The role of economic policy uncertainty in predicting US recessions : a mixed-frequency markov-switching vector autoregressive approach

This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, the authors apply a mixed-frequency Markov-switching vector autoregressive (MF-MS-VAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. Their results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. The results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.http://www.economics-ejournal.orgam2016Economic

Forecasting the price of gold

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases

Forecasting home sales in the four census regions and the aggregate US economy using singular spectrum analysis

Accurate forecasts of home sales can provide valuable information for not only, policy makers, but also financial institutions and real estate professionals. Given this, our analysis compares the ability of two different versions of Singular Spectrum Analysis (SSA) meth- ods, namely Recurrent SSA (RSSA) and Vector SSA (VSSA), in univariate and multivariate frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy and its four census regions (Northeast, Midwest, South and West). We compare the perfor- mance of the SSA-based models with classical and Bayesian variants of the autoregressive and vector autoregressive models. Using an out-of-sample period of 1979:8-2014:6, given an in-sample period of 1973:1-1979:7, we find that the univariate VSSA is the best performing model for the aggregate US home sales, while the multivariate versions of the RSSA is the outright favorite in forecasting home sales for all the four census regions. Our results high- light the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.http://link.springer.com/journal/106142017-12-31hb2016Economic

Modeling and forecasting the volatility of carbon dioxide emission allowance prices : a review and comparison of modern volatility models

The launch of the markets for carbon dioxide emission allowances was guided by the aim to use the supposedly efficient price formation mechanism of an organized exchange to optimally allocate a certain quantity of emissions among potential polluters. While this introduction of a centralized trading arrangement should have helped to achieve required emission reductions with a minimum of economic losses, from the viewpoint of market participants it has raised concerns about appropriate risk management provisions to cope with the fluctuations of time-varying allowance prices. The present review provides an overview over state-of-the-art models for price volatility expanding the scope from relatively simple GARCH-type models to models with longterm dependence and regime switches including the relatively recent class of so-called multifractal models. We provide a comparative application of these models to carbon dioxide emission allowance prices from the European Union Emission Trading Scheme and evaluate their performance with up-to-date model comparison tests based on out-of-sample forecasts of future volatility and value-at-risk.http://www.elsevier.com/locate/rser2018-03-31hb2017Economic

The Role of Economic Policy Uncertainty in Predicting U.S. Recessions: A Mixed-frequency Markov-switching Vector Autoregressive Approach

This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, the authors apply a mixed-frequency Markov-switching vector autoregressive (MF-MS-VAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. Their results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. The results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.http://www.economics-ejournal.orgam2016Economic