93,755 research outputs found
Out-of-sample equity premium prediction: A complete subset quantile regression approach
This paper extends the complete subset linear regression framework to a quantile regression setting. We employ complete subset combinations of quantile forecasts in order to construct robust and accurate equity premium predictions. Our recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile succeeds in identifying the best subset in a time- and quantile-varying manner. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average benchmark and the complete subset mean regression approach
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Forecasting audience increase on YouTube
User profiles constructed on Social Web platforms are often motivated by the need to maximise user reputation within a community. Subscriber, or follower, counts are an indicator of the influence and standing that the user has, where greater values indicate a greater perception or regard for what the user has to say or share. However, at present there lacks an understanding of the factors that lead to an increase in such audience levels, and how a user’s behaviour can a!ect their reputation. In this paper we attempt to fill this gap, by examining data collected from YouTube over regular time intervals. We explore the correlation between the subscriber counts and several behaviour features - extracted from both the user’s profile and the content they have shared. Through the use of a Multiple Linear Regression model we are able to forecast the audience levels that users will yield based on observed behaviour. Combining such a model with an exhaustive feature selection process, we yield statistically significant performance over a baseline model containing all features
Short and long-term wind turbine power output prediction
In the wind energy industry, it is of great importance to develop models that
accurately forecast the power output of a wind turbine, as such predictions are
used for wind farm location assessment or power pricing and bidding,
monitoring, and preventive maintenance. As a first step, and following the
guidelines of the existing literature, we use the supervisory control and data
acquisition (SCADA) data to model the wind turbine power curve (WTPC). We
explore various parametric and non-parametric approaches for the modeling of
the WTPC, such as parametric logistic functions, and non-parametric piecewise
linear, polynomial, or cubic spline interpolation functions. We demonstrate
that all aforementioned classes of models are rich enough (with respect to
their relative complexity) to accurately model the WTPC, as their mean squared
error (MSE) is close to the MSE lower bound calculated from the historical
data. We further enhance the accuracy of our proposed model, by incorporating
additional environmental factors that affect the power output, such as the
ambient temperature, and the wind direction. However, all aforementioned
models, when it comes to forecasting, seem to have an intrinsic limitation, due
to their inability to capture the inherent auto-correlation of the data. To
avoid this conundrum, we show that adding a properly scaled ARMA modeling layer
increases short-term prediction performance, while keeping the long-term
prediction capability of the model
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
Call centers' managers are interested in obtaining accurate point and
distributional forecasts of call arrivals in order to achieve an optimal
balance between service quality and operating costs. We present a strategy for
selecting forecast models of call arrivals which is based on three pillars: (i)
flexibility of the loss function; (ii) statistical evaluation of forecast
accuracy; (iii) economic evaluation of forecast performance using money
metrics. We implement fourteen time series models and seven forecast
combination schemes on three series of daily call arrivals. Although we focus
mainly on point forecasts, we also analyze density forecast evaluation. We show
that second moments modeling is important both for point and density
forecasting and that the simple Seasonal Random Walk model is always
outperformed by more general specifications. Our results suggest that call
center managers should invest in the use of forecast models which describe both
first and second moments of call arrivals
Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package
Raftery, Karny, and Ettler (2010) introduce an estimation technique, which
they refer to as Dynamic Model Averaging (DMA). In their application, DMA is
used to predict the output strip thickness for a cold rolling mill, where the
output is measured with a time delay. Recently, DMA has also shown to be useful
in macroeconomic and financial applications. In this paper, we present the eDMA
package for DMA estimation implemented in R. The eDMA package is especially
suited for practitioners in economics and finance, where typically a large
number of predictors are available. Our implementation is up to 133 times
faster then a standard implementation using a single-core CPU. Thus, with the
help of this package, practitioners are able to perform DMA on a standard PC
without resorting to large clusters, which are not easily available to all
researchers. We demonstrate the usefulness of this package through simulation
experiments and an empirical application using quarterly U.S. inflation data.Comment: 21 pages, 5 figures, 2 table
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
Formalizing judgemental adjustment of model-based forecasts
In business and in macroeconomics it is common practice to use econo-metric models to generate forecasts. These models can take any degree ofsophistication. Sometimes it is felt by an expert that the model-based fore-cast needs adjustment. This paper makes a plea for a formal approach to suchan adjustment, more precise, for the creation of detailed logbooks which con-tain information on why and how model-based forecasts have been adjusted.The reasons for doing so are that such logbooks allow for (i) the preservationof expert knowledge, (ii) for the possible future modi¯cation of econometricmodels in case adjustment is almost always needed, and (iii) for the evaluationof adjusted forecasts. In this paper I put forward an explicit mathematicalexpression for a judgementally adjusted model-based forecast. The key pa-rameters in the expression should enter the logbook. In a limited simulationexperiment I illustrate an additional use of this expression, that is, lookingwith hindsight if adjustment would have led to better results. The resultsof the simulation suggest that always adjusting forecasts leads to very poorresults. Also, it is documented that small adjustments are better that largeadjustments, even in case large adjustments are felt necessary.forecasting;judgemental adjustment
Forecasting inflation using dynamic model averaging
We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period
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