The contribution of structural break models to forecasting macroeconomic series

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving 60 macroeconomic quarterly and monthly time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. We find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling window based forecasts perform well

Experiences and Satisfaction of High-Risk Mothers Who Gave Birth in Select Facilities in Legazpi City, Philippines

Among the interventions implemented to curb maternal mortality in the Philippines, service delivery networks (SDNs) have been one of the most important. However, due to their recent implementation, frameworks to assess the quality of care they provide have not yet been established. To address this need, we had formulated the Integrated Patient-Centered Health Service Framework and used it to explore the satisfaction of mothers who gave birth in select facilities Legazpi City Philippines. We conducted key-informant interviews with 14 mothers. We found out that they were satisfied with the quality of care during pre-pregnancy, pregnancy, and post-partum; however, the quality of care during labor and delivery, adherence to referral protocols, and respect for patient‟s autonomy can still be improved. The framework should be used by program managers to have a qualitative measure of quality of care provided by SDNs, as well as to ensure that referral protocols were implemented

Forecasting Global Equity Indices using Large Bayesian VARs

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co-movement commonly observed in global stock markets. Moreover, the time-varying specification of the covariance structure accounts for sudden shifts in the level of volatility. In an out-of-sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy-and-hold strategy

PID control as a process of active inference with linear generative models

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation provides also a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional

On the predictability of emerging market sovereign credit spreads

This paper examines the quarter-ahead out-of-sample predictability of Brazil, Mexico, the Philippines and Turkey credit spreads before and after the Lehman Brothers’ default. A model based on the country-specific credit spread curve factors predicts no better than the random walk and slope regression benchmarks. Model extensions with the global yield curve factors and with both global and domestic uncertainty indicators notably outperform both benchmarks post-Lehman. The finding that bond prices better reflect fundamental information after the Lehman Brothers’ failure indicates that this landmark of the recent global financial crisis had wake-up call effects on emerging market bond investors

Can behavioral biases explain the rejections of the expectation hypothesis of the term structure of interest rates?

We test whether the rejections of the expectations hypothesis can be explained by two behavioral biases: the law of small numbers and conservatism. We use the term structure to decompose excess bond returns into components related to expectation errors and expectation revisions, enabling a direct test of behavioral models using the expectations of market participants. We find systematic patterns in expectation errors, and expectation revisions, which are consistent with these two biases. We show that a trading strategy that exploits these biases delivers significant economic profits and that our results are unlikely to be driven by a time-varying risk premium

Real-Time Monitoring and Analysis of Zebrafish Electrocardiogram with Anomaly Detection.

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR), and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA) membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine

A new parametric equation of state and quark stars

It is still a matter of debate to understand the equation of state of cold supra-nuclear matter in compact stars because of unknown on-perturbative strong interaction between quarks. Nevertheless, it is speculated from an astrophysical view point that quark clusters could form in cold quark matter due to strong coupling at realistic baryon densities. Although it is hard to calculate this conjectured matter from first principles, one can expect the inter-cluster interaction to share some general features to nucleon-nucleon interaction. We adopt a two-Gaussian component soft-core potential with these general features and show that quark clusters can form stable simple cubic crystal structure if we assume Gaussian form wave function. With this parameterizing, Tolman-Oppenheimer-Volkoff equation is solved with reasonable constrained parameter space to give mass-radius relation of crystalline solid quark star. With baryon densities truncated at 2 times nuclear density at surface and range of interaction fixed at 2fm we can reproduce similar mass-radius relation to that obtained with bag model equations of state. The maximum mass ranges from about 0.5 to 3 solar mass. Observed maximum pulsar mass (about 2 solar mass) is then used to constrain parameters of this simple interaction potential.Comment: 5 pages, 2 figure

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance

Anchoring the Yield Curve Using Survey Expectations

The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations can accurately predict yields, but they are typically not available for all maturities and/or forecast horizons. We show how survey expectations can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible exponential tilting method that anchors the model forecasts to the survey expectations, and we develop a test to guide the choice of the anchoring points. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to - such as information about the current state of the economy or forward-looking information contained in monetary policy announcements - without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates