In this paper we develop methods for estimation and forecasting in large time-varying parameter v... more In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computa-tional constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting fac-tor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.
This paper proposes a mean field variational Bayes algorithm for efficient posterior and predicti... more This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive inference in time-varying parameter models. Our approach involves: i) computationally trivial Kalman filter updates of regression coefficients, ii) a dynamic variable selection prior that removes irrelevant variables in each time period, and iii) a fast approximate state-space estimator of the regression volatility parameter. In an exercise involving simulated data we evaluate the new algorithm numerically and establish its computational advantages. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts over a number of alternatives.
In this paper we develop methods for estimation and forecasting in large time-varying parameter v... more In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computa-tional constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting fac-tor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.
We forecast quarterly US ination based on the generalized Phillips curve using econometric method... more We forecast quarterly US ination based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe ¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe ¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.
The evolution of monetary policy in the U.S. is examined based on structural dynamic factor model... more The evolution of monetary policy in the U.S. is examined based on structural dynamic factor models. I extend the current literature which questions the stability of the monetary transmission mechanism, by proposing and studying time-varying parameters factor-augmented vector autoregressions (TVP-FAVAR), which allow for fast and efficient inference based on hundreds of explanatory variables. Different specifications are compared where the factor loadings, VAR coefficients and error covariances, or combinations of those, may change gradually in every period or be subject to small breaks. The model is applied to 157 post-World War II U.S. quarterly macroeconomic variables. The results clearly suggest that the propagation of the monetary and non-monetary (exogenous) shocks has altered its behavior, and specifically in a fashion which supports smooth evolution rather than abrupt change. The most notable changes were in the responses of real activity measures, prices and monetary aggregat...
We develop efficient Bayesian estimation algorithms for dynamic factor models with time-varying c... more We develop efficient Bayesian estimation algorithms for dynamic factor models with time-varying coefficients and stochastic volatilities for the purpose of monitoring and forecasting with possibly large macroeconomic datasets in the presence of structual breaks. One algorithm can approximate the posterior mean, and the second algorithm samples from the full joint parameter posteriors. We show that our proposed algorithms are fast, numerically stable, and easy to program, which makes them ideal for real time monitoring and forecasting using flexible factor model structures. We implement two forecasting exercises in order to evaluate the performance of our algorithms, and compare them with traditional estimation methods such as principal components and Markov-Chain Monte Carlo.
ABSTRACT We use Bayesian factor regression models to construct a financial conditions index (FCI)... more ABSTRACT We use Bayesian factor regression models to construct a financial conditions index (FCI) for the U.S. Within this context we develop Bayesian model averaging methods that allow the data to select which variables should be included in the FCI or not. We also examine the importance of different sources of instability in the factors, such as stochastic volatility and structural breaks. Our results indicate that ignoring structural breaks in the loadings can be quite costly in terms of the forecasting performance of the FCI. Additionally, Bayesian model averaging can improve in specific cases the performance of the FCI, by means of discarding irrelevant financial variables during the estimation of the factor.
In this paper we develop methods for estimation and forecasting in large time-varying parameter v... more In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computa-tional constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting fac-tor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.
This paper proposes a mean field variational Bayes algorithm for efficient posterior and predicti... more This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive inference in time-varying parameter models. Our approach involves: i) computationally trivial Kalman filter updates of regression coefficients, ii) a dynamic variable selection prior that removes irrelevant variables in each time period, and iii) a fast approximate state-space estimator of the regression volatility parameter. In an exercise involving simulated data we evaluate the new algorithm numerically and establish its computational advantages. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts over a number of alternatives.
In this paper we develop methods for estimation and forecasting in large time-varying parameter v... more In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computa-tional constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting fac-tor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.
We forecast quarterly US ination based on the generalized Phillips curve using econometric method... more We forecast quarterly US ination based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe ¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe ¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.
The evolution of monetary policy in the U.S. is examined based on structural dynamic factor model... more The evolution of monetary policy in the U.S. is examined based on structural dynamic factor models. I extend the current literature which questions the stability of the monetary transmission mechanism, by proposing and studying time-varying parameters factor-augmented vector autoregressions (TVP-FAVAR), which allow for fast and efficient inference based on hundreds of explanatory variables. Different specifications are compared where the factor loadings, VAR coefficients and error covariances, or combinations of those, may change gradually in every period or be subject to small breaks. The model is applied to 157 post-World War II U.S. quarterly macroeconomic variables. The results clearly suggest that the propagation of the monetary and non-monetary (exogenous) shocks has altered its behavior, and specifically in a fashion which supports smooth evolution rather than abrupt change. The most notable changes were in the responses of real activity measures, prices and monetary aggregat...
We develop efficient Bayesian estimation algorithms for dynamic factor models with time-varying c... more We develop efficient Bayesian estimation algorithms for dynamic factor models with time-varying coefficients and stochastic volatilities for the purpose of monitoring and forecasting with possibly large macroeconomic datasets in the presence of structual breaks. One algorithm can approximate the posterior mean, and the second algorithm samples from the full joint parameter posteriors. We show that our proposed algorithms are fast, numerically stable, and easy to program, which makes them ideal for real time monitoring and forecasting using flexible factor model structures. We implement two forecasting exercises in order to evaluate the performance of our algorithms, and compare them with traditional estimation methods such as principal components and Markov-Chain Monte Carlo.
ABSTRACT We use Bayesian factor regression models to construct a financial conditions index (FCI)... more ABSTRACT We use Bayesian factor regression models to construct a financial conditions index (FCI) for the U.S. Within this context we develop Bayesian model averaging methods that allow the data to select which variables should be included in the FCI or not. We also examine the importance of different sources of instability in the factors, such as stochastic volatility and structural breaks. Our results indicate that ignoring structural breaks in the loadings can be quite costly in terms of the forecasting performance of the FCI. Additionally, Bayesian model averaging can improve in specific cases the performance of the FCI, by means of discarding irrelevant financial variables during the estimation of the factor.
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Papers by Dimitris Korobilis