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on Econometric Time Series |
| By: | Christian Kleiber (University of Basel) |
| Abstract: | This paper studies a Stieltjes-type moment problem defined by the generalized lognormal distribution, a heavy-tailed distribution with applications in economics, finance and related fields. It arises as the distribution of the exponential of a random variable following a generalized error distribution, and hence figures prominently in the EGARCH model of asset price volatility. Compared to the classical lognormal distribution it has an additional shape parameter. It emerges that moment (in)determinacy depends on the value of this parameter: for some values, the distribution does not have finite moments of all orders, hence the moment problem is not of interest in these cases. For other values, the distribution has moments of all orders, yet it is moment-indeterminate. Finally, a limiting case is supported on a bounded interval, and hence determined by its moments. For those generalized lognormal distributions that are moment-indeterminate Stieltjes classes of moment-equivalent distributions are presented. |
| Keywords: | Generalized error distribution, generalized lognormal distribution, lognormal distribution, moment problem, size distribution, Stieltjes class, volatility model |
| JEL: | C46 C02 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:bsl:wpaper:2012/15&r=ets |
| By: | Neil R. Ericsson; Erica L. Reisman |
| Abstract: | Global vector autoregressions (GVARs) have several attractive features: multiple potential channels for the international transmission of macroeconomic and financial shocks, a standardized economically appealing choice of variables for each country or region examined, systematic treatment of long-run properties through cointegration analysis, and flexible dynamic specification through vector error correction modeling. Pesaran, Schuermann, and Smith (2009) generate and evaluate forecasts from a paradigm GVAR with 26 countries, based on Dées, di Mauro, Pesaran, and Smith (2007). The current paper empirically assesses the GVAR in Dées, di Mauro, Pesaran, and Smith (2007) with impulse indicator saturation (IIS)—a new generic procedure for evaluating parameter constancy, which is a central element in model-based forecasting. The empirical results indicate substantial room for an improved, more robust specification of that GVAR. Some tests are suggestive of how to achieve such improvements. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:1056&r=ets |
| By: | Xu Cheng (Department of Economics, University of Pennsylvania); Bruce E. Hansen (Department of Economics, University of Wisconsin-Madison) |
| Abstract: | This paper considers forecast combination with factor-augmented regression. In this framework, a large number of forecasting models are available, varying by the choice of factors and the number of lags. We investigate forecast combination using weights that minimize the Mallows and the leave-h-out cross validation criteria. The unobserved factor regressors are estimated by principle components of a large panel with N predictors over T periods. With these generated regressors, we show that the Mallows and leave-h-out cross validation criteria are approximately unbiased estimators of the one-step-ahead and multi-step-ahead mean squared forecast errors, respectively, provided that N, T —› ∞. In contrast to well-known results in the literature, the generated-regressor issue can be ignored for forecast combination, without restrictions on the relation between N and T. Simulations show that the Mallows model averaging and leave-h-out cross-validation averaging methods yield lower mean squared forecast errors than alternative model selection and averaging methods such as AIC, BIC, cross validation, and Bayesian model averaging. We apply the proposed methods to the U.S. macroeconomic data set in Stock and Watson (2012) and find that they compare favorably to many popular shrinkage-type forecasting methods. |
| Keywords: | Cross-validation, factor models, forecast combination, generated regressors, Mallows |
| JEL: | C52 C53 |
| Date: | 2012–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:12-046&r=ets |
| By: | Dungey, Mardi; Milunovich, George; Thorp, Susan; Yang, Minxian (School of Economics and Finance, University of Tasmania) |
| Abstract: | Detecting contagion during financial crises requires demarcation of crisis periods. This paper presents a method for endogeneous dating of both the start and finish of crises, coupled with the statistical detection of contagion effects. We couple smooth transition functions with structural GARCH to identify both features of markets in crisis, and provide conditions under which these eects will be identified. To illustrate we apply the framework to US financial returns in REITS, S&P500 and Treasury bonds indices over the period 2001 to 2010, and clearly identify four phases consistent with a pre-crisis period to October 2007, two phases of crisis up to and following late August 2008, and a post-crisis phase dating from June 2009. The evidence strongly supports changes in the transmission mechanisms of shocks between asset returns during the crisis, and particularly contagion from equity markets to REITS. The post-crisis period has not returned to pre-crisis relationships. |
| Keywords: | Contagion, Structural GARCH, Global Financial Crisis |
| JEL: | G01 C51 |
| Date: | 2012–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:15030&r=ets |