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on Forecasting |
| By: | Foroni, Claudia; Marcellino, Massimiliano; Stevanović, Dalibor |
| Abstract: | Temporal aggregation in general introduces a moving average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed frequency model. The MA component is generally neglected, likely to preserve the possibility of OLS estimation, but the consequences have never been properly studied in the mixed frequency context. In this paper, we show, analytically, in Monte Carlo simulations and in a forecasting application on U.S. macroeconomic variables, the relevance of considering the MA component in mixed-frequency MIDAS and Unrestricted-MIDAS models (MIDAS-ARMA and UMIDAS-ARMA). Specifically, the simulation results indicate that the short-term forecasting performance of MIDAS-ARMA and UMIDAS-ARMA is better than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting U.S. GDP growth, investment growth and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS-ARMA is better than UMIDAS-ARMA. JEL Classification: E37, C53 |
| Keywords: | ARMA models, MIDAS models, temporal aggregation |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20182206&r=for |
| By: | Steel, Mark F. J. |
| Abstract: | The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas, among which growth economics, production modelling, finance and forecasting macroeconomic quantities. |
| Keywords: | Bayesian methods; Model uncertainty; Normal linear model; Prior specification; Robustness |
| JEL: | C11 C15 C20 C52 O47 |
| Date: | 2017–09–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:90110&r=for |
| By: | Isabel Casas (BCAM; and Department of Business Economics, University of Southern Denmark); Jiti Gao (Monash University); Shangyu Xie (University of International Business and Economics) |
| Abstract: | Income elasticity dynamics of health expenditure is considered for the OECD and the Eurozone over the period 1995-2014. This paper studies a novel non-linear cointegration model with fixed effects, controlling for cross-section dependence and unobserved heterogeneity. Most importantly, its coefficients can vary over time and its variables can be non-stationary. The resulting asymptotic theory is fundamentally different with a faster rate of convergence to similar kernel smoothing methodologies. A fully modified kernel regression method is also proposed to reduce the asymptotic bias. Results show a steep increase in the income elasticity for the OECD and a small increase for the Eurozone. |
| Keywords: | Cross-sectional dependence, Health expenditure, Income elasticity, Nonparametric kernel smoothing, Non-stationarity, Super-consistency. |
| JEL: | C14 C23 G13 H51 |
| Date: | 2111 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2018-29&r=for |