Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation

The weak-constraint inverse for nonlinear dynamical models is discussed and derived in terms of a probabilistic formulation. The well-known result that for Gaussian error statistics the minimum of the weak-constraint inverse is equal to the maximum-likelihood estimate is rederived. Then several methods based on ensemble statistics that can be used to find the smoother (as opposed to the filter) solution are introduced and compared to traditional methods. A strong point of the new methods is that they avoid the integration of adjoint equations, which is a complex task for real oceanographic or atmospheric applications. they also avoid iterative searches in a Hilbert space, and error estimates can be obtained without much additional computational effort. the feasibility of the new methods is illustrated in a two-layer quasigeostrophic model.

Bank loan terms and conditions: is there a macro effect?

We examine whether macroeconomic factors contain significant information for bank loan contracting terms and conditions (T&Cs), over and above that of standard firm-specific or country-level institutional factors. Our estimation is based on a seemingly unrelated mixed-processes methodology that accommodates two salient data properties: (i) the fact that loan contract terms are determined jointly as a single lending contract, and (ii) the fact that the elements of loan T&Cs are generated by different distributional formats. Our findings indicate that cross-country variation accounts for a significant portion of observed variation in loan T&Cs. In addition, macroeconomic fundamentals significantly explain the “package” of loan T&Cs offered to corporate borrowers, with this effect being distinct from any influence that T&Cs receive from firm-specific factors, and also from country-specific institutional factors.