Forecasting the Spanish economy with an augmented VAR–DSGE model (original) (raw)
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Policy-Oriented Macroeconomic Forecasting With Hybrid DSGE and Time-Varying Parameter VAR Models
SSRN Electronic Journal, 2000
Micro-founded dynamic stochastic general equilibrium (DSGE) models appear to be particularly suited to evaluating the consequences of alternative macroeconomic policies. Recently, increasing efforts have been undertaken by policymakers to use these models for forecasting, although this proved to be problematic due to estimation and identification issues. Hybrid DSGE models have become popular for dealing with some of the model misspecifications and the trade-off between theoretical coherence and empirical fit, thus allowing them to compete in terms of predictability with VAR models. However, DSGE and VAR models are still linear and they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy in a robust manner. This study conducts a comparative evaluation of the out-of-sample predictive performance of many different specifications of DSGE models and various classes of VAR models, using datasets for the real GDP, the harmonized CPI and the nominal short-term interest rate series in the euro area. Simple and hybrid DSGE models were implemented, including DSGE-VAR and factor-augmented DGSE, and tested against standard, Bayesian and factor-augmented VARs. Moreover, a new state-space time-varying VAR model is presented. The total period spanned from 1970:Q1 to 2010:Q4 with an out-of-sample testing period of 2006:Q1-2010:Q4, which covers the global financial crisis and the EU debt crisis. The results of this study can be useful in conducting monetary policy analysis and macro-forecasting in the euro area. compared to alternative non-structural models. In the macro-econometric literature, hybrid or mixture DSGE models have become popular for dealing with some of the model misspecifications as well as the trade-off between theoretical coherence and empirical fit (Schorfheide, 2010). They are categorized in additive hybrid models and hierarchical hybrid models. The hybrid models provide a complete analysis of the data law of motion and better capture the dynamic properties of the DSGE models. In the recent literature, different attempts using hybrid models have been introduced for solving, estimating and forecasting with DSGEs. and proposed augmenting a DSGE model with measurement error terms that follow a first-order autoregressive process, known as the DSGE-AR approach. Ireland (2004) proposed a method that is similar to the DSGE-AR, but imposing no restriction on the measurement errors, assuming that residuals follow a first-order vector autoregression (DSGE-AR, in the manner of Ireland). A different approach called DSGE-VAR was proposed by Del and was based on the works of and Ingram and Whiteman (1994). The main idea behind the DSGE-VAR is the use of the VAR representation as an econometric tool for empirical validation, combining prior information derived from the DSGE model in estimation. However, it has several problems. One of the main problems in finding a statistical representation for the data by using a VAR, is overfitting due to the inclusion of too many lags and too many variables, some of which may be insignificant. The problem of overfitting results in multicollinearity and loss of degrees of freedom, leading to inefficient estimates and large out-of-sample forecasting errors. It is possible to overcome this problem by using the well-known 'Minnesota' priors . The use of 'Minnesota' priors has been proposed to shrink the parameters space and thus overcome the curse of dimensionality. Following this idea in combining the DSGE model information and the VAR representation, two alternative econometric tools have been also introduced: the DSGE-FAVAR and the augmented VAR-DSGE model (Fernández-de-Córdoba and Torres, 2011). The main idea behind the factor-augmented DSGE (DSGE-FAVAR) is the use of factors to improve the statistical identification in validating the models. Consequently, the VAR representation is replaced by a FAVAR model as the statistical benchmark.
Policy-Oriented Macroeconomic Forecasting with Hybrid DGSE and Time-Varying Parameter VAR Models
Journal of Forecasting, 2016
Micro-founded dynamic stochastic general equilibrium (DSGE) models appear to be particularly suited for evaluating the consequences of alternative macroeconomic policies. Recently, increasing e¤orts have been undertaken by policymakers to use these models for forecasting, although this proved to be problematic due to estimation and identi…cation issues. Hybrid DSGE models have become popular for dealing with some of model misspeci…cations and the trade-o¤ between theoretical coherence and empirical …t, thus allowing them to compete in terms of predictability with VAR models. However, DSGE and VAR models are still linear and they do not consider time-variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy in a robust manner. This study conducts a comparative evaluation of the out-of-sample predictive performance of many di¤erent speci…cations of DSGE models and various classes of VAR models, using datasets for the real GDP, the harmonized CPI and the nominal short-term interest rate series in the Euro area. Simple and hybrid DSGE models were implemented including DSGE-VAR and Factor Augmented DGSE, and tested against standard, Bayesian and Factor Augmented VARs. Moreover, a new state-space time-varying VAR model is presented. The total period spanned from 1970:1 to 2010:4 with an out-of-sample testing period of 2006:1-2010:4, which covers the global …nancial crisis and the EU debt crisis. The results of this study can be useful in conducting monetary policy analysis and macro-forecasting in the Euro area.
A DSGE Model for the Spanish Economy
In this paper, we provide a brief introduction to a new macroeconometric model of the Spanish economy named MEDEA (Modelo de Equilibrio Dinámico de la Economía EspañolA). MEDEA is a dynamic stochastic general equilibrium (DSGE) model that aims to describe the main features of the Spanish economy for policy analysis, counterfactual exercises, and forecasting. MEDEA is built in the tradition of New Keynesian models with real and nominal rigidities, but it also incorporates aspects such as a small open economy framework, an outside monetary authority such as the ECB, and population growth, factors that are important in accounting for aggregate fluctuations in Spain. The model is estimated with Bayesian techniques and data from the last two decades. Beyond describing the properties of the model, we perform different exercises to illustrate the potential of MEDEA, including historical decompositions, long-run and short-run simulations, and counterfactual experiments.
Bayesian Forecasting with a Factor-Augmented Vector Autoregressive DSGE model
In this paper we employ advanced Bayesian methods 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. Very recently, hybrid models have become popular for dealing with some of the DSGE model misspeci…cations. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and e¤ectively deal with more complex real-world problems as richer sources of data become available. This study includes a comparative evaluation of the out-of-sample predictive performance of many di¤erent speci…cations of estimated DSGE models and various classes of VAR models, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and Factor Augmented VARs. In this study we focus on a Factor Augmented DSGE model that is estimated using Bayesian approaches. The investigated period spans 1960:Q4 to 2010:Q4 for the real GDP, the harmonized CPI and the nominal short-term interest rate. We produce their forecasts 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.
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 relax the stationarity assumptions in VAR or DSGE models, thus capturing the adaptive underlying structure of the economy in a robust manner. In this paper, we present a state space time-varying parameter VAR model. Moreover, we focus on the DSGE-VAR that combines a micro-founded 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.
Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models
Computational Statistics & Data Analysis, 2014
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.
An evaluation of the forecast performance of DSGE and VAR Models: The case of a developing country
Business Review, 2019
This paper estimates a DSGE model and three versions of VAR models (VARX, BVARX and BVAR) to analyze forecasting performance of these models in context of Pakistan. VAR models and a medium-scale DSGE model are estimated using quarterly data (1980Q4-2017Q2). Expanding window recursive out-of-sample forecasts for GDP growth, call money rate, CPI inflation and percent change in exchange rate are generated and compared over the period 2009Q1-2017Q2. Forecasting performance is analyzed by the comparison of bias and root mean squared errors (RMSE). Analysis of forecasting performance over 1-8 quarters forecast horizon reveals that BVAR model provides relatively better forecast in case of GDP growth, interest rate and inflation while BVARX provides more accurate forecast in case of exchange rate. In case of GDP growth, inflation and exchange rate, forecasting performance of DSGE model considerably improves as forecasting horizon expands. For longer forecast horizons, divergence between DSGE and Bayesian VAR forecasts tends to disappear. This implies that DSGE model is more relevant for medium term forecasting rather than short term forecasting. Structural interpretation of DSGE forecast errors reveals that there has been unutilized growth potential in economic activity. This slack in economic activity might be attributable to unnecessarily high interest rate and overvalued exchange rate.
A DSGE-VAR model for forecasting key South African macroeconomic variables
Economic Modelling, 2013
The paper develops a Small Open Economy New Keynesian DSGE-VAR (SOENKDSGE-VAR) model of the South African economy, characterised by incomplete pass-through of exchange rate changes, external habit formation, partial indexation of domestic prices and wages to past inflation, and staggered price and wage setting. The model is estimated using Bayesian techniques on data from the period 1980Q1 to 2003Q2, and then used to forecast output, inflation and nominal short-term interest rate for one-to eight-quarters-ahead over an out-of sample horizon of 2003Q3 to 2010Q4. When the forecast performance of the SOENKDSGE-VAR model is compared with an independently estimated DSGE model, the classical VAR and six alternative BVAR models, we find that, barring the BVAR model based on the SSVS prior on both VAR coefficients and the error covariance, the SOENKDSGE-VAR model is found to perform competitively, if not, better than all the other VAR 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.