This paper employs Hamilton’s (1989) original Markov-switching model and time-varying Markov-switching model developed by Filardo (1994), respectively, to investigate the business cycle and evaluate the usefulness of the coincident and leading indexes in dating the business cycle and in predicting future GDP in Taiwan. The empirical results do suggest that these two indexes help date the busine...

Currently available asymptotic results in the literature suggest that matching estimators have higher variance than reweighting estimators. The extant literature comparing the finite sample properties of matching to specific reweighting estimators, however, has concluded that reweighting performs far worse than even the simplest matching estimator. We resolve these puzzling conclusions. Specifi...

There has been serious suspicion of a spurious rejection of the unit roots in panel studies of PPP due to the failure to control for cross-sectional dependence. This article presents evidence of mean-reversion in industrial country real exchange rates in a set up that accounts naturally for cross-sectional dependence, is invariant to the benchmark currency and capable of detecting against regim...

In this paper, we use the adaptive lasso estimator to choose the relevant instruments and eliminate the irrelevant instruments. The limit theory of Zou (2006) is extended from univariate iid case to heteroskedastic and non Gaussian data. Then we use the selected instruments in generalized empirical likelihood estimators (GEL). In this sense, these are called hybrid GEL. It is also shown that th...

In Markov-switching regression models, we use Kullback–Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. Specifically, we derive a new information criterion, Markov switching criterion (MSC), which is an estimate of KL divergence. MSC imposes an appropriate penalty to mitigate the overretention of states in the Markov chai...

In this paper Efficient Importance Sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure for the Monte Carlo (MC) evaluation of high-dimensional interdependent integrals. It can be used to carry out ML-estimation of SV models as we...

Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model. For the AR(p) model, there exists a correction function to fix the incidental parameter problem when t...

This paper explores the ability of some popular income distributions to model observed skewness and kurtosis. We present the generalized beta type 1 (GB1) and type 2 (GB2) distributions’ skewnesskurtosis spaces and clarify and expand on previously known results on other distributions’ skewnesskurtosis spaces. Data from the Luxembourg Income Study are used to estimate sample moments and explore ...

This paper considers the problem of speci cation testing in general parametric models and shows that for a wide class of models the hypothesis of correct speci cation is equivalent to a continuum of moment equalities. Using these moment equalities we construct a class of speci cation tests that have correct asymptotic size in general parametric models, including stationary time series models, a...

A Note on Adapting Propensity Score Matching and Selection Models to Choice Based Samples The probability of selection into treatment plays an important role in matching and selection models. However, this probability can often not be consistently estimated, because of choicebased sampling designs with unknown sampling weights. This note establishes that the selection and matching procedures ca...