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on Forecasting |
| By: | Athanasopouolos, George (Monash University); Poskitt, Don (Monash University); Vahid, Farshid (Monash University); Yao, Wenying (School of Business and Economics, University of Tasmania) |
| Abstract: | This article studies error correction vector autoregressive moving average (ECVARMA) models. A complete procedure for identifying and estimating EC-VARMA models is proposed. The cointegrating rank is estimated in the first stage using an extension of the non-parametric method of Poskitt (2000). Then, the structure of the VARMA model for variables in levels is identified using the scalar component model (SCM) methodology developed in Athanasopoulos and Vahid (2008), which leads to a uniquely identifiable VARMA model. In the last stage, the VARMA model is estimated in its error correction form. Monte Carlo simulation is conducted using a 3-dimensional VARMA(1,1) DGP with cointegrating rank 1, in order to evaluate the forecasting performances of the EC-VARMA models. This algorithm is illustrated further using an empirical example of the term structure of U.S. interest rates. The results reveal that the out-of-sample forecasts of the EC-VARMA model are superior to those produced by error correction vector autoregressions (VARs) of finite order, especially in short horizons. |
| Keywords: | cointegration, VARMA model, iterative OLS, scalar component modelNote: |
| JEL: | C1 C32 C53 |
| Date: | 2014–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:17835&r=for |
| By: | Rangan Gupta; Patrick T. Kanda; Mampho P. Modise; Alessia Paccagnini |
| Abstract: | Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation. |
| Keywords: | DSGE model; Inflation; Core variables; Noncore variables |
| JEL: | C11 C32 C53 E27 E47 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ucn:oapubs:10197/7351&r=for |
| By: | Domit, Sílvia (Bank of England); Monti, Francesca (Bank of England); Sokol, Andrej (Bank of England) |
| Abstract: | We estimate a Bayesian VAR analogue to the Bank of England’s DSGE model (COMPASS) and assess their relative performance in forecasting GDP growth and CPI inflation in real time between 2000 and 2012. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, the performance of these models was similar when forecasting CPI. We also find that, despite underpredicting inflation at most forecast horizons, the BVAR density forecasts outperformed those of COMPASS. Both models overpredicted GDP growth at all forecast horizons, but the BVAR outperformed COMPASS at forecast horizons up to one year ahead. The BVAR’s point and density forecast performance is also comparable to that of a Bank of England in-house statistical suite for both GDP and CPI inflation and to the Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies. |
| Keywords: | Forecasting; Bayesian VARs; macro-modelling |
| JEL: | C53 E12 E17 |
| Date: | 2016–01–25 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0583&r=for |
| By: | Francesco Ravazzolo; Philip Rothman |
| Abstract: | We carry out a pseudo out-of-sample density forecasting study for U.S. GDP with an autoregressive benchmark and alternatives to the benchmark than include both oil prices and stochastic volatility. The alternatives to the benchmark produce superior density forecasts. This comparative density performance appears to be driven more by stochastic volatility than by oil prices. We use our density forecasts to compute a recession risk indicator around the Great Recession. The alternative model that includes the real price of oil generates the earliest strong signal of a recession; but it also shows increased recession risk after the Great Recession. |
| Date: | 2015–10 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0038&r=for |
| By: | Jaydip Sen; Tamal Datta Chaudhuri |
| Abstract: | With the rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, the research community has started spending considerable effort in technical analysis of such data. Forecasting is also an area which has witnessed a paradigm shift in its approach. In this work, we have used the time series of the index values of the Auto sector in India during January 2010 to December 2015 for a deeper understanding of the behavior of its three constituent components, e.g., the Trend, the Seasonal component, and the Random component. Based on this structural analysis, we have also designed three approaches for forecasting and also computed their accuracy in prediction using suitably chosen training and test data sets. The results clearly demonstrate the accuracy of our decomposition results and efficiency of our forecasting techniques, even in presence of a dominant Random component in the time series. |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.02407&r=for |
| By: | Ravazzolo, Francesco (Norges Bank and BI Norwegian Business School); Vespignani, Joaquin (Tasmanian School of Business & Economics, University of Tasmania) |
| Abstract: | In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian’s index of global real economic activity and the index of OECD World industrial production. We develop an econometric approach based on desirable econometric properties in relation to the quarterly measure of World or global gross domestic product to evaluate and to choose across different alternatives. The method is designed to evaluate short-term, long-term and predictability properties of the indicators. World steel production is proven to be the best monthly indicator of global economic activity in terms of our econometric properties. Kilian’s index of global real economic activity also accurately predicts World GDP growth rates. When extending the analysis to an out-ofsample exercise, both Kilian’s index of global real economic activity and the World steel production produce accurate forecasts for World GDP, confirming evidence provided by the econometric properties. Specifically, a forecast combination of the three indices produces statistically significant gains up to 40% at nowcast and more than 10% at longer horizons relative to an autoregressive benchmark. |
| Keywords: | Global real economic activity, World steel production, Forecasting |
| JEL: | E1 E3 C1 C5 C8 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:22664&r=for |
| By: | Artur Aiguzhinov (Faculty of Economics & cef.up, University of Porto; Institute for Systems and Computer Engineering, Technology and Science); Ana Paula Serra (Faculty of Economics & cef.up, University of Porto); Carlos Soares (Faculty of Engineering, University of Porto; Institute for Systems and Computer Engineering, Technology and Science) |
| Abstract: | Several institutions issue rankings of financial analysts based on the accuracy of their price and EPS forecasts. Given that these rankings are expost they may not be useful to investors. In this paper we show that trading strategies based on perfect foresight and on past rankings outperform a passive strategy. In addition, we report that investors are better off following analysts that issue accurate price targets rather than following those with accurate EPS forecasts. |
| Keywords: | Keywords: financial analysts; rankings; target price forecasts; earnings forecasts; portfolio management |
| JEL: | G11 G14 G24 G29 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:por:cetedp:1604&r=for |