|
on Forecasting |
| By: | Yeboah Asuamah, Samuel |
| Abstract: | The aim of the paper is to contribute to the body of knowledge in the area of forecasting using Autoregressive Integrated Moving Average (ARIMA) modelling for liquefied petroleum gas (LPG) for Ghana using monthly data for the period 2000-2011. The ARIMA (1, 1, 1) model was identified as suitable model. The findings show that the forecasted values insignificantly underestimate the actual consumption and thus indicate consistency of the results. The values of the evaluation statistics such as the ME; MSE; RMSE; MAE, and Theil’s statistic, on the accuracy of the model indicate that the estimated model is suitable for forecasting LPG. The findings support the continuous use of the ARIMA model in forecasting, in econometric time series forecast. Future study should consider modelling other energy sources that are used in Ghana and other developing economies such as kerosene. |
| Keywords: | Liquefied petroleum gas, autoregressive integrated moving average, Forecasting, Diagnostic statistics |
| JEL: | C51 C52 C53 E17 Q47 |
| Date: | 2015–07–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:67834&r=for |
| By: | Gary Koop; Dimitris Korobilis |
| Abstract: | In this paper, we develop econometric methods for estimating large Bayesian timevarying parameter panel vector autoregressions (TVP-PVARs) and use these methods to forecast inflation for euro area countries. Large TVP-PVARs contain huge numbers of parameters which can lead to over-parameterization and computational concerns. To overcome these concerns, we use hierarchical priors which reduce the dimension of the parameter vector and allow for dynamic model averaging or selection over TVP-PVARs of different dimension and different priors. We use forgetting factor methods which greatly reduce the computational burden. Our empirical application shows substantial forecast improvements over plausible alternatives. |
| Keywords: | Panel VAR, inflation forecasting, Bayesian, time-varying parameter model |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:gla:glaewp:2015_25&r=for |
| By: | Kapetanious, George (Bank of England); Price, Simon (Bank of England); Theodoridis, Konstantinos (Bank of England) |
| Abstract: | DSGE models are of interest because they offer structural interpretations, but are also increasingly used for forecasting. Estimation often proceeds by methods which involve building the likelihood by one-step ahead (h=1) prediction errors. However in principle this can be done using different horizons where h>1. Using the well-known model of Smets and Wouters (2007), for h=1 classical ML parameter estimates are similar to those originally reported. As h extends some estimated parameters change, but not to an economically significant degree. Forecast performance is often improved, in several cases significantly. |
| Keywords: | DSGE models; multi-step prediction errors; forecasting. |
| Date: | 2015–11–20 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0567&r=for |
| By: | Chatziantoniou, Ioannis; Degiannakis, Stavros; Eeckels, Bruno; Filis, George |
| Abstract: | This study utilizes both disaggregated data and macroeconomic indicators in order to examine the importance of the macroeconomic environment of origin countries for analysing destinations’ tourist arrivals. In particular, it is the first study to present strong empirical evidence that both of these features in tandem provide statistically significant information of tourist arrivals in Greece. The forecasting exercises presented in our analysis show that macroeconomic indicators conducive to better forecasts are mainly origin country-specific, thus highlighting the importance of considering the apparent sharp national contrasts among origin countries when investigating domestic tourist arrivals. Given the extent of the dependency of the Greek economy on tourism income, but also, given the perishable nature of the tourist product itself, results have important implications for policy makers in Greece. |
| Keywords: | Tourist arrivals forecasting, seasonal ARIMA, Diebold-Mariano test, disaggregated data, macroeconomic indicators. |
| JEL: | C22 C53 F19 O10 |
| Date: | 2015–11–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:68062&r=for |
| By: | Tamás Ilyés (Magyar Nemzeti Bank (Central Bank of Hungary)); Lóránt Varga (Magyar Nemzeti Bank (Central Bank of Hungary)) |
| Abstract: | In our paper, we introduce the Hungarian Payment System Model (HUPS), a computable general equilibrium model with detailed payment services which can be used for policy evaluation and forecast. In the last years, several studies investigated different aspects of payment systems and some papers used equilibrium theory to study a specific segment or question of retail payments. In our paper, we take a step forward as we extend this research using the general equilibrium approach. The HUPS model is a large and highly disaggregated computable general equilibrium model with 25 economic agents and nearly 100 payment services, which cover most of the payment system supply chain in Hungary. It contains 7 types of costs for each payment service, varying degree of economies of scale, oligopoly and cross-product pricing and agent behaviour adjustments to payment method costs. In our model, the payment sector is an integrated part of the economy as every actor has to make payment decisions related to its activities. As a result, the model can be used for thorough economic evaluation of many kinds of policies and other changes in the field of retail payments. The HUPS model is calibrated on the large and up-to-date information base of Hungarian payment statistics, surveys and studies – most notably the Hungarian cost of payments study – which makes it a powerful and robust modelling and forecasting tool. |
| Keywords: | payment economics, CGE modelling, retail payments, cost of payments. |
| JEL: | C68 E27 E42 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:mnb:wpaper:2015/3&r=for |
| By: | Askitas, Nikos (IZA) |
| Abstract: | Traffic jams are an important problem both on an individual and on a societal level and much research has been done on trying to explain their emergence. The mainstream approach to road traffic monitoring is based on crowdsourcing roaming GPS devices such as cars or cell phones. These systems are expectedly able to deliver good results in reflecting the immediate present. To my knowledge there is as yet no system which offers advance notice on road conditions. Google Search intensity for the German word stau (i.e. traffic jam) peaks 2 hours ahead of the number of traffic jam reports as reported by the ADAC, a well known German automobile club and the largest of its kind in Europe. This is true both in the morning (7 am to 9 am) and in the evening (5 pm to 7 pm). I propose such searches as a way of forecasting road conditions. The main result of this paper is that after controlling for time of day and day of week effects we can still explain a significant portion of the variation of the number of traffic jam reports with Google Trends and we can thus explain well over 80% of the variation of road conditions using Google search activity. A one percent increase in Google stau searches implies a .4 percent increase of traffic jams. Our paper is a proof of concept that aggregate, timely delivered behavioural data can help fine tune modern societies. |
| Keywords: | stau, traffic jams, highways, road conditions, Google Trends, prediction, forecasting, complexity, endogeneity, behaviour, big data, data science, computational social science, complex systems |
| JEL: | R41 |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp9503&r=for |
| By: | Luiz Hotta; Carlos Trucíos; Esther Ruiz |
| Abstract: | Bootstrap procedures are useful in GARCH models to obtain forecast densities for returns and volatilities.In this paper, we analyze the effect of outliers on the finite sample properties of these densities when they are based on standard maximum likelihood and robust procedures. We show that when the former procedure is implemented, the bootstrap densities are badly affected by the presence of outliers. However,the robust estimator based on variance targeting with an adequate modification of the volatility filter has the best performance when compared with alternative robust procedures. The results are illustrated withboth simulated and real data. |
| Keywords: | BM estimator , Outliers , Smooth bootstrap , Variance targeting , Winsorized bootstrap |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws1523&r=for |