This paper argues that probability forecasts convey information on the uncertainties that surround macro-economic forecasts in a straightforward manner which is preferable to other alternatives, including the use of confidence intervals. Probability forecasts obtained using a small benchmark macroeconometric model as well as a number of other alternatives are presented and evaluated using recursive forecasts generated over the period 1999q1-2001q1. Out of sample probability forecasts of inflation and output growth are also provided over the period 2001q2-2003q1, and their implications discussed in relation to the Bank of England’s inflation target and the need to avoid recessions, both as separate events and jointly. The robustness of the results to parameter and model uncertainties is also investigated by a pragmatic implementation of the Bayesian model averaging approach.

have provided excellent research assistance. I thank Stephen Machin and three referees for

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The recent controversy over model selection in the context of ‘growth regressions ’ has led to some remarkably numerous ‘estimation ’ strategies, including 4 million regressions by Sala-i-Martin (1997b). Only one regression is really needed, namely the general unrestricted model, appropriately reduced to a parsimonious encompassing congruent representation. Such an outcome was achieved in one run on PcGets, within 15 minutes of receiving from Professor Ley the data set in Fernández et al (2001). We reproduce that equation, and corroborate the findings in Hoover and Perez (2004), who also adopt an automatic general-to-simple approach.

We investigate country heterogeneity in cross-country growth regressions. In contrast to the previous literature that focuses on low-income countries, this study also highlights growth determinants in high-income (OECD) countries. We introduce Iterative Bayesian Model Averaging (IBMA) to address not only potential parameter heterogeneity, but also the model uncertainty inherent in growth regressions. IBMA is essential to our estimation because the simultaneous consideration of model uncertainty and parameter heterogeneity in standard growth regressions increases the number of candidate regressors beyond the processing capacity of ordinary BMA algorithms. Our analysis generates three results that strongly support different dimensions of parameter heterogeneity. First, while a large number of regressors can be identified as growth determinants in Non-OECD countries, the same regressors are irrelevant for OECD countries. Second, Non-OECD countries and the global sample feature only a handful of common growth determinants. Third, and most devastatingly, the long list of variables included in popular cross-country datasets does not contain regressors that begin to satisfactorily characterize the basic

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Trade theories covering Preferential Trade Agreements (PTAs) are as diverse as the literature in search of their empirical support. To account for the model uncertainty that surrounds the validity of the competing PTA theories, we introduce Bayesian Model Averaging (BMA) to the PTA literature. BMA minimizes the sum of Type I and Type II error, the mean squared error, and generates predictive distributions with optimal predictive performance. Once model uncertainty is addressed as part of the empirical strategy, we report clear evidence of Trade Creation, Trade Diversion, and Open Bloc effects. After controlling for natural trading partner effects, Trade Creation is weaker – except for the EU. To calculate the actual effects of PTAs on trade flows we show that the analysis must be comprehensive: it must control for Trade Creation and Diversion as well as all possible PTAs. Several prominent control variables are also shown to be robustly related to Trade Creation; they relate to factor endowments and economic policy.

The exact specification and motivation of the Environmental Kuznets Curve (EKC) is the subject of a vast literature in environmental economics. A remarkably diverse set of econometric approaches has been employed to support or reject a specific relationship between environmental quality and pollution. Nevertheless, methods employed to date have not addressed the issue of model uncertainty, given that a sizable number of competing theories exist that can explain the income/pollution relationship. We introduce Bayesian Model Averaging to the EKC analysis to examine a) whether a sulphur dioxide EKC exists, and if so, b) which income/pollution specification is most strongly supported by the data. We find only weak support for an EKC, which disappears altogether when we address oversampling issues in the data. In contrast, our results highlight the relative importance of political economy and site-specific variables in explaining pollution outcomes. Trade is also shown to play an important indirect role. It moderates the influence of the composition effect on pollution. Our findings run contrary to the deterministic view of the income/pollution relationship that is persistent in the literature.

This study investigates U.S. state economic growth from 1970-1999. I innovate on previous studies by developing a new approach for addressing “model uncertainty ” issues associated with estimating growth equations. My approach borrows from the “extreme bounds analysis ” (EBA) approach of Leamer (1985), while also addressing concerns raised by Granger and Uhlig (1990), Salai-Martin (1997) and others that not all specifications are equally likely to be true. I then apply this approach to identify “robust” determinants of state economic growth. My analysis confirms the importance of productivity characteristics of the labor force and industrial composition of a state’s economy. I also find that policy variables such as (i) size and structure of government and (ii) taxation are “robust ” and economically important determinants of state economic growth.

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