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韩山师范学院财务管理课件第四章 Financial Forecasting。
FUNCTIONAL COEFFICIENT MOVING AVERAGE MODEL WITH APPLICATIONS TO FORECASTING CHINESE CPI
Moving Average model functional coefficient model fore- casting Consumer Price Index
2016/1/26
This article establishes the functional coefficient moving average mod-el (FMA), which allows the coefficient of the classical moving average model to adapt with a covariate. The functional coefficien...
Forecasting Equity Premium: Global Historical Average versus Local Historical Average and Constraints
Equity premium Nonparametric local historical average model Positivity con- straint Bagging
2016/1/25
The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric ker...
Forecasting Equity Premium: Global Historical Average versus Local Historical Average and Constraints
Equity premium Nonparametric local historical average model Positivity con- straint Bagging Model averaging
2016/1/20
The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric ker...
Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting
Nonlinearity Nonparametric regression Semiparametric regression Local mono- tonicity Bagging
2016/1/20
This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang(2001). Asymptotic properties of ...
Does "model-free" forecasting really outperform the "true" model? A reply to Perretti et al
"model-free" forecasting outperform "true" model
2013/6/14
Estimating population models from uncertain observations is an important problem in ecology. Perretti et al. observed that standard Bayesian state-space solutions to this problem may provide biased pa...
Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
Comparison nonhomogeneous regression models probabilistic wind speed forecasting
2013/6/14
In weather forecasting, nonhomogeneous regression is used to statistically postprocess forecast ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regressi...
Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components
Bayesian model averaging continuous ranked probability score ensemble calibration truncated normal distribution
2013/6/13
Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to ...
Markov Switching Component ARCH Model: Stability and Forecasting
ARCH models Markov process Stability Component GARCH models Forecasting Bayesian inference Griddy Gibbs sampling
2013/4/28
This paper introduces an extension of the Markov switching ARCH model where the volatility in each state is a convex combination of two different ARCH components with time varying weights with differe...
Probabilistic temperature forecasting with statistical calibration in Hungary
Probabilistic temperature forecasting with statistical calibration in Hungary
2013/4/27
Weather forecasting is mostly based on the outputs of deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result in forecast ensembles w...
Inter Time Series Sales Forecasting
Association mining Combining Decomposition Forecasting Inter time series.
2013/4/27
Combining forecast from different models has shown to perform better than single forecast in most time series. To improve the quality of forecast we can go for combining forecast. We study the effect ...
Finite sample forecasting with estimated temporally aggregated linear processes
linearmodels ARMA temporal aggregation forecasting finite sample forecasting flow temporal aggregation stock temporal aggregation multistep forecasting
2012/11/22
We propose a finite sample based predictor for estimated linear one dimensional time series models and compute the associated total forecasting error. The expression for the error that we present take...
Causal band-limited approximation and forecasting for discrete time processes
band-limited processes discrete time processes causal filters sampling low-pass filters forecasting.
2012/9/18
We study causal dynamic approximation of non-bandlimited discretetime processes by band-limited discrete time processes such that a part of the historical path of the underlying process is approximate...
Bayesian semi-parametric forecasting with penalised splines and autoregressive errors
splines, autoregressive errors, semi-parametric regression, Bayesian
2012/9/18
Observational time series data often exhibit both cyclic temporal trends and autocorrelation and may also depend on covariates. As such, there is a need for exible regression models that are able to c...
Forecasting electricity consumption by aggregating specialized experts
Prediction with expert advice Specialized experts Application to real data
2012/9/19
We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We rst provide a review of the relevant literature and
present two theoretical contributions: a ...