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22
SHORT AND LONGRUN TAX ELASTICITIES THE CASE OF THE
, 2007
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COST DYNAMICS INSIGHTS GAINED FROM CONVERSATIONS WITH LABOR MARKET DECISION MAKERS 1
, 2007
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WAGE AND LABOUR COST DYNAMICS WAGE INEQUALITY IN SPAIN RECENT DEVELOPMENTS 1
, 2007
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THE ECONOMIC IMPACT OF MERGER CONTROL WHAT IS SPECIAL ABOUT BANKING? 1
, 2007
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particle filter analysis of dynamic economic models
, 2008
"... inference based only on simulated likelihood: ..."
ESRI Discussion Paper Series No.231 Timevarying Analysis of Dynamic Stochastic General Equilibrium Models Based on Sequential Monte Carlo Methods
, 2010
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RWP 0904Yield Curve in an Estimated Nonlinear Macro Model
, 2009
"... This paper estimates a sticky price macro model with US macro and term structure data using Bayesian methods. The model is solved by a nonlinear method. The posterior distribution of the parameters in the model is found to be bimodal. The degree of nominal rigidity is high at one mode (“sticky pric ..."
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This paper estimates a sticky price macro model with US macro and term structure data using Bayesian methods. The model is solved by a nonlinear method. The posterior distribution of the parameters in the model is found to be bimodal. The degree of nominal rigidity is high at one mode (“sticky price mode”) but is low at the other mode (“flexible price mode”). I find that the degree of nominal rigidity is important for identifying macro shocks that affect the yield curve. When prices are more flexible, a slowly varying inflation target of the central bank is the main driver of the overall level of the yield curve by changing longrun inflation expectations. In contrast, when prices are more sticky, a highly persistent markup shock is the main driver. The posterior probability of each mode is sensitive to the use of observed proxies for inflation expectations. Ignoring additional information from survey data on inflation expectations significantly reduces the posterior probability of the flexible price mode. Incorporating this additional information suggests that yield curve fluctuations can be better understood by focusing on the flexible price mode. Considering nonlinearities of the model solution also increases the posterior probability of the flexible price mode, although to a lesser degree than using survey data information.
Efficient econometric inference based on estimated likelihoods
, 2008
"... Suppose we wish to carry out likelihood based inference but we solely have an unbiased simulation based estimator of the likelihood. We note that unbiasedness is enough when the estimated likelihood is used inside a MetropolisHastings algorithm. This result has recently been introduced in statistic ..."
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Suppose we wish to carry out likelihood based inference but we solely have an unbiased simulation based estimator of the likelihood. We note that unbiasedness is enough when the estimated likelihood is used inside a MetropolisHastings algorithm. This result has recently been introduced in statistics literature by Andrieu, Doucet, and Holenstein (2007) and is perhaps surprising given the celebrated results on maximum simulated likelihood estimation. It can be widely applied in microeconomics, macroeconomics and financial econometrics. One way of generating unbiased estimates of the likelihood is by the use of a particle filter. We illustrate these methods on three problems in econometrics, producing rather generic methods. Taken together, these methods imply that if we can simulate from an economic model we can carry out likelihood based inference using its simulations.
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"... Abstract. We apply a kernel smoother to the particles in the standard SIR filter for nonlinear state space models with additive Gaussian observation noise. This reduces the Monte Carlo error in the estimates of both the posterior density of the states and the marginal density of the observation at ..."
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Abstract. We apply a kernel smoother to the particles in the standard SIR filter for nonlinear state space models with additive Gaussian observation noise. This reduces the Monte Carlo error in the estimates of both the posterior density of the states and the marginal density of the observation at each time point. We correct for variance inflation in the smoother, which together with the use of Gaussian kernels, results in a Gaussian (Kalman) update when the amount of smoothing turns to infinity. Our main contribution is a study of different criteria for choosing the optimal bandwidth h in the kernel smoother. We derive the rate at which h → 0 as the number of particles increases. The resulting formula is used to show consistency of posterior and marginal densities, and to study the effect of state dimension and the effect of correlation among the state variables on the optimal h. Further, we study the limit h → ∞, and thereby shed light on the effect of nonGaussianity on the currently popular Ensemble Kalman Filter. Finally, we illustrate our approach using examples from econometrics. Our filter is shown to be highly suited for dynamic models with high signaltonoise ratio, for which the SIR filter has problems.
Bayesian Inference for Nonlinear Structural Time Series Models
, 2014
"... This article discusses a partially adapted particle filter for estimating the likelihood of nonlinear structural econometric state space models whose state transition density cannot be expressed in closed form. The filter generates the disturbances in the state transition equation and allows for mul ..."
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This article discusses a partially adapted particle filter for estimating the likelihood of nonlinear structural econometric state space models whose state transition density cannot be expressed in closed form. The filter generates the disturbances in the state transition equation and allows for multiple modes in the conditional disturbance distribution. The particle filter produces an unbiased estimate of the likelihood and so can be used to carry out Bayesian inference in a particle Markov chain Monte Carlo framework. We show empirically that when the signal to noise ratio is high, the new filter can be much more efficient than the standard particle filter, in the sense that it requires far fewer particles to give the same accuracy. The new filter is applied to several simulated and real examples and in particular to a dynamic stochastic general equilibrium model.