nep-ecm New Economics Papers
on Econometrics
Issue of 2009‒10‒17
twelve papers chosen by
Sune Karlsson
Orebro University

  1. Minimum Distance Estimation and Testing of DSGE Models from Structural VARs By Fève, P.; Matheron, J.; Sahuc, J-G.
  2. Estimation of quasi-rational DSGE monetary models By Luca Fanelli
  3. Semiparametric Efficiency Bound for Models of Sequential Moment Restrictions Containing Unknown Functions By Chunrong Ai; Xiaohong Chen
  4. Comparing forecast accuracy: A Monte Carlo investigation By Fabio Busetti; Juri Marcucci; Giovanni Veronese
  5. Macroeconomic Forecasting and Structural Change By Antonello D'Agostino; Luca Gambetti; Domenico Giannone
  6. Identification of a Heterogeneous Generalized Regression Model with Group Effects By Steven T. Berry; Philip A. Haile
  7. Nowcasting Euro Area Economic Activity in Real-Time: The Role of Confidence Indicator By Domenico Giannone; Lucrezia Reichlin; Saverio Simonelli
  8. Multiple Testing Techniques in Growth Econometrics By Deckers, Thomas; Hanck, Christoph
  9. State price density estimation via nonparametric mixtures By Ming Yuan
  10. Inference in Mixed Proportional Hazard Models with K Random Effects By Guillaume Horny.
  11. A Method for Implementing Counterfactual Experiments in Models with Multiple Equilibria By Victor, Aguirregabiria
  12. Bayesian estimation of Cox models with non-nested random effects: an application to the ratification of ILO conventions by developing countries By Bernhard Boockmann.; Dragana Djurdjevic.; Guillaume Horny.; François Laisney.

  1. By: Fève, P.; Matheron, J.; Sahuc, J-G.
    Abstract: The aim of this paper is to complement the MDE--SVAR approach when the weighting matrix is not optimal. In empirical studies, this choice is motivated by stochastic singularity or collinearity problems associated with the covariance matrix of Impulse Response Functions. Consequently, the asymptotic distribution cannot be used to test the economic model's fit. To circumvent this difficulty, we propose a simple simulation method to construct critical values for the test statistics. An empirical application with US data illustrates the proposed method.
    Keywords: MDE, SVAR, DSGE models.
    JEL: C15 C32 E32
    Date: 2009
  2. By: Luca Fanelli (Alma Mater Studiorum Università di Bologna)
    Abstract: This paper proposes the estimation of small-scale dynamic stochastic general equilibrium (DSGE) monetary models under the quasi-rational expectations (QRE) hypothesis. The QRE-DSGE model is based on the idea that the determinate reduced form solution associated with the structural model, if it exists, must have the same lag structure as the ‘best fitting’ vector autoregressive (VAR) model for the observed time series. After discussing solution properties and the local identifiability of the model, a likelihood-based iterative algorithm for estimating the structural parameters and testing the data adequacy of the system is proposed. A Monte Carlo experiment shows that, even controlling for the omitted dynamics bias, the over-rejection of the nonlinear cross-equation restrictions when asymptotic critical values are used and variables are highly persistent is a relevant issue in finite samples. An application based on euro area data illustrates the advantages of using error-correcting formulations of the QRE-DSGE model when the inflation rate and the short-term interest rate are approximated as difference stationary processes. A parametric bootstrap version of the likelihood-ratio test for the implied cross-equation restrictions does not reject the estimated QRE-DSGE model.
    Keywords: Dynamic stochastic general equilibrium model, Maximum Likelihood estimation, Quasi-Rational Expectations, VAR. Modelli DSGE, Stima di massima verosimiglianza, Aspettative Quasi-Razionali, Modelli VAR.
    Date: 2009
  3. By: Chunrong Ai (Dept. of Economics, University of Florida); Xiaohong Chen (Cowles Foundation, Yale University)
    Abstract: This paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions. Our results extend those of Chamberlain (1992b) and Ai and Chen (2003) for semiparametric conditional moment restriction models with identical information sets to the case of nested information sets, and those of Chamberlain (1992a) and Brown and Newey (1998) for models of sequential moment restrictions without unknown functions to cases with unknown functions of possibly endogenous variables. Our bound results are applicable to semiparametric panel data models and semiparametric two stage plug-in problems. As an example, we compute the efficiency bound for a weighted average derivative of a nonparametric instrumental variables (IV) regression, and find that the simple plug-in estimator is not efficient. Finally, we present an optimally weighted, orthogonalized, sieve minimum distance estimator that achieves the semiparametric efficiency bound.
    Keywords: Sequential moment models, Semiparametric efficiency bounds, Optimally weighted orthogonalized sieve minimum distance, Nonparametric IV regression, Weighted average derivatives, Partially linear quantile IV
    JEL: C14 C22
    Date: 2009–10
  4. By: Fabio Busetti (Bank of Italy); Juri Marcucci (Bank of Italy); Giovanni Veronese (Bank of Italy)
    Abstract: The size and power properties of several tests of equal Mean Square Prediction Error (MSPE) and of Forecast Encompassing (FE) are evaluated, using Monte Carlo simulations, in the context of dynamic regressions. For nested models, the F-type test of forecast encompassing proposed by Clark and McCracken (2001) displays overall the best properties. However its power advantage tends to become smaller as the prediction sample increases and for multi-step ahead predictions; in these cases a standard FE test based on Gaussian critical values becomes relatively more attractive. The ranking among the tests remains broadly unaltered for one-step and multi-step ahead predictions, for partially misspecified models and for highly persistent data. A similar setup is then used to analyze the case of non-nested models. Again it is found that FE tests have a significantly better performance than tests of equal MSPE for discriminating between correct and misspecified models. An empirical application evaluates the predictive ability of nested and non-nested models for GDP in Italy and the euro-area.
    Keywords: Forecast encompassing, Model evaluation, Nested models, Non-nested models, Equal predictive ability
    JEL: C12 C52 C53
    Date: 2009–09
  5. By: Antonello D'Agostino; Luca Gambetti; Domenico Giannone
    Abstract: The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coe±cients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the naaive random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation.
    Keywords: Forecasting, infation, stochastic Volatility, time varying vector autoregression.
    JEL: C32 E37 E47
    Date: 2009
  6. By: Steven T. Berry (Cowles Foundation, Yale University); Philip A. Haile (Cowles Foundation, Yale University)
    Abstract: We consider identification in a "generalized regression model" (Han, 1987) for panel settings in which each observation can be associated with a "group" whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully nonparametric and allows for the endogeneity of group-specific observables, which might include prices, policies, and/or treatments. The model features heterogeneous responses to observables and unobservables, and arbitrary heteroskedasticity. We provide sufficient conditions for full identification of the model, as well as weaker conditions sufficient for identification of the latent group effects and the distribution of outcomes conditional on covariates and the group effect.
    Keywords: Nonparametric identification, Binary choice, Threshold crossing, Censored regression, Proportional hazard model
    JEL: C23 C24 C25
    Date: 2009–10
  7. By: Domenico Giannone; Lucrezia Reichlin; Saverio Simonelli
    Abstract: This paper assesses the role of surveys for the early estimates of GDP in the euro area in a model-based automated procedures which exploits the timeliness of their release. The analysis is conducted using both an historical evaluation and a real time case study on the current conjuncture.
    Keywords: Forecasting, factor model, real time data, large data sets, survey.
    JEL: E52 C33 C53
    Date: 2009
  8. By: Deckers, Thomas; Hanck, Christoph
    Abstract: This paper discusses two longstanding questions in growth econometrics which involve multiple hypothesis testing. In cross sectional GDP growth regressions many variables are simultaneously tested for significance. Similarly, when investigating pairwise convergence of output for $n$ countries, $n(n-1)/2$ tests are performed. We propose to control the false discovery rate (FDR) so as not to erroneously declare variables significant in these multiple testing situations only because of the large number of tests performed. Doing so, we provide a simple new way to robustly select variables in economic growth models. We find that few other variables beyond the initial GDP level are needed to explain growth. We also show that convergence of per capita output using a time series definition with the necessary condition of no unit root in the log per-capita output gap of two economies does not appear to hold
    Keywords: Growth Empirics; Multiple Testing; Convergence; Bootstrap
    JEL: O47 C12
    Date: 2009–10–10
  9. By: Ming Yuan
    Abstract: We consider nonparametric estimation of the state price density encapsulated in option prices. Unlike usual density estimation problems, we only observe option prices and their corresponding strike prices rather than samples from the state price density. We propose to model the state price density directly with a nonparametric mixture and estimate it using least squares. We show that although the minimization is taken over an infinitely dimensional function space, the minimizer always admits a finite dimensional representation and can be computed efficiently. We also prove that the proposed estimate of the state price density function converges to the truth at a ``nearly parametric'' rate.
    Date: 2009–10
  10. By: Guillaume Horny.
    Abstract: A general formulation of Mixed Proportional Hazard models with K random effects is provided. It enables to account for a population stratified at K different levels. We then show how to approximate the partial maximum likelihood estimator using an EM algorithm. In a Monte Carlo study, the behavior of the estimator is assessed and I provide an application to the ratification of ILO conventions. Compared to other procedures, the results indicate an important decrease in computing time, as well as improved convergence and stability.
    Keywords: EM algorithm, penalized likelihood, partial likelihood, frailties.
    JEL: C13 C14 C41
    Date: 2009
  11. By: Victor, Aguirregabiria
    Abstract: This paper proposes a method for implementing counterfactual experiments in estimated models that have multiple equilibria. The method assumes that the researcher does not know the equilibrium selection mechanism and wants to impose minimum restrictions on it. Our key assumption is that the equilibrium selection function does not jump discontinuously between equilibria as we change marginally the structural parameters of the model. Under this assumption, we show that, although the equilibrium selection function is unknown, the researcher can obtain an approximation of this function in a neighborhood of the estimated values of the structural parameters. Under the additional assumption that the counterfactual equilibrium is stable, this approximation can be combined with iterations in the equilibrium mapping to obtain the exact counterfactual equilibrium. We illustrate the differences between our approach and other methods, such as the selection of a counterfactual equilibrium that is closer to the equilibrium in the data, and equilibrium mapping iterations using the equilibrium in the data as the initial value. We show that, in general, these alternative methods are not consistent with the assumption that the equilibrium selection mechanism is continuous with respect to the structural parameters.
    Keywords: Structural models with multiple equilibria; Counterfactual experiments; Equilibrium selection.
    JEL: C10 C60
    Date: 2009–10–10
  12. By: Bernhard Boockmann.; Dragana Djurdjevic.; Guillaume Horny.; François Laisney.
    Abstract: We use a multivariate hazard model to analyse the ratification behaviour of ILO conventions by developing countries. The model accounts for two random effects: one at the country level, the other at the convention level. After investigating identification, we use a semi-parametric Bayesian approach based on the partial likelihood. We find diverging results between Bayesian and frequentist estimates concerning the importance of the two unobserved heterogeneities.
    Keywords: Gibbs sampling, partial likelihood, frailties, duration analysis.
    JEL: C11 C14 C15 C41 D78 J80 O19
    Date: 2009

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