Understanding factors controlling primary production is fundamental for the protection, management, and restoration of ecosystems. Tropical seagrass ecosystems are among the most productive ...ecosystems worldwide, yielding tremendous services for society. Yet they are also among the most impaired from anthropogenic stressors, prompting calls for ecosystem‐based restoration approaches. Artificial reefs (ARs) are commonly applied in coastal marine ecosystems to rebuild failing fisheries and have recently gained attention for their potential to promote carbon sequestration. Nutrient hotspots formed via excretion from aggregating fishes have been empirically shown to enhance local primary production around ARs in seagrass systems. Yet, if and how increased local production affects primary production at ecosystem scale remains unclear, and empirical tests are challenging. We used a spatially explicit individual‐based simulation model that combined a data‐rich single‐nutrient primary production model for seagrass and bioenergetics models for fish to test how aggregating fish on ARs affect seagrass primary production at patch and ecosystem scales. Specifically, we tested how the aggregation of fish alters (i) ecosystem seagrass primary production at varying fish densities and levels of ambient nutrient availability and (ii) the spatial distribution of seagrass primary production. Comparing model ecosystems with equivalent nutrient levels, we found that when fish aggregate around ARs, ecosystem‐scale primary production is enhanced synergistically. This synergistic increase in production was caused by nonlinear dynamics associated with nutrient uptake and biomass allocation that enhances aboveground primary production more than belowground production. Seagrass production increased near the AR and decreased in areas away from the AR, despite marginal reductions in seagrass biomass at the ecosystem level. Our simulation's findings that ARs can increase ecosystem production provide novel support for ARs in seagrass ecosystems as an effective means to promote (i) fishery restoration (increased primary production can increase energy input to the food web) and (ii) carbon sequestration, via higher rates of primary production. Although our model represents a simplified, closed seagrass system without complex trophic interactions, it nonetheless provides an important first step in quantifying ecosystem‐level implications of ARs as a tool for ecological restoration.
Overreaction in Macroeconomic Expectations Bordalo, Pedro; Gennaioli, Nicola; Ma, Yueran ...
The American economic review,
09/2020, Letnik:
110, Številka:
9
Journal Article
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We study the rationality of individual and consensus forecasts of macroeconomic and financial variables using the methodology of Coibion and Gorodnichenko (2015), who examine predictability of ...forecast errors from forecast revisions. We find that individual forecasters typically overreact to news, while consensus forecasts underreact relative to full-information rational expectations. We reconcile these findings within a diagnostic expectations version of a dispersed information learning model. Structural estimation indicates that departures from Bayesian updating in the form of diagnostic overreaction capture important variation in forecast biases across different series, yielding a belief distortion parameter similar to estimates obtained in other settings.
Abstract This paper presents an overview of the research progress on Multiphysics simulation of MEMS electrostatic actuators. MEMS electrostatic actuators have shown significant development potential ...in applications such as ADWSS and VOA. However, the increasing size and complexity of these devices have posed challenges to traditional Multiphysics simulation methods, leading to lengthy computational times that hinder the overall device development process. In order to enhance simulation efficiency and meet the demands of device development, the use of simplified single physics field models have been explored. The results have demonstrated that the simplified single physics filed simulation model yields comparable results to traditional Multiphysics simulations with an error tolerance of within 10%, while reducing simulation time by 80-90%.
Large time-varying parameter VARs Koop, Gary; Korobilis, Dimitris
Journal of econometrics,
12/2013, Letnik:
177, Številka:
2
Journal Article
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In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from ...the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.
Using data from the European Central Bank's Survey of Professional Forecasters and ECB/Eurosystem staff projections, we analyze the role of ex-ante conditioning variables for macroeconomic forecasts. ...In particular, we test to which extent the updating and ex-post performance of predictions for inflation, real GDP growth and unemployment are related to beliefs about future oil prices, exchange rates, interest rates and wage growth. While oil price and exchange rate predictions are updated more frequently than macroeconomic forecasts, the opposite is true for interest rate and wage growth expectations. Beliefs about future inflation are closely associated with oil price expectations, whereas expected interest rates are related to predictions of output growth and unemployment. Exchange rate predictions also matter for macroeconomic forecasts, albeit less so than the other variables. With regard to forecast errors, wage growth and GDP growth closely comove, but only during the period when interest rates are at the effective zero lower bound.
•Macroeconomic forecasts are based on assumptions about future economic conditions.•Survey-based inflation forecasts are closely associated with oil price expectations.•GDP growth and unemployment forecasts are related to interest rate expectations.•Exchange rate and wage growth assumptions are less important for macro forecasts.•Survey participants can increase forecast accuracy by reducing assumption errors.
Fundamental disagreement Andrade, Philippe; Crump, Richard K.; Eusepi, Stefano ...
Journal of monetary economics,
10/2016, Letnik:
83
Journal Article
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We document a novel set of facts about disagreement among professional forecasters: (1) forecasters disagree at all horizons, including the long run; (2) the term structure of disagreement is ...downward sloping for real output growth, relatively flat for inflation, and upward sloping for the federal funds rate; (3) disagreement is time varying at all horizons. We propose a generalized model of imperfect information that can jointly explain these facts. We further use the term structure of disagreement to show that the monetary policy rule perceived by professional forecasters features a high degree of interest-rate smoothing and time variation in the intercept.
•We document new facts about disagreement among professional forecasters.•These facts present a challenge to benchmark models of expectation formation.•We propose a multivariate model with informational frictions that jointly explains the facts.•Professional forecasters perceive monetary policy to be highly inertial.•They also perceive a rule with time-variation in the intercept.
This paper argues for a careful (re)consideration of the expectations formation process and a more systematic inclusion of real-time expectations through survey data in macroeconomic analyses. While ...the rational expectations revolution has allowed for great leaps in macroeconomic modeling, the surveyed empirical microevidence appears increasingly at odds with the full-information rational expectation assumption. We explore models of expectation formation that can potentially explain why and how survey data deviate from full-information rational expectations. Using the New Keynesian Phillips curve as an extensive case study, we demonstrate how incorporating survey data on inflation expectations can address a number of otherwise puzzling shortcomings that arise under the assumption of full-information rational expectations.
This paper compares alternative models of time-varying volatility on the basis of the accuracy of real-time point and density forecasts of key macroeconomic time series for the USA. We consider ...Bayesian autoregressive and vector autoregressive models that incorporate some form of time-varying volatility, precisely random walk stochastic volatility, stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The results show that the AR and VAR specifications with conventional stochastic volatility dominate other volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree.
Abstract
The sole method available to humans for precise control of power energy is power electronic technology, which is also a key trend in developing the future power system and the entire energy ...structure. PWM control technology regulates the pulse signal’s width, and this circuit can alter the fundamental amplitude and form of the input voltage. This study will evaluate the three-phase inverter circuit’s operating principle, develop its control strategy, create a SIMULINK simulation model, and do a rough analysis using an LC filter.
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