General equilibrium restrictions for dynamic factor models

This paper proposes the use of dynamic factor models as an alternative to the VAR-based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Beyond the weak restrictions, which are given by the number of shocks and the number of state variables, the behavioural restrictions embedded in the utility and production functions of the model economy contribute to achieve further parsimony. Such parsimony reduces the number of parameters to be estimated, potentially helping the general equilibrium environment improve forecast accuracy. In turn, the DSGE model is considered to be misspecified when it is outperformed by the state-space representation that only incorporates the weak restriction

Nowcasting Belgium

This paper proposes a method that takes into account the calendar of European and Belgian intraquarterly data releases to automatically update GDP growth expectations or nowcasts in realtime. The role of surveys is well known in the nowcasting literature, but this is the first paper that has attempted to isolate quality from timeliness as independent properties that can be expressed in function of the model parameters. The modeling framework allows for the incorporation of different kinds of survey data directly in levels and features a parsimonious specification of the GDP revision process which does not impose strict assumptions regarding the rationality of the statistical agency. The results in the empirical section emphasize the quality of survey data, which allows the model to produce accurate real GDP growth nowcasts for Belgium three months prior to the publication of the official flash estimate

Nowcasting real economic activity in the euro area: Assessing the impact of qualitative surveys. National Bank of Belgium Working Paper No. 331

This paper analyses the contribution of survey data, in particular various sentiment indicators, to nowcasts of quarterly euro area GDP. It uses a genuine real-time dataset that is constructed from original press releases in order to transform the actual dataflow into an interpretable flow of news. The latter is defined as the difference between the released values and the prediction of a mixedfrequency dynamic factor model. Our purpose is twofold. First, we aim to quantify the specific value added for nowcasting GDP from a set of heterogeneous data releases including not only sentiment indicators constructed by Eurostat, Markit, the National Bank of Belgium, IFO, ZEW, GfK or Sentix, but also hard data regarding industrial production or retail sales in the aggregate euro area and individually in some of the largest euro area countries. Second, our quantitative analysis is used to draw up an overall ranking of the indicators, on the basis of their average contribution to updates of the nowcast. Among the survey indicators, we find the strongest impact for the Markit Manufacturing PMI and the Business Climate Indicator in the euro area, and the IFO Business Climate and IFO Expectations in Germany. The widely monitored consumer confidence indicators, on the other hand, typically do not lead to significant revisions of the nowcast. In addition, even if euro area industrial production is a relevant predictor, hard data generally contribute less to the nowcasts: they may be more closely correlated with GDP but their relatively late availability implies that they can to a large extent be anticipated by nowcasting on the basis of survey data and, hence, their ‘news’ component is smaller. Finally, we also show that, in line with the previous literature, the NBB’s own business confidence indicator appears to be useful for predicting euro area GDP. The prevalence of survey data remains also under a counterfactual scenario in which hard data are released without any delay. This finding confirms that, in addition to being available in a more timely manner, survey data also contain relevant information that does not seem to be captured by hard data

Can inflation expectations in business or consumer surveys improve inflation forecasts? National Bank of Belgium, Working Paper No. 348

In this paper we develop a new model that incorporates inflation expectations and can be used for the structural analysis of inflation, as well as for forecasting. In this latter connection, we specifically look into the usefulness of real-time survey data for inflation projections. We contribute to the literature in two ways. First, our model extracts the inflation trend and its cycle, which is linked to real economic activity, by exploiting a much larger information set than typically seen in this class of models and without the need to resort to Bayesian techniques. The reason is that we use variables reflecting inflation expectations from consumers and firms under the assumption that they are consistent with the expectations derived from the model. Thus, our approach represents an alternative way to shrink the model parameters and to restrict the future evolution of the factors. Second, the inflation expectations that we use are derived from the qualitative questions on expected price developments in both the consumer and the business surveys. This latter source, in particular, is mostly neglected in the empirical literature. Our empirical results suggest that overall, inflation expectations in surveys provide useful information for inflation forecasts. In particular for the most recent period, models that include survey expectations on prices tend to outperform similar models that do not, both for Belgium and the euro area. Furthermore, we find that the business survey, i.e. the survey replies by the price-setters themselves, contributes most to these forecast improvements

Precariedad, exclusión social y modelo de sociedad: lógicas y efectos subjetivos del sufrimiento social contemporáneo (IV). Innovación docente en Filosofía

El PIMCD “Precariedad, exclusión social y modelo de sociedad: lógicas y efectos subjetivos del sufrimiento social contemporáneo (IV). Innovación docente en Filosofía” constituye la cuarta edición de un PIMCD que ha recibido financiación en las últimas convocatorias de PIMCD UCM, de los que se han derivado actividades de formación para estudiantes de Grado, Máster y Doctorado y al menos 3 publicaciones colectivas publicadas por Ediciones Complutense, Siglo XXI y Palgrave McMillan

Structural models for macroeconomics and forecasting

This Thesis is composed by three independent papers that investigatecentral debates in empirical macroeconomic modeling.Chapter 1, entitled “A Model for Real-Time Data Assessment with an Application to GDP Growth Rates”, provides a model for the datarevisions of macroeconomic variables that distinguishes between rational expectation updates and noise corrections. Thus, the model encompasses the two polar views regarding the publication process of statistical agencies: noise versus news. Most of the studies previous studies that analyze data revisions are basedon the classical noise and news regression approach introduced by Mankiew, Runkle and Shapiro (1984). The problem is that the statistical tests available do not formulate both extreme hypotheses as collectively exhaustive, as recognized by Aruoba (2008). That is, it would be possible to reject or accept both of them simultaneously. In turn, the model for theDPP presented here allows for the simultaneous presence of both noise and news. While the “regression approach” followed by Faust et al. (2005), along the lines of Mankiew et al. (1984), identifies noise in the preliminaryfigures, it is not possible for them to quantify it, as done by our model. The second and third chapters acknowledge the possibility that macroeconomic data is measured with errors, but the approach followed to model the missmeasurement is extremely stylized and does not capture the complexity of the revision process that we describe in the first chapter.Chapter 2, entitled “Revisiting the Success of the RBC model”, proposes the use of dynamic factor models as an alternative to the VAR based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Our empirical illustration compares the out-of-sample forecasting performance of a simple RBC model augmented with a serially correlated noise component against several specifications belonging to classes of dynamic factor and VAR models. Although the performance of the RBC model is comparableto that of the reduced form models, a formal test of predictive accuracy reveals that the weak restrictions are more useful at forecasting than the strong behavioral assumptions imposed by the microfoundations in the model economy.The last chapter, “What are Shocks Capturing in DSGE modeling”, contributes to current debates on the use and interpretation of larger DSGEmodels. Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies point out that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin andSala, 2005, Uhlig, 2004). In this Chapter, we propose to reconcile both views by considering an alternative DSGE estimation approach whichmodels explicitly the statistical agency along the lines of Sargent (1989). This enables us to distinguish whether the exogenous shocks in DSGEmodeling are structural or instead serve the purpose of fitting the data in presence of misspecification and measurement problems. When applied to the original Smets and Wouters (2007) model, we find that the explanatory power of the structural shocks decreases at high frequencies. This allows us to back out a smoother measure of the natural output gap than thatresulting from the original specification.Doctorat en Sciences économiques et de gestioninfo:eu-repo/semantics/nonPublishe

Nowcasting Spanish GDP growth in real time : "one and a half months earlier"

El fuerte descenso de la actividad económica registrado en España durante 2009 y 2010 no tiene precedentes en la historia más reciente. Tras diez años de prosperidad con un crecimiento medio del 3.7%, el escenario macroeconómico actual somete a estrés los modelos de predicción automáticos. En este artículo se evalua la capacidad de varias combinaciones de modelos multivariantes autoregresivos con retardos distribuidos (ADL) para obtener "nowcasts" o estimaciones del PIB anteriores a la publicación oficial. Dichos modelos requiren la estimación de un elevado número de parámetros cuando desea construirse una predicción condicional a un amplio conjunto de variables. Para hacer frente a la llamada "maldición de la dimensionalidad", utilizamos información a priori proveniente de la literatura sobre Vectores Autorregresivos Bayesianos (BVAR). Nuestro procedimiento puede interpretarse como un método simple y oportuno para aproximar la mezcla de herramientas contables, modelos y juicio que se utiliza en cualquier agencia estadística durante el proceso de construcción de las cifras del PIB agregado. La evaluación en tiempo real durante la fase más severa de la actual recesión muestra que nuestro método permite obtener predicciones fiables del PIB real español casi un mes y medio antes de que las cifras oficiales se hagan pública

Structural models for macroeconomics and forecasting

This Thesis is composed by three independent papers that investigatecentral debates in empirical macroeconomic modeling.Chapter 1, entitled “A Model for Real-Time Data Assessment with an Application to GDP Growth Rates”, provides a model for the datarevisions of macroeconomic variables that distinguishes between rational expectation updates and noise corrections. Thus, the model encompasses the two polar views regarding the publication process of statistical agencies: noise versus news. Most of the studies previous studies that analyze data revisions are basedon the classical noise and news regression approach introduced by Mankiew, Runkle and Shapiro (1984). The problem is that the statistical tests available do not formulate both extreme hypotheses as collectively exhaustive, as recognized by Aruoba (2008). That is, it would be possible to reject or accept both of them simultaneously. In turn, the model for theDPP presented here allows for the simultaneous presence of both noise and news. While the “regression approach” followed by Faust et al. (2005), along the lines of Mankiew et al. (1984), identifies noise in the preliminaryfigures, it is not possible for them to quantify it, as done by our model. The second and third chapters acknowledge the possibility that macroeconomic data is measured with errors, but the approach followed to model the missmeasurement is extremely stylized and does not capture the complexity of the revision process that we describe in the first chapter.Chapter 2, entitled “Revisiting the Success of the RBC model”, proposes the use of dynamic factor models as an alternative to the VAR based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Our empirical illustration compares the out-of-sample forecasting performance of a simple RBC model augmented with a serially correlated noise component against several specifications belonging to classes of dynamic factor and VAR models. Although the performance of the RBC model is comparableto that of the reduced form models, a formal test of predictive accuracy reveals that the weak restrictions are more useful at forecasting than the strong behavioral assumptions imposed by the microfoundations in the model economy.The last chapter, “What are Shocks Capturing in DSGE modeling”, contributes to current debates on the use and interpretation of larger DSGEmodels. Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies point out that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin andSala, 2005, Uhlig, 2004). In this Chapter, we propose to reconcile both views by considering an alternative DSGE estimation approach whichmodels explicitly the statistical agency along the lines of Sargent (1989). This enables us to distinguish whether the exogenous shocks in DSGEmodeling are structural or instead serve the purpose of fitting the data in presence of misspecification and measurement problems. When applied to the original Smets and Wouters (2007) model, we find that the explanatory power of the structural shocks decreases at high frequencies. This allows us to back out a smoother measure of the natural output gap than thatresulting from the original specification.Doctorat en Sciences économiques et de gestioninfo:eu-repo/semantics/nonPublishe

General Equilibrium Restrictions for Dynamic Factor Models

This paper proposes the use of dynamic factor models as an alternative to the VAR-based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Beyond the weak restrictions, which are given by the number of shocks and the number of state variables, the behavioural restrictions embedded in the utility and production functions of the model economy contribute to achieve further parsimony. Such parsimony reduces the number of parameters to be estimated, potentially helping the general equilibrium environment improve forecast accuracy. In turn, the DSGE model is considered to be misspecified when it is outperformed by the state-space representation that only incorporates the weak restrictions.dynamic and static rank, factor models, DSGE models, forecasting