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Central Limit Theorems for Supercritical Branching Markov Processes
Central limit theorem branching Markov process supercritical
2016/1/20
In this paper we establish spatial central limit theorems for a large class of supercritical branching Markov processes with general spatial-dependent branching mechanisms. These are generalizations o...
Estimation of fixed effects panel regression models with separable and nonseparable space-time filters
Spatial autoregression Panel data Spatial cointegration Explosive roots Fixed e¤ects
2016/1/20
This paper considers a quasi-maximum likelihood estimation for a linear panel data model with time and individual …xed e¤ects, where the disturbances have dynamic and spatial correlations which might ...
Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
2016/1/20
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
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 ...
On Implied Volatility for Options – Some Reasons to Smile and More to Correct
Bias correction Implied volatility Kernel estimator Pricing errors
2016/1/20
We analyze the properties of the implied volatility, the commonly used volatility estimator by direct option price inversion. It is found that the implied volatility is subject to a systematic bias in...
Bessel Processes, Stochastic Volatility, and Timer Options
Bessel Processes Stochastic Volatility Timer Options
2016/1/20
Motivated by analytical valuation of timer options (an important innovation in realized variance based derivatives), we explore their novel mathematical connection with stochastic volatility and Besse...
Bounds for the Sum of Dependent Risks and Worst Value-at-Risk with Monotone Marginal Densities
Complete mixability Monotone density Sum of dependent risks Value-at- Risk
2016/1/20
In quantitative risk management, it is important and challenging to find sharp bounds for the distribution of the sum of dependent risks with given marginal distributions, but an unspecified dependenc...
Optimal reinsurance minimizing the distortion risk measure under general reinsurance premium principles Matrices
Optimal reinsurance Distortion risk measure Wang’s premium principle VaR TVaR
2016/1/20
Recently the optimal reinsurance strategy concerning the insurer’s risk attitude and the reinsurance premium principle is an interesting topic. This paper discusses the optimal reinsurance problem wit...
Strong law of large number of a class of super-diffusions
Spatial autoregression Dynamic panels Fixed e¤ects Quasi-maximum likelihood estima
2016/1/19
Strong law of large number of a class of super-diffusions.
Estimation for spatial dynamic panel data with fixed effects: the case of spatial cointegration
Dynamic panels Fixed e¤ects Quasi-maximum likelihood estima- tion Bias correction Generalized method of moments Spatial cointegration
2016/1/19
Yu, de Jong and Lee (2008) establish asymptotic properties of quasi-maximum likelihood estimators for a stable spatial dynamic panel model with …xed e¤ects when both the number of individuals n and th...
On the Approximate Maximum Likelihood Estimation for Diffusion Processes
Asymptotic expansion Asymptotic normality Consistency Dis- crete time observation Maximum likelihood estimation
2016/1/19
The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. A...
Spatial Panels: Random Components vs. Fixed Effects
Random components Fixed e¤ects Maximum likelihood estimation Pooling
2016/1/19
This paper investigates spatial panel data models with a space-time …lter in disturbances. We consider their estimation by both …xed e¤ects and random e¤ects speci…cations. With a between equation pro...
On BIC's Selection Consistency for Discriminant Analysis
BIC Discriminant Analysis Selection Consistency
2016/1/19
Linear and quadratic discriminant analysis are two very useful classification methods, for which the problem of variable selection is of fundamental impor-tance. To this end, a BIC-type selection crit...
Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions
Conditional characteristic function Diffusion processes Empirical likelihood Kernel smoothing L′ evy driven processes
2016/1/19
Markov processes are used in a wide range of disciplines including finance.The transition densities of these processes are often unknown. However, the conditionalcharacteristic functions are more like...
Effcient GMM estimation of spatial dynamic panel data models with fixed effects
Spatial autoregression Dynamic panels Fixed e¤ects Generalized method of moment Many moments
2016/1/19
In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with …xed e¤ects when n is large, and T can be large, but small relative to n. The GMM esti...