VARs, common factors and the empirical validation of equilibrium business cycle models

Giannone, Domenico; Reichlin, Lucrezia; Sala, Luca

doi:10.1016/j.jeconom.2005.01.030

“…Cogley (2001) and Gorodnichenko and Ng (2010) were concerned with the problem of estimating the structural parameters of a cyclical DSGE when the trend specification is incorrect, but did not investigate what consequences imperfect filtering has on the properties of the cyclical component or their implications for structural estimates. Giannone, Reichlin, and Sala (2006) emphasized that if model variables are measured with error, the solution has a natural factor structure, and exploited this feature to compare VAR and factor model impulse responses. Rather than consider a factor structure for the endogenous variables in terms of the states, we construct an estimable structure where vectors of filtered observable data have a factor structure in terms of the variables of the model.…”

Section: The Relationship With the Literaturementioning

confidence: 99%

Multiple Filtering Devices for the Estimation of Cyclical DSGE Models

Canova

¹

2009

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We propose a method to estimate time invariant cyclical dynamic stochastic general equilibrium models using the information provided by a variety of filters. We treat data filtered with alternative procedures as contaminated proxies of the relevant model-based quantities and estimate structural and nonstructural parameters jointly using a signal extraction approach. We employ simulated data to illustrate the properties of the procedure and compare our conclusions with those obtained when just one filter is used. We revisit the role of money in the transmission of monetary business cycles. Terms of use: Documents in EconStor may

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“…Even though Agent Based Models (ABMs) have often been advocated as promising alternatives to neoclassical models rooted in the dogmatic paradigms of rational expectations and representative agents, there are still some concerns about how to bring them down to the data (Windrum et al, 2007;Gallegati and Richiardi, 2009;Grazzini and Richiardi, 2015). In macroeconomics, for example, Giannone et al (2006), Canova and Sala (2009) and Paccagnini (2009) provide details about how to estimate and validate Dynamic Stochastic General Equilibrium models. However, their approach cannot be extended to settings where an analytical solution of the model (or an equilibrium) does not exist, which are typical cases in ABMs, system dynamics and complex systems more in general.…”

Section: Introductionmentioning

confidence: 99%

Empirical Validation of Simulated Models through the GSL-div: An Illustrative Application

Lamperti

¹

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractA major concern about the use of simulation models regards their relationship with the empirical data. The identification of a suitable indicator quantifying the distance between the model and the data would help and guide model selection and output validation. This paper proposes the use of a new criterion, called GSL-div and developed in Lamperti (2015), to assess the degree of similarity between the dynamics observed in the data and those generated by the numerical simulation of models. As an illustrative application, this approach is used to distinguish between different versions of the well known asset pricing model with heterogeneous beliefs proposed in Brock and Hommes (1998). Once the discrimination ability of the GSL-div is proved, model's dynamics are directly compared with actual data coming from two major stock market indexes (EuroSTOXX 50 for Europe and CSI 300 for China).Results show that the model, once calibrated, is fairly able to track the evolution of both the two indexes, even though a better fit is reported for the Chinese stock market. However, I also find that many different combinations of traders behavioural rules are compatible with the same observed dynamics. Within this heterogeneity, an emerging common trait is found: to be empirically valid, the model has to account for a strong trend following component, which might either come from a unique trend type that heavily extrapolates information from past observations or the combinations of different types with milder, or even opposite, attitudes towards the trend.

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“…The factor model employed here should be distinguished from what studied in the traditional factor literature (see Sargent and Sims, 1977, Geweke, 1977, Geweke and Singleton, 1981, Altug, 1989, Sargent, 1989, Giannone, Reichlin and Sala, 2006). Since our model is approximate and feasible for large panels we need less stringent assumptions to identify the common from the idiosyncratic component (we do not need to impose cross-sectional orthogonality of the idiosyncratic residuals).…”

Section: Introductionmentioning

confidence: 99%

Opening the Black Box: Structural Factor Models With Large Cross Sections

Forni

¹

,

Giannone

²

,

Lippi

³

et al. 2009

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This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We establish sufficient conditions for identification of the structural shocks and the associated impulse-response functions. In particular, we argue that, if the data follow an approximate factor structure, the "problem of fundamentalness", which is intractable in structural VARs, can be solved provided that the impulse responses are sufficiently heterogeneous. Finally, we propose a consistent method (and n, T rates of convergence) to estimate the impulse-response functions, as well as a bootstrapping procedure for statistical inference. Non Technical SummaryAgents and policy makers have access to rich information, coming from data on different sectors of the economy. However, standard macro time series models are typically based on few selected variables. Recent econometric literature has introduced models that can exploit large data-sets and still retain simplicity (parsimony). These models -known in the literature as dynamics factor models -are based on the idea that the macroeconomy is driven by few shocks, common to all variables. Since a robust empirical characteristics of macroeconomic time series is that they exhibit strong co-movements, common shocks generate the bulk of the observed dynamics in macro variables.Dynamic factor models have been shown to be successful to forecast macroeconomic variables, but only few applications have considered these models for identifying and estimating structural shocks, as, for example, it is done in the VAR literature.The aim of this paper is to develop the estimation and identification theory needed to study structural shocks and their impulse response functions in dynamic factor models.The analysis of the paper and the empirical application we present show that dynamic factor models are suitable for structural macroeconomic modelling and constitute an interesting alternative to structural VARs. In particular, if the information used by economic agents cannot be captured by the small set of variables considered in a typical VAR, an econometric model based on large information can recover the structural shocks while the small VAR cannot. The factor model framework is also useful when the aim is to study the effect of macroshocks on many variables in the economy, possibly sectoral and regional, rather than studying the effect of these shocks to core macro variables only.

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VARs, common factors and the empirical validation of equilibrium business cycle models

Cited by 61 publications

References 21 publications

Multiple Filtering Devices for the Estimation of Cyclical DSGE Models

Multiple Filtering Devices for the Estimation of Cyclical DSGE Models

Empirical Validation of Simulated Models through the GSL-div: An Illustrative Application

Opening the Black Box: Structural Factor Models With Large Cross Sections

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