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on Forecasting |
| By: | Balcilar, Mehmet; Gupta, Rangan; Segnon, Mawuli |
| Abstract: | 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-MSVAR) 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. The 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. |
| Keywords: | business cycles,economic policy uncertainty,mixed frequency,Markov-switching VAR models |
| JEL: | E32 E37 C32 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwedp:201614&r=for |
| By: | Amélie Charles (Audencia Recherche - Audencia); Olivier Darné (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes); Jae H. Kim (La Trobe University [Melbourne]) |
| Abstract: | This paper evaluates the predictability of monthly stock return using out-of-sample (multi-step ahead and dynamic) prediction intervals. Past studies have exclusively used point forecasts, which are of limited value since they carry no information about the intrinsic predictive uncertainty associated. We compare empirical performances of alternative prediction intervals for stock return generated from a naive model, univariate autoregressive model, and multivariate model (predictive regression and VAR), using the U.S. data from 1926. For evaluation free from data snooping bias, we adopt moving sub-sample windows of different lengths. It is found that the naive model often provides the most informative prediction intervals, outperforming those generated from the univariate model and multivariate models incorporating a range of economic and financial predictors. This strongly suggests that the U.S. stock market has been informationally efficient in the weak-form as well as in the semi-strong form, subject to the information set considered in this study. |
| Keywords: | Autoregressive Model, Bootstrapping, Financial Ratios, Forecasting, Interval Score, Market Efficiency |
| Date: | 2016–03–30 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01295037&r=for |
| By: | Jennifer Castle; David Hendry; Michael P. Clements |
| Abstract: | Abstract: Economic forecasting may go badly awry when there are structural breaks, such that the relationships between variables that held in the past are a poor basis for making predictions about the future. We review a body of research that seeks to provide viable strategies for economic forecasting when past relationships can no longer be relied upon. |
| Keywords: | Business Cycles, Forecasting, Breaks |
| JEL: | C51 C22 |
| Date: | 2016–02–02 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:779&r=for |
| By: | Martyna Marczak (University of Hohenheim); Tommaso Proietti (CEIS and DEF,University of Rome "Tor Vergata"); Stefano Grassi (University of Kent) |
| Abstract: | This article presents a robust augmented Kalman filter that extends the data– cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one–step–ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M–type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series. |
| Keywords: | robust filtering, augmented Kalman filter, structural time series model, additive outlier, innovation outlier |
| JEL: | C32 C53 C63 |
| Date: | 2016–03–31 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:374&r=for |
| By: | Sid Ghoshal; Stephen Roberts |
| Abstract: | Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measure the performance gain, quantified in terms of Normalised Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson correlation, from fusing each of four separate data domains: time series technicals, sentiment analysis, options market data and broker recommendations. We show evidence that ARD kernels produce meaningful feature rankings that help retain salient inputs and reduce input dimensionality, providing a framework for sifting through financial complexity. We measure the performance gain from fusing each domain's heterogeneous data streams into a single probabilistic model. In particular our findings highlight the critical value of options data in mapping out the curvature of price space and inspire an intuitive, novel direction for research in financial prediction. |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1603.06202&r=for |
| By: | Hännikäinen, Jari |
| Abstract: | This paper analyzes the predictive content of the level, slope and curvature of the yield curve for U.S. real activity in a data-rich environment. We find that the slope contains predictive power, but the level and curvature are not successful leading indicators. The predictive power of each of the yield curve factors fluctuates over time. The results show that economic conditions matter for the predictive ability of the slope. In particular, inflation persistence emerges as a key variable that affects the predictive content of the slope. The slope tends to forecast output growth better when inflation is highly persistent. |
| Keywords: | yield curve; factor model; data-rich environment; forecasting; macroeconomic regimes; conditional predictive ability |
| JEL: | C53 E43 E44 E47 E52 |
| Date: | 2016–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:70489&r=for |
| By: | Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi |
| Abstract: | Abstract. The paper compares the pseudo real-time forecasting performance of threeDynamic Factor Models: (i) The standard principal-component model, Stock and Watson(2002a), (ii) The model based on generalized principal components, Forni et al. (2005),(iii) The model recently proposed in Forni et al. (2015) and Forni et al. (2016). We employa large monthly dataset of macroeconomic and financial time series for the US economy,which includes the Great Moderation, the Great Recession and the subsequent recovery.Using a rolling window for estimation and prediction, we find that (iii) neatly outperforms(i) and (ii) in the Great Moderation period for both Industrial Production and Inflation,and for Inflation over the full sample. However, (iii) is outperfomed by (i) and (ii) over thefull sample for Industrial Production. |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/228908&r=for |
| By: | Davide Pettenuzzo (Brandeis University); Gary Koop (NUniversity of Strathclyde); Dimitris Korobilis (University of Glasgow) |
| Abstract: | Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR methods to forecast better than either factor methods or large VAR methods involving prior shrinkage. |
| Keywords: | multivariate time series, random projection, forecasting |
| JEL: | C11 C32 C53 |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:brd:wpaper:103&r=for |
| By: | Yuri Danilov (Department of Economics, Lomonosov Moscow State University) |
| Abstract: | The article discusses the main elements of the "new" economic geography, adequately describes the current patterns of spatial development. In the context of reducing the share of transportation costs in the total cost of production is enhanced agglomeration effect, resulting in a further concentration of production and population. The article presents the basic tools and measures used by the "new" economic geography. The article formulates proposals for the application of developments and achievements of the "new" economic geography in the strategic planning of spatial development of the Russian Federation. Forecasting and strategic planning indicators are proposed, which most adequately reflect the processes of spatial development taking place in the modern economy |
| Keywords: | spatial development, indicators of spatial development, new economic geography, distances interpretation, agglomeration effect, strategic planning |
| JEL: | R58 N90 B20 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:upa:wpaper:0028&r=for |
| By: | Alina Mika; Robert Zymek |
| Abstract: | We re-visit the evidence about the trade benefits of European Monetary Union (EMU), focusing on the experience of countries which adopted the common currency since 2002. Based on “state of the art†gravity estimations for the period 1992-2013, we reach three main conclusions. First, estimates from an appropriately specified and estimated gravity equation provide no evidence of a euro effect on trade flows among early euro adopters up to the year 2002. Second, this finding is robust to extending the sample period to incorporate data up to 2013, covering five additional euro accessions. Third, while there is no robust evidence of a euro effect, there is evidence that intra-EU trade flows have expanded faster than the global average during the 2002-2013 period. Using the functional form of a theory-consistent gravity equation, we perform pseudo out-of-sample forecasts of trade flows for recent euro joiners. In line with our estimation results, we show that pseudo forecasts of the change in trade flows after euro accession, assuming no euro effect, outperform forecasts based on the expectation of a significantly positive effect. This suggests that euro accession countries should not expect a significant boost to their trade from joining EMU. |
| Keywords: | euro, trade, gravity, poisson |
| JEL: | F14 F15 F17 F33 |
| Date: | 2016–02–02 |
| URL: | http://d.repec.org/n?u=RePEc:edn:esedps:269&r=for |