We derive new results on the asymptotic behavior of the estimated parameters of a linear asset pricing model and their associated t-statistics in the presence of a factor that is independent of the returns. The inclusion of this useless factor in the model leads to a violation of the full rank...

In this paper, we extend the results in Hansen (1982) regarding the asymptotic distribution of generalized method of moments (GMM) sample moment conditions. In particular, we show that the part of the scaled sample moment conditions that gives rise to degeneracy in the asymptotic normal...

Similar to Ingram and Whiteman (1994), De Jong et al. (1993) and Del Negro and Schorfheide (2004) this study proposes a methodology of constructing Dynamic Stochastic General Equilibrium (DSGE) consistent prior distributions for Bayesian Vector Autoregressive (BVAR) models. The moments of the...

Conditional heteroskedasticity can be exploited to identify the structural vector autoregressions (SVAR) but the implications for inference on structural impulse responses have not been investigated in detail yet. We consider the conditionally heteroskedastic SVAR-GARCH model and propose a...

In this paper, we propose a model based on multivariate decomposition of multiplicative - absolute values and signs - components of several returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional...

This paper seeks to investigate and remedy the apparent inability of Markov regime switching models to predict future states in the medium to long term. We show that projected time varying transition probability series in the model may be biased towards predicting regime switches with high...

This paper researches small-sample properties of the Markov-switching model with time-varying transition probabilities. By means of simulation, it is shown that the likelihood ratio statistic is over-sized for sample sizes relevant in many empirical applications. The number of regime switches...

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling...

Inference on the long-run properties of a Vector Autoregression (VAR) consisting wholly of I(1) variables are made using Bayesian methods. In particular, the implications on the forecast and impulse response function distributions of directly estimating and restricting the drift parameters of...

I introduce a technique to estimate parameters in regressions with reduced rank parameters in a general setting. The framework can handle a general class of parameter restrictions and allows for specifications with heteroskedastic and autocorrelated regression errors. Applications of this...