This paper introduces quasi-maximum likelihood estimator for multivariate diffusions based on discrete observations. A numerical solution to the stochastic differential equation is obtained by higher order Wagner-Platen approximation and it is used to derive the first two conditional moments. Monte Carlo simulation shows that the proposed method has good finite sample property for both normal a...

We examine the long-run relationship between tourism development and economic growth using Nonlinear Autoregressive Distributed Lag (NARDL) model for Bangladesh annual data from 1980-2016. find an asymmetric Bangladesh's economy since a one percent increase in receipt increases by about 0.19 percent. On contrary, due to decline receipt, will decrease 0.66 So, have higher negative impact on than...

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...

In this paper we consider the dynamics of spot and futures prices in the presence of arbitrage. We propose a partially linear error correction model where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. We estimate our model using data on the DAX index and the DAX futures contract. We find that the adjustment is indeed nonlinear. The linear alternati...

The paper develops a new and easy-to-use F test in a time series GMM framework that allows for general forms of serial dependence. The test is based on the Wald statistic with a multiplicative correction factor and employs critical values from a standard F distribution. The F critical values are high-order correct under the conventional asymptotics. Monte Carlo simulations show that the F test ...

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on or with methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for ...

This paper introduces various consistent tests for the null hypothesis of stationarity with possibly unknown multiple structural break points against the alternative of nonstationarity that can be applied to multiple as well as univariate time series. These tests can be applied to either partial or pure structural breaks. It is shown that tests for stationarity become divergent when structural ...

Often we are interested in the largest root of an autoregressive process. Available methods rely on inverting t-tests to obtain confidence intervals. However, for large autoregressive roots, t-tests do not approximate asymptotically uniformly most powerful tests and do not have optimality properties when inverted for confidence intervals. We exploit the relationship between the power of tests a...

Using a time series framework, the paper studies the interactions of the annual real per capita GDP data of the G7 countries. We find evidence of six common nonstationary processes behind the international output dynamics. In addition, there is evidence for the existence of a common business cycle among these countries. The trend and cycle components of each output series are obtained with a pr...