Разработан концептуальный подход к управлению интеграционными процессами в экономике с учетом нелинейной модели развития инноваций, подтверждающий, что...

This paper analyses the growth of American cities, understood as the growth of the population or of the per capita income, from 1990 to 2000. This empirical analysis uses data from all the cities (incorporated places) with more than 25,000 inhabitants in the year 2000 (1152 cities). The results...

Innovation is a multilevel phenomenon. Not only attributes of firms but also the framework conditions within which firms operate matter. Although this has been recognized in the literature for a long time, a quantitative test that explicitly considers this hypothesis has been lacking. Using a...

In this paper we discuss tests for residual cross section dependence in nonlinear panel data models. The tests are based on average pair-wise residual correlation coefficients. In nonlinear models, the definition of the residual is ambiguous and we consider two approaches: deviations of the...

Two Bayesian optimal design criteria for hierarchical linear models are discussed - the psi_beta criterion for the estimation of individual-level parameters beta, and the psi_theta criterion for the estimation of hyper-parameters theta. While the psi_beta criterion involves only the...

Time series analysis is a tremendous research area in statistics and econometrics. As remarked in a review by Howell Tong in 2001, for about 100 years up to 2001 Biometrika (alone) published over 400 papers on the subject. [Tong (2001)] Furthermore, in the review, Howell Tong is able break down...

We propose a new nonlinear filtering model for a better estimation of credit rating transition matrix consistent with the hypothesis that rating transition intensities as well as dynamics of financial asset prices depend on some unobservable macroeconomic factor. We attempt a branching particle...

The primary objective of this paper is to propose two nonlinear extensions for macroeconomic forecasting using large datasets. First, we propose an alternative technique for factor estimation, i.e., kernel principal component analysis, which allows the factors to have a nonlinear relationship to...

The debate on the forecasting ability in economics of non-linear models has a long history, and the Great Recession provides us with an opportunity for a re-assessment of the forecasting performance of several classes of non-linear models, widely used in applied macroeconomic research. In this...

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...