In this paper, we survey some recent results on parametric and nonparametric statistical estimation about the spectrum of stationary models with tapered data, as well as a question concerning robustness of inferences ...
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In this paper, we survey some recent results on parametric and nonparametric statistical estimation about the spectrum of stationary models with tapered data, as well as a question concerning robustness of inferences carried out on a linear stationary process contaminated by a small trend. We also discuss some questions concerning tapered Toeplitz matrices and operators, central limit theorems for tapered Toeplitz-type quadratic functionals, and tapered Fejer-type singular integrals. These are the main tools for obtaining the corres ponding results, and also are of interest in themselves. The processes considered will be discrete-time and continuous-time Gaussian, linear or Levy-driven linear processes with memory.
This is a survey of recent results on central and non-central limit theorems for quadratic functionals of stationary processes. The underlying processes are Gaussian, linear or Levy-driven linear processes with memory...
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This is a survey of recent results on central and non-central limit theorems for quadratic functionals of stationary processes. The underlying processes are Gaussian, linear or Levy-driven linear processes with memory, and are defined either in discrete or continuous time. We focus on limit theorems for Toeplitz and tapered Toeplitz type quadratic functionals of stationary processes with applications in parametric and nonparametric statistical estimation theory. We discuss questions concerning Toeplitz matrices and operators, Fejer-type singular integrals, and Levy-Ito-type and Stratonovich-type multiple stochastic integrals. These are the main tools for obtaining limit theorems.
Nowadays, modeling and forecasting electricity spot prices are challenging due to their specific features, including multiple seasonalities, calendar effects, and extreme values (also known as jumps, spikes, or outlie...
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Nowadays, modeling and forecasting electricity spot prices are challenging due to their specific features, including multiple seasonalities, calendar effects, and extreme values (also known as jumps, spikes, or outliers). This study aims to provide a comprehensive analysis of electricity price forecasting by comparing several outlier filtering techniques followed by various modeling frameworks. To this end, extreme values are first treated with five different filtering techniques and are then replaced by four different outlier replacement approaches. Next, the spikes-free series is divided into deterministic and stochastic components. The deterministic component includes long-term trend, yearly and weekly seasonalities, and bank holidays and is estimated through parametric and nonparametric approaches. On the other hand, the stochastic component accounts for the short-run dynamics of the price time series and is modeled using different univariate and multivariate models. The one-day-ahead out-of-sample forecast results for the Italian Power Exchange (IPEX), obtained for a whole year, suggest that the outliers pre-filtering give a high accuracy gain. In addition, multivariate modeling for the stochastic component outperforms univariate models.
In this paper, we survey some recent results on statistical inference (parametric and nonparametric statistical estimation, hypotheses testing) about the spectrum of stationary models with tapered data. We also discus...
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In this paper, we survey some recent results on statistical inference (parametric and nonparametric statistical estimation, hypotheses testing) about the spectrum of stationary models with tapered data. We also discuss some questions concerning tapered Toeplitz matrices and operators, central limit theorems for tapered Toeplitz type quadratic functionals, and tapered Fejer-type kernels and singular integrals. These are the main tools for obtaining the corresponding results, and also are of interest in themselves. The processes considered will be discrete-time and continuous-time Gaussian, linear or Levy-driven linear processes with memory.
The study applies parametric and nonparametric estimation methods to determine hedonic prices of rice quality attributes, and a partial equilibrium model to determine the payoff to investing in quality improvement in ...
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The study applies parametric and nonparametric estimation methods to determine hedonic prices of rice quality attributes, and a partial equilibrium model to determine the payoff to investing in quality improvement in five countries in Sub-Saharan Africa. Results indicate that consumers are willing to pay price premiums for head rice, slender grains, peak viscosity, parboiled rice, and rice sold in urban markets. However, they strongly discount amylose content, rice with impurities and imported rice. Investing in quality improvement through amylose content reduction leads to net welfare gains with a benefit-cost ratio of 47.86 and internal rate of return of 90%.
The fields of statistics and econometrics have developed powerful methods for testing the validity (specification) of a model based on its fit to underlying data. Unlike statisticians, managers are typically more inte...
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The fields of statistics and econometrics have developed powerful methods for testing the validity (specification) of a model based on its fit to underlying data. Unlike statisticians, managers are typically more interested in the performance of a decision rather than the statistical validity of the underlying model. We propose a framework and a statistical test that incorporate decision performance into a measure of statistical validity. Under general conditions on the objective function, asymptotic behavior of our test admits a sharp and simple characterization. We develop our approach in a revenue management setting and apply the test to a data set used to optimize prices for consumer loans. We show that traditional model-based goodness-of-fit tests may consistently reject simple parametric models of consumer response (e. g., the ubiquitous logit model), while at the same time these models may "pass" the proposed performance-based test. Such situations arise when decisions derived from a postulated (and possibly incorrect) model generate results that cannot be distinguished statistically from the best achievable performance-i.e., when demand relationships are fully known.
U processes are the most common form of continuous semimartingales, and include diffusion processes. This paper is concerned with the nonparametric regression relationship between two such U processes. We are interest...
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U processes are the most common form of continuous semimartingales, and include diffusion processes. This paper is concerned with the nonparametric regression relationship between two such U processes. We are interested in the quadratic variation (integrated volatility) of the residual in this regression, over a unit of time (such as a day). A main conceptual finding is that this quadratic variation can be estimated almost as if the residual process were observed, the difference being that there is also a bias which is of the same asymptotic order as the mixed normal error term. The proposed methodology, "ANOVA for diffusions and Ito processes," can be used to measure the statistical quality of a parametric model and, nonparametrically, the appropriateness of a one-regressor model in general. On the other hand, it also helps quantify and characterize the trading (hedging) error in the case of financial applications.
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