Imposing parsimony in cross-country growth regressions

The number of variables related to long-run economic growth is large compared with the number of countries. Bayesian model averaging is often used to impose parsimony in the cross-country growth regression. The underlying prior is that many of the considered variables need to be excluded from the model. This paper, instead, advocates priors that impose parsimony without excluding variables. The resulting models fit the data better and are more robust to revisions of income data. The positive relationship between measures of trade openness and growth is much stronger than found in the literature. JEL Classification: C20, C52, O40, O47Adaptive Ridge Regression, Bayesian model averaging, Economic Growth, measurement error

Determinants of economic growth: will data tell?

Many factors inhibiting and facilitating economic growth have been suggested. Will international income data tell which matter when all are treated symmetrically a priori? We find that growth determinants emerging from agnostic Bayesian model averaging and classical model selection procedures are sensitive to income differences across datasets. For example, many of the 1975-1996 growth determinants according to World Bank income data turn out to be irrelevant when using Penn World Table data instead (the WB adjusts for purchasing power using a slightly different methodology). And each revision of the 1960-1996 PWT income data brings substantial changes regarding growth determinants. We show that research based on stronger priors about potential growth determinants is more robust to imperfect international income data. JEL Classification: E01, O47Growth regressions, robust growth determinants

House Prices and the stance of Monetary Policy.

This paper estimates a Bayesian VAR for the US economy which includes a housing sector and addresses the following questions. Can developments in the housing sector be explained on the basis of developments in real and nominal GDP and interest rates? What are the effects of housing demand shocks on the economy? How does monetary policy affect the housing market? What are the implications of house price developments for the stance of monetary policy? Regarding the latter question, we implement a version of a Monetary Conditions Index (MCI) due to Céspedes et al. (2006). JEL Classification: E3, E4Bayesian VAR, conditional forecast, House prices, monetary conditions index, monetary policy shock

Responses to monetary policy shocks in the east and the west of Europe: a comparison

This paper compares impulse responses to monetary policy shocks in the euro area countries before the EMU and in the New Member States (NMS) from central-eastern Europe. We mitigate the small sample problem, which is especially acute for the NMS, by using a Bayesian estimation that combines information across countries. The impulse responses in the NMS are broadly similar to those in the euro area countries. There is some evidence that in the NMS, which have had higher and more volatile inflation, the Phillips curve is steeper than in the euro area countries. This finding is consistent with economic theory. JEL Classification: C11, C32, C33, E40, E52Bayesian estimation, exchangeable prior, Monetary policy transmission, Structural VAR

Temperature Shocks and Economic Growth: Evidence from the Last Half Century

This paper uses historical fluctuations in temperature within countries to identify its effects on aggregate economic outcomes. We find three primary results. First, higher temperatures substantially reduce economic growth in poor countries. Second, higher temperatures may reduce growth rates, not just the level of output. Third, higher temperatures have wide-ranging effects, reducing agricultural output, industrial output, and political stability. These findings inform debates over climate's role in economic development and suggest the possibility of substantial negative impacts of higher temperatures on poor countries

Priors about Observables in Vector Autoregressions *

Abstract We formulate a prior about observables in a vector autoregression (VAR) and then solve the deconvolution problem for the implied prior about VAR parameters. Formulating a prior about observables is more intuitive than formulating a prior about VAR parameters directly, because VAR parameters are hard to interpret. Our numerical algorithm for approximating the implied prior about parameters works well even in high-dimensional problems and can be applied also for models other than VARs. In the empirical application we formulate a prior about growth rates of the observables in a VAR model of the United States economy. We find that this prior makes a big difference for the estimated persistence of output responses to monetary policy shocks, compared with the results of standard priors for VARs