Informing implementation of quality improvement in Australian primary care

Background: Quality Improvement (QI) initiatives in primary care are effective at improving uptake of evidence based guidelines, but are difficult to implement and sustain. In Australia meso-level health organisations such as Primary health care Organisations (PHCO) offer new opportunities to implement area-wide QI programs. This study sought to identify enablers and barriers to implementation of an existing Australian QI program and to identify strategic directions that PHCOs can use in the ongoing development of QI in this environment. Methods: Semi-structured telephone interviews were conducted with 15 purposively selected program staff and participants from the Australian Primary Care Collaborative (APCC) QI program. Interviewees included seven people involved in design, administration and implementation of the APCC program and eight primary care providers (seven General Practitioners (GPs) and one practice nurse) who had participated in the program from 2004 to 2014. Interviewees were asked to describe their experience of the program and reflect on what enabled or impeded its implementation. Interviews were recorded, transcribed and iteratively analysed, with early analysis informing subsequent interviews. Identified themes and their implications were reviewed by a GP expert reference group. Results: Implementation enablers and barriers were grouped into five thematic areas: (1) leadership, particularly the identification and utilisation of change champions; (2) organisational culture that supports quality improvement; (3) funding incentives that support a culture of quality and innovation; (4) access to and use of accurate data; and 5) design and utilisation of clinical systems that enable and support these issues. In all of these areas, the active involvement of an overarching external support organisation was considered a key ingredient to successful implementation. Conclusion: There are substantial opportunities for PHCOs to play a pivotal role in QI implementation in Australia and internationally. In developing QI programs and policies, such organisations ought to invest their efforts in: (1) identifying and mentoring local leaders; (2) fostering QI culture via development of local peer networks; (3) developing and advocating for alternative funding models to support and incentivise these activities; (4) investing in data and audit tool infrastructure; and (5) facilitation of systems implementation within primary care practices

Fitting Weibull ACD Models to High Frequency Transactions Data: A Semi-parametric Approach based on Estimating Functions

Autoregressive conditional duration (ACD) models play an important role in financial modeling. This paper considers the estimation of the Weibull ACD model using a semi-parametric approach based on the theory of estimating functions (EF). We apply the EF and the maximum likelihood (ML) methods to a data set given in Tsay (2003, p203) to compare these two methods. It is shown that the EF approach is easier to apply in practice and gives better estimates than the MLE. Results show that the EF approach is compatible with the ML method in parameter estimation. Furthermore, the computation speed for the EF approach is much faster than for the MLE and therefore offers a significant reduction of the completion time.Volatility, Option pricing, Volatility of volatility, Forecasting

Comparison of Alternative ACD Models via Density and Interval Forecasts: Evidence from the Australian Stock Market

In this paper a number of alternative ACD models are compared using a sample of data for three major companies traded on the Australian Stock Exchange. The comparison is performed by employing the methodology for evaluating density and interval forecasts, developed by Diebold, Gunther and Tay (1998) and Christoffersen (1998), respectively. Our main finding is that the generalized gamma and log-normal distributions for the error terms have similar performance and perform better that the exponential and Weibull distributions. Additionally, there seems to be no substantial difference between the standard ACD specification of Engle and Russel (1998) and the log-ACD specification of Bauwens and Giot (2000).ACD models, Density forecasts Acknowledgements: This paper forms part of an ARC Linkage Grant research project, ÃModelling stock market liquidity in Australia and the Asia Pacific RegionÓ. We are grateful to the Australian Research Council for financial support. The financial data has been graciously provided by the Securities Research Institute (SIRCA) which is our industry partner.

Finite Sample Properties of the QMLE for the Log-ACD Model: Application to Australian Stocks

This paper is concerned with the finite sample properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Although the distribution of the QMLE for the log-ACD model is unknown, it is an important issue as it is used widely for testing various market microstructure models and effects. Knowledge of the distribution of the QMLE is crucial for purposes of valid inference and diagnostic checking. This paper investigates the structural and statistical properties of the log-ACD model by establishing the relationship between the log-ACD model and the Autoregressive-Moving Average (ARMA) model. The theoretical results developed in the paper are evaluated using Monte Carlo experiments. The experimental results also provide insights into the finite sample properties of the log-ACD model under different distributional assumptions.Conditional duration, Asymmetry, ACD, Log-ACD, Monte Carlo simulation Acknowledgement: The authors are grateful for the financial support of the Australian Research Council.

GARMA, HAR and rules of thumb for modelling realized volatility

This paper features an analysis of the relative effectiveness, in terms of the Adjusted R-Square, of a variety of methods of modelling realized volatility (RV), namely the use of Gegenbauer processes in Auto-Regressive Moving Average format, GARMA, as opposed to Heterogenous Auto-Regressive HAR models and simple rules of thumb. The analysis is applied to two data sets that feature the RV of the S&P500 index, as sampled at 5 min intervals, provided by the OxfordMan RV database. The GARMA model does perform slightly better than the HAR model, but both models are matched by a simple rule of thumb regression model based on the application of lags of squared, cubed and quartic, demeaned daily returns

Some statistical models for durations and their applications in finance

This paper considers a new class of time series models called Autoregressive Conditional Duration (ACD) models. Various statistical properties of this class of ACD models are given. A minimum mean square error (mmse) forecast function is obtained as it plays an important role in many practical applications. The theory is illustrated using a potential application based on financial data

Generalized moving average models and applications in high frequency data

This paper considers a new class of first order moving average type time series model with index δ (\u3e 0) to describe some hidden features of a time series. It is shown that this class of models provides a valid, simple solution to a new direction of time series modelling. In particular, for suitably chosen parameters (coefficient β and index δ) this type of models could be used to describe data with low or high frequency components. Various new results associated with this class are given in a general form. A simulation study is carried out to justify the theory. We justify the importance of this class of models in practice using a set of real time series data

Analysis and applications of autoregressive moving average models with stochastic variance

It is known that volatility plays a central role in financial modelling problems. This paper studies, in detail, a class of discrete time stochastic volatility (SV) models driven by ARMA models with innovations having a stochastic variances. The auto- correlation function of this class of models is derived and methods of identification of such processes are described. An example is added to illustrate the development of the theory over the standard methods

Analysis and Applications of Autoregressive Moving Average Models with Stochastic Variance

It is known that volatility plays a central role in ?nancial modelling problems. This paper studies, in detail, a class of discrete time stochastic volatility (SV) models driven by ARMA models with innovations having a stochastic variances. The auto-correlation function of this class of models is derived and methods of identi?cation of such processes are described. An example is added to illustrate the development of the theory over the standard methods.GARCH models, Volatility, Stochastic variance, Innovations, Heteroscedasticity, Random, Conditional expectation, Autocorrelation, Estimation