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Baltagi, Badi H. - 2013

This chapter reviews the panel data forecasting literature. Starting with simple forecasts based on fixed and random … well as spatial autoregressive and moving average type disturbances. These forecasting methods are then studied in the … context of seemingly unrelated regressions. We highlight several forecasting empirical applications using panel data, as well …

Persistent link: https://www.econbiz.de/10014025230

Revisiting the great ratios hypothesis

Chudik, Alexander; Pesaran, M. Hashem; Smith, Ron - 2022

The idea that certain economic variables are roughly constant in the long-run is an old one. Kaldor described them as stylized facts, whereas Klein and Kosobud labelled them great ratios. While such ratios are widely adopted in theoretical models in economics as conditions for balanced growth,...

Persistent link: https://www.econbiz.de/10013041372

Large panel data models with cross-sectional dependence : a survey

Chudik, Alexander; Pesaran, M. Hashem - 2013

This paper provides an overview of the recent literature on estimation and inference in large panel data models with cross-sectional dependence. It reviews panel data models with strictly exogenous regressors as well as dynamic models with weakly exogenous regressors. The paper begins with a...

Persistent link: https://www.econbiz.de/10009786037

Continuous-time modelling with spatial dependence

Oud, Johannes H. L.; Folmer, Henk; Patuelli, Roberto; … - 2011

Persistent link: https://www.econbiz.de/10009720763

SUR estimation of error components models with AR(1) disturbances and unobserbed endogenous effects

Egger, Peter - 2001

This paper focusses on the estimation of error components models in the presence of a correlation of the disturbances across equations and AR(1) of the remainder disturbances for panel data with endogenous unobserved effects. Additionally, the set-up allows for unequally spaced panel data and...

Persistent link: https://www.econbiz.de/10011492665

Observation-driven hierarchical density models for missing data imputation

Khanna, Yonas; Lucas, André - 2025

We propose an observation-driven dynamic common factor model for missing value imputation in high-dimensional panel data. The model exploits both serial and cross-sectional information in the data and can easily cope with time-variation in conditional means and variances, as well as with either...

Persistent link: https://www.econbiz.de/10015373862

Prediction in a spatial nested error components panel data model

Baltagi, Badi H.; Pirotte, Alain - In: International journal of forecasting 30 (2014) 3, pp. 407-414

Persistent link: https://www.econbiz.de/10010511584

Bayesian SAR model with stochastic volatility and multiple time-varying weights

Costola, Michele; Iacopini, Matteo; Wichers, Casper - 2023

A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in...

Persistent link: https://www.econbiz.de/10014416011

Exports and labour demand : searching for functional structure in multi-output multi-skill technologies

Koebel, Bertrand M. (contributor) - 2005

In order to simplify the representation of a technological relationship between inputs and outputs, a production unit's technology must typically satisfy some restrictive conditions, some of them being well known in the literature. This paper presents new results for aggregating labour inputs...

Persistent link: https://www.econbiz.de/10003011513

Deflation and economic growth in long-term perspective

Ryska, Pavel - 2014

This paper deals with the relationship between deflation and economic growth. Although there are numerous theories on the potential effects of deflation on real output, empirical evidence in this field is still scarce and partial. In order to explore the relationship between prices and output in...

Persistent link: https://www.econbiz.de/10010358347