On Public Inefficiencies in a Mixed Duopoly

The aim of this paper is to investigate the welfare effect of a change in the public firms objective function in oligopoly when the government takes into account the distortionary effect of rising funds by taxation (shadow cost of public funds). We analyze the impact of a shift from welfare- to profit-maximizing behaviour of the public firm on the timing of competition by endogenizing the determination of simultaneous (Nash-Cournot) versus sequential (Stackelberg) games using the game with observable delay proposed by Hamilton and Slutsky (1990). Differently from previous work that assumed the timing of competition, we show that, absent efficiency gains, instructing the public firm to play as a private one never increases welfare. Moreover, even when large efficiency gains result from the shift in public firm's objective, an inefficient public firm that maximizes welfare may be preferred.

Delay and Haircuts in Sovereign Debt: Recovery and Sustainability

One of the striking aspects of recent sovereign debt restructurings is, conditional on default, delay length is positively correlated with the size of haircut. In this paper, we develop an incomplete information model of debt restructuring where the prospect of uncertain economic recovery and the signalling about sustainability concerns together generate multi-period delay. The results from our analysis show that there is a correlation between delay length and size of haircut. Such results are supported by evidence. We show that Pareto ranking of equilibria, conditional on default, can be altered once we take into account the ex ante incentive of sovereign debtor. We use our results to evaluate proposals advocated to ensure orderly resolution of sovereign debt crises.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

A labor market with targeted wage offers

We model a market for highly skilled workers, such as the academic job market. The outputs of firm-worker matches are heterogeneous and common knowledge. Wage setting is synchronous with search: firms simultaneously make one personalized o¤er each to the worker of their choice. With large frictions (delay costs), efficient coordination is not possible, but for small frictions efficient matching with Diamond-type monopsony wages is an equilibrium.

Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.

Large Bayesian VARMAs

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.

Delay and Haircuts in Sovereign Debt: Recovery and Sustainability

One of the striking aspects of recent sovereign debt restructurings is, conditional on default, delay length is positively correlated with the size of "haircut", which is size of creditor losses. In this paper, we develop an incomplete information model of debt restructuring where the prospect of uncertain economic recovery and the signalling about sustainability concerns together generate multi-period delay. The results from our analysis show that there is a correlation between delay length and size of haircut. Such results are supported by evidence. We show that Pareto ranking of equilibria, conditional on default, can be altered once we take into account the ex ante incentive of sovereign debtor. We use our results to evaluate proposals advocated to ensure orderly resolution of sovereign debt crises.