For the estimation of structural gravity models using PPML with countrypair, exportertime and importer-time effects it proves useful to exploit the equilibrium restrictions imposed by the system of multilateral resistances. This yields an iterative projection based PPML estimator that is...

Indian Banking, especially the PSU segment has been facing the fire for quite some time now. Be it NPA, or be it digitization or even be it cash management, they are finding it hard to cope up with the changes. ‘Earnings per Share' (EPS) is a cardinal parameter which will impact the...

The purpose of this paper is to present two different approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002...

We show that adding countries as a panel dimension to macroeconomic data can statistically significantly improve the generalization ability of structural and reduced-form models, as well as allow machine learning methods to outperform these and other macroeconomic forecasting models. Using GDP...

Artificial neural networks have become increasingly popular for statistical model fitting over the last years, mainly due to increasing computational power. In this paper, an introduction to the use of artificial neural network (ANN) regression models is given. The problem of predicting the GDP...

We leverage insights from machine learning to optimize the trade off between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of...

This paper focuses on the spatial variation in the uptake of social security benefits following a large and detrimental exogenous shock. Specifically, we focus on the Global Financial Crisis (GFC) and the onset of the COVID-19 pandemic. We construct a two-period panel of 66 Territorial...