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on Forecasting |
| By: | Rangan Gupta (Department of Economics, University of Pretoria); Kevin Kotze (School of Economics, University of Cape Town, Rondebosch, 7700, South Africa) |
| Abstract: | This paper considers whether the use of real oil price data can improve upon the forecasts of the interest rate in South Africa. We employ various Bayesian vector autoregressive (BVAR) 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, ex- change rates, interest rates and oil prices, where the initial in-sample extends from 1979q1 to 1997q4. We then perform rolling estimations and one- to eight-step ahead forecasts over the out-of-sample period 1998q1 to 2014q4. The results suggest that models that includes information relating to oil prices outperform the model that does not include this in- formation, when comparing their out-of-sample properties. In addition, the model with the positive component of oil price tends to perform bet- ter 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 at longer horizons, where the im- provement 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. |
| Keywords: | Interest rate, oil price, forecasting, South Africa |
| JEL: | C32 C53 E43 E47 Q41 |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201531&r=for |
| By: | Dongkoo Kim; Tae-hwan Rhee; Keunkwan Ryu; Changmock Shin |
| Abstract: | Economic forecasts are quite essential in our daily lives, which is why many research institutions periodically make and publish forecasts of main economic indicators. We ask (1) whether we can consistently have a better prediction when we combine multiple forecasts of the same variable and (2) if we can, what will be the optimal method of combination. We linearly combine multiple linear combinations of existing forecasts to form a new forecast (“combination of combinations”), and the weights are given by Bayesian model averaging. In the case of forecasts on Germany’s real GDP growth rate, this new forecast dominates any single forecast in terms of root-mean-square prediction errors. |
| Keywords: | Combination of forecasts; Bayesian model averaging |
| JEL: | E32 E37 |
| Date: | 2015–03 |
| URL: | http://d.repec.org/n?u=RePEc:rwi:repape:0546&r=for |
| By: | Chiu, Ching-Wai (Jeremy) (Bank of England); Mumtaz, Haroon (Queen Mary University of London); Pinter, Gabor (Bank of England) |
| Abstract: | In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature Student’s t distribution and time-varying variance. We estimate this model using US data on output growth, inflation, interest rates and stock returns. In terms of in-sample fit, the VAR model featuring both stochastic volatility and t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference is especially stark over the recent Great Recession. Further international evidence confirms that accounting for both stochastic volatility and Student’s t-distributed disturbances may lead to improved forecast accuracy. |
| Keywords: | Bayesian VAR; fat-tails; stochastic volatility; Great Recession |
| JEL: | C11 C32 C52 |
| Date: | 2015–05–29 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0528&r=for |
| By: | Duncan, Roberto (Ohio University); Martinez-Garcia, Enrique (Federal Reserve Bank of Dallas) |
| Abstract: | This paper provides both theoretical insight as well as empirical evidence in support of the view that inflation is largely a global phenomenon. First, we show that inflation across countries incorporates a significant common factor captured by global inflation. Second, we show that in theory a role for global inflation in local inflation dynamics emerges over the business cycle even without common shocks, and under flexible exchange rates and complete international asset markets. Third, we identify a strong "error correction mechanism" that brings local inflation rates back in line with global inflation which explains the relative success of inflation forecasting models based on global inflation (e.g., Ciccarelli and Mojon (2010). Fourth, we argue that the workhorse New Open Economy Macro (NOEM) model of Martínez-García and Wynne (2010) can be approximated by a finiteorder VAR and estimated using Bayesian techniques to forecast domestic inflation incorporating all relevant linkages with the rest of the world. This NOEM-BVAR provides a tractable model of inflation determination that can be tested empirically in forecasting. Finally, we use pseudo-out-of-sample forecasts to assess the NOEM-BVAR at different horizons (1 to 8 quarters ahead) across 17 OECD countries using quarterly data over the period 1980Q1-2014Q4. In general, we find that the NOEM-BVAR model produces a lower root mean squared prediction error (RMSPE) than its competitors—which include most conventional forecasting models based on domestic factors and also the recent models based on global inflation. In a number of cases, the gains in smaller RMSPEs are statistically significant. The NOEM-BVAR model is also accurate in predicting the direction of change for inflation, and often better than its competitors along this dimension too. |
| JEL: | E31 F41 F42 F47 |
| Date: | 2015–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:235&r=for |
| By: | Ca'Zorzi, Michele (European Central Bank); Muck, Jakub (National Bank of Poland and Warsaw School of Economics); Rubaszek, Michal (National Bank of Poland and Warsaw School of Economics) |
| Abstract: | This paper brings four new insights into the Purchasing Power Parity (PPP) debate. First, we show that a half-life PPP (HL) model is able to forecast real exchange rates better than the random walk (RW) model at both short and long-term horizons. Second, we find that this result holds if the speed of adjustment to the sample mean is calibrated at reasonable values rather than estimated. Third, we find that it is preferable to calibrate, rather than to elicit as a prior, the parameter determining the speed of adjustment to PPP. Fourth, for most currencies in our sample, the HL model outperforms the RW also in terms of nominal effective exchange rate forecasting. |
| JEL: | C32 F31 F37 |
| Date: | 2015–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:229&r=for |
| By: | Camba-Méndez, Gonzalo; Kapetanios, George; Papailias, Fotis; Weale, Martin R. |
| Abstract: | This paper assesses the forecasting performance of various variable reduction and variable selection methods. A small and a large set of wisely chosen variables are used in forecasting the industrial production growth for four Euro Area economies. The results indicate that the Automatic Leading Indicator (ALI) model performs well compared to other variable reduction methods in small datasets. However, Partial Least Squares and variable selection using heuristic optimisations of information criteria along with the ALI could be used in model averaging methodologies. JEL Classification: C11, C32, C52 |
| Keywords: | Bayesian shrinkage regression, dynamic factor model, euro area, forecasting, Kalman filter, partial least squares |
| Date: | 2015–04 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20151773&r=for |
| By: | Sentyureva, N (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Yagîvkinà, Vità (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Kolomak, Evgeniya (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Uskov, D (Russian Presidential Academy of National Economy and Public Administration (RANEPA)) |
| Abstract: | Forecasting of tax revenues is an integral part of the budget process, the implementation of which is taking the necessary political, financial and socio-economic solutions. Under the conditions of existence of financial instability in the economy, improved forecasting tool designed to solve the problems of prediction and prevention of the negative effects of economic shocks, ensure the financial sustainability of the budget system of the Russian Federation. |
| Keywords: | tax revenues, forecasting, budget, financial sustainability |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:ppaper:mn20&r=for |
| By: | Droumaguet, Matthieu; Warne, Anders; Woźniak, Tomasz |
| Abstract: | We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for posterior inference include a novel block Metropolis-Hastings sampling algorithm for the estimation of the restricted models. We analyze a system of monthly US data on money and income. The test results in MS-VARs contradict those in linear VARs: the money aggregate M1 is useful for forecasting income and for predicting the next period’s state. JEL Classification: C11, C12, C32, C53, E32 |
| Keywords: | Bayesian hypothesis testing, block Metropolis-Hastings sampling, Markov-switching models, mixture models, posterior odds ratio |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20151794&r=for |
| By: | Mottaghi, Aliasghar |
| Abstract: | This study investigates the predictive ability of current and past cash flows with respect to the estimation of future cash flow, and compares this predictive ability with that of current and past earnings. Future cash flow is estimated in this study on the basis of a model hierarchy that initially incorporates aggregated predictors and then their disaggregated components, with the objective of improving on conventional research design with respect to the problematic issues surrounding missing values in source databases, extreme values in the sampled data and variability in fiscal year length. In determining whether the disaggregation of earnings into cash flow, accruals and their components adds to the predictive ability of cash flow, the present thesis also documents out-of-sample accuracy tests for the UK based on initial in-sample estimations, with accruals being computed using both the information in the Statement of Cash Flows and the information that may be derived from Balance Sheet changes. Using the information in the Statement of Cash Flows, the results of the in-sample estimation indicate that, whilst there is no notable difference between the ability of cash flow and aggregate earnings to predict future cash flow, the disaggregation of earnings into cash flow and accruals improves the prediction. The out-of-sample accuracy tests confirm the standard result that this disaggregated earnings model is a better predictor of future cash flow. In contrast, this thesis shows that, when using information in the Balance Sheet, by way of changes from one period to the next, the results of both the in-sample estimation and the out-of-sample accuracy tests show that disaggregated earnings is unable to outperform aggregate earnings in predicting future cash flow. Nevertheless, when the total accrual is further disaggregated into its deferral and accrual components, in-sample estimation reveals additional improvement in predictive ability, using each of the two sources of information to compute total accruals (the Statement of Cash Flows and Balance Sheet changes), although this is less evident with the out-of-sample tests. Whilst further analysis indicates that disaggregation is more informative when the firm size is large, the magnitude of accruals is low and the firm reports a positive CFO and EBIT, the thesis shows that the ability of the estimation models to predict future cash flow differs across industries in the UK, and that the findings are generally sensitive to the effect of database choice, the fiscal year length, and the identification and treatment of unrecorded data. |
| URL: | http://d.repec.org/n?u=RePEc:sus:susphd:0711&r=for |
| By: | Forni, Mario; Hallin, Marc; Lippi, Marco; Zaffaroni, Paolo |
| Abstract: | Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional factor space. A Monte Carlo experiment corroborates those theoretical results and demonstrates the excellent performance of those estimators in out-of-sample forecasting. |
| Keywords: | Consistency and rates.; Generalized dynamic factor models.; High -dimensional time series.; One-sided representations of dynamic factor models.; Vector processes with singular spectral density |
| JEL: | C0 C01 E0 |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:10618&r=for |
| By: | Olga Y. Uritskaya; Vadim M. Uritsky |
| Abstract: | In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti-persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from one hour to one month. For both Canadian markets, the price movements reveal three types of correlated behavior which can be used for forecasting. The discovered scenarios remain the same on different time scales up to one month as well as for on- and off- peak electricity data. These scenarios represent sharp increases of prices and are not present in the Mid-C market due to its lower volatility. We argue that extreme price movements in this market should follow the same tendency as the more volatile Canadian markets. The estimated values of the Pareto indices suggest that the prediction of these events can be statistically stable. The results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day. |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1505.08117&r=for |