Are Hodrick-Prescott `forecasts' rational?

We evaluate the usefulness of the Hodrick-Prescott (HP) filter as a proxy for rational expectations, using long runs of annual US inflation data. Our conclusion is that while the HP series are not fully rational in the sense of Muth (1961), they do generally meet the criterion of `weak rationality' recently proposed by Grant and Thomas (1999). They are also rational proxy predictors of direction for, following Merton (1981), agents would not change their prior in the opposite direction to these `forecasts'. However, smoother HP `forecasts' are more prone to inefficiency and less useful predictors of direction. First Version Received: May 2000/Final Version Received: May 2001     

A review and validation of overall survival extrapolation in health technology assessments of cancer immunotherapy by the National Institute for Health and Care Excellence: how did the initial best estimate compare to trial data subsequently made available?

Background: Validation of overall survival (OS) extrapolations of immune-checkpoint inhibitors (ICIs) during the National Institute for Health and Care Excellence (NICE) Single Technology Assessment (STA) process is limited due to data still maturing at the time of submission. Inaccurate extrapolation may lead to inappropriate decision-making. The availability of more mature trial data facilitates a retrospective analysis of the plausibility and validity of initial extrapolations. This study compares these extrapolations to subsequently available longer-term data.

Methods: A systematic search of completed NICE appraisals of ICIs from March 2000 to December 2017 was performed. A targeted search was also undertaken to procure published OS data from the pivotal clinical trials for each identified STA made available post-submission to NICE. Initial Kaplan-Meier curves and associated extrapolations from NICE documentation were extracted to compare the accuracy of OS projections versus the most mature data.

Results: The review identified 11 STAs, of which 10 provided OS data upon submission to NICE. The extrapolations undertaken considered parametric or piecewise survival models. Additional data cut-offs provided a mean of 18 months of OS beyond the end of the original data. Initial extrapolations typically under-estimated OS from the most mature data cut-off by 0.4–2.7%, depending on the choice of assessment method and use of the manufacturer- or ERG-preferred extrapolation.

Conclusion: Long-term extrapolation of OS is required for NICE STAs based on initial immature OS data. The results of this study demonstrate that the initial OS extrapolations employed by manufacturers and ERGs generally predicted OS reasonably well when compared to more mature data (when available), although on average they appeared to underestimate OS. This review and validation shows that, while the choice of OS extrapolation is uncertain, the methods adopted are generally aligned with later-published follow-up data and appear appropriate for informing HTA decisions.     

Evaluating cost center productivity

The monthly and yearly productivity summaries were developed and applied to a computer spreadsheet to aid the nurse manager in better understanding and communicating budget issues for diverse ambulatory care departments. A computerized spreadsheet using a commercially available personal computer program, such as Lotus, Quattro Pro, or Excel, can be used to more quickly and accurately track and summarize monthly budget reports. The data can be entered into the spreadsheet either manually or imported by query from the financial mainframe system. Contact your agency's finance or information department for information on how to accomplish this. Periodically acuity and resources should be measured and compared with quality monitors to maintain standards. For the past 10 years, our facility has successfully used this tool to make more informed decisions by identifying trouble spots early, and taking corrective action to avoid crisis management.     

Population growth and per capita income： A panel cointegration analysis

The noteworthy effects of demographic factors on economic development have been well documented. However, the empirical formulation grounds heavily on theoretical framework. Moreover, there is no set agreement on the relationship between population growth and per capita income. This paper retraces the famous pastime relationship for a sample of thirty-two countries classified as developing and developed over the period of 1970-2007. The methodology includes panel unit root tests and panel cointegration analysis. The main conclusion of the study is that population growth is an indicator of per capita income. The empirical findings display the existence of cointegration for both country groups. Moreover, the results support a significantly positive effect for developed countries; whereas a significantly negative effect for developing countries.     

Determinants of Commercial Bank Interest Margins and Profitability: Some International Evidence

Using bank-level data for 80 countries in the years 1988–95,this article shows that differences in interest margins andbank profitability reflect a variety of determinants: bank characteristics,macroeconomic conditions, explicit and implicit bank taxation,deposit insurance regulation, overall financial structure, andunderlying legal and institutional indicators. A larger ratioof bank assets to gross domestic product and a lower marketconcentration ratio lead to lower margins and profits, controllingfor differences in bank activity, leverage, and the macroeconomicenvironment. Foreign banks have higher margins and profits thandomestic banks in developing countries, while the opposite holdsin industrial countries. Also, there is evidence that the corporatetax burden is fully passed onto bank customers, while higherreserve requirements are not, especially in developing countries.     

Performance-weighted ensembles of random forests for predicting price impact

For any large player in financial markets, the impact of their trading activity represents a substantial proportion of transaction costs. This paper proposes a novel machine learning algorithm for predicting the price impact of order book events. Specifically, we introduce a prediction system based on ensembles of random forests (RFs). The system is trained and tested on depth-of-book data from the BATS and Chi-X exchanges and performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression. The results show that recency-weighted ensembles of RFs produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Feature importance ranking is used to explore the significance of various market features on the price impact, finding them to be highly variable through time. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process.