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on Econometric Time Series |
| By: | Franses, Ph.H.B.F.; Wiemann, T. |
| Abstract: | This paper adapts the non-parametric Dynamic Time Warping (DTW) technique in an application to examine the temporal alignment and similarity across economic time series. DTW has important advantages over existing measures in economics as it alleviates concerns regarding a pre-defined fixed temporal alignment of series. For example, in contrast to current methods, DTW can capture alternations between leading and lagging relationships of series. We illustrate DTW in a study of US states’ business cycles around the Great Recession, and find considerable evidence that temporal alignments across states dynamic. Trough cluster analysis, we further document state-varying recoveries from the recession. |
| Keywords: | Business cycles, Non-parametric method, Dynamic Time Warping |
| JEL: | C14 C50 C87 E32 |
| Date: | 2018–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:109916&r=ets |
| By: | Chevillon, Guillaume (ESSEC Research Center, ESSEC Business School) |
| Abstract: | This paper studies the properties of multi-step projections, and forecasts that are obtained using either iterated or direct methods. The models considered are local asymptotic: they allow for a near unit root and a local to zero drift. We treat short, intermediate and long term forecasting by considering the horizon in relation to the observable sample size. We show the implication of our results for models of predictive regressions used in the financial literature. We show here that direct projection methods at intermediate and long horizons are robust to the potential misspecification of the serial correlation of the regression errors. We therefore recommend, for better global power in predictive regressions, a combination of test statistics with and without autocorrelation correction. |
| Keywords: | Multi-step Forecasting; Predictive Regressions; Local Asymptotics; Dynamic Misspecification; Finite Samples; Long Horizons |
| JEL: | C22 C52 C53 |
| Date: | 2017–07–23 |
| URL: | http://d.repec.org/n?u=RePEc:ebg:essewp:dr-17010&r=ets |
| By: | Haroon Mumtaz (Queen Mary University of London) |
| Abstract: | This paper extends the procedure developed by Jurado et al. (2015) to allow the estimation of measures of uncertainty that can be attributed to specific structural shocks. This enables researchers to investigate the 'origin' of a change in overall macroeconomic uncertainty. To demonstrate the proposed method we consider two applications. First, we estimate UK macroeconomic uncertainty due to external shocks and show that this component has become increasingly important over time for overall uncertainty. Second, we estimate US macroeconomic uncertainty conditioned on monetary policy shocks with the results suggesting that while policy uncertainty was important during early 1980s, recent contributions are estimated to be modest. |
| Keywords: | FAVAR, Stochastic volatility, Proxy VAR, Uncertainty measurement |
| JEL: | C2 C11 E3 |
| Date: | 2018–08–15 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:864&r=ets |
| By: | Richard Pincak; Kabin Kanjamapornkul |
| Abstract: | We solved a stylized fact on a long memory process of volatility cluster phenomena by using Minkowski metric for GARCH(1,1) under assumption that price and time can not be separated. We provide a Yang-Mills equation in financial market and anomaly on superspace of time series data as a consequence of the proof from the general relativity theory. We used an original idea in Minkowski spacetime embedded in Kolmogorov space in time series data with behavior of traders.The result of this work is equivalent to the dark volatility or the hidden risk fear field induced by the interaction of the behavior of the trader in the financial market panic when the market crashed. |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1808.04231&r=ets |
| By: | Dolatabadi, Sepideh; Kumar Narayan, Paresh; Orregaard Nielsen, Morten; Xu, Ke |
| Abstract: | Based on recent evidence of fractional cointegration in commodity spot and futures mar- kets, we investigate whether a fractionally cointegrated model can provide statistically and/or economically signicant forecasts of commodity returns. Specically, we propose to model and forecast commodity spot and futures prices using a fractionally cointegrated vector autoregres- sive (FCVAR) model that generalizes the more well-known (non-fractional) CVAR model to allow fractional integration. We derive the best linear predictor for the FCVAR model and perform an out-of-sample forecast comparison with the non-fractional model. In our empirical analysis to daily data on 17 commodity markets, the fractional model is found to be superior in terms of in-sample t and also out-of-sample forecasting based on statistical metrics of forecast comparison. We analyze the economic signicance of the forecasts through a dynamic trading strategy based on a portfolio with weights derived from a mean-variance utility function. Al- though there is much heterogeneity across commodity markets, this analysis leads to statistically signicant and economically meaningful prots in most markets, and shows that prots from both the fractional and non-fractional models are higher on average and statistically more signif- icant than prots derived from a simple moving-average strategy. The analysis also shows that, in spite of the statistical advantage of the fractional model, the fractional and non-fractional models generate very similar prots with only a slight advantage to the fractional model on average. |
| Keywords: | Financial Economics, Marketing |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:ags:quedwp:274663&r=ets |
| By: | Katarina Juselius (Department of Economics, University of Copenhagen) |
| Abstract: | This survey paper discusses the Cointegrated VAR methodology and how it has evolved over the last 30 years. The ?first section is a description of major steps in the econometric development of the CVAR model that facilitated serious real world applications. The next three sections are primarily methodological and discuss (i) difficulties and puzzles when confronting theory with the data, (ii) the formulation of a viable link between theory and the data, a so called theory-consistent CVAR scenario, and (iii) how all this was inspired by Trygve Haavelmo and his Nobel prize winning monograph "The Probability Approach to Economics". The next two sections discuss early applications of the Cointegrated VAR model to monetary transmission mechanisms, international transmission mechanisms and wage, price and unemployment dynamics. They report puzzling evidence, discuss the need for new theory, and propose a method for combining partial CVAR analyses into a larger macroeconomic model. The following sections propose a new, empirically-based, approach to macroeconomics in which imperfect knowledge based expectations replace so called rational expectations and in which the fi?nancial sector plays a key role for understanding the long persistent movements in the data. The last section argues that the CVAR can act as a "design of experiment for passive observations" and illustrates with several applications including unemployment dynamics under crises periods and aid effectiveness in South Saharan African countries. |
| Keywords: | Cointegrated VAR methodology, Linking theory and evidence, Empirically based macroeconomics |
| JEL: | B41 C32 C51 C52 |
| Date: | 2018–08–14 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuiedp:1807&r=ets |