The paper considers estimation and inference of time series GMM models where a subset of parameters are time varying. The magnitude of the time variation in the unstable parameters is such that efficient tests detect the instability with (possibly high) probability smaller than one, even in the limit. We show that for many forms of parameter instability and for a large class of GMM models, stan...

In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead of the mere word-level models, to discover causes of microblogging users’ diversified emotions on specific hot event. A modified topic-supervised biterm topic model is utilized in CECM to detect ‘emotion topics’ in event-related tweets, and then context-sensitive topical PageRank is utilized to detect meaningful multi...

When impulse responses in dynamic multivariate models such as identified VARs are given economic interpretations, it is important that reliable statistical inferences be provided. Before probability assessments are provided, however, the model must be normalized. Contrary to the conventional wisdom, this paper argues that normalization, a rule of reversing signs of coefficients in equations in ...

Regulatory reform in the Nordic electricity-supply markets has resulted in a single integrated Nordic electricity market. This paper performs an econometric study of market power in the spot market of Nord Pool, the joint Nordic power exchange. I use a dynamic extension of the Bresnahan-Lau model, and weekly data for the period from 1996 through April 1999. To my knowledge, this is the first st...

We develop a test for the linear no cointegration null hypothesis in a threshold vector error correction model. We adopt a sup-Wald type test and derive its null asymptotic distribution. A residual-based bootstrap is proposed, and the first-order consistency of the bootstrap is established. A set of Monte Carlo simulations shows that the bootstrap corrects size distortion of asymptotic distribu...

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/ √ T . This local embedding makes the problem asymptotically a normal-normal Bayes problem, resulting in closed-form solutions for th...

In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application docume...

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method provides a computationally more effic...

This paper evaluates the interacted effects of the fiscal and monetary policies on the nominal and real macro-variables of the Iranian economy. Our analysis is thus based on the optimal control theory by which the optimal path of the control variables including monetary and fiscal tools are determined over the period 1963-2006. We also use a macro-econometric model in form of a simultaneous equ...

This paper discusses inference about the pre and post break value of a scalar parameter in GMM time series models with a single break at an unknown point in time. We show that treating the break date estimated by least squares as the true break date leads to substantially oversized tests and confidence intervals unless the break is large. We develop an alternative test that controls size unifor...