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BAYESIAN MODEL SELECTION AND FORECASTING IN NONCAUSAL AUTOREGRESSIVE MODELS (...
In this paper, we propose a Bayesian estimation and forecasting procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the... -
A RANK-ORDERED LOGIT MODEL WITH UNOBSERVED HETEROGENEITY IN RANKING CAPABILIT...
To study preferences, respondents to a survey are usually asked to select their most preferred option from a set. Preferences can be estimated more efficiently if respondents... -
FOSTERING EDUCATIONAL ENROLMENT THROUGH SUBSIDIES: THE ISSUE OF TIMING (repli...
The purpose of this paper is to build a dynamic structural model of educational choices in which cognitive skills shape decisions. The model is estimated by maximum likelihood... -
‘DUAL’ GRAVITY: USING SPATIAL ECONOMETRICS TO CONTROL FOR MULTILATERAL RESIST...
We derive a quantity-based structural gravity equation system in which both trade flows and error terms are cross-sectionally correlated. This system can be estimated using... -
WEIGHTED SMOOTH TRANSITION REGRESSIONS (replication data)
A new procedure is proposed for modelling nonlinearity of a smooth transition form, by allowing the transition variable to be a weighted function of lagged observations. This... -
A comparison of treatment effects estimators using a structural model of AMI ...
We compare the performance of various matching estimators using a novel approach that is feasible in the absence of experimental data. We estimate a structural model of hospital... -
Non-parametric bounds on quantiles under monotonicity assumptions: with an ap...
Within the inferential context of predicting a distribution of potential outcomes P[y(t)] under a uniform treatment assignment t ∈ T, this paper deals with partial... -
Simulation-based tests of forward-looking models under VAR learning dynamics ...
In this paper we propose a simulation-based technique to investigate the finite sample performance of likelihood ratio (LR) tests for the nonlinear restrictions that arise when... -
Forecasting large datasets with Bayesian reduced rank multivariate models (re...
The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and... -
Conditional Markov chain and its application in economic time series analysis...
Motivated by the great moderation in major US macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long-run... -
On nonparametric estimation of a hedonic price function (replication data)
Recently, using mixed data on Canadian housing, Parmeter, Henderson, and Kumbhakar (Journal of Applied Econometrics 2007; 22: 695-699) found that a nonparametric approach for...
