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

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

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

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

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 probabilistic interpretation of PID controllers using active inference

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition within general mathematical frameworks derived from information and control theory, statistical physics and machine learning. The connections between information and control theory have been discussed since the 1950’s by scientists like Shannon and Kalman and have recently risen to prominence in modern stochastic optimal control theory. However, the implications of the confluence of these two theoretical frameworks for the biological sciences have been slow to emerge. Here we argue that if the active inference proposal is to be taken as a general process theory for biological systems, we need to consider how existing control theoretical approaches to biological systems relate to it. In this work we will focus on PID (Proportional-Integral-Derivative) controllers, one of the most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation under simple linear generative models.most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation under simple linear generative models

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

The effect of earned versus house money on price bubble formation in experimental asset markets

Does house money exacerbate price bubbles? We compare house money asset market experiments with an earned money treatment where initial portfolios are constructed from a real effort task. Bubbles occur; however, trading volumes and earnings dispersion are significantly higher with house money. We investigate the role of cognitive ability in accounting for the differences in earnings distribution across treatments by using the cognitive reflection test (CRT). Low CRT subjects earned less than high CRT subjects. Low CRT subjects were net purchasers (sellers) of shares when the price was above (below) fundamental value. The opposite was true for high CRT subjects

Predicting growth rates and recessions: assessing US leading indicators under real-time conditions

In this paper we analyze the power of various indicators to predict growth rates of aggregate production using real-time data. In addition, we assess their ability to predict turning points of the economy. We consider four groups of indicators: survey data, composite indicators, real economic indicators, and financial data. Almost all indicators are found to improve short-run growth forecasts whereas the results for four-quarter-ahead growth forecasts and the prediction of recession probabilities in general are mixed. We can confirm the result that an indicator suited to improve growth forecasts does not necessarily help to produce more accurate recession forecasts. Only composite leading indicators perform generally well in both forecasting exercises