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Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/26
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
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...
Applying the job demands–resources model to migrant workers: Exploring how and when geographical distance increases quit propensity
Applying the job demands–resources model migrant workers Exploring how
2016/1/26
We extend the job demands–resources model to explain how and when rural migrants whoworkfarfrom theirfamilies andprovincial hometownsaremore likely toleave jobs. Through two studies, we found that the...
Identification of universally optimal circular designs for the interference model
Approximate design theory circular design interference model linear equations system universal optimality
2016/1/20
Many applications of block designs exhibit neighbor and edge ef-fects. A popular remedy is to use the circular design coupled with the interference model. The search for optimal or efficient designs h...
Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/20
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
CreditRisk Model with Dependent Risk Factors
CreditRisk + model conditional independence dependent risk factors
2016/1/20
The CreditRisk + model is widely used in industry for computing the loss of a credit port-folio. The standard CreditRisk + model assumes independence among a set of common risk factors, a simplified a...
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...
Robust efficient frontier analysis with a separable uncertainty model
Robust efficient frontier analysis separable uncertainty model
2015/7/10
Mean-variance (MV) analysis is often sensitive to model mis-specification or uncertainty, meaning that the MV efficient portfolios constructed with an estimate of the model parameters (i.e., the expec...
The Future Has Thicker Tails than the Past: Model Error As Branching Counterfactuals
Fukushima Counterfactual histories Risk management Epistemology of probability Model errors Fragility and Antifragility Fourth Quadrant
2012/11/23
Ex ante forecast outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. Reapplying measurements of uncertainty about the estimation errors...
Model selection by LASSO methods in a change-point model
change-points selection criterion asymptotic behavior
2011/7/19
The paper considers a linear regression model with multiple change-points occurring at unknown times.
Monte Carlo algorithms for model assessment via conflicting summaries
Metropolis-Hastings Sequential Monte Carlo model choice
2011/7/6
The development of statistical methods and numerical algorithms for model choice is vital to many real-world applications. In practice, the ABC approach can be instrumental for sequential model design...
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization, Sparsity
2011/7/6
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a...
Adaptive estimation in the nonparametric random coefficients binary choice model by needlet thresholding
Discrete choice models random coefficients inverse problems
2011/7/6
In this article we consider the estimation of the joint distribution of the random coefficients and error term in the nonparametric random coefficients binary choice model. In this model from economic...
A Functional Version of the ARCH Model
ARCH financial data functional time series high-frequency data weak-dependence
2011/6/16
Improvements in data acquisition and processing techniques have lead to an almost continuous
flow of information for financial data. High resolution tick data are available and can be quite convenien...
Parameter estimation in a spatial unit root autoregressive model
Spatial autoregressive processes unit root models
2011/3/23
Spatial autoregressive model $X_{k,\ell}=\alpha X_{k-1,\ell}+\beta X_{k,\ell-1}+\gamma X_{k-1,\ell-1}+\epsilon_{k,\ell}$ is investigated in the unit root case, that is when the parameters are on the b...