Now showing 1 - 9 of 9
  • Publication
    Oil price forecastability and economic uncertainty
    Information on economic policy uncertainty does matter in predicting the change in oil prices. We compare the forecastability of standard, Bayesian and time-varying VAR against univariate models. The time-varying VAR model outranks all alternative models over the period 2007:1–2014:2.
      609Scopus© Citations 93
  • Publication
    Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a predictability analysis versus TVP-VARs
    In DSGE literature there has been an increasing awareness on the role that the banking sector can play in macroeconomic activity. In most of recent works, purely financial instabilities and frictions are derived from intermediaries that affect the real economy by means of a credit channel or a balance sheet channel. We model financial intermediation as in Gertler and Karadi (2011) to take into account the bank leverage constraint in the propagation of shocks to the real economy. Within this framework, the evolution of estimated shocks and the instabilities in the structural parameters show that time-variation should be crucial in any attempted empirical analysis. However, DSGE modelling usually fails to take into account inherent nonlinearities of the economy, especially in crisis time periods. Hence, we propose a novel time-varying parameter (TVP) state-space estimation method for VAR processes both for homoskedastic and heteroskedastic error structures. We conduct an exhaustive empirical exercise that includes the comparison of the out-of-sample predictive performance of the estimated DSGE model with that of standard VARs, Bayesian VARs and TVP-VARs. Overall, a first attempt is made to find macro-financial micro-founded DSGE models as well as adaptive TVP-VARs, which are able to deal with financial instabilities via incorporating banking intermediation.
      334Scopus© Citations 11
  • Publication
    Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models
    Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. A comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models is performed, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE–VAR and Factor Augmented DSGEs and tested against standard, Bayesian and Factor Augmented VARs. Moreover, small scale models including the real gross domestic product, the harmonized consumer price index and the nominal short-term federal funds interest rate, are comparatively assessed against medium scale models featuring additionally sticky nominal prices, wage contracts, habit formation, variable capital utilization and investment adjustment costs. The investigated period spans 1960:Q4–2010:Q4 and forecasts are produced for the out-of-sample testing period 1997:Q1–2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.
      522Scopus© Citations 18
  • Publication
    Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a predictability analysis versus TVP-VARs
    (University College Dublin. School of Economics, 2016-08) ; ; ;
    In the dynamic stochastic general equilibrium (DSGE) literature there has been an increasing awareness on the role that the banking sector can play in macroeconomic activity. We present a DSGE model with financial intermediation as in Gertler and Karadi (2011). The estimation of shocks and of the structural parameters shows that time-variation should be crucial in any attempted empirical analysis. Since DSGE modelling usually fails to take into account inherent nonlinearities of the economy, we propose a novel time-varying parameter (TVP) state-space estimation method for VAR processes both for homoskedastic and heteroskedastic error structures. We conduct an exhaustive empirical exercise to compare the out-of-sample predictive performance of the estimated DSGE model with that of standard ARs, VARs, Bayesian VARs and TVP-VARs. We find that the TVP-VAR provides the best forecasting performance for the series of GDP and net worth of financial intermediaries for all steps-ahead, while the DSGE model outperforms the other specifications in forecasting inflation and the federal funds rate at shorter horizons.
      488
  • Publication
    The Effectiveness of Forward Guidance in an Estimated DSGE Model for the Euro Area: the Role of Expectations
    (University College Dublin. School of Economics, 2017-01) ; ;
    We assess the effectiveness of the forward guidance undertaken by European Central Bank using a standard medium-scale DSGE model à la Smets and Wouters (2007). Exploiting data on expectations from surveys, we show that incorporating expectations should be crucial in performance evaluation of models for the forward guidance. We conduct an exhaustive empirical exercise to compare the pseudo out-of-sample predictive performance of the estimated DSGE model with a Bayesian VAR and a DSGE-VAR models. DSGE model with expectations outperforms others for inflation; while for output and short term-interest rate the DSGE-VAR with expectations reports the best prediction.
      232
  • Publication
    On the predictability of time-varying VAR and DSGE models
    Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic fluctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspecifications as they are able to solve the trade-off between theoretical coherence and empirical fit. However, these models are still linear and they do not consider time variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this article, we present a state-space time-varying parameter VAR model. Moreover, we focus on the DSGE–VAR that combines a microfounded DSGE model with the flexibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP–VAR and the DSGE–VAR outperform the other models.
      464Scopus© Citations 14
  • Publication
    Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model
    Although policymakers and practitioners are particularly interested in dynamic stochastic general equilibrium (DSGE) models, these are typically too stylized to be applied directly to the data and often yield weak prediction results. Very recently, hybrid DSGE models have become popular for dealing with some of the model misspecifications. Major advances in estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. In this study we introduce a Bayesian approach to estimate a novel factor augmented DSGE model that extends the model of Consolo et al. [Consolo, A., Favero, C.A., and Paccagnini, A., 2009. On the Statistical Identification of DSGE Models. Journal of Econometrics, 150, 99–115]. We perform a comparative predictive evaluation of point and density forecasts for many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy including real-time data. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and factor augmented VARs. The results can be useful for macro-forecasting and monetary policy analysis.
      496Scopus© Citations 6
  • Publication
    On the predictability of time-varying VAR and DSGE models
    Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic fluctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspecifications as they are able to solve the trade-off between theoretical coherence and empirical fit. However, these models are still linear and they do not consider time variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this article, we present a state-space time-varying parameter VAR model. Moreover, we focus on the DSGE–VAR that combines a microfounded DSGE model with the flexibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP–VAR and the DSGE–VAR outperform the other models.
      394Scopus© Citations 14
  • Publication
    Macroprudential policy and forecasting using Hybrid DSGE models with financial frictions and State space Markov-Switching TVP-VARs
    (Cambridge University Press, 2015-10) ;
    We focus on the interaction of frictions both at the firm level and in the banking sector in order to examine the transmission mechanism of the shocks and to reflect on the response of the monetary policy to increases in interest rate spreads, using DSGE models with financial frictions. However, VAR models are linear and the solutions of DSGEs are often linear approximations; hence they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy, especially in crisis periods. A novel method for time-varying VAR models is introduced. As an extension to the standard homoskedastic TVP-VAR, we employ a Markov-switching heteroskedastic error structure. Overall, we conduct a comparative empirical analysis of the out-of-sample performance of simple and hybrid DSGE models against standard VARs, BVARs, FAVARs, and TVP-VARs, using data sets from the U.S. economy. We apply advanced Bayesian and quasi-optimal filtering techniques in estimating and forecasting the models.
      693Scopus© Citations 10