Showing 1 - 10 of 127
We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure...
Persistent link: https://www.econbiz.de/10013314694
This paper considers a first-order autoregressive panel data model with individual-specific effects and a heterogeneous … autoregressive coefficient. It proposes estimators for the moments of the cross-sectional distribution of the autoregressive coefficients …, with a focus on the first two moments, assuming a random coefficient model for the autoregressive coefficients without imposing …
Persistent link: https://www.econbiz.de/10014347822
Despite the fact that many aggregates are nonlinear functions and the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the case of linear aggregates with fixed, time-invariant aggregation weights. In this study a...
Persistent link: https://www.econbiz.de/10010270456
This paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to...
Persistent link: https://www.econbiz.de/10010264361
This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial … autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the … case of spatial autoregressive models with spatial autoregressive disturbances of arbitrary (finite) order (SARAR(R,S)). We …
Persistent link: https://www.econbiz.de/10010264403
Kelejian and Prucha (1998, 1999) for the spatial autoregressive parameter in the disturbance process. We prove the consistency …
Persistent link: https://www.econbiz.de/10010264476
In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step...
Persistent link: https://www.econbiz.de/10010264508
This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial … autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional … assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance …
Persistent link: https://www.econbiz.de/10010264566
dynamic systems. Restrictions on the coefficients of an unrestricted VAR are proposed that are binding only in a limit as the … number of endogenous variables tends to infinity. It is shown that under such restrictions, an infinite-dimensional VAR (or … IVAR) can be arbitrarily well characterized by a large number of finite-dimensional models in the spirit of the global VAR …
Persistent link: https://www.econbiz.de/10010276215
. While we concede that typical VAR models put forward in the literature fail to identify oil price shocks that significantly … VAR based search on these periods, we are able to identify an oil price shock that affects the German production even on …
Persistent link: https://www.econbiz.de/10010274918