Real-time inflation forecast densities from ensemble phillips curves

A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock andWatson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight combination strategies for density forecasting using a time-varying Phillips curve relationship between inflation and the output gap. The densities reflect the uncertainty across a large number of models using many statistical measures of the output gap, allowing for a single structural break of unknown timing. We use real-time data for the US, Australia, New Zealand and Norway. Our main finding is that the recursive-weight strategy performs well across the real-time data sets, consistently giving well-calibrated forecast densities. The equal-weight strategy generates poorly-calibrated forecast densities for the US and Australian samples. There is little difference between the two strategies for our New Zealand and Norwegian data. We also find that the ensemble modeling approach performs more consistently with real-time data than with revised data in all four countries

A Real Time Tax Smoothing Based Fiscal Policy Rule

In this paper we consider the real-time implementation of a fiscal policy rule based on tax smoothing (Barro (1979) and Bohn (1998)). We show that the tax smoothing approach, augmented by fiscal habit considerations, provides a surprisingly accurate description of US budget surplus movements. In order to investigate the robustness of the policy implications of the rule, we construct a real-time US fiscal data set, complementing the data documented by Croushore and Stark (2001). For each variable we record the different vintages, reflecting the remeasurements that occur over time. We demonstrate that the easily constructed rule provided a useful benchmark for policy analysis that is robust to real-time remeasurements

A Real Time Tax Smoothing Based Fiscal Policy Rule

We consider the real-time implementation of a fiscal policy rule based on tax smoothing (Barro (1979), Bohn (1998)). We show that the tax smoothing approach, augmented by fiscal habit considerations, provides a surprisingly accurate description of US budget surplus movements. In order to investigate the robustness of the policy implications of the rule, we construct a real-time US fiscal data set, complementing the data documented by Croushore and Stark (2001). For each variable, we record the different vintages, reflecting the remeasurements that occur over time. We demonstrate that the rule provides a useful benchmark for policy analysis that is robust to real-time remeasurements.fiscal rules, tax smoothing, fiscal habits, real-time data

Common trends and common cycles in Canada: who knew so much has been going on?

It is generally accepted that convergence is well established for regional Canadian per capita outputs. The authors present evidence that long-run movements are driven by two stochastic common trends in this time series. This evidence casts doubt on the convergence hypothesis for Canada. Another prevalent belief is that Canada forms an optimal currency area (OCA). The authors uncover three serially correlated common cycles whose asymmetries suggest Canada is not an OCA. Their common trend-common cycle decomposition of regional outputs also reveals that trend shocks dominate fluctuations in Ontario, Quebec, and the Maritimes in the short run and long run but not in British Columbia and the Prairie region. Thus, regional Canadian economic fluctuations are driven by a rich, diverse, and economically important set of propagation and growth mechanisms.

Does estimated glomerular filtration rate predict in-hospital mortality in acutely unwell hospitalized oldest old?

Funding: This research received no external funding. Acknowledgments: We gratefully acknowledge the contribution of team members who contributed to data collection.Peer reviewedPublisher PD

Control of Noise in Chemical and Biochemical Information Processing

We review models and approaches for error-control in order to prevent the buildup of noise when gates for digital chemical and biomolecular computing based on (bio)chemical reaction processes are utilized to realize stable, scalable networks for information processing. Solvable rate-equation models illustrate several recently developed methodologies for gate-function optimization. We also survey future challenges and possible new research avenues.Comment: 39 pages, 8 figures, PD

Optimization of Enzymatic Biochemical Logic for Noise Reduction and Scalability: How Many Biocomputing Gates Can Be Interconnected in a Circuit?

We report an experimental evaluation of the "input-output surface" for a biochemical AND gate. The obtained data are modeled within the rate-equation approach, with the aim to map out the gate function and cast it in the language of logic variables appropriate for analysis of Boolean logic for scalability. In order to minimize "analog" noise, we consider a theoretical approach for determining an optimal set for the process parameters to minimize "analog" noise amplification for gate concatenation. We establish that under optimized conditions, presently studied biochemical gates can be concatenated for up to order 10 processing steps. Beyond that, new paradigms for avoiding noise build-up will have to be developed. We offer a general discussion of the ideas and possible future challenges for both experimental and theoretical research for advancing scalable biochemical computing