|
on Forecasting |
| By: | Nikoleta Anesti (Bank of England); Ana Beatriz Galvao (University of Warwick); Silvia Miranda-Agrippino (Centre for Macroeconomics (CFM); Bank of England) |
| Abstract: | We design a new econometric framework to nowcast macroeconomic data subject to revisions, and use it to predict UK GDP growth in real-time. To this aim, we assemble a novel dataset of monthly and quarterly indicators featuring over ten years of real-time data vintages. Successive monthly estimates of GDP growth for the same quarter are treated as correlated observables in a Dynamic Factor Model (DFM) that also includes a large number of mixed-frequency predictors, leading to the release-augmented DFM (RA-DFM). The framework allows for a simple characterisation of the stochastic process for the revisions as a function of the observables, and permits a detailed assessment of the contribution of the data flow in informing (i) forecasts of quarterly GDP growth; (ii) the evolution of forecast uncertainty; and (iii) forecasts of revisions to early released GDP data. By evaluating the real-time performance of the RA-DFM, we find that the model’s predictions have information about the latest GDP releases above and beyond that contained in the statistical office earlier estimates; predictive intervals are well-calibrated; and UK GDP growth real-time estimates are commensurate with professional nowcasters. We also provide evidence that statistical office data on production and labour markets, subject to large publication delays, account for most of the forecastability of the revisions. |
| Keywords: | Nowcasting, Data revisions, Dynamic factor model |
| JEL: | C51 C53 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:cfm:wpaper:1824&r=for |
| By: | Holtemöller, Oliver; Schult, Christoph |
| Abstract: | In this paper, we document the forecasting performance of estimated basic dynamic stochastic general equilibrium (DSGE) models and compare this to extended versions which consider alternative expectation formation assumptions and financial frictions. We also show how standard model features, such as price and wage rigidities, contribute to forecasting performance. It turns out that neither alternative expectation formation behaviour nor financial frictions can systematically increase the forecasting performance of basic DSGE models. Financial frictions improve forecasts only during periods of financial crises. However, traditional price and wage rigidities systematically help to increase the forecasting performance. |
| Keywords: | business cycles,economic forecasting,expectation formation,financial frictions,macroeconomic modelling |
| JEL: | C32 C53 E37 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:152018&r=for |
| By: | Nyoni, Thabani |
| Abstract: | In the financial as well as managerial decision making process, forecasting is a crucial element (Majhi et al, 2009). Most research have been made on forecasting of financial and economic variables through the help of researchers in the last decades using series of fundamental and technical approaches yielding different results (Musa et al, 2014). The theory of forecasting exchange rate has been in existence for many centuries where different models yield different forecasting results either in the sample or out of sample (Onasanya & Adeniji, 2013). A country’s exchange rate is one of the most closely monitored indicators, as fluctuations in exchange rates can have far reaching economic consequences (Ribeiro, 2016). The recent financial turmoil all over the world demonstrates the urgency of perfect information of the exchange rates (Shim, 2000). Understanding the forecasting of exchange rate behaviour is important to monetary policy (Simwaka, 2007). One of the important variables that have considerable influence on other socio – economic variables in Nigeria is the Nigerian naira / dollar exchange rate (Ismail, 2009). Owing to the critical role played by exchange rate dynamics in international trade and overall economic performance of all countries in general, the need for a good forecasting tool cannot be ruled out. In this study, we model and forecast the Naira / USD exchange rates over the period 1960 – 2017. Our diagnostic tests such as the ADF test indicate that EXC time series data is I (1). Based on the minimum AIC value, the study presents the ARIMA (1, 1, 1) model as the optimal model. The ADF test further indicates that the residuals of the ARIMA (1, 1, 1) model are stationary and thus bear the characteristics of a white noise process. It is also important to note that our forecast evaluation statistics, namely ME, RMSE, MAE, MPE, MAPE and Theil’s U absolutely show that our forecast accuracy is quite good. Our forecast actually indicates that the Naira will continue to depreciate. The main policy implication from this study is that the Central Bank of Nigeria (CBN), should devalue the Naira in order to not only restore exchange rate stability but also encourage local manufacturing and promote foreign capital inflows. |
| Keywords: | ARIMA, Exchange rate, Forecasting, Nigeria |
| JEL: | C53 E37 E47 F31 F37 O24 |
| Date: | 2018–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:88622&r=for |
| By: | Massimo Guidolin; Manuela Pedio |
| Abstract: | We test whether three well-known commodity-specific variables (basis, hedging pressure, and momentum) may improve the predictive power for commodity futures returns of models otherwise based on macroeconomic factors. We compute recursive, out-of-sample forecasts for fifteen monthly commodity futures return series, when estimation is based on a stepwise regression approach under a probability-weighted regime-switching regression that identifies different volatility regimes. Comparisons with an AR(1) benchmark show that the inclusion of commodity-specific factors does not improve the forecasting power. We perform a back-testing exercise of a meanvariance investment strategy that exploits any predictability of the conditional risk premium of commodities, stocks, and bond returns, also taking into account transaction costs caused by portfolio rebalancing. The risk-adjusted performance of this strategy does not allow us to conclude that any forecasting approach outperforms the others. However, there is evidence that investment strategies based on commodity-specific predictors outperform the remaining strategies in the high-volatility state. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp1886&r=for |
| By: | Francis X. Diebold; Minchul Shin |
| Abstract: | Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality ("partially-egalitarian LASSO"). Ex-post analysis reveals that the optimal solution has a very simple form: The vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts – indeed they perform approximately as well as the ex-post best forecaster. |
| JEL: | C53 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24967&r=for |
| By: | Asgari, Mahdi; Nemati, Mehdi; Zheng, Yuqing |
| Abstract: | Predicting financial market movements in today’s fast-paced and complex environment is challenging more than ever. For many investors, online resources are a major source of information. Researchers can use Google Trends to access the number of search queries of a particular topic by internet users. The search volume index provided by Google then can be used as a proxy for importance of that topic. To predict the collective response to a particular news, we can use the search index for relevant search terms in our forecasting model. The focus of our study is forecasting food stock movement. A unique feature of the food industry is that besides common fundamental information, stakeholders are responsive to food safety news. In this study, we test whether including relevant search terms would reduce the forecasting error and improve the predictive power of traditional models. We use the market data and Google Trends index for 46 listed food companies. The empirical results show that on average the use of search terms reduces forecasting error by 2 to 31 percent for predicting trading volume, and reduces forecasting error by 3.5 to 77 percent for predicting the closing price, depending on the company. We also applied a model confidence set (MCS) to create a set of specifications that have statistically least forecasting error. The average forecasting error of the models in the set is lower than all models with search terms which implies that the MCS approach is efficient in identifying models with best predictive power. |
| Keywords: | Agribusiness, Research Methods/ Statistical Methods |
| Date: | 2018–02–06 |
| URL: | http://d.repec.org/n?u=RePEc:ags:saea18:266323&r=for |