8,687 research outputs found
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting
This paper explores and develops alternative statistical representations and
estimation approaches for dynamic mortality models. The framework we adopt is
to reinterpret popular mortality models such as the Lee-Carter class of models
in a general state-space modelling methodology, which allows modelling,
estimation and forecasting of mortality under a unified framework. Furthermore,
we propose an alternative class of model identification constraints which is
more suited to statistical inference in filtering and parameter estimation
settings based on maximization of the marginalized likelihood or in Bayesian
inference. We then develop a novel class of Bayesian state-space models which
incorporate apriori beliefs about the mortality model characteristics as well
as for more flexible and appropriate assumptions relating to heteroscedasticity
that present in observed mortality data. We show that multiple period and
cohort effect can be cast under a state-space structure. To study long term
mortality dynamics, we introduce stochastic volatility to the period effect.
The estimation of the resulting stochastic volatility model of mortality is
performed using a recent class of Monte Carlo procedure specifically designed
for state and parameter estimation in Bayesian state-space models, known as the
class of particle Markov chain Monte Carlo methods. We illustrate the framework
we have developed using Danish male mortality data, and show that incorporating
heteroscedasticity and stochastic volatility markedly improves model fit
despite an increase of model complexity. Forecasting properties of the enhanced
models are examined with long term and short term calibration periods on the
reconstruction of life tables.Comment: 46 page
Dynamic Bayesian Predictive Synthesis in Time Series Forecasting
We discuss model and forecast combination in time series forecasting. A
foundational Bayesian perspective based on agent opinion analysis theory
defines a new framework for density forecast combination, and encompasses
several existing forecast pooling methods. We develop a novel class of dynamic
latent factor models for time series forecast synthesis; simulation-based
computation enables implementation. These models can dynamically adapt to
time-varying biases, miscalibration and inter-dependencies among multiple
models or forecasters. A macroeconomic forecasting study highlights the dynamic
relationships among synthesized forecast densities, as well as the potential
for improved forecast accuracy at multiple horizons
Moment estimation in Auerbach-Kotlikoff models: How well do they match the data?
Despite their widespread use for the analysis of economic questions, a formal and systematic calibration methodology has not yet been developed for Auerbach-Kotlikoff (Auerbach and Kotlikoff 1987) overlapping generations (AK-OLG) models. Calibration as estimation in macroeconomics involves choosing free parameters by matching moments of simulated models with those of the data. This paper maps this approach into the framework of AK-OLG models. The paper further evaluates the back-fitting properties of three different versions of a prototype AK-OLG model along a number of dimensions of mostly US data for the time period 1960-2003.
Estimating Nonlinear Dynamic Equilibrium economies: A Likelihood Approach
This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. We develop a Sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for parameter estimation and for model comparison. The algorithm can deal both with nonlinearities of the economy and with the presence of non-normal shocks. We show consistency of the estimate and its good performance in finite simulations. This new algorithm is important because the existing empirical literature that wanted to follow a likelihood approach was limited to the estimation of linear models with Gaussian innovations. We apply our procedure to estimate the structural parameters of the neoclassical growth model.Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Sequential Monte Carlo)
Gaussian process single-index models as emulators for computer experiments
A single-index model (SIM) provides for parsimonious multi-dimensional
nonlinear regression by combining parametric (linear) projection with
univariate nonparametric (non-linear) regression models. We show that a
particular Gaussian process (GP) formulation is simple to work with and ideal
as an emulator for some types of computer experiment as it can outperform the
canonical separable GP regression model commonly used in this setting. Our
contribution focuses on drastically simplifying, re-interpreting, and then
generalizing a recently proposed fully Bayesian GP-SIM combination, and then
illustrating its favorable performance on synthetic data and a real-data
computer experiment. Two R packages, both released on CRAN, have been augmented
to facilitate inference under our proposed model(s).Comment: 23 pages, 9 figures, 1 tabl
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