We examine the largest cost component in offshore development projects, drilling rates, which have been high in recent years. To our knowledge, rig rates have not been analysed empirically before in the economic literature. Using econometric analysis, we examine the effects of gas and oil...

Reliable local demographic schedules are in high demand, but small area problems pose a challenge to estimation. The literature has directed little attention to the opportunities created by increased availability of high-quality geo-coded data. We propose the use of empirical Bayes methods based...

The notion of the group of orthogonal matrices acting on the set of all feasible identification schemes is used to characterize the identification problem arising in structural vector autoregressions. This approach presents several conceptual advantages. First, it provides a fundamental...

What difference does government support of business R&D make to the rate of innovation? Addressing this important question has deep theoretical roots and broadening practical applications in OECD countries. The analysis of output additionality has been hampered by incomplete data combined with...

This paper revisits the estimation of private returns to R&D. In an extension of the standard approach, we allow for endogeneity of production decisions, heterogeneity of R&D elasticities, and asymmetric treatment of intramural and extramural R&D. Our empirical analyses are based on an extended...

We investigate the accuracy of ex ante assessments of vulnerability to income poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply different regression models, based on household covariates and previous-year equivalence income, to classify a...

In the absence of reliable and exhaustive income data, Proxy Means Tests (PMTs) are frequently employed as a cost-effective way to identify income-poor beneficiaries of targeted anti-poverty programs. However, their usefulness depends on whether proxies accurately identify the income poor. Based...

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES)...

This paper investigates the determinants of Norwegian import prices of manufactures over the period 1970(1) - 1991(4). Multivariate cointegration analysis establishes a long-run relationship between import prices, foreign prices, the exchange rate and domestic unit labour costs. Normalized on...