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on Econometric Time Series |
| By: | W. Robert Reed (University of Canterbury); Min Zhu |
| Abstract: | A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment. This note points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results. Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. |
| Keywords: | Hurwicz bias, Auto-Regressive Distributed-Lag (ARDL) models, Dynamic Panel Data (DPD) models, DPD estimators, long-run impact, long-run propensity, Fieller’s method, indirect inference, jackknifing |
| JEL: | C22 C23 |
| Date: | 2016–07–28 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:16/16&r=ets |
| By: | Peter Reinhard Hansen (University of North Carolina at Chapel Hill, United States); Pawel Janus (UBS Global Asset Management, Zürich, Switzerland); Siem Jan Koopman (VU University Amsterdam, the Netherlands) |
| Abstract: | We propose a novel multivariate GARCH model that incorporates realized measures for the variance matrix of returns. The key novelty is the joint formulation of a multivariate dynamic model for outer-products of returns, realized variances and realized covariances. The updating of the variance matrix relies on the score function of the joint likelihood function based on Gaussian and Wishart densities. The dynamic model is parsimonious while each innovation still impacts all elements of the variance matrix. Monte Carlo evidence for parameter estimation based on different small sample sizes is provided. We illustrate the model with an empirical application to a portfolio of 15 U.S. financial assets. |
| Keywords: | high-frequency data; multivariate GARCH; multivariate volatility; realised covariance; score; Wishart density |
| JEL: | C32 C52 C58 |
| Date: | 2016–08–11 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20160061&r=ets |
| By: | Shinichiro Shirota (Department of Statistical Science, Duke University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo); Hedibert. F. Lopes (Insper Institute of Education and Research); Haixiang Piao (Nippon Life Insurance Company) |
| Abstract: | Multivariate stochastic volatility models with leverage are expected to play important roles in financial applications such as asset allocation and risk management. However, these models suffer from two major difficulties: (1) there are too many parameters to estimate by using only daily asset returns and (2) estimated covariance matrices are not guaranteed to be positive definite. Our approach takes advantage of realized covariances to achieve the efficient estimation of parameters by incorporating additional information for the co-volatilities, and considers Cholesky decomposition to guarantee the positive definiteness of the covariance matrices. In this framework, a exible model is proposed for stylized facts of financial markets, such as dynamic correlations and leverage effects among volatilities. By using the Bayesian approach, Markov Chain Monte Carlo implementation is described with a simple but efficient sampling scheme. Our model is applied to the data of nine U.S. stock returns, and it is compared with other models on the basis of portfolio performances. |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2016cf1019&r=ets |
| By: | Antonia Arsova (Leuphana University Lueneburg, Germany); Deniz Dilan Karaman Örsal (Leuphana University Lueneburg, Germany) |
| Abstract: | This paper takes a multiple testing perspective on the problem of determining the cointegrating rank in macroeconometric panel data with cross-sectional dependence. The testing procedure for a common rank among the panel units is based on Simes’ (1986) intersection test and requires only the p-values of suitable individual test statistics. A Monte Carlo study demonstrates that this simple test is robust to crosssectional dependence and has reasonable size and power properties. A multivariate version of Kendall’s tau is used to test an important assumption underlying Simes’ procedure for dependent statistics. The method is illustrated by testing the validity of the monetary exchange rate model for 8 OECD countries in the post-Bretton Woods era. |
| Keywords: | panel cointegration rank test, cross-sectional dependence, multiple testing, common factors, likelihood-ratio |
| JEL: | C12 C15 C33 |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:lue:wpaper:357&r=ets |