Unpredictable Components Research Articles

Central Bank Sentiment and Policy Expectations

We explore empirically the theoretical prediction that optimism or pessimism have aggregate effects, in the context of monetary policy. First, we quantify the tone conveyed by FOMC policymakers in their statements using computational linguistics. Second, we identify sentiment as the unpredictable component of tone, orthogonal to fundamentals, expectations, monetary shocks and investors’ sentiment. Third, we estimate the impact of FOMC sentiment on the term structure of private interest rate expectations using a high-frequency methodology and an ARCH model. Optimistic FOMC sentiment increases policy expectations primarily at the one-year maturity. We also find that sentiment affects inflation and industrial production beyond monetary shocks.

The welfare effects of coffee price volatility for Ethiopian coffee producers

Purpose This paper aims to estimate the welfare effects for Ethiopian coffee producers from eliminating coffee price volatility. Design/methodology/approach To estimate volatility, the generalised autoregressive conditional heteroskedasticity technique is applied to monthly coffee prices in Ethiopia for the period 1976-2012. To distinguish between the unpredictable and predictable components of volatility, we obtain separate estimates of the conditional and unconditional variance of the residual. This is combined with estimates of the coefficient of relative risk aversion to measure the welfare effects from eliminating the unpredictable component of price volatility. Findings A key finding is that the welfare gain from eliminating coffee price volatility is small; the gain per producer comes to a meagre US$0.76 in a year. Originality/value This has important policy implications for the efficacy of price stabilisation mechanisms for coffee producers, i.e. any attempt to eliminate coffee price volatility at a cost may not be a preferred outcome for Ethiopian producers. The contribution of the paper lies in using the unconditional variance, as it more truly reflects price risk faced by coffee producers without overestimating it.

Disagreement after News: Gradual Information Diffusion or Differences of Opinion?

This paper explores the long-standing empirical fact of increased trading volume around news releases through the lens of canonical models of gradual information diffusion and differences of opinion. I use a unique dataset of clicks on news by key finance professionals to distinguish between trading among investors who see the news at different times and trading among investors who see the same news but disagree regarding its interpretation. Consistent with gradual information diffusion, dispersion in the timing of investors' attention is strongly predictive of daily volume around earnings announcements and volume within minutes of individual news articles. Furthermore, delayed attention is predictive of minute-level return continuation, daily-level post-earnings-announcement drift, and monthly-level return momentum. The results are robust to using the unpredictable component of news positioning as an instrument for delayed attention. Differences of opinion, measured as heterogeneity in the investors clicking on the news, is generally weaker in explaining trading volume around news, but plays a larger role when the news is more textually ambiguous.

‘Echoes from the Field’ Study Outcome and Discussion: Reflections on Software Systems Engineering Practice.

AbstractTo date, despite that various software development approaches have evolved and demonstrated success, many software development projects still fail. This paper argues that the reason for such failure is the result of not addressing the human‐side of the Software Systems Engineering (SWSE) adequately. Humans are relatively unpredictable components in the larger system of the sociotechnical environment, therefore requires adequate consideration. The study investigated the experience and perception of software engineers for the contemporary practice of SWSE. A suite of multidisciplinary theories was consulted and adapted to broaden the understanding of the role human‐element plays in SWSE, such as the Knowledge Creation Theory (KCT), Toyota Production System (TPS) known as “Lean” and Emotional Intelligence (EI). An overall conclusion is that SWSE is a human‐centric activity, and its success is heavily reliant on the human‐element and its related social dynamics. The findings can help viewing SWSE from a different prospective that could help improving the practice.

Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

SummaryTrain dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so‐called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off‐peak hours separately. The peak‐hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off‐peak‐hour dwell times are estimated using a non‐parametric regression model, in particular, the k‐nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%–88.5% during peak hours and 80.1% during off‐peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible. Copyright © 2016 John Wiley & Sons, Ltd.

Estimating ENSO predictability based on multi-model hindcasts

Based on hindcasts of seasonal forecast systems participating in the North American Multi-Model Ensemble, the seasonal dependence of predictability of the El Nino–Southern Oscillation (ENSO) was estimated. The results were consistent with earlier analyses in that the predictability of ENSO was highest in winter and lowest in spring and summer. Further, predictability as measured by the relative amplitude of predictable and unpredictable components was dominated by the ensemble mean instead of the spread (or dispersion) among ensemble members. This result was consistent with previous analysis that most of ENSO predictability resides in the shift of the probability density function (PDF) of ENSO sea surface temperature (SST) anomalies (i.e., changes in the first moment of the PDF that is associated with the ensemble mean of ENSO SST anomalies) rather than due to changes in the spread of the PDF. The analysis establishes our current best estimate of ENSO predictability that can serve as a benchmark for quantifying further improvements resulting from advances in observing, assimilation, and seasonal prediction systems.

Electricity Consumption Modelling: A Case of Germany

Recent research has found that electricity consumption is a very useful variable in economics. In many applications it might be desirable to decompose electricity consumption into unpredictable and deterministic (or highly predictable) component. We want to find out whether forecasting works better if we model electricity load independently for each hour or if we model in the first step average daily consumption and in a second step we model for each hour deviation from this average. We therefore compare two simple, yet flexible models for hourly electricity consumption in Germany. Both models use temperature, industrial production, hours of daylight and dummies for days of the week and month of the year as explanatory variables. We find that the first model, despite being simpler, forecasts hourly electricity demand more precisely. This indicates that hourly electricity consumption represent various goods, and should be modelled separately for each hour.

Discussion

Discussion

How Informative are the Unpredictable Components of Earnings Forecasts?

markdownabstract__Abstract__ An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the unpredictable components, which may be viewed as the personal expertise of the earnings forecasters, we see that small adjustments to the model forecasts lead to more forecast accuracy. Based on past track records, it is possible to predict the future track record of individual forecasters.

Warpage control of thin-walled injection molding using local mold temperatures

Currently, 3C products are required to be lightweight, portable, and convenient. Injection molding is among the most used techniques for mass production in plastic processing industries; however, producing thinner parts that do not warp is challenging. Although plastic components warp for numerous complicated reasons, warpage primarily is caused by variations in shrinkage during the injection process of plastic part manufacturing. Material properties, part design, mold design, and processing conditions are factors influencing variations in the part shrinkage. For example, inconsistent thickness in component geometry, poor sprue–runner–gate or cooling design in the injection mold, and improper molding condition settings may cause plastic parts to warp excessively. Warpage causes unpredictable component shapes, which may cause poor assembly quality. Although mold cooling achieved by adjusting mold temperatures improves warpage, the conventional single mold temperature setting for each male or female mold plate limits the cooling capability. Therefore, this paper describes local mold temperature settings for a cooling system that can prevent severe warpage in an asymmetric plastic cover for handheld communication devices. The neutral axis theory is introduced to analyze the temperature distribution in the cross section of a part, and then predict the warping trend. Through simulation and experiments conducted in this study, the feasibility of using an effective local mold temperature setting in a cooling system to reduce part warpage was verified.

Genotype and environment interaction and stability analysis for seed yield in yellow mung bean (Vigna radiata L.)

Nine yellow seeded mung (Vigna radiata L.) genotypes were evaluated along with three checks for their yield performance during three years (2007, 2010 and 2011). Pooled analysis of variance and stability analysis were performed. The genotypic (G) × environment (E) interaction and both variance due to genotypes and environment were significant. The portioning of G × E interaction into linear and non-linear components indicated that both predictable and unpredictable components shared the interaction. On the basis of stability parameters, the top yielding genotypes such as BGS-9 (605.444 Kg/Ha), Sel-4 (519.778 Kg/Ha) and China mung (567.000 Kg/Ha) exhibited high mean yield. Based on stability parameters the genotypes YM-5 (459.889 Kg/Ha), YM-8 (451.333 Kg/Ha) exhibited low mean performance along with regression value nearer to unity (bi=1) and non significant deviation from regression (S2 di=0) indicating the high stability and wider adaptability across the three environments. The genotypes BGS-9 (605.444 Kg/Ha) and Sel-4 (519.778 Kg/Ha) exhibited high mean value and bi values (bi>1) and non significant deviation (S2 di < 0) value indicating adapted for high performance environments (These genotypes are sensitive to environments and give maximum yield when inputs are not limited).

Examining the Success of the Central Banks in Inflation Targeting Countries: The Dynamics of Inflation Gap and the Institutional Characteristics

This paper analyzes the performance of central banks in 27 inflation targeting countries by examining their success in achieving their explicit inflation targets. For this purpose, we decompose the inflation gap, the difference between actual inflation and inflation target, into predictable and unpredictable components. We argue that the central banks are successful if the predictable component in the inflation gap diminishes over time. The predictable component of inflation gap is measured by the conditional mean of a time-varying autoregressive model. Our results find considerable heterogeneity in the success of these IT countries in achieving their targets at the start of this policy regime. Our findings also suggest that the central banks of the IT adopting countries started targeting inflation implicitly before becoming an explicit inflation targeter. The panel data analysis suggests that the relative success of these countries in reducing the gap is influenced by their institutional characteristics particularly by fiscal discipline and macroeconomic performance.

Employer Learning, Productivity, and the Earnings Distribution: Evidence from Performance Measures

Two ubiquitous empirical regularities in pay distributions are that the variance of wages increases with experience, and innovations in wage residuals have a large, unpredictable component. The leading explanations for these patterns are that over time, either firms learn about worker productivity but productivity remains fixed or workers’ productivities themselves evolve heterogeneously. In this paper, we seek to disentangle these two models and place magnitudes on their relative importance. We derive a dynamic model of learning and productivity that nests both models and allows them to coexist. We estimate our model on a 20-year panel of pay and performance measures from a single, large firm (the Baker-GibbsHolmstrom data). Incorporating performance measures yields two key innovations. First, the panel structure implies that we have repeat measures of correlates of productivity, as opposed to the empirical evidence on employer learning which uses one fixed measure. Second, we can separate productivity from pay, whereas the previous literature on productivity evolution could not. We find that both models are important in explaining the data. However, the predominant effect is that worker productivity evolves idiosyncratically over time, implying firms must continuously learn about a moving target. Therefore, while the majority of pay dispersion is driven by variation in individual productivity, wages differ significantly from individual productivity at all experience levels due to imperfect information. We believe this represents a significant reinterpretation of the empirical literature on employer learning.

Extracting predictable components and forecasting techniques in extended-range numerical weather prediction

This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales. Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth. The predictable components are defined as those with a slow error growth at a given range. A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast (DERF) model of the National Climate Center. At the same time, useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data, reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components. Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method. The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.

The Welfare Gain from Eliminating Coffee Price Volatility: The Case of Indian Coffee Producers

It is commonly felt that the liberalisation of commodity markets has increased the exposure of commodity producers to price volatility. Using a generalized autoregressive conditional heteroskedasticity framework, we make a distinction between the predictable and unpredictable components of volatility, the latter exposing producers to price risk. By using empirical estimates of the coefficient of relative risk aversion drawn from the literature, we show that the welfare gain from eliminating this price risk for Indian coffee producers is on average 4.8 percent of their revenue from coffee sales, which for a poor producer may be more than a month’s income. This underlines the need for providing producers access to suitable price-risk management or hedging mechanisms.

Playing smart vs. playing safe: the joint expression of phenotypic plasticity and potential bet hedging across and within thermal environments.

Adaptive phenotypic plasticity evolves when cues reliably predict fitness consequences of life-history decisions, whereas bet hedging evolves when environments are unpredictable. These modes of response should be jointly expressed, because environmental variance is composed of both predictable and unpredictable components. However, little attention has been paid to the joint expression of plasticity and bet hedging. Here, I examine the simultaneous expression of plasticity in germination rate and two potential bet-hedging traits - germination fraction and within-season diversification in timing of germination - in seeds from multiple seed families of five geographically distant populations of Lobelia inflata (L.) subjected to a thermal gradient. Populations differ in germination plasticity to temperature, in total germination fraction and in the expression of potential diversification in the timing of germination. The observation of a negative partial correlation between the expression of plasticity and germination variance (potential diversification), and a positive correlation between plasticity and germination fraction is suggestive of a trade-off between modes of response to environmental variance. If the observed correlations are indicative of those between adaptive plasticity and bet hedging, we expect an optimal balance to exist and differ among populations. I discuss the challenges involved in testing whether the balance between plasticity and bet hedging depends on the relative predictability of environmental variance.

Stochastic forcing of north tropical Atlantic sea surface temperatures by the North Atlantic Oscillation

The North Atlantic Oscillation (NAO) is a rapidly decorrelating process that strongly affects the climate over the Atlantic and the surrounding continents. Although the NAO itself is basically unpredictable on seasonal timescales using statistical methods, NAO forcing is here shown to significantly affect sea surface temperatures (SSTs) evolving on those timescales. Results using linear inverse modeling (LIM) imply that the NAO index and its convolution with deterministic SST dynamics account for nearly half the unpredictable component of north tropical Atlantic SST at lead times greater than 9 months; adding this component to hindcasts at a lead of 48 weeks increases correlation with north tropical Atlantic SST from about 0.4 to about 0.6. Rapid fluctuations during boreal winter and spring, when the NAO is strongest, affect SST predictability throughout the entire year.

Lipid-Modifying Enzymes in Human Tear Fluid and Corneal Epithelial Stress Response

Purpose.: Since homeostasis at the ocular surface requires a delicate balance between numerous factors, and the external environment contributes as an unpredictable component, we aimed to understand the role that various lipids and their regulators have in the complex process that maintains a healthy corneal surface.

Variability and predictability of Northeast China climate during 1948–2012

In this work, authors examine the variabilities of precipitation and surface air temperature (T2m) in Northeast China during 1948–2012, and their global connection, as well as the predictability. It is noted that both the precipitation and T2m variations in Northeast China are dominated by interannual and higher frequency variations. However, on interdecadal time scales, T2m is shifted significantly from below normal to above normal around 1987/1988. Statistically, the seasonal mean precipitation and T2m are largely driven by local internal atmospheric variability rather than remote forcing. For the precipitation variation, circulation anomalies in the low latitudes play a more important role in spring and summer than in autumn and winter. For T2m variations, the associated sea surface pressure (SLP) and 850-hPa wind (uv850) anomalies are similar for all seasons in high latitudes with significantly negative correlations for SLP and westerly wind anomaly for uv850, suggesting that a strong zonal circulation in the high latitudes favors warming in Northeast China. The predictability of precipitation and T2m in Northeast China is assessed by using the Atmospheric Model Inter-comparison Project type experiments which are forced by observed sea surface temperature (SST) and time-evolving greenhouse gas (GHG) concentrations. Results suggest that T2m has slightly higher predictability than precipitation in Northeast China. To some extent, the model simulates the interdecadal shift of T2m around 1987/1988, implying a possible connection between SST (and/or GHG forcing) and surface air temperature variation in Northeast China on interdecadal time scales. Nevertheless, the precipitation and T2m variations are mainly determined by the unpredictable components which are caused by the atmospheric internal dynamic processes, suggesting low predictability for the climate variation in Northeast China.

Employer Learning, Productivity and the Earnings Distribution: Evidence from Performance Measures

Two ubiquitous empirical regularities in pay distributions are that the variance of wages increases with experience, and innovations in wage residuals have a large, unpredictable component. The leading explanations for these patterns are that over time, either firms learn about worker productivity but productivity remains fixed or workers' productivities themselves evolve heterogeneously. In this paper, we seek to disentangle these two models and place magnitudes on their relative importance. We derive a dynamic model of learning and productivity that nests both models and allows them to coexist. We estimate our model on a 20-year panel of pay and performance measures from a single, large firm (the Baker-Gibbs-Holmstrom data). Incorporating performance measures yields two key innovations. First, the panel structure implies that we have repeat measures of correlates of productivity, as opposed to the empirical evidence on employer learning which uses one fixed measure. Second, we can separate productivity from pay, whereas the previous literature on productivity evolution could not. We find that both models are important in explaining the data. However, the predominant effect is that worker productivity evolves idiosyncratically over time, implying firms must continuously learn about a moving target. Therefore, while the majority of pay dispersion is driven by variation in individual productivity, wages differ significantly from individual productivity at all experience levels due to imperfect information. We believe this represents a significant reinterpretation of the empirical literature on employer learning.