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on Forecasting |
| By: | Rangan Gupta (Department of Economics, University of Pretoria); Kevin Kotze (School of Economics, University of Cape Town) |
| Abstract: | This paper considers whether the use of real oil price data can improve upon the forecasts for the nominal interest rate in South Africa. We employ Bayesian vector autoregressive models that make use of various measures of oil prices and compare the forecasting results of these models with those that do not make use of this data. The real oil price data is also disaggregated into positive and negative components to establish whether this would improve upon the forecasting performance of the model. The full dataset includes quarterly measures of output, consumer prices, exchange rates, interest rates and oil prices, where the initial in-sample period extends from 1979q1 to 1997q4. We then perform recursive estimations and one- to eight-step ahead forecasts over the out-of-sample period 1998q1 to 2014q4. The results suggest that the models that include information relating to oil prices outperform the model that does not include this information, when comparing their out-of-sample properties. In addition, the model with the positive component of oil price tends to perform better than other models over the short to medium horizons. Then lastly, the model that includes both the positive and negative components of the oil price, provides superior forecasts over longer horizons, where the improvement is large enough to ensure that it is the best forecasting model on average. Hence, not only do real oil prices matter when forecasting interest rates, but the use of disaggregate oil price data may facilitate additional improvements. |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:ctn:dpaper:2016-01&r=for |
| By: | Constantin Burgi (The George Washington University) |
| Abstract: | In this paper, I use the Bloomberg Survey of forecasts to assess if evaluating the distribution of expectations will lead to important additional insights over the evaluation of the simple average. I first introduce new approaches that allow me to assess the forecast accuracy and the information rigidity at the individual level despite a large share of missing data. Applying these new approaches, I find that taking into account the distribution can significantly improve the predictive power of the survey. For example, I find that the part of uncertainty measured by disagreement can improve the prediction of recessions in a dynamic probit model relative to the simple average. On information rigidity, I find that some of the rigidity found at the aggregate level likely stems from the aggregation process. Together, my findings suggest that we should look at individual expectations whenever possible as important insights are lost by just looking at aggregate expectations. |
| Keywords: | Expectations, Bloomberg Survey, Forecast Evaluation, Uncertainty, Dynamic Probit |
| JEL: | C22 C52 C53 E17 E37 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2016-013&r=for |
| By: | Magdalena Petrovska (National Bank of the Republic of Macedonia); Aneta Krstevska (National Bank of the Republic of Macedonia); Nikola Naumovski (National Bank of the Republic of Macedonia) |
| Abstract: | This paper aims to assess the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the ESI within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed by analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia. |
| Keywords: | Forecasting, Business cycle turning points, Qual VAR, MCMC, Latent variable |
| JEL: | F42 C25 C22 |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:mae:wpaper:2016-05&r=for |
| By: | Wagner Piazza Gaglianone; Gabriel Jaqueline Terra Moura Marins |
| Abstract: | In this paper, we construct multi-step-ahead point and density forecasts of the exchange rate, from statistical or economic-driven approaches, using financial or macroeconomic data and using parametric or nonparametric distributions. We employ a set of statistical tools, from di¤erent strands of the literature, to identify which models work in practice, in terms of forecast accuracy across di¤erent data frequencies and forecasting horizons. We propose a novel full-density/local analysis approach to collect the many test results, and deploy a simple risk based decision rule to rank models. An empirical exercise with Brazilian daily and monthly data reveals that macro fundamentals matter when modeling the risk of exchange rate appreciation, whereas models using survey information or ?nancial data are the best way to account for the depreciation risk. These findings have relevance for econometricians, risk managers or policymakers interested in evaluating the accuracy of competing exchange rate models. |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:446&r=for |
| By: | Faria, Gonçalo; Verona, Fabio |
| Abstract: | We forecast stock market returns by applying, within a Ferreira and Santa-Clara (2011) sum-of-the-parts framework, a frequency decomposition of several predictors of stock returns. The method delivers statistically and economically significant improvements over historical mean forecasts, with monthly out-of-sample R2 of 3.27% and annual utility gains of 403 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the predictors with the highest predictive power from the noisy parts, and from the fact that the frequency-decomposed predictors carry complementary information that captures both the long-term trend and the higher frequency movements of stock market returns. |
| Keywords: | predictability, stock returns, equity premium, asset allocation, frequency domain, wavelets |
| JEL: | G11 G12 G14 G17 |
| Date: | 2016–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:bof:bofrdp:2016_029&r=for |
| By: | Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Greece); Juncal Cunado (Department of Economics, University of Navarra, Spain); Rangan Gupta (Department of Economics, University of Pretoria, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha USA, and School of Business and Economics, Loughborough University, UK) |
| Abstract: | This paper analyzes to what extent a selection of leading indicators are able to forecast U.S. recessions by means of both dynamic probit models and Support Vector Machines (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models foresee U.S. recession periods more closely than SVM models for up to 6 months ahead, while the SVM models are more accurate at longer horizons. Furthermore, SVM models appear to discriminate between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models include oil, stock returns and the term spread as leading indicators. |
| Keywords: | Dynamic Probit Models, Support Vector Machines, U.S. Recessions |
| JEL: | C53 E32 E37 |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201685&r=for |