A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum Schwarz Bayesian Information Criterion (Minimum...

This paper demonstrates that if we intend to optimally rank order n objects (candidates) each of which has m attributes or rank scores awarded by m evaluators, then the ordinal ranking of objects by the conventional principal component based factor scores turns out to be suboptimal. Three...

In this report, we outline a method for approximating a Markovian (or feedback-Nash) equilibrium of a dynamic game, possibly subject to coupled-constraints. We treat such a game as a "multiple" optimal control problem. A method for approximating a solution to a given optimal control problem via...

Rank-ordering of individuals or objects on multiple criteria has many important practical applications. A reasonably representative composite rank ordering of multi-attribute objects/individuals or multi-dimensional points is often obtained by the Principal Component Analysis, although much...

This document describes program code for the solution and estimation of dynamic discrete games of incomplete information using the Nested Pseudo Likelihood (NPL) method in Aguirregabiria and Mira (2007). The code is illustrated using a dynamic game of store location by retail chains, and actual...

In simulation we often have to generate correlated random variables by giving a reference intercorrelation matrix, R or Q. The matrix R is positive definite and a valid correlation matrix. The matrix Q may appear to be a correlation matrix but it may be invalid (negative definite). With R(m,m)...

Computing a numerical solution to a periodic optimal control problem is difficult. A method of approximating a solution to a given (stochastic) optimal control problem using Markov chains was developed in [3]. This paper describes an attempt at applying this method to a periodic optimal control...

The stochastic simulation of an econometric model is an application of Monte Carlo methods. Deterministic simulation is performed setting error terms to zero. Stochastic simulation, on the contrary, takes into account the disturbance terms, solving the model after adding a vector of...

Numerical simulation methods can overcome the difficulties and limitations of analytical methods, when analyzing dynamic properties of econometric models.

Experiments of stochastic simulation on a macro model of the Italian economy; this paper describes the first results produced by the research team.