We develop a non‐parametric technique framework for estimating firm‐level Total Factor
Productivity (TFP). Our paper has two major novelties: first, we propose a modelling of …

In this paper we propose a single‐step generalized method of moments (GMM) approach to
estimate a production function with multiple quasi‐fixed and variable inputs as well as …

Given a set of moment restrictions (MRs) that overidentify a parameter θ, we investigate a
semiparametric Bayesian approach for inference on θ that does not restrict the data …

Energy stocks are potentially a hedge against inflation and have a number of advantages
over other forms of energy investing. This motivates us to study on portfolio management of …

How do ventures manage adjustment costs, input elasticities, and productivity growth? We
draw on the intertemporal production decisions of ventures that are quasi-fixed, costly to …

The paper examines the sensitivity for the solution of linear programming problems using
Bayesian techniques, when samples for the coefficients of the objective function are …

We study semiparametric inference in a small-dimensional vector autoregressive (VAR)
model of order p augmented by unobservable common factors with a dynamic described by …

In this paper, we introduce a new model to estimate efficiency by generalizing the state-of-
the-art panel stochastic frontier model, the salient feature of which is decomposition of …

Macro variables frequently display time-varying distributions, driven by the dynamic and
evolving characteristics of economic, social, and environmental factors that consistently …

Existing methods for the measurement of technical efficiency in the dynamic production
models obtain it from the implied distance functions without making use of the information …