Data generating processt distributionBayesian analysisAs the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions. In this paper, we propose a novel way to incorporate uncertainty about the DGP into ...
As the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions. In this paper, we propose a novel way to incorporate uncertainty about the DGP into portfolio analysis. We find that accounting for fat...
For the statistical foundation of the proposed technique I will use DGP 3 from section 1 of Chapter 2. Furthermore I employ the usual DE A assumption of a convex production technology satisfying strong disposability. This technology will be primarily described by Farrell output-efficient boundaries ...
Data generation process 1 We first consider the following data generation process (DGP): D=α1+δ1X1+δ2X22+δ3X1X2+δ4|X3|+δ5X43+U (5) Y=α2+Dθ+γ1X1+γ2X22+γ3X1X2+γ4|X3|+γ5X43+V (6) where X=(X1,X2,X3,X4) are randomly generated from a multivariate normal...
This paper is organized as follows. In the main text we first consider synthetic time series generated with three stochastic data generating processes (DGPs) where the underlying causality is known: a linear system, a nonlinear system, and the chaotic Lorenz system (sectionModelspresents the details...
This paper explores the specification and testing of some modified count data models. These alternatives permit more flexible specification of the data-generating process (dgp) than do familiar count data models (e.g., the Poisson), and provide a natural means for modeling data that are over- ...
The data generating process (DGP) for generic dynamic panel data consists of a law of state dynamics g , a selection or attrition rule h , and an initial condition F . I study nonparametric identifiability of this complete DGP ( g , h , F ) using short unbalanced panel data, allowing ...
These alternatives permit more flexible specification of the data-generating process (dgp) than do familiar count data models (e.g., the Poisson), and provide a natural means for modeling data that are over- or underdispersed by the standards of the basic models. In the cases considered, ...
This is where being Yahoo or Google, firms whose ad platforms reach large numbers of users, have a clear advantage of scale. They can run massive experiments involving ten or hundreds of millions of users. But this leads to another, bigger problem: the Data Generating Process (DGP). ...
In the first step, we estimate a multivariate model based on a high-dimensional panel of time-series data from a pool of untreated peers without any stringent assumptions about the data generating process (DGP). Then, we compute the counterfactual by extrapolating the model with data after the...