasts generated with VARs or with direct forecast methods from possibly infinite order data generating processes. Given this density, we use Scheffé’s Smethod to construct simultaneous confidence regions for the forecast path and show how to construct path forecasts conditional on assumed paths for a subset of the system’s variables, along with their conditional predictive density and a test on the assumed path’s likelihood. We then introduce the mean square forecast path metric to compare the predictive ability between competing models, and appropriately modify DieboldMarianoWest and GiacominiWhite predictive ability tests. Finally, as empirical illustrations of the theoretical concepts, we evaluate path forecasts from a system of U.S. macroeconomic variables, and examine the role of monetary aggregates in forecasting inflation.
including © notice, is given to the source. The HarrodBalassaSamuelson Hypothesis: Real Exchange Rates and their LongRun Equilibrium
, 2010
"... Jordà is grateful for the support from the Spanish MICINN National Grant SEJ20076309 and the hospitality of the Federal Reserve Bank of San Francisco during preparation of this manuscript. Taylor also gratefully acknowledges research support from the Center for the Evolution of the Global Economy a ..."
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Jordà is grateful for the support from the Spanish MICINN National Grant SEJ20076309 and the hospitality of the Federal Reserve Bank of San Francisco during preparation of this manuscript. Taylor also gratefully acknowledges research support from the Center for the Evolution of the Global Economy at the University of California, Davis. The views expressed herein are those of the authors and do not
November 2008 Path Forecast Evaluation ∗
"... A path forecast refers to the sequence of forecasts 1 to H periods into the future. A summary of the range of possible paths the predicted variable may follow for a given confidence level requires construction of simultaneous confidence regions that adjust for any covariance between the elements of ..."
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A path forecast refers to the sequence of forecasts 1 to H periods into the future. A summary of the range of possible paths the predicted variable may follow for a given confidence level requires construction of simultaneous confidence regions that adjust for any covariance between the elements of the path forecast. This paper shows how to construct such regions with the joint predictive density and Scheffé’s (1953) Smethod. In addition, the joint predictive density can be used to construct simple statistics to evaluate the local internal consistency of a forecasting exercise of a system of variables. Monte Carlo simulations demonstrate that these simultaneous confidence regions provide approximately correct coverage in situations where traditional error bands, based on the collection of marginal predictive densities for each horizon, are vastly off mark. The paper showcases these methods with an application to the most recent monetary episode of interest rate hikes in the U.S. macroeconomy.
QuasiBayesian Model Selection∗
, 2014
"... In this paper we establish the consistency of the model selection criterion based on the quasimarginal likelihood obtained from Laplacetype estimators (LTE). We consider cases in which parameters are strongly identified, weakly identified and partially identified. Our Monte Carlo results confirm ..."
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In this paper we establish the consistency of the model selection criterion based on the quasimarginal likelihood obtained from Laplacetype estimators (LTE). We consider cases in which parameters are strongly identified, weakly identified and partially identified. Our Monte Carlo results confirm our consistency results. Our proposed procedure is applied to select among monetary macroeconomic models using US data. ∗We thank Matias Cattaneo, Larry Christiano, Kengo Kato, Lutz Kilian JaeYoung Kim and Vadim Marmer for helpful discussions and Mathias Trabandt for providing the data and code. We also thank the seminar and conference participants for helpful comments at the Bank of Canada, Gakushuin University, Hi