<Para ID="Par1">Strong branching is an effective branching technique that can significantly reduce the size of the branch-and-bound tree for solving mixed integer nonlinear programming (MINLP) problems. The focus of this paper is to demonstrate how to effectively use “discarded” information from strong...</para>

Any non-stationary series can be decomposed into permanent (or "trend") and transitory (or "cycle") components. Typically some atheoretic pre-filtering procedure is applied to extract the permanent component. This paper argues that analysis of the fundamental underlying stationary economic...

Optimal monetary stabilisation in the standard New Keynesian framework usually assumes a policy loss function from outside the model. In this paper, in contrast, the objective arises directly from the model. Credit constraints and sticky nominal debt contracts imply that monetary stabilisation...

We construct a dynamic general equilibrium model in which household debt is sticky in nominal terms and debtor households are credit constrained. Interest payments on debt contracts may be at floating rates or fixed for the duration of the contract. A key result is that a simple static Taylor...

The ratio of Indian to US per capita output over the past 45 years has displayed a distinctive "V"-shaped pattern. We show that a strikingly similar V-shaped pattern is visible not just in aggregate output figures, but also as the primary determinant of long-term movements in the cross-sectional...

In Ghate & Wright Journal of Development Economics, vol. 99 (2012) pp 58–67, it was noted that there was considerable variation in the extent to which different Indian states participated in the Great Indian Growth Turnaround. In this paper it is investigated whether there was any...

We implement a dynamic programming algorithm on a computational grid consisting of loosely coupled processors, possibly including clusters and individual workstations. The grid changes dynamically during the computation, as processors enter and leave the pool of workstations. The algorithm is...

A predictive regression for y(t) and a time series representation of the predictors, x(t), together imply a univariate reduced form for y(t). In this paper we work backwards, and ask: if we observe y(t), what do its univariate properties tell us about any x(t) in the "predictive space"...