international econometric journal
in Russian language
International conference “Modern econometric tools and
applications”
Mikusheva, Anna. Estimation of dynamic stochastic general
equilibrium models
This
essay is a survey of the main econometric approaches to estimation of the
dynamic stochastic general equilibrium (DSGE) models widely used by central
banks and federal reserves. The paper discusses in
detail the main econometric problems arising in inferences about the parameters
of the log-linearized DSGE models. We examine three
main estimation approaches: the minimum distance method, the maximum likelihood
method and the Bayesian approach. We focus on the problems of weak
identification that are due to scarcity of macro data. The issues of economic modelling and methods of solving dynamic models are beyond
the scope of the current essay.
Jones, Callum; Kulish, Mariano. A practical introduction to DSGE modeling with Dynare
This
document is a practical introduction to Dynare. It
shows how to install Dynare and write a DSGE model in
Dynare notation, and goes through the output from
running the model, where output is stored in the Matlab
workspace, as well as common Dynare errors. We use Dynare to do some useful analysis. We briefly discuss
estimation and forecasting using Dynare. The document
closes with a research application.
Tsyplakov, Alexander. A mini-dictionary of English econometric
terminology III
This part of the dictionary comments on English econometric terms
estimator, quantile, nested, marginal, and some
others. Emphasis
is again placed on accurate definitions of their meaning to avoid possible
confusion and incorrect interpretation.
Groshev, Oleg. Time varying vine copulas for multivariate returns
We
analyze the multivariate distribution of financial returns using time varying
conditional vine copulas. We present the d-Stage Maximum Likelihood (dSML) estimator which is shown to be not only consistent
and asymptotically normal, but also more computationally attractive than the
standard ML or Patton's 2SML. Using dSML, we fit vine
copulas to returns of a portfolio on emerging market currencies.
Franguridi, Grigory. Higher order
conditional moment dynamics and forecasting value-at-risk
We
empirically investigate the possibilities for enhancing value-at-risk
predictions by explicit modelling conditional higher
order moment dynamics of financial returns. Using one-day-ahead VaR forecasts for 5 highly liquid constituents of the
S&P500 index from different industrial sectors, we compare performances of
the benchmark GARCH model with skewed generalized Student's innovations with a
set of models allowing for time-varying asymmetry and kurtosis such as
ARCD-type models with normal inverse gaussian and
skewed generalized Student's errors. As predictive accuracy tests we exploit
both the scoring rules for left tail forecasts and likelihood-ratio tests for
correct (un)conditional quantile forecasts. We also
propose a parsimonious ARCD model with the skewed generalized error
distribution for innovations, asymmetric power ARCH for volatility and autoregressive
dynamics for skewness and kurtosis related parameters which is shown to perform not worse than the
aforementioned models in terms of VaR prediction
accuracy, while being computationally less demanding.