Managing COVID-19 related distress in primary care:principles of assessment and management

COVID-19 will cause normal feelings of worry and stress and many of those who experience higher levels of distress will experience resolution of their symptoms as society returns to pre-COVID-19 functioning. Only a minority are likely to develop a psychiatric disorder. Certain individuals may be vulnerable to experiencing persisting symptoms, such as those with pre-existing comorbidity. Management approaches could centre around using collaborative approaches to provide and build on already existing socioeconomic support structures, the avoidance of over-medicalisation, watchful waiting and finally treating those who do meet the criteria for psychiatric diagnosis. Primary care clinicians are likely be the first healthcare point of contact for most COVID-19 related distress and it is important that they are able to provide evidence based and evidence informed responses, which includes social, psychological and pharmacological approaches. This expert opinion paper serves to summarise some approaches, based primarily on indirect extrapolation of evidence concerning the general management of psychological distress, in the absence of COVID-19 specific evidence, to assist primary care clinicians in their assessment and management of COVID-19 related distress

Using Covariates to Improve the Efficacy of Univariate Bubble Detection Methods

We explore how information additional to a specific price series can be used to improve the power of popular univariate autoregressive-based methods for detecting and dating speculative price bubble episodes. Following Phillips, Wu and Yu (2011) and Phillips, Shi and Yu (2015) we base our approach on sequences of sub-sample regression-based augmented Dickey-Fuller [ADF] statistics. Our point of departure from these extant procedures is to allow for additional information in the testing and dating procedures. To do so we follow the approach of Hansen (1995) and augment the sub-sample ADF regressions with covariate regressors. The limiting null distributions of the resulting statistics depend on the long-run squared correlation between the covariates and the regression error. We show that this dependence can be accounted for by using a residual bootstrap re-sampling method. Simulation evidence shows that including relevant covariates can significantly improve the efficacy of both the resulting bubble detection tests and the associated date-stamping procedure, relative to using standard sub-sample ADF statistics. An empirical application of the proposed methodology to monthly S&P 500 data is considered, using a variety of candidate covariates. Using these covariates, the onset of the dotcom bubble and the bubble associated with Black Monday are both identified significantly earlier than when using standard methods

Tests for an end-of-sample bubble in financial time series

In this paper we examine the issue of detecting explosive behaviour in economic and financial time series when an explosive episode is both ongoing at the end of the sample, and of finite length. We propose a testing strategy based on the sub-sampling method of Andrews (2003), in which a suitable test statistic is calculated on a finite number of end-of-sample observations, with a critical value obtained using sub-sample test statistics calculated on the remaining observations. This approach also has the practical advantage that, by virtue of how the critical values are obtained, it can deliver tests which are robust to, among other things, conditional heteroskedasticity and serial correlation in the driving shocks. We also explore modifications of the raw statistics to account for unconditional heteroskedasticity using studentisation and a White-type correction. We evaluate the finite sample size and power properties of our proposed procedures, and find that they offer promising levels of power, suggesting the possibility for earlier detection of end-of-sample bubble episodes compared to existing procedures

Bonferroni Type Tests for Return Predictability and the Initial Condition

We develop tests for predictability that are robust to both the magnitude of the initial condition and the degree of persistence of the predictor. While the popular Bonferroni Q test of Campbell and Yogo (2006) displays excellent power properties for strongly persistent predictors with an asymptotically negligible initial condition, it can suffer from severe size distortions and power losses when either the initial condition is asymptotically non-negligible or the predictor is weakly persistent. The Bonferroni t-test of Cavanagh et al. (1995), although displaying power well below that of the Bonferroni Q test for strongly persistent predictors with an asymptotically negligible initial condition, displays superior size control and power when the initial condition is asymptotically nonnegligible. In the case where the predictor is weakly persistent, a conventional regression t-test comparing to standard normal quantiles is known to be asymptotically optimal under Gaussianity. Based on these properties, we propose two asymptotically size controlled hybrid tests that are functions of the Bonferroni Q, Bonferroni t, and conventional t tests. Our proposed hybrid tests exhibit very good power regardless of the magnitude of the initial condition or the persistence degree of the predictor. An empirical application to the data originally analysed by Campbell and Yogo (2006) shows our new hybrid tests are much more likely to find evidence of predictability than the Bonferroni Q test when the initial condition of the predictor is estimated to be large in magnitude

CUSUM-Based Monitoring for Explosive Episodes in Financial Data in the Presence of Time-Varying Volatility

We generalise the Homm and Breitung (2012) CUSUM-based procedure for the real-time detection of explosive autoregressive episodes in financial price data to allow for time-varying volatility. Such behaviour can heavily in ate the false positive rate [FPR] of the CUSUM based procedure to spuriously signal the presence of an explosive episode. Our modified procedure involves replacing the standard variance estimate in the CUSUM statistics with a nonparametric kernel-based spot variance estimate. We show that the sequence of modified CUSUM statistics has a joint limiting null distribution which is invariant to any time-varying volatility present in the innovations and that this delivers a real-time monitoring procedure whose theoretical FPR is controlled. Simulations show that the modification is effective in controlling the empirical FPR of the procedure, yet sacrifices only a small amount of power to detect explosive episodes, relative to the standard procedure, when the shocks are homoskedastic. An empirical illustration using Bitcoin price data is provided

Robust tests for a linear trend with an application to equity indices

In this paper we develop a testing procedure for the presence of a deterministic linear trend in a univariate time series which is robust to whether the series is I(0) or I(1) and requires no knowledge of the form of weak dependence present in the data. Our approach is motivated by the testing procedures of Vogelsang [1998, Econometrica, vol 66, p123–148] and Bunzel and Vogelsang [2005, Journal of Business and Economic Statistics, vol 23, p381–394], but utilises an auxiliary unit root test to switch between critical values in the exact I(1) and I(0) environments, rather than using this unit root test to scale the test statistic as is done in the aforementioned procedures. We show that our proposed tests have uniformly greater local asymptotic power than the tests of Vogelsang (1998) and Bunzel and Vogelsang (2005) when the error process is exact I(1), identical local asymptotic when the error process is I(0), and have better overall local asymptotic power when the error process is near I(1). Our proposed tests also display superior finite sample power to the tests of Vogelsang (1998) and Bunzel and Vogelsang (2005) and are competitive in finite samples with tests designed to be optimal in both the exact I(1) and I(0) environments. We apply our test procedures to a number of equity indices and find that these series appear to have a significant upward deterministic trend, yet are also highly persistent about this long run growth path

Outside/inside:social determinants of mental health

Individuals’ mental health and wellbeing are dependent on many social factors including housing, employment, education and adequate nutrition among others. These factors can influence at personal, family and community levels. The interlinked and cumulative impact of these social determinants needs to be ascertained to aid appropriate patient management, as well as to establish prevention and health education programmes. Some of these determinants also have to be recognised at policy level. It is crucial for clinicians to understand the role social determinants play in the genesis and perpetuation of mental and physical illnesses, so that appropriate social interventions can be set in place. Clinicians have a role to play in their clinical practice, as well as advocates for their patients and policy leaders. In order to ensure that health is joined up with other sectors, such as education, employment, judiciary and housing, policy-makers must avoid silos. Every policy must have an impact assessment on physical health and mental health. Policy-makers need to understand scientific evidence and must work with researchers, clinicians, communities and patients to help develop and implement rights-based policies

Robust and powerful tests for nonlinear deterministic components

We develop a test for the presence of nonlinear deterministic components in a univariate time series, approximated using a Fourier series expansion, designed to be asymptotically robust to the order of integration of the process and to any weak dependence present. Our approach is motivated by the Wald-based testing procedure of Harvey, Leybourne and Xiao (2010) [Journal of Time Series Analysis, vol. 31, p.379-391], but uses a function of an auxiliary unit root statistic to select between the asymptotic I(0) and I(1) critical values, rather than modifying the Wald test statistic as in Harvey et al.. We show that our proposed test has uniformly greater local asymptotic power than the test of Harvey et al. when the shocks are I(1), identical local asymptotic power when the shocks are I(0), and also improved .nite sample properties. We also consider the issue of determining the number of Fourier frequencies used to specify any nonlinear deterministic components, evaluating the performance of algorithmic- and information criterion-based model selection procedures

Bonferroni Type Tests for Return Predictability and the Initial Condition^*

We develop tests for predictability that are robust to both the magnitude of the initial condition and the degree of persistence of the predictor. While the popular Bonferroni Q test of Campbell and Yogo (2006) displays excellent power properties for strongly persistent predictors with an asymptotically negligible initial condition, it can suffer from severe size distortions and power losses when either the initial condition is asymptotically non-negligible or the predictor is weakly persistent. The Bonferroni t test of Cavanagh et al. (1995), although displaying power well below that of the Bonferroni Q test for strongly persistent predictors with an asymptotically negligible initial condition, displays superior size control and power when the initial condition is asymptotically non-negligible. In the case where the predictor is weakly persistent, a conventional regression t test comparing to standard normal quantiles is known to be asymptotically optimal under Gaussianity. Based on these properties, we propose two asymptotically size controlled hybrid tests that are functions of the Bonferroni Q, Bonferroni t, and conventional t tests. Our proposed hybrid tests exhibit very good power regardless of the magnitude of the initial condition or the persistence degree of the predictor. An empirical application to the data originally analysed by Campbell and Yogo (2006) shows our new hybrid tests are much more likely to find evidence of predictability than the Bonferroni Q test when the initial condition of the predictor is estimated to be large in magnitude.</p