The Contribution of Sectoral Productivity Differentials to Inflation in Greee

This paper estimates the magnitude of the Balassa-Samuelson effect for Greece. We calculate the effect directly, using sectoral national accounts data, which permits estimation of total factor productivity (TFP) growth in the tradeables and nontradeables sectors. Our results suggest that it is difficult to produce one estimate of the BS effect. Any particular estimate is contingent on the definition of the tradeables sector and the assumptions made about labour shares. Moreover, there is also evidence that the effect has been declining through time as Greek standards of living have caught up on those in the rest of the world and as the non-tradeables sector within Greece catches up with the tradeables.Balassa-Samuelson effect, inflation, productivity

Some results on contractive mappings as related to pattern recognition

Several of the techniques used in pattern recognition are reformulated as the problem of determining fixed points of a function. If x sub 0 is a fixed point of f and if f is contractive at x sub 0, then, for any y belonging to a sufficiently small neighborhood of x sub 0 the orbit of y will converge to x sub 0. Several general results regarding contractive mappings are developed with emphasis on functions

Minimum Decision Cost for Quantum Ensembles

For a given ensemble of $N$ independent and identically prepared particles, we calculate the binary decision costs of different strategies for measurement of polarised spin 1/2 particles. The result proves that, for any given values of the prior probabilities and any number of constituent particles, the cost for a combined measurement is always less than or equal to that for any combination of separate measurements upon sub-ensembles. The Bayes cost, which is that associated with the optimal strategy (i.e., a combined measurement) is obtained in a simple closed form.Comment: 11 pages, uses RevTe

Logic gates at the surface code threshold: Superconducting qubits poised for fault-tolerant quantum computing

A quantum computer can solve hard problems - such as prime factoring, database searching, and quantum simulation - at the cost of needing to protect fragile quantum states from error. Quantum error correction provides this protection, by distributing a logical state among many physical qubits via quantum entanglement. Superconductivity is an appealing platform, as it allows for constructing large quantum circuits, and is compatible with microfabrication. For superconducting qubits the surface code is a natural choice for error correction, as it uses only nearest-neighbour coupling and rapidly-cycled entangling gates. The gate fidelity requirements are modest: The per-step fidelity threshold is only about 99%. Here, we demonstrate a universal set of logic gates in a superconducting multi-qubit processor, achieving an average single-qubit gate fidelity of 99.92% and a two-qubit gate fidelity up to 99.4%. This places Josephson quantum computing at the fault-tolerant threshold for surface code error correction. Our quantum processor is a first step towards the surface code, using five qubits arranged in a linear array with nearest-neighbour coupling. As a further demonstration, we construct a five-qubit Greenberger-Horne-Zeilinger (GHZ) state using the complete circuit and full set of gates. The results demonstrate that Josephson quantum computing is a high-fidelity technology, with a clear path to scaling up to large-scale, fault-tolerant quantum circuits.Comment: 15 pages, 13 figures, including supplementary materia

Risk estimation using probability machines

BACKGROUND: Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. RESULTS: We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. CONCLUSIONS: The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from

Quantifying the Relationship between Capability and Health in Older People: Can't Map, Won't Map

BACKGROUND: Intuitively, health and capability are distinct but linked concepts. This study aimed to quantify the link between a measure of health status (EQ-5D-3L) and capability (ICECAP-O) using regression-based methods. METHODS: EQ-5D-3L and ICECAP-O data were collected from a sample of older people ( n = 584), aged over 65 years, requiring a hospital visit and/or care home resident, and recruited to one of 3 studies forming the Medical Crisis in Older People (MCOP) program in England. The link of EQ-5D-3L with 1) ICECAP-O tariff scores were estimated using ordinary least squares (OLS) or censored least absolute deviation (CLAD) regression models; and 2) ICECAP-O domain scores was estimated using multinomial logistic (MNL) regression. Mean absolute error (MAE), root mean squared error (RMSE), absolute difference (AD) between mean observed and estimated values, and the R(2) statistic were used to judge model performance. RESULTS: In this sample of older people ( n = 584), higher scores on the EQ-5D-3L were shown to be linked with higher ICECAP-O scores when using linear regression. An OLS-regression model was identified to be the best performing model with the lowest error statistics (AD = 0.0000; MAE = 0.1208; MSE = 0.1626) and highest goodness of fit ( R(2) = 0.3532); model performance was poor when predicting the lower ICECAP-O tariff scores. The three domains of the EQ-5D-3L showing a statistically significant quantifiable link with the ICECAP-O tariff score were self-care, usual activities, and anxiety/depression. CONCLUSION: A quantifiable, but weak, link between health (EQ-5D-3L) and capability (ICECAP-O) was identified. The findings from this study add further support that the ICECAP-O is providing complimentary information to the EQ-5D-3L. Mapping between the 2 measures is not advisable and the measures should not be used as direct substitutes to capture the impact of interventions in economic evaluations