Time series forecasting with the WARIMAX-GARCH method

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet “EVs” (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting

How strong is the linkage between tourism and economic growth in Europe?

In this study, we examine the dynamic relationship between tourism growth and economic growth, using a newly introduced spillover index approach. Based on monthly data for 10 European countries over the period 1995{2012, our analysis reveals the following empirical regularities. First, the tourism-economic growth relationship is not stable over time in terms of both magnitude and direction, indicating that the tourism{led economic growth (TLEG) and the economic{driven tourism growth (EDTG) hypotheses are time{dependent. Second, the aforementioned relationship is also highly economic event{dependent, as it is influenced by the Great Recession of 2007 and the ongoing Eurozone debt crisis that began in 2010. Finally, the impact of these economic events is more pronounced in Cyprus,Greece, Portugal and Spain, which are the European countries that have witnessed the greatest economic downturn since 2009. Plausible explanations of these results are provided and policy implications are drawn

Optimal military spending in the US: A time series analysis

This paper extends previous work on the optimal size of government spending by including nested functional decompositions of military spending into consumption and investment. Post World War II US data are then used to estimate nested non-linear growth models using semi-parametric methods. As expected, investments in military and non-military expenditure are both found to be productive expenditures with respect to the private production. Moreover there is little evidence to suggest that current military spending is having a negative impact on economic growth in the US, while civilian consumption only tends to have only a weak impact. This does not imply that society will necessarily benefit from a reallocation of more spending to the military sector, nor that it is the best way to achieve economic growth. © 2010 Elsevier B.V

Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR(∞) Models *

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101814/1/1468-2354.t01-1-00016.pd

Evaluation of bottom-up and top-down strategies for aggregated forecasts: state space models and arima applications

Abstract. In this research, we consider monthly series from the M4 competition to study the relative performance of top-down and bottom-up strategies by means of implementing forecast automation of state space and ARIMA models. For the bottomup strategy, the forecast for each series is developed individually and then these are combined to produce a cumulative forecast of the aggregated series. For the top-down strategy, the series or components values are first combined and then a single forecast is determined for the aggregated series. Based on our implementation, state space models showed a higher forecast performance when a top-down strategy is applied. ARIMA models had a higher forecast performance for the bottom-up strategy. For state space models the top-down strategy reduced the overall error significantly. ARIMA models showed to be more accurate when forecasts are first determined individually. As part of the development we also proposed an approach to improve the forecasting procedure of aggregation strategies

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p Peer reviewe

Do dividends signal future earnings in the Nordic stock markets?

We study the informational content of dividends on three Nordic civil law markets, where other simultaneous but blurring motives for dividends may be weaker. Using aggregate data on real earnings per share and payout ratios, long time series from 1969 to 2010, and methodologies which address problems of endogeneity, non-stationarity and autocorrelation (including a Vector Error Correction Model approach), we find evidence on dividend signaling in Nordic markets. However, we also find heterogeneity in the relationship between dividends and earnings on markets similar in many respects, suggesting that even small variations in the institutional surroundings may be important for the results