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on Forecasting |
| By: | Gergely Akos Ganics (Banco de España) |
| Abstract: | How should researchers combine predictive densities to improve their forecasts? I propose consistent estimators of weights which deliver density forecast combinations approximating the true predictive density, conditional on the researcher’s information set. Monte Carlo simulations confi rm that the proposed methods work well for sample sizes of practical interest. In an empirical example of forecasting monthly US industrial production, I demonstrate that the estimator delivers density forecasts which are superior to well-known benchmarks, such as the equal weights scheme. Specifi cally, I show that housing permits had valuable predictive power before and after the Great Recession. Furthermore, stock returns and corporate bond spreads proved to be useful predictors during the recent crisis, suggesting that fi nancial variables help with density forecasting in a highly leveraged economy. |
| Keywords: | density forecasts, forecast combinations, probability integral transform, Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling, Kullback-Leibler information criterion |
| JEL: | C13 C22 C53 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1751&r=for |
| By: | Moses Tule (Monetary Policy Department, Central Bank of Nigeria Abuja); Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Charles Chimeke (Monetary Policy Department, Central Bank of Nigeria Abuja) |
| Abstract: | In this study, we propose a supply-side augmented Phillips curve for the Nigerian economy, which significantly enhances its inflation forecasts. We argue for the role of oil price as a good proxy for the supply side of inflation given the structure of the Nigerian economy, which essentially relies on oil revenue. Thus, we compare the forecast results of the oil-based augmented Phillips curve with the traditional variant, as well as time series models such as ARIMA and ARFIMA. We also test for any probable asymmetric response of Nigeria inflation forecast to oil price changes. The forecast analyses are conducted for both in-sample and out-of-sample periods using alternative forecast measures. We also consider alternative estimators such as Lewellen (2004) [LW hereafter] and Westerlund and Narayan (2012, 2015) [WN hereafter] estimators which account for relevant statistical properties of the predictors and their results are compared with the standard OLS estimator. The results suggest that the choice of estimator does matter for accurate inflation forecast for Nigeria, whether for in-sample or out-of-sample forecast and the WN estimator is preferred particularly when compared with OLS estimator. Secondly, the augmented model outperforms its traditional version, as well as time series models for both forecast samples. However, oil price asymmetries become evident when large samples are used. Our results are robust to alternative oil price proxies and forecast measures. |
| Keywords: | OECD; Nigeria, Phillips curve, Oil price, Inflation forecasts, Forecast evaluation |
| JEL: | C53 E31 E37 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:cui:wpaper:0040&r=for |
| By: | Chiu, Ching-Wai (Jeremy); Mumtaz, Haroon; Pinter, Gabor |
| Abstract: | We introduce a Bayesian VAR model with non-Gaussian disturbances that are modelled with a finite mixture of normal distributions. Importantly, we allow for regime switching among the different components of the mixture of normals. Our model is highly flexible and can capture distributions that are fat-tailed, skewed and even multimodal. We show that our model can generate large out-of-sample forecast gains relative to standard forecasting models, especially during tranquil periods. Our model forecasts are also competitive with those generated by the conventional VAR model with stochastic volatility. |
| JEL: | C11 C32 C52 |
| Date: | 2016–02–29 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:86238&r=for |
| By: | Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Raymond Swaray (Economics Subject Group, University of Hull Business, University of Hull, Cottingham Road, UK); Idris Adediran (Department of Economics, Obafemi Awolowo University, Nigeria.) |
| Abstract: | In this paper, we set forth to answer the research question: can coffee price predict US inflation? Motivated by the importance of coffee to Americans and the significance of the coffee subsector to the US economy, we pursue three notable innovations. First, we augment the traditional Phillips curve model with coffee price as a predictor and show that the resulting model outperforms the traditional variant in both in-sample and out-of-sample predictability of US inflation. Second, we justify the need to account for the inherent statistical features in the predictor as well as make a case for the superiority of Westerlund and Narayan (WN) estimator especially in the in-sample predictive model. These answer the research question in the affirmative with strong evidence that coffee price indeed serves as a good predictor, along with economic activity, for US inflation. These further challenge the position of Stock and Watson (1999, 2007, 2008) and establish that economic models can outperform statistical models for forecasting inflation. |
| Keywords: | OECD; US, Phillips curve, Coffee price, Inflation forecasts, Forecast evaluation |
| JEL: | C53 E31 E37 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:cui:wpaper:0041&r=for |
| By: | Christian R. Proaño; Artur Tarassow |
| Abstract: | We investigate the probability forecasting performance of a three-regime dynamic ordered probit model framework suitable to forecast recessions, low growth periods and accelerations for the U.S. and Japan. In a first step, we apply a non-parametric dating algorithm for the identification of these three phases. We compare the pseudo-out-of-sample forecasting skills of an otherwise standard binary dynamic probit model with a three-regime dynamic ordered probit framework by means of a rolling-window exercise combined with time-varying indicator selection. Based on a set of monthly macroeconomic and financial leading indicators, the results show the superiority of the ordered probit framework to forecast all three business cycle phases up to six months ahead under real-time conditions. Apart from standard probability forecast evaluation measures, receiver-operating curves and related summarizing statistics are computed. |
| Keywords: | Forecasting, Recession, Stagnation, ROC |
| JEL: | C52 C53 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:imk:wpaper:188-2017&r=for |
| By: | Becker, Janis; Leschinski, Christian |
| Abstract: | The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The analysis is carried out using a data set consisting of all stocks that were part of the Dow Jones Industrial Average in 1996. After selecting the relevant explanatory variables in the subsample from 1996 to 2003, forecast evaluations are conducted in an out-of-sample environment for the period from 2004 to 2017. Since the model selection and the forecasting period are strictly separated, the procedure mimics the situation a forecaster would face in real time. It is found that the sign of daily returns is predictable to an extent that is statistically significant. Moreover, trading strategies based on these forecasts generate positive alpha, even after accounting for transaction costs. This underlines the economic significance of the predictability and implies that there are periods during which markets are not fully efficient. |
| Keywords: | Asset Pricing; Market Efficiency; Directional Predictability; Statistical Classification |
| JEL: | G12 G14 G17 C38 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-624&r=for |
| By: | Lei Fang; Wolfgang K. Härdle; Juhyun Park |
| Abstract: | Mortality is different across countries, states and regions. Several empirical research works however reveal that mortality trends exhibit a common pattern and show similar structures across populations. The key element in analyzing mortality rate is a time-varying indicator curve. Our main interest lies in validating the existence of the common trends among these curves, the similar gender differences and their variability in location among the curves at the national level. Motivated by the empirical findings, we make the study of estimating and forecasting mortality rates based on a semi-parametric approach, which is applied to multiple curves with the shape-related nonlinear variation. This approach allows us to capture the common features contained in the curve functions and meanwhile provides the possibility to characterize the nonlinear variation via a few deviation parameters. These parameters carry an instructive summary of the time-varying curve functions and can be further used to make a suggestive forecast analysis for countries with barren data sets. In this research the model is illustrated with mortality rates of Japan and China, and extended to incorporate more countries. All numerical procedures are transparent and reproduced on www.quantlet.de. |
| Keywords: | Nonparametric smoothing, Parametric modeling, Common trend, Mortality,; Lee-Carter method, Multi-populations |
| JEL: | C14 C32 C38 J11 J13 |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2016-023&r=for |
| By: | Xinjue Li; Lenka Zbonakova; Wolfgang Karl Härdle |
| Abstract: | In the present paper we propose a new method, the Penalized Adaptive Method (PAM), for a data driven detection of structure changes in sparse linear models. The method is able to allocate the longest homogeneous intervals over the data sample and simultaneously choose the most proper variables with help of penalized regression models. The method is simple yet exible and can be safely applied in high-dimensional cases with di erent sources of parameter changes. Comparing with the adaptive method in linear models, its combination with dimension reduction yields a method which selects proper signi cant variables and detects structure breaks while steadily reduces the forecast error in high-dimensional data. When applying PAM to bond risk premia modelling, the locally selected variables and their estimated coecient loadings identi ed in the longest stable subsamples over time align with the true structure changes observed throughout the market. |
| Keywords: | SCAD penalty, propagation-separation, adaptive window choice, multiplier bootstrap, bond risk premia |
| JEL: | C13 C20 E37 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2017-023&r=for |
| By: | Manh Ha Nguyen (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes); Olivier Darné (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes) |
| Abstract: | This paper analyzes volatility models and their risk forecasting abilities with the presence of jumps for the Vietnam Stock Exchange (VSE). We apply GARCH-type models, which capture short and long memory and the leverage effect, estimated from both raw and filtered returns. The data sample covers two VSE indexes, the VN index and HNX index, provided by the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX), respectively, during the period 2007 - 2015. The empirical results reveal that the FIAPARCH model is the most suitable model for the VN index and HNX index. |
| Keywords: | Vietnam Stock exchange,volatility,GARCH models,Value-at-Risk. |
| Date: | 2018–01–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01679456&r=for |