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The UK relationship between nominal wage inflation and the unemployment rate is unstable. Over sub‐periods of the last 160 years of turbulent data, Phillips curve slopes range from strongly negative, slightly negative, flat, slightly positive and strongly positive. Our constant‐parameter congruent model of real wages explains these instabilities, yet also implies a constant negative relationship between nominal wage inflation and the unemployment rate when corrected by its regressors. Disentangling these effects reveals that structural breaks in the real‐wage model's variables do not explain the instabilities, which instead occur during sub‐periods when some of its explanatory variables are insignificant. [ABSTRACT FROM AUTHOR]

UK top income shares have varied hugely over the past two centuries, ranging from more than 30% to less than 7% of pre‐tax national income allocated to the top 1 percentile. We build a congruent dynamic linear regression model of the top 1% income share allowing for economic, political and social factors. Saturation estimation is used to model outliers and trend breaks, proxying underlying structural changes driving income inequality in the UK. We use the model to forecast the top 1% income share over the last 15 years, and compare to a range of forecast devices. Despite a well‐specified constant parameter model conditioning on significant explanatory variables, the best performing forecasts are obtained from a random walk and a smoothed random walk. These results are explained by the presence of shifts in the income share over the forecast period, resulting in forecasts from equilibrium correction models converging to the wrong equilibrium. Our best prediction for 2026 based on the most recent data from 2021 (a 5‐year ahead projection) is that the pre‐tax top 1% income share will remain at the most recent realized value of 12.7%, but there is a large degree of uncertainty, with a 95% confidence band ranging from 10% to 15.7%. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. David Forbes Hendry received his MSc and PhD from the LSE. [ABSTRACT FROM AUTHOR]

We review key stages in the development of general‐to‐specific modelling (Gets). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, Gets applications faced many criticisms, especially from accusations of 'data mining'—no longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non‐stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables. [ABSTRACT FROM AUTHOR]

The decision-theoretic Downsian model and other related accounts predict that increasing perceptions of election closeness will increase turnout. Does this prediction hold? Past observational and experimental tests raise generalizability and credible inference issues. Prior field experiments either (1) compare messages emphasizing election closeness to non-closeness messages, potentially conflating changes in closeness perceptions with framing effects of the voter encouragement message, or (2) deliver information about a particular race's closeness, potentially altering beliefs about the features of that election apart from its closeness. We address the limitations of prior work in a large-scale field experiment conducted in seven states and find that a telephone message describing a class of contests as decided by fewer, as opposed to more, votes increases voter turnout. Furthermore, this effect exceeds that of a standard election reminder. The results imply expected electoral closeness affects turnout and that perceptions of closeness can be altered to increase participation. [ABSTRACT FROM AUTHOR]

Political campaigns frequently emphasize the material stakes at play in election outcomes to motivate participation. However, field-experimental academic work has given greater attention to other aspects of voters' decisions to participate despite theoretical models of turnout and substantial observational work signaling that a contest's perceived importance affects the propensity to vote. We identify two classes of treatments that may increase the material incentive to participate and test these messages in a large-scale placebo-controlled field experiment in which approximately 24,500 treatment letters were delivered during Connecticut's 2013 municipal elections. We find some evidence that these messages are effective in increasing participation, as well as that some of them may be more effective than typical nonpartisan get-out-the-vote appeals. While these results remain somewhat preliminary, our findings have important implications for our understanding of how voters decide whether to participate and how best to mobilize citizens who would otherwise sit out elections. [ABSTRACT FROM AUTHOR]

Hypercalcaemia is common in patients with malignancy, but is rare in seminoma with only eight cases reported in the literature. We present an unusual case of a 36-year-old man who presented with hypercalcaemia and stage 3 acute kidney injury. He presented initially with headache and malaise, and was found to have markedly deranged blood tests. He underwent a renal biopsy before imaging confirmed an unexpected large abdominal mass, which was confirmed histologically to be a seminoma. He was referred to a tertiary oncology centre, and underwent emergency chemotherapy and radical resection with no evidence of recurrence to this date and with return to normocalcaemia. [ABSTRACT FROM AUTHOR]

The name I Cobb-Douglas i derives from Douglas's first published time-series study with Cobb (Cobb and Douglas [14]), after which Cobb ceased to participate. En route, Hendrik Houthakker ([45]) (who references Horst Mendershausen's highly critical [61] article on Douglas's production function research) had established that aggregating microlevel Leontief production functions could lead to macro Cobb-Douglas. Biddle remarks that "the original time-series regression of the 1928 Cobb-Douglas paper arose out of Douglas's prior research interests and represented cutting-edge work in empirical economics" (4). Chapter 5 discusses the take up of the Cobb-Douglas production function in agricultural economics, which kept associated research work alive while Douglas was in the Senate. [Extracted from the article]

By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems. [ABSTRACT FROM AUTHOR]

This article presents a large-scale, empirical evaluation of the psychophysiological correlates of political ideology and, in particular, the claim that conservatives react with higher levels of electrodermal activity to threatening stimuli than liberals. We (1) conduct two large replications of this claim, using locally representative samples of Danes and Americans; (2) reanalyze all published studies and evaluate their reliability and validity; and (3) test several features to enhance the validity of psychophysiological measures and offer a number of recommendations. Overall, we find little empirical support for the claim. This is caused by significant reliability and validity problems related to measuring threat sensitivity using electrodermal activity. When assessed reliably, electrodermal activity in the replications and published studies captures individual differences in the physiological changes associated with attention shifts, which are unrelated to ideology. In contrast to psychophysiological reactions, self-reported emotional reactions to threatening stimuli are reliably associated with ideology. [ABSTRACT FROM AUTHOR]

Objective: We analyze the number of recorded cases and deaths of COVID‐19 in many parts of the world, with the aim to understand the complexities of the data, and produce regular forecasts. Methods: The SARS‐CoV‐2 virus that causes COVID‐19 has affected societies in all corners of the globe but with vastly differing experiences across countries. Health‐care and economic systems vary significantly across countries, as do policy responses, including testing, intermittent lockdowns, quarantine, contact tracing, mask wearing, and social distancing. Despite these challenges, the reported data can be used in many ways to help inform policy. We describe how to decompose the reported time series of confirmed cases and deaths into a trend, seasonal, and irregular component using machine learning methods. Results: This decomposition enables statistical computation of measures of the mortality ratio and reproduction number for any country, and we conduct a counterfactual exercise assuming that the United States had a summer outcome in 2020 similar to that of the European Union. The decomposition is also used to produce forecasts of cases and deaths, and we undertake a forecast comparison which highlights the importance of seasonality in the data and the difficulties of forecasting too far into the future. Conclusion: Our adaptive data‐based methods and purely statistical forecasts provide a useful complement to the output from epidemiological models. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes. Using the same methods, we forecast aggregate UK unemployment over the pandemic. The forecasts rapidly adapt to the employment policies implemented when the UK entered the first lockdown. The difference between our statistical and theory based forecasts provides a measure of the effect of furlough policies on stabilising unemployment, establishing useful scenarios had furlough policies not been implemented. [ABSTRACT FROM AUTHOR]

In this article, we first offer a model of design knowledge types (know-about, know-that, know-how) and their interrelationships in value sensitive design. Then we demonstrate that value sensitive design is a formative framework, which provides a shaping influence on practice, enables creative appropriation, and supports theory and method development. [ABSTRACT FROM AUTHOR]

In this article, we report on eight grand challenges for value sensitive design, which were developed at a one-week workshop, Value Sensitive Design: Charting the Next Decade, Lorentz Center, Leiden, The Netherlands, November 14–18, 2016. A grand challenge is a substantial problem, opportunity, or question that motives sustained research and design activity. The eight grand challenges are: (1) Accounting for Power, (2) Evaluating Value Sensitive Design, (3) Framing and Prioritizing Values, (4) Professional and Industry Appropriation, (5) Tech policy, (6) Values and Human Emotions, (7) Value Sensitive Design and Intelligent Algorithms, and (8) Value Tensions. Each grand challenge consists of a discussion of its importance and a set of tractable key questions. [ABSTRACT FROM AUTHOR]

Climate econometrics is a new sub-discipline that has grown rapidly over the last few years. As greenhouse gas emissions like carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) are a major cause of climate change, and are generated by human activity, it is not surprising that the tool set designed to empirically investigate economic outcomes should be applicable to studying many empirical aspects of climate change. Economic and climate time series exhibit many commonalities. Both data are subject to non-stationarities in the form of evolving stochastic trends and sudden distributional shifts. Consequently, the well-developed machinery for modeling economic time series can be fruitfully applied to climate data. In both disciplines, we have imperfect and incomplete knowledge of the processes actually generating the data. As we don't know that data generating process (DGP), we must search for what we hope is a close approximation to it. The data modeling approach adopted at Climate Econometrics (http://www.climateeconometrics.org/) is based on a model selection methodology that has excellent properties for locating an unknown DGP nested within a large set of possible explanations, including dynamics, outliers, shifts, and non-linearities. The software we use is a variant of machine learning which implements multi-path block searches commencing from very general specifications to discover a well-specified and undominated model of the processes under analysis. To do so requires implementing indicator saturation estimators designed to match the problem faced, such as impulse indicators for outliers, step indicators for location shifts, trend indicators for trend breaks, multiplicative indicators for parameter changes, and indicators specifically designed for more complex phenomena that have a common reaction 'shape' like the impacts of volcanic eruptions on temperature reconstructions. We also use combinations of these, inevitably entailing settings with more candidate variables than observations. Having described these econometric tools, we take a brief excursion into climate science to provide the background to the later applications. By noting the Earth's available atmosphere and water resources, we establish that humanity really can alter the climate, and is doing so in myriad ways. Then we relate past climate changes to the 'great extinctions' seen in the geological record. Following the Industrial Revolution in the mid-18th century, building on earlier advances in scientific, technological and medical knowledge, real income levels per capita have risen dramatically globally, many killer diseases have been tamed, and human longevity has approximately doubled. However, such beneficial developments have led to a global explosion in anthropogenic emissions of greenhouse gases. These are also subject to many relatively sudden shifts from major wars, crises, resource discoveries, technology and policy interventions. Consequently, stochastic trends, large shifts and numerous outliers must all be handled in practice to develop viable empirical models of climate phenomena. Additional advantages of our econometric methods for doing so are detecting the impacts of important policy interventions as well as improved forecasts. The econometric approach we outline can handle all these jointly, which is essential to accurately characterize non-stationary observational data. Few approaches in either climate or economic modeling consider all such effects jointly, but a failure to do so leads to mis-specified models and hence incorrect theory evaluation and policy analyses. We discuss the hazards of modeling wide-sense non-stationary data (namely data not just with stochastic trends but also distributional shifts), which also serves to describe our notation. The application of the methods is illustrated by two detailed modeling exercises. The first investigates the causal role of CO2 in Ice Ages, where a simultaneous-equations system is developed to characterize land ice volume, temperature and atmospheric CO2 levels as non-linear functions of measures of the Earth's orbital path round the Sun. The second turns to analyze the United Kingdom's highly non-stationary annual CO2 emissions over the last 150 years, walking through all the key modeling stages. As the first country into the Industrial Revolution, the UK is one of the first countries out, with per capita annual CO2 emissions now below 1860's levels when our data series begin, a reduction achieved with little aggregate cost. However, very large decreases in all greenhouse gas emissions are still required to meet the UK's 2050 target set by its Climate Change Act in 2008 of an 80% reduction from 1970 levels, since reduced to a net zero target by that date, as required globally to stabilize temperatures. The rapidly decreasing costs of renewable energy technologies offer hope of further rapid emission reductions in that area, illustrated by a dynamic scenario analysis. [ABSTRACT FROM AUTHOR]

The literature currently maintains the assumption that mutual coupling is the mechanism that leads to the degradation of isolation between the active ports of antenna arrays. However, this paper shows that a coupled-resonator model with series-coupled mutual coupling breaks down when applied to multi-mode/multi-port antenna arrays and limits the achievable isolation. An improved model based on parallel-coupled modes is presented. This model suggests that poor isolation is more accurately caused by unbalanced antenna modes. Using this model, several novel examples of simple and compact dual- and triple-mode antennas show how to maximize isolation in multi-mode antennas by balancing the modes’ resonant frequencies and radiation bandwidths. For comparison, the mutually coupled assumption limits isolation to around 20 dB in the proposed novel structures, whereas 40–70 dB is possible when parallel-coupled modes are considered. Based on the developed concept, a compact triple-mode two-pole-filtering antenna is fabricated at 3500 MHz with 160 MHz of bandwidth and more than 40 dB of isolation between the antenna ports. [ABSTRACT FROM AUTHOR]

A generalized frequency response for cavity filters is demonstrated from six modes coupled in parallel. The six modes belong to two parallel-coupled triple-mode spheres and are coupled in parallel via two half-wavelength transmission-line resonators. The theory of phase and amplitude control of parallel couplings into spherical cavities is introduced by way of coupled resonator models, and the structure of two combined spheres is detailed. The theory and simulations prove that multiple transmission zeros can be arbitrarily placed, including inside the passband to create multiple bands and beyond infinity (imaginary frequencies) for flattened group delay. Several design examples of two spheres in parallel are presented to demonstrate a high degree of flexibility of transmission zero placement, and one example is fabricated and successfully tested. [ABSTRACT FROM AUTHOR]

Purpose: Our aim was to determine if self-determined motivation (SDM) in elite, men's soccer changes over time and differs as a function of age, skill-grouping, and engagement in soccer play and practice. We tested predictions from the Developmental Model of Sport Participation (DMSP) regarding relations between practice and play and SDM among both elite and non-elite samples. Methods: Elite youth soccer players in the United Kingdom (n = 31; from the Under 13/U13 and U15 years age groups) completed practice history and motivation questionnaires at time 1 (T1) and ∼2 years later (T2: now U15 and U17 years). Non-elite players (n = 32; from U15 and U17 years) completed the same questionnaires at T2 only. Results: For the elite groups, global SDM decreased over time for the current U17 group (from U15), but no change was seen for the current U15 group (from U13). Age group differences at T2 mirrored these data, with U17 players showing lower global SDM and higher controlled motivation than U15 elites. The non-elite players did not show age group differences, but elites scored higher for global SDM and autonomous motivation than non-elites. The recent hours accumulated in practice negatively correlated with global SDM in elite and non-elite groups, but play was unrelated to measures of motivation. Conclusion: Differences in SDM as a function of age and skill point toward the dynamic nature of these motivations over time, likely a result of proximity to external rewards related to professional status. Although high volumes of practice are related to lower global SDM in both skill groups, the absence of any relations between SDM and soccer play does not support a key prediction related to the DMSP. [ABSTRACT FROM AUTHOR]

In this article, we introduce the Special Issue, Value Sensitive Design: Charting the Next Decade, which arose from a week-long workshop hosted by Lorentz Center, Leiden, The Netherlands, November 14–18, 2016. Forty-one researchers and designers, ranging in seniority from doctoral students to full professors, from Australia, Europe, and North America, and representing a wide range of academic fields participated in the workshop. The first article in the special issue puts forward eight grand challenges for value sensitive design to help guide and shape the field. It is followed by 16 articles consisting of value sensitive design nuggets—short pieces of writing on a new idea, method, challenge, application, or other concept that engages some aspect of value sensitive design. The nuggets are grouped into three clusters: theory, method, and applications. Taken together the grand challenges and nuggets point the way forward for value sensitive design into the next decade and beyond. [ABSTRACT FROM AUTHOR]

Elite soccer players (~15 yr) from professional academies in the UK were rated on technical, tactical, physical and creative skills by coaches at time 1 (T1). Players estimated accumulated hours in soccer practice (coach-led activities) and play (self-led activities) during childhood. Coach-ratings were again collected 2.5 yr later (T2) for players that received a professional contract (~17 yr). Adult-professional status was determined at T3 (~ 20 yr). Skill ratings distinguished across the three “future” groups (academy-only, youth-professional only and adult-professional), for all but creative skill. For players that transitioned to youth-professional, medium sized correlations were noted between childhood practice amounts and technical and creative skill ratings at T1, although practice amounts correlated more strongly with skill ratings at T2. Play amounts were not related to any skills. Within groups of youth elite athletes, domain-specific play in childhood, and to a lesser degree practice, were generally not good discriminators of specific-skills. The effects of sport-specific practice on the development (and discriminability) of skills takes time to emerge. We consider the implications for talent development models and purported links between play and creativity. [ABSTRACT FROM AUTHOR]

The adoption as policy models by central banks of representative agent New Keynesian dynamic stochastic general equilibrium models has been widely criticised, including for their simplistic micro-foundations. At the Bank of England, the previous generation of policy models is seen in its 1999 medium-term macro model (MTMM). Instead of improving that model to correct its considerable flaws, many shared by other non-DSGE policy models such as the Federal Reserve's FRB/US, it was replaced in 2004 by the DSGE-based BEQM. Though this clearly failed during and after the global financial crisis, it was replaced in 2011 by the DSGE COMPASS, complemented by a 'suite of models'. We provide a general critique of DSGE models for explaining, forecasting and policy analyses at central banks, and suggest new directions for improving current empirical macroeconomic models based on empirical modelling broadly consistent with better theory, rather than seeking to impose simplistic and unrealistic theory. [ABSTRACT FROM AUTHOR]

Democratic theory and prior empirical work support the view that political participation, by promoting social integration and pro-social attitudes, reduces one's propensity for anti-social behavior, such as committing crimes. Previous investigations examine observational data, which are vulnerable to bias if omitted factors affect both propensity to participate and risk of criminality or their reports. A field experiment encouraging 552,525 subjects aged 18-20 to register and vote confirms previous observational findings of the negative association between participation and subsequent criminality. However, comparing randomly formed treatment and control groups reveals that the intervention increased participation but did not reduce subsequent criminality. Our results suggest that while participation is correlated with criminality, it exerts no causal effect on subsequent criminal behavior. [ABSTRACT FROM AUTHOR]

Key Clinical Message Intravesical instillation of Bacillus Calmette-Guérin ( BCG) has been shown to be an effective form of immunotherapy for bladder cancer. This case report describes a patient who develops systemic BCG-osis following intravesical BCG instillation and demonstrates the importance of being aware of more severe complications associated with BCG immunotherapy. [ABSTRACT FROM AUTHOR]

The rise in mass incarceration provides a growing impetus to understand the effect that interactions with the criminal justice system have on political participation. While a substantial body of prior research studies the political consequences of criminal disenfranchisement, less work examines why eligible ex-felons vote at very low rates. We use administrative data on voting and interactions with the criminal justice system from Pennsylvania to assess whether the association between incarceration and reduced voting is causal. Using administrative records that reduce the possibility of measurement error, we employ several different research designs to investigate the possibility that the observed negative correlation between incarceration and voting might result from differences across individuals that lead both to incarceration and to low participation. As this selection bias issue is addressed, we find that the estimated effect of serving time in prison on voting falls dramatically and for some research designs vanishes entirely. [ABSTRACT FROM AUTHOR]

A triple-mode silver-plated ceramic cavity filter with wide spurious-free response is presented. It uses single mode ceramic slabs as interfacing resonators with the cube to give wide spurious-free performance up to 4 GHz for an 1800-MHz base station filter. Three square apertures at the slab-cube interface give simple and flexible control of transmission zero placement while maintaining low insertion loss. The parallel nature of the slab-to-cube coupling allows a simple cuboid structure without chamfers or intermode coupling elements. A seven-pole filter in the DCS-1800 transmit band is built using a slab-slab-cube-slab-slab structure and measured. Along with wide spurious-free performance, the filter achieves around 1 dB of band edge insertion loss with more than 50 dB of attenuation, 20 MHz from the band edges, and from an 11-cm3 package. The near band performance shows all of the seven poles in the return loss. The insertion loss is 1.1 dB on the low side (1805 MHz), 1.0 dB on the high side (1880 MHz), and 0.6 dB at the band center. The wideband response shows more than 40 dB of attenuation up to the frequency 3.7 GHz. [ABSTRACT FROM PUBLISHER]

Economic policy agencies produce forecasts with accompanying narratives, and base policy changes on the resulting anticipated developments in the target variables. Systematic forecast failure, defined as large, persistent deviations of the outturns from the numerical forecasts, can make the associated narrative false, which would in turn question the validity of the entailed policy implementation. We establish when systematic forecast failure entails failure of the accompanying narrative, which we call forediction failure, and when that in turn implies policy invalidity. Most policy regime changes involve location shifts, which can induce forediction failure unless the policy variable is super exogenous in the policy model. We propose a step-indicator saturation test to check in advance for invariance to policy changes. Systematic forecast failure, or a lack of invariance, previously justified by narratives reveals such stories to be economic fiction. [ABSTRACT FROM AUTHOR]

Data spanning long time periods, such as that over 1860–2012 for the UK, seem likely to have substantial errors of measurement that may even be integrated of order one, but which are probably cointegrated for cognate variables. We analyze and simulate the impacts of such measurement errors on parameter estimates and tests in a bivariate cointegrated system with trends and location shifts which reflect the many major turbulent events that have occurred historically. When trends or shifts therein are large, cointegration analysis is not much affected by such measurement errors, leading to conventional stationary attenuation biases dependent on the measurement error variance, unlike the outcome when there are no offsetting shifts or trends. [ABSTRACT FROM AUTHOR]

Purpose Addressing the question, how might socio-technical systems help homeless young people to succeed broadly in employment, the purpose of this paper is to present a future vision, the U-District Job Co-op, where youth take on “mini-jobs” offered by neighborhood stakeholders.Design/methodology/approach Drawing on value sensitive design, design-based, and qualitative research methods, the Job Co-op is explicated by reporting on three linked studies.Findings First, based on empirical research with varied neighborhood stakeholders, barriers and possible solutions to employment for homeless young people are presented. Second, three design insights for shaping a solution space of socio-technical systems for job search are presented and used analytically to examine six existing systems. Third, findings from a co-design study in which homeless young people expressed their understandings for web-based job services explicate the vision of the Job Co-op.Social implications This study offers a socio-technical approach, grounded in the neighborhood context, for supporting homeless young people in job search and related activities.Originality/value The studies reported in this paper demonstrate how methods for information system design can be used to generate and clarify opportunities for human benefit and for the development of socio-technical systems that account for human values. [ABSTRACT FROM AUTHOR]

Research on how economic factors affect attitudes toward immigration often focuses on labor market effects, concluding that, because workers' skill levels do not predict opposition to low- versus highly skilled immigration, economic self-interest does not shape policy attitudes. We conduct a new survey to measure beliefs about a range of economic, political, and cultural consequences of immigration. When economic self-interest is broadened to include concerns about the fiscal burdens created by immigration, beliefs about these economic effects strongly correlate with immigration attitudes and explain a significant share of the difference in support for highly versus low-skilled immigration. Although cultural factors are important, our results suggest that previous work underestimates the importance of economic self-interest as a source of immigration policy preferences and attitudes more generally. [ABSTRACT FROM AUTHOR]

Triple-mode bandstop ceramic cavity filters utilizing parallel-coupled resonators are presented. A transmission line, etched onto the face of a silver-plated ceramic cube, couples to three modes in parallel, avoiding any interresonator coupling perturbations. By varying only a few structural parameters, the coupling strengths into each mode can be controlled, and highly flexible bandstop filtering functions are demonstrated. A 30-MHz bandstop filter at 1800 MHz, from a roughly 27 mm3 ceramic block mounted to a copper base, is fabricated and measured. It achieves 26 dB of attenuation, with less than 0.3 dB of insertion loss 20 MHz away from the stopband, and less than 0.3 dB of reflection loss in the stopband, closely matching the simulation. [ABSTRACT FROM AUTHOR]

A design and analysis approach that enables extracting the full filtering potential of triple-mode filters while greatly simplifying the construction and hence reducing the filter cost is presented. The presented way views multimode resonators of cavity filters as resonators coupled in parallel, with every mode within the structure completely orthogonal; hence, there are no interresonator couplings. Each resonator then independently sees the interfacing input and output while the strength and phase of the coupling, along with the resonator frequencies, dictate the transmission zero placement. Thus, a completely general frequency response can be attained with as many arbitrarily placed zeros as there are multimode resonators. The absence of intracavity couplings allows using a simple rectangular cuboid while the input-to-output coupling via a single printed circuit board interface manages three controllable transmission zeros per cuboid. To verify the proposed approach, multiple designs operating at the DCS-1800 Band-3 are presented. [ABSTRACT FROM PUBLISHER]

We present a methodology for detecting breaks at any point in time-series regression models using an indicator saturation approach, applied here to modelling climate change. Building on recent developments in econometric model selection for more variables than observations, we saturate a regression model with a full set of designed break functions. By selecting over these break functions using an extended general-to-specific algorithm, we obtain unbiased estimates of the break date and magnitude. Monte Carlo simulations confirm the approximate properties of the approach. We assess the methodology by detecting volcanic eruptions in a time series of Northern Hemisphere mean temperature spanning roughly 1200 years, derived from a fully coupled global climate model simulation. Our technique demonstrates that historic volcanic eruptions can be statistically detected without prior knowledge of their occurrence or magnitude- and hence may prove useful for estimating the past impact of volcanic events using proxy reconstructions of hemispheric or global mean temperature, leading to an improved understanding of the effect of stratospheric aerosols on temperatures. The break detection procedure can be applied to evaluate policy impacts as well as act as a robust forecasting device. [ABSTRACT FROM AUTHOR]

One of the most important recent developments in social psychology is the discovery of minor interventions that have large and enduring effects on behavior. A leading example of this class of results is in the work by Bryan et al. [Bryan CJ, Walton GM, Rogers T, Dweck CS (2011) Proc Natl Acad Sci USA 108(31):12653–12656], which shows that administering a set of survey items worded so that subjects think of themselves as voters (noun treatment) rather than as voting (verb treatment) substantially increases political participation (voter turnout) among subjects. We revisit these experiments by replicating and extending their research design in a large-scale field experiment. In contrast to the 11 to 14% point greater turnout among those exposed to the noun rather than the verb treatment reported in the work by Bryan et al., we find no statistically significant difference in turnout between the noun and verb treatments (the point estimate of the difference is approximately zero). Furthermore, when we benchmark these treatments against a standard get out the vote message, we estimate that both are less effective at increasing turnout than a much shorter basic mobilization message. In our conclusion, we detail how our study differs from the work by Bryan et al. and discuss how our results might be interpreted. [ABSTRACT FROM AUTHOR]

It is argued that model selection and robust estimation should be handled jointly. Impulse indicator saturation makes that possible, but leads to the situation where there are more variables than observations. This is illustrated by revisiting the analysis of Tobin's food data. [ABSTRACT FROM AUTHOR]

How does America's high rate of incarceration shape political participation? Few studies have examined the direct effects of incarceration on patterns of political engagement. Answering this question is particularly relevant for the 93% of formerly incarcerated individuals who are eligible to vote. Drawing on new administrative data from Connecticut, we present evidence from a field experiment showing that a simple informational outreach campaign to released felons can recover a large proportion of the reduction in participation observed following incarceration. The treatment effect estimates imply that efforts to reintegrate released felons into the political process can substantially reduce the participatory consequences of incarceration. [ABSTRACT FROM AUTHOR]

An underlying assumption of proportional hazards models is that the effect of a change in a covariate on the hazard rate of event occurrence is constant over time. For scholars using the Cox model, a Schoenfeld residual-based test has become the disciplinary standard for detecting violations of this assumption. However, using this test requires researchers to make a choice about a transformation of the time scale. In practice, this choice has largely consisted of arbitrary decisions made without justification. Using replications and simulations, we demonstrate that the decision about time transformations can have profound implications for the conclusions reached. In particular, we show that researchers can make far more informed decisions by paying closer attention to the presence of outlier survival times and levels of censoring in their data. We suggest a new standard for best practices in Cox diagnostics that buttresses the current standard with in-depth exploratory data analysis. [ABSTRACT FROM AUTHOR]

Trygve Haavelmo’s Probability Approach aimed to implement economic theories, but he later recognized their incompleteness. Although he did not explicitly consider model selection, we apply it when theory-relevant variables, {Xt}, are retained without selection while selecting other candidate variables, {Wt}. Under the null that the {Wt} are irrelevant, by orthogonalizing with respect to the {Xt}, the estimator distributions of the Xt’s parameters are unaffected by selection even for more variables than observations and for endogenous variables. Under the alternative, when the joint model nests the generating process, an improved outcome results from selection. This implements Haavelmo’s program relatively costlessly. [ABSTRACT FROM AUTHOR]

Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem), using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem) while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem), using a viable approach that resolves the computational problem of immense numbers of possible models. [ABSTRACT FROM AUTHOR]