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on Econometric Time Series |
| By: | Gergely Akos Ganics (Banco de España) |
| Abstract: | How should researchers combine predictive densities to improve their forecasts? I propose consistent estimators of weights which deliver density forecast combinations approximating the true predictive density, conditional on the researcher’s information set. Monte Carlo simulations confi rm that the proposed methods work well for sample sizes of practical interest. In an empirical example of forecasting monthly US industrial production, I demonstrate that the estimator delivers density forecasts which are superior to well-known benchmarks, such as the equal weights scheme. Specifi cally, I show that housing permits had valuable predictive power before and after the Great Recession. Furthermore, stock returns and corporate bond spreads proved to be useful predictors during the recent crisis, suggesting that fi nancial variables help with density forecasting in a highly leveraged economy. |
| Keywords: | density forecasts, forecast combinations, probability integral transform, Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling, Kullback-Leibler information criterion |
| JEL: | C13 C22 C53 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1751&r=ets |
| By: | Ryo Okui; Wendun Wang |
| Abstract: | This paper develops a new model and a new estimation procedure for panel data that allow us to identify heterogeneous structural breaks. In many applications, there are good reasons to suspect that structural breaks occur at different time points across individual units and the sizes of the breaks differ too. We model individual heterogeneity using a grouped pattern such that individuals within a given group share the same regression coefficients. For each group, we allow common structural breaks in the coefficients, while the number of breaks, the break points, and the size of breaks can differ across groups. To estimate the model, we develop a hybrid procedure of the grouped fixed effects approach and adaptive group fused Lasso (least absolute shrinkage and selection operator). We show that our method can consistently identify the latent group structure, detect structural breaks, and estimate the regression parameters. Monte Carlo results demonstrate a good performance of the proposed method in finite samples. We apply our method to two cross-country empirical studies and illustrate the importance of taking heterogeneous structural breaks into account. |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1801.04672&r=ets |
| By: | Pierre Guérin (OECD); Danilo Leiva-Leon (Banco de España); Massimiliano Marcellino (Bocconi University, IGIER and CEPR) |
| Abstract: | We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression fi lter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass regression fi lter (MS-3PRF), is suitable for data sets with large cross-sectional dimensions, since estimation and inference are straightforward, as opposed to existing regime-switching factor models where computational complexity limits applicability to few variables. In a Monte Carlo experiment, we study the finite sample properties of the MS-3PRF and fi nd that it performs favourably compared with alternative modelling approaches whenever there is structural instability in factor loadings. For empirical applications, we consider forecasting economic activity and bilateral exchange rates, finding that the MS-3PRF approach is competitive in both cases. |
| Keywords: | factor model, Markov-switching, forecasting |
| JEL: | C22 C23 C53 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1748&r=ets |
| By: | M. D. Gadea-Rivas (UNIVERSITY OF ZARAGOZA); Ana Gómez-Loscos (Banco de España); Eduardo Bandrés (UNIVERSITY OF ZARAGOZA) |
| Abstract: | The aim of this paper is to show the usefulness of Finite Mixture Markov Models (FMMMs) for regional analysis. FMMMs combine clustering techniques and Markov Switching models, providing a powerful methodological framework to jointly obtain business cycle datings and clusters of regions that share similar business cycle characteristics. An illustration with European regional data shows the sound performance of the proposed method. |
| Keywords: | business cycles, clusters, regions, finite mixture Markov models |
| JEL: | C22 C32 E32 R11 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1744&r=ets |
| By: | Josep Lluís Carrion-i-Silvestre (AQR-IREA Research Group, Department of Econometrics, Statistics, and Spanish Economy, University of Barcelona. Av. Diagonal, 690. 08034 Barcelona.); María Dolores Gadea (Department of Applied Economics, University of Zaragoza. Gran Vía, 4, 50005 Zaragoza (Spain).); Antonio Montañés (Department of Applied Economics, University of Zaragoza. Gran Vía, 4, 50005 Zaragoza (Spain).) |
| Abstract: | The paper investigates the estimation bias of autoregressive models for bounded stochastic processes and the performance of the standard procedures in the literature that aim to correcting the estimation bias. It is shown that, in some cases, the bounded nature of the stochastic processes worsen the estimation bias effect, which suggests the design of bound-specific bias correction methods. The paper focuses on two popular autoregressive estimation bias correction procedures which are extended to cover bounded stochastic processes. Finite sample performance analysis of the new proposal is carried out using Monte Carlo simulations which reveal that accounting for the bounded nature of the stochastic processes leads to improvements in the estimation of autoregressive models. Finally, an illustration is given using the current account balance of some developed countries, whose shocks persistence measures are computed. |
| Keywords: | Bounded stochastic processes, estimation bias, unit root tests, current account balance. JEL classification: C22, C32, E32, Q43. |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201719&r=ets |
| By: | Likai Chen; Weining Wang; Wei Biao Wu |
| Abstract: | For change-point analysis of high dimensional time series, we consider a semiparametric model with dynamic structural break factors. The observations are described by a few low dimensional factors with time-invariate loading functions of covariates. The unknown structural break in time models the regime switching e ects introduced by exogenous shocks. In particular, the factors are assumed to be nonstationary and follow a Vector Autoregression (VAR) process with a structural break. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic convergence results for making inference on the common breakpoint in time. The estimation precision is evaluated via a simulation study. Finally we present two empirical illustrations on modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset. |
| Keywords: | high dimensional time series, change-point analysis, temporal and cross-sectional dependence, vector autoregressive process |
| JEL: | C00 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2017-026&r=ets |
| By: | Chiu, Ching-Wai (Jeremy); Mumtaz, Haroon; Pinter, Gabor |
| Abstract: | We introduce a Bayesian VAR model with non-Gaussian disturbances that are modelled with a finite mixture of normal distributions. Importantly, we allow for regime switching among the different components of the mixture of normals. Our model is highly flexible and can capture distributions that are fat-tailed, skewed and even multimodal. We show that our model can generate large out-of-sample forecast gains relative to standard forecasting models, especially during tranquil periods. Our model forecasts are also competitive with those generated by the conventional VAR model with stochastic volatility. |
| JEL: | C11 C32 C52 |
| Date: | 2016–02–29 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:86238&r=ets |