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Working paper
Econometric estimation in long–range dependent volatility models: theory and practice
In: Journal of Econometrics, Band 147, Heft 1, S. 72-83
It is commonly accepted that some financial data may exhibit long-range dependence, while other financial data exhibit intermediate-range dependence or short-range dependence. These behaviours may be fitted to a continuous-time fractional stochastic model. The estimation procedure proposed in this paper is based on a continuous-time version of the Gauss–Whittle objective function to find the parameter estimates that minimize the discrepancy between the spectral density and the data periodogram. As a special case, the proposed estimation procedure is applied to a class of fractional stochastic volatility models to estimate the drift, standard deviation and memory parameters of the volatility process under consideration. As an application, the volatility of the Dow Jones, S&P 500, CAC 40, DAX 30, FTSE 100 and NIKKEI 225 is estimated.
Nonparametric Kernel Estimation of the Impact of Tax Policy on the Demand for Private Health Insurance in Australia
In: IZA Discussion Paper No. 9265
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A Localized Neural Network with Dependent Data: Estimation and Inference
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Semiparametric Single-Index Estimation for Average Treatment Effects
In: Monash Econometrics and Business Statistics Working Paper Series 10/22
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Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach
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On Income and Price Elasticities for Energy Demand: A Panel Data Study
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An Integrated Panel Data Approach to Modelling Economic Growth
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Estimation and Testing for High-dimensional Near Unit Root Time Series
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