Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.

Adaptive-multi-reference least means squares filter

Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.

A primer on phylogenetic generalised least squares

Phylogenetic generalised least squares (PGLS) is one of the most commonly employed phylogenetic comparative methods. The technique, a modification of generalised least squares, uses knowledge of phylogenetic relationships to produce an estimate of expected covariance in cross-species data. Closely related species are assumed to have more similar traits because of their shared ancestry and hence produce more similar residuals from the least squares regression line. By taking into account the expected covariance structure of these residuals, modified slope and intercept estimates are generated that can account for interspecific autocorrelation due to phylogeny. Here, we provide a basic conceptual background to PGLS, for those unfamiliar with the approach. We describe the requirements for a PGLS analysis and highlight the packages that can be used to implement the method. We show how phylogeny is used to calculate the expected covariance structure in the data and how this is applied to the generalised least squares regression equation. We demonstrate how PGLS can incorporate information about phylogenetic signal, the extent to which closely related species truly are similar, and how it controls for this signal appropriately, thereby negating concerns about unnecessarily ‘correcting’ for phylogeny. In addition to discussing the appropriate way to present the results of PGLS analyses, we highlight some common misconceptions about the approach and commonly encountered problems with the method. These include misunderstandings about what phylogenetic signal refers to in the context of PGLS (residuals errors, not the traits themselves), and issues associated with unknown or uncertain phylogeny.

Corrigendum to “Least squares learning and the US treasury bill rate” [Econ. Syst. 38 (2014) 194–204]

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning.

14N overtone magic angle spinning NMR

Overtone NMR spectroscopy has the potential to provide high-resolution ¹⁴N solid-state NMR spectra. The technique was first developed during the 1980s but has only recently been successfully combined with magic angle spinning (MAS), providing improved sensitivity and resolution as well as enabling more advanced approaches such as indirect detection and signal enhancement methods. This report provides a brief background to ¹⁴N overtone NMR, describing the ways in which it differs from conventional NMR and the challenges that arise as a result. The effects of MAS on the overtone spectrum are then presented and illustrated with numerous experimental and simulated examples. Finally, several recent developments enabled by MAS are described and some potential future directions are suggested.

Promoting reasoning through the magic V task

Reasoning in mathematics plays a critical role in developing mathematical understandings. In this article, Bragg, Loong, Widjaja, Vale and Herbert explore an adaptation of the Magic V Task and how was used in several classrooms to promote and develop reasoning skills.

Use and spatial patterns of newly developed public squares in urban villages in Shenzhen

This paper investigates the use and spatial patterns of newly developed public squares in urban villages in the City of Shenzhen, China. Given the lack of information about how this type of public space has been used by the Chinese, this paper provides insights that enable the development of more user friendly public space in China. The research is based on the fieldwork carried out in 2014 to examine public squares in four urban villages in Shenzhen. Direct observation and activity mapping have been used as major methodology for this research. The focus of this paper will be placed not only the formal aspects such as the design aspiration, scale and provision of public amenity, but also on the usage that includes types of users, there daily activity as well as their location preference.

Neither fixed nor random: weighted least squares meta-analysis

This study challenges two core conventional meta-analysis methods: fixed effect and random effects. We show how and explain why an unrestricted weighted least squares estimator is superior to conventional random-effects meta-analysis when there is publication (or small-sample) bias and better than a fixed-effect weighted average if there is heterogeneity. Statistical theory and simulations of effect sizes, log odds ratios and regression coefficients demonstrate that this unrestricted weighted least squares estimator provides satisfactory estimates and confidence intervals that are comparable to random effects when there is no publication (or small-sample) bias and identical to fixed-effect meta-analysis when there is no heterogeneity. When there is publication selection bias, the unrestricted weighted least squares approach dominates random effects; when there is excess heterogeneity, it is clearly superior to fixed-effect meta-analysis. In practical applications, an unrestricted weighted least squares weighted average will often provide superior estimates to both conventional fixed and random effects.

Neither fixed nor random: weighted least squares meta-regression

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of‘mixed-effects’ or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the ‘true’ regression coefficient.

A magic pill? A qualitative analysis of patients' views on the role of antidepressant therapy in inflammatory bowel disease (IBD).

BACKGROUND: Studies with healthy volunteers have demonstrated that antidepressants can improve immunoregulatory activity and thus they may have a potential to positively impact the disease course in inflammatory bowel disease (IBD), a chronic and incurable condition. However, patients' views on the role of antidepressants in the management of their IBD are unknown. Thus, this study aimed to explore patients' experiences and opinions regarding the effect of antidepressants on IBD course before possibly undertaking future treatment trials with antidepressants. METHODS: Semi-structured in-depth interviews with open-ended questions were conducted with a randomly selected sample of IBD patients recruited at the Australian public hospital IBD clinic and currently receiving antidepressants. A qualitative content analysis was undertaken to summarise patients' responses. A Visual Analogue Scale was used to provide a quantitative assessment of patients' experiences with antidepressants. RESULTS: Overall, 15 IBD sufferers currently on antidepressants (nine females, six males) were interviewed. All 15 reported a positive response to antidepressants reporting they improved their quality of life, with minimal side-effects. Five patients (33.3%) felt the antidepressant had specifically improved their IBD course. Three patients noted how they believed the reduction in feelings of stress mediated the positive influence of the antidepressant on IBD course. Ten patients (66.7%) felt the antidepressants had not specifically influenced their IBD. Nine patients (60.0%) had a generally positive attitude towards antidepressants, four patients (26.7%) were ambivalent, and two patients (13.3%) held a negative view towards antidepressants. Twelve patients (80.0%) stated that they would be willing to participate in clinical trials. CONCLUSIONS: Antidepressants seem to be well tolerated by IBD patients. One third of patients reported an observable improvement of their IBD under the influence of this treatment. The positive attitude towards antidepressants in these participants may make the conduct of clinical trials to further assess for any specific role on IBD course feasible. However, due to a small sample size, a qualitative nature of this study and in light of the results of studies on other populations indicating reluctance to taking antidepressants at least in some patients, these results should be interpreted with caution until confirmed in quantitative studies.