Bayesian multivariate time series models for forecasting European macroeconomic series

Qiang, Fu

Abstract

Research on and debate about 'wise use' of explicitly Bayesian forecasting procedures has been widespread and often heated. This situation has come about partly in response to the dissatisfaction with the poor forecasting performance of conventional methods and partly in view of the development of computational capacity and macro-data availability. Experience with Bayesian econometric forecasting schemes is still rather limited, but it seems to be an attractive alternative to subjectively adjusted statistical models [see, for example, Phillips (1995a), Todd (1984) and West & Harrison (1989)]. It provides effective standards of forecasting performance and has demonstrated success in forecasting macroeconomic variables. Therefore, there would seem a case for seeking some additional insights into the important role of such methods in achieving objectives within the macroeconomics profession.

The primary concerns of this study, motivated by the apparent deterioration of mainstream macroeconometric forecasts of the world economy in recent years [Wallis (1989), pp.34-43], are threefold. The first is to formalize a thorough, yet simple, methodological framework for empirical macroeconometric modelling in a Bayesian spirit. The second is to investigate whether improved forecasting accuracy is feasible within a European-based multicountry context. This is conducted with particular emphasis on the construction and implementation of Bayesian vector autoregressive (BVAR) models that incorporate both a priori and cointegration restrictions. The third is to extend the approach and apply it to the joint-modelling of system-wide interactions amongst national economies. The intention is to attempt to generate more accurate answers to a variety of practical questions about the future path towards a united Europe.

The use of BVARs has advanced considerably. In particular, the value of joint-modelling with time-varying parameters and much more sophisticated prior distributions has been stressed in the econometric methodology literature. See e.g. Doan et al. (1984).

Kadiyala and Karlsson (1993, 1997), Litterman (1986a), and Phillips (1995a, 1995b). Although trade-linked multicountry macroeconomic models may not be able to clarify all the structural and finer economic characteristics of each economy, they do provide a flexible and adaptable framework for analysis of global economic issues.

In this thesis, the forecasting record for the main European countries is examined using the 'post mortem' of IMF, DECO and EEC sources. The formulation, estimation and selection of BVAR forecasting models, carried out using Microfit, MicroTSP, PcGive and RATS packages, are reported. Practical applications of BVAR models especially address the issues as to whether combinations of forecasts explicitly outperform the forecasts of a single model, and whether the recent failures of multicountry forecasts can be attributed to an increase in the 'internal volatility' of the world economic environment. See Artis and Holly (1992), and Barrell and Pain (1992, p.3).

The research undertaken consolidates existing empirical and theoretical knowledge of BVAR modelling. It provides a unified coverage of economic forecasting applications and develops a common, effective and progressive methodology for the European economies. The empirical results reflect that in simulated 'out-of-sample' forecasting performances, the gains in forecast accuracy from imposing prior and long-run constraints are statistically significant, especially for small estimation sample sizes and long forecast horizons.

Citation

Qiang, F. (2000). Bayesian multivariate time series models for forecasting European macroeconomic series. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4215096

• Thesis Type: Thesis
• Deposit Date: Feb 7, 2014
• Publicly Available Date: Feb 23, 2023
• Keywords: Business
• Public URL: https://hull-repository.worktribe.com/output/4215096
• Additional Information: Business School, The University of Hull
• Award Date: Apr 1, 2000