This paper estimates the distribution-led regime of the US economy for the period 1947-2019. We use a time varying parameter model, which allows for changes in the regime over time. To the best of our knowledge this is the first paper that has attempted to do this. This innovation is important,...

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of...

In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M0 and M1, introduced by Piccolo in 1990. In particular, we show...

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of...

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general nonlinear, possibly non-smooth and over-identified moment restrictions, where the sampling information consists of a primary sample and an auxiliary sample. The variables of interest in the...

This paper describes a modelling methodology for multivariate stochastic processes. The concept of multiple causality is discussed and a procedure to detect multiple causality is suggested. The data of a major Canadian supermarket is analyzed and a multivariate autoregressive model for this...

We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence...

Causal inference methods are widely used in empirical research; however, there is a paucity of evidence on the properties of shared latent factor estimators in the presence of contaminated instrumental variable (IV) when a strong IV may not be available. We present a theoretical formulation to...

In the present study, we propose a simple test approach based on the work of Breitung and Candelon (2006), which allows us to test for asymmetric predictability at a pre-specified frequency. The test approach can also be used to test for causality in cointegrated systems, as illustrated by...

David Hendry and Hans-Martin Krolzig have demonstrated that PCGets, an automatic model selection algorithm that implements general-to-specific search procedures, can be successfully applied to the individual equations of vector autoregressions (VARs), provided that the contemporaneous causal...