We investigate the relationship between household debt and health outcomes for OECD countries over the period 1995 to 2012. Using a dataset of aggregated, standardized and objective measures of household debt and health outcomes, we estimate an instrumental variable (GMM) model in order to deal with endogeneity and reverse causality concerns between health and debt. We find that higher househol...

Institutions affect the process of economic growth through their many indicators. The authors have checked the relationship between institutions and economic growth in selected countries of South Asia by using data from 1995 to 2010. The results of ‘Fixed Effects Estimation’ and ‘Generalize Method of Moments’ confirmed the significant positive relationship between institutions and economic grow...

A Simple GMM Estimator for the Semi-Parametric Mixed Proportional Hazard Model Ridder and Woutersen (2003) have shown that under a weak condition on the baseline hazard there exist root-N consistent estimators of the parameters in a semiparametric Mixed Proportional Hazard model with a parametric baseline hazard and unspecified distribution of the unobserved heterogeneity. We extend the Linear ...

In this paper we examine the link between stock market uncertainty and monetary policy in the US. There are strong arguments why central banks should account for stock market uncertainty in their strategy. Amongst others, they can maintain the functioning of financial markets and moderate possible economic downswings. To describe the behavior of the Federal Reserve Bank, augmented forward-looki...

Phillips curves are central to discussions of inflation dynamics and monetary policy. New Keynesian Phillips curves describe how past inflation, expected future inflation, and a measure of real marginal cost or an output gap drive the current inflation rate. This paper studies the (potential) weak identification of these curves under generalized methods of moments (GMM) and traces this syndrome...

The paper considers likelihood-based estimation of multivariate models, in which only marginal distributions are correctly specified. The unknown joint distribution is modelled with a copula function, which may be misspecified. In a GMM framework, we study robustness and efficiency of resulting estimators, propose improvements to existing estimators and discuss tests of copula validity. It is s...

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decade. However, they are generally trained using the generative criterion of maximum likelihood estimation. In this paper, we propose a simple and efficient discriminative approach to learn GMM with a large margin criterion to solve the classification problem. Our approach is based on a ...

This paper focuses on the automatic detection of a person’s blood level alcohol based on automatic speech processing approaches. We compare 5 different feature types with different ways of modeling. Experiments are based on the ALC corpus of IS2011 Speaker State Challenge. The classification task is restricted to the detection of a blood alcohol level above 0.5 ‰. Three feature sets are based o...

Our research aims to determine the effect of electricity distribution and energy consumption on industrial development dynamics that occur between regions in Indonesia by adding investment inflation as control variables. The analysis tools we use are static (Fixed Effect) dynamic (GMM) panel data model with a dataset 34 provinces for 2012-2019 period. results state has significant positive indu...

This paper considers two-step effi cient GMM estimation and inference where the weighting matrix and asymptotic variance matrix are based on the series long run variance estimator. We propose a simple and easy-to-implement modification to the trinity of test statistics in the two-step effi cient GMM setting and show that the modified test statistics are all asymptotically F distributed under th...