Oxford Bulletin of Economics and Statistics最新文献

We estimate the short-run effects of weather-related disasters on local economic activity and cross-border spillovers that operate through economic linkages between U.S. states. To this end, we use emergency declarations triggered by natural disasters and estimate their effects using a monthly global vector autoregressive (GVAR) model for U.S. states. Impulse responses highlight the nationwide effects of weather-related disasters that hit individual regions. Taking into account economic linkages between states allows capturing much stronger spillovers than those associated with mere spatial proximity. The results underscore the importance of geographic heterogeneity for impact evaluation and the critical role of supply-side propagation mechanisms.

This article is concerned with the estimation of aggregate relationships among a potentially large number of panel data variables in the presence of unobserved heterogeneity in the form of interactive effects, an empirically very relevant scenario that has not been considered before. One of our findings is that if the regressors load on the same set of latent factors as the dependent variable, which seems a priori likely since many variables are co-moving, the aggregation automatically accounts for the unobserved heterogeneity. In order to also account for the many regressors, the aggregate model is estimated using a version of LASSO. It is shown that under suitable regulatory conditions, the estimator is oracle efficient and selection consistent, properties that are verified in small samples using Monte Carlo simulations. The empirical usefulness of the estimator is illustrated using as an example the gravity equation of trade.

Recursive SVAR models are identified by covariance conditions derived from the assumption of uncorrelated shocks. Recent literature has advocated using additional higher-order moment conditions implied by independent shocks. This paper characterizes the redundancy properties of these higher-order coskewness and cokurtosis conditions by showing that recursive SVAR estimators that rely exclusively on covariance conditions, neglecting the additional identifying information in higher-order moments, are asymptotically inefficient. Moreover, we prove that some higher-order moment conditions are always redundant and provide no improvement in asymptotic efficiency. A simulation demonstrates that excluding redundant conditions is essential to achieve performance gains in small samples.

We examine the propagation of localised banking panics across the United States using digitised state-level balance sheet data from National Banks for the 1870–1929 period. Our findings reveal that such panics spill over beyond state borders, triggering moderately persistent credit contractions and liquid asset accumulation. We develop a tractable model illustrating a key trade-off: while interbank markets—exemplified by the pyramidal reserve structure of the National Banking Era—enable banks to access lower-cost funding, they also transmit panic effects nationwide.

This paper extends prior research by introducing a novel econometric framework—Regime-Dependent Granger Causality—to analyse the systematic elements of United States (US) monetary policy. Using vector autoregressive models allowing for temporary Granger causality, we examine the association between monetary policy regimes—Taylor rules and Monetary Feedback rules—and the tenures of Federal Reserve Chairs. The analysis identifies the Global Financial Crisis period as a critical juncture. Taylor rule regimes predominated between 1965 and 2004, whereas Monetary Feedback regimes became more prominent from 1984 to 2019, with notable deviations during the Burns–Miller tenure and post-2004 under Bernanke. Monte Carlo simulations affirm the robustness of these findings. The study underscores the increasing relevance of Monetary Feedback rules in understanding the evolving nature of systematic US monetary policy, particularly in the aftermath of the crisis.

Originally written in 1991 to advance the formal analysis of macroeconomic forecasting models and methods following the development of cointegration, alternative forecasting devices, conditional and unconditional forecasts, and data accuracy are considered. Macro-econometric model forecasts, forecast errors, and forecast variances are described. Forecast comparisons using mean square errors are criticised, as is pooling disparate forecasts, which violates forecast encompassing. The non-stationarity of economic data is discussed in terms of unit roots, stochastic trends, cointegration, technical progress, and regime shifts. Sources of forecast error are delineated, concluding with evaluating its recommendations.

For binary outcome models, an endogeneity correction based on nonlinear rank-based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: Either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the dependence between the endogenous and exogenous regressors is nonlinear. Under these conditions, we prove consistency and asymptotic normality. Monte Carlo simulations and an application to German insolvency data illustrate the usefulness of the method.