J.F. Kaashoek (Johan)
http://repub.eur.nl/ppl/9/
List of Publicationsenhttp://repub.eur.nl/logo.jpg
http://repub.eur.nl/
RePub, Erasmus University RepositoryOn the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks
http://repub.eur.nl/pub/11158/
Sun, 01 Jul 2007 00:00:01 GMT<div>L.F. Hoogerheide</div><div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
Likelihoods and posteriors of instrumental variable (IV) regression models with strong endogeneity and/or weak instruments may exhibit rather non-elliptical contours in the parameter space. This may seriously affect inference based on Bayesian credible sets. When approximating posterior probabilities and marginal densities using Monte Carlo integration methods like importance sampling or Markov chain Monte Carlo procedures the speed of the algorithm and the quality of the results greatly depend on the choice of the importance or candidate density. Such a density has to be ‘close’ to the target density in order to yield accurate results with numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate densities, as they have a universal approximation property and are easy to sample from. A key step in the proposed class of methods is the construction of a neural network that approximates the target density. The methods are tested on a set of illustrative IV regression models. The results indicate the possible usefulness of the neural network approach.'Rotterdam econometrics': an analysis of publications of the econometric institute 1956-2004
http://repub.eur.nl/pub/11136/
Mon, 01 May 2006 00:00:01 GMT<div>H.K. van Dijk</div><div>J.F. Kaashoek</div><div>A.P.M. Wagelmans</div>
The high ranking of the Econometric Institute, as listed in recent leading scientific journals, is examined for a 50-year period using similar standard measures. The distribution of the publications over different research areas is analyzed and a time-series model is specified to describe and forecast the publication pattern."Rotterdam econometrics": publications of the econometric institute 1956-2005
http://repub.eur.nl/pub/7452/
Mon, 20 Feb 2006 00:00:01 GMT<div>H.K. van Dijk</div><div>J.F. Kaashoek</div><div>A.P.M. Wagelmans</div>
This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005."Rotterdam Econometrics": an analysis of publications of the econometric institute 1956-2004
http://repub.eur.nl/pub/7244/
Mon, 02 Jan 2006 00:00:01 GMT<div>H.K. van Dijk</div><div>J.F. Kaashoek</div><div>A.P.M. Wagelmans</div>
The high ranking of the Econometric Institute,
as listed in recent leading scientific journals, is examined
for a fifty year period using similar standard measures. The distribution of the publications
over different research areas is analyzed and a time-series model is specified to describe
and forecast the publication pattern.On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks
http://repub.eur.nl/pub/2007/
Thu, 31 Mar 2005 00:00:01 GMT<div>L.F. Hoogerheide</div><div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
Likelihoods and posteriors of instrumental variable regression models with strong
endogeneity and/or weak instruments may exhibit rather non-elliptical contours in
the parameter space. This may seriously affect inference based on Bayesian credible
sets. When approximating such contours using Monte Carlo integration methods like
importance sampling or Markov chain Monte Carlo procedures the speed of the algorithm
and the quality of the results greatly depend on the choice of the importance or
candidate density. Such a density has to be `close' to the target density in order to
yield accurate results with numerically efficient sampling. For this purpose we
introduce neural networks which seem to be natural importance or candidate densities,
as they have a universal approximation property and are easy to sample from.
A key step in the proposed class of methods is the construction of a neural network
that approximates the target density accurately. The methods are tested on a set of
illustrative models. The results indicate the feasibility of the neural network
approach.Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models
http://repub.eur.nl/pub/1281/
Wed, 19 May 2004 00:00:01 GMT<div>L.F. Hoogerheide</div><div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
Likelihoods and posteriors of econometric models with strong endogeneity and weak
instruments may exhibit rather non-elliptical contours in the parameter space.
This feature also holds for cointegration models when near non-stationarity occurs
and determining the number of cointegrating relations is a nontrivial issue, and
in mixture processes where the modes are relatively far apart. The performance of
Monte Carlo integration methods like importance sampling or Markov Chain
Monte Carlo procedures greatly depends in all these cases on the choice of the
importance or candidate density. Such a density has to be `close' to the target
density in order to yield numerically accurate results with efficient sampling.
Neural networks seem to be natural importance or candidate densities, as they have
a universal approximation property and are easy to sample from. That is, conditionally
upon the specification of the neural network, sampling can be done either directly or
using a Gibbs sampling technique, possibly using auxiliary variables. A key step in
the proposed class of methods is the construction of a neural network that approximates
the target density accurately. The methods are tested on a set of illustrative models
which include a mixture of normal distributions, a Bayesian instrumental variable
regression problem with weak instruments and near non-identification, a cointegration
model with near non-stationarity and a two-regime growth model for US recessions
and expansions. These examples involve experiments with non-standard, non-elliptical
posterior distributions. The results indicate the feasibility of the
neural network approach.Neural network approximations to posterior densities: an analytical approach
http://repub.eur.nl/pub/1047/
Thu, 07 Aug 2003 00:00:01 GMT<div>L.F. Hoogerheide</div><div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
In Hoogerheide, Kaashoek and Van Dijk (2002) the class of neural network
sampling methods is introduced to sample from a target (posterior)
distribution that may be multi-modal or skew, or exhibit strong correlation
among the parameters. In these methods the neural network is used as an
importance function in IS or as a candidate density in MH. In this note we
suggest an analytical approach to estimate the moments of a certain (target)
distribution, where `analytical' refers to the fact that no sampling
algorithm like MH or IS is needed.We show an example in which our analytical
approach is feasible, even in a case where a `standard' Gibbs approach would
fail or be extremely slow.Functional approximations to posterior densities: a neural network approach to efficient sampling
http://repub.eur.nl/pub/1727/
Tue, 31 Dec 2002 00:00:01 GMT<div>L.F. Hoogerheide</div><div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
The performance of Monte Carlo integration methods like importance sampling or Markov Chain Monte Carlo procedures greatly depends on the choice of the importance or candidate density. Usually, such a density has to be "close" to the target density in order to yield numerically accurate results with efficient sampling. Neural networks seem to be natural importance or candidate densities, as they have a universal approximation property and are easy to sample from. That is, conditional upon the specification of the neural network, sampling can be done either directly or using a Gibbs sampling technique, possibly using auxiliary variables. A key step in the proposed class of methods is the construction of a neural network that approximates the target density accurately. The methods are tested on a set of illustrative models which include a mixture of normal distributions, a Bayesian instrumental variable regression problem with weak instruments and near-identification, and two-regime growth model for US recessions and expansions. These examples involve experiments with non-standard, non-elliptical posterior distributions. The results indicate the feasibility of the neural network approach.Neural network analysis of varying trends in real exchange rates
http://repub.eur.nl/pub/11339/
Sun, 01 Dec 2002 00:00:01 GMT<div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
Neural networks are fitted to real exchange rates of several industrialized countries. The size and topology of the networks is found through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell. These pruned neural networks are good approximations to varying non-linear trends in real exchange rates. Non-linear dynamic analysis shows that the long-term equilibrium values of several European currencies correspond to the actual values within the European Monetary System. Based on its long-term equilibrium value, the Euro appears to be undervalued vis-à-vis the US dollar at the introduction of the Euro on 1 January 1999.Neural networks as econometric tool
http://repub.eur.nl/pub/1670/
Mon, 19 Feb 2001 00:00:01 GMT<div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
The flexibility of neural networks to handle complex data patterns of economic variables is well known. In this survey we present a brief introduction to a neural network and focus on two aspects of its flexibility . First, a neural network is used to recover the dynamic properties of a nonlinear system, in particular, its stability by making use of the Lyapunov exponent. Second, a two-stage network is introduced where the usual nonlinear model is combined with time transitions, which may be handled by neural networks. The connection with time-varying smooth transition models is indicated. The procedures are
illustrated using three examples: a structurally unstable chaotic model, nonlinear trends in real exchange rates and a time-varying Phillips curve using US data from 1960-1997.Neural networks as econometric tool
http://repub.eur.nl/pub/1661/
Wed, 25 Oct 2000 00:00:01 GMT<div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
The flexibility of neural networks to handle complex data patterns of economic variables is well known. In this survey we present a brief introduction to a neural network and focus on two aspects of its flexibility . First, a neural network is used to recover the dynamic properties of a nonlinear system, in particular, its stability by making use of the Lyapunov exponent.
Second, a two-stage network is introduced where the usual nonlinear model is combined with time transitions, which may be handled by neural networks. The connection with time-varying smooth transition models is indicated. The procedures are
illustrated using three examples: a structurally unstable chaotic model, nonlinear trends in real exchange rates and a time-varying Phillips curve using US data from 1960-1997.A bilinear programming solution to the quadratic assignment problem
http://repub.eur.nl/pub/1629/
Wed, 22 Dec 1999 00:00:01 GMT<div>J.F. Kaashoek</div><div>J.H.P. Paelinck</div>
The quadratic assignment problem (QAP) or maximum acyclical graph problem is well documented (see e.g. Pardalos and Wolkowicz, 1994). One of the authors has published some material, in which it was tried, by structuring the problem additionally, to bring it as closely as possible in the neighbourhood of a binary solution (see
Paelinck, 1983, pp. 251-256 and 273-277); good but not optimal solutions could so be obtained (see Paelinck, 1985, pp. 247-254). The problem is taken up again here, in the same spirit but at the
same time in a different vein.Neural network analysis of varying trends in real exchange rates
http://repub.eur.nl/pub/1569/
Wed, 31 Mar 1999 00:00:01 GMT<div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
In this paper neural networks are fitted to the real exchange rates of seven industrialized countries. The size and topology of the used networks is found by reducing the size of the network through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell.A simple strategy to prune neural networks with an application to economic time series
http://repub.eur.nl/pub/1523/
Thu, 31 Dec 1998 00:00:01 GMT<div>J.F. Kaashoek</div><div>H.K. van Dijk</div>
A major problem in applying neural networks is specifying the size of the network. Even for moderately sized networks the number of
parameters may become large compared to the number of data. In this paper network performance is examined while reducing the size of the
network through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network
output per hidden layer cell and input layer cell.