Multi-sector inflation forecasting - quarterly models for South Africa.

Inflation is a far from homogeneous phenomenon, a fact often neglected in modeling consumer price inflation. Using a novel methodology grounded in theory, the ten sub-components of the consumer price index (excluding mortgage interest rates), are modeled separately and forecast, four-quartersahead. Equilibrium correction models in a rich multivariate form employ general and sectoral information, and take account of structural breaks and institutional changes. Our methods allow for longer lags than conventionally considered in VARs, but in a parsimonious manner. Sign priors are imposed on long-run effects and automatic model selection is used to select parsimonious models from more general ones. The models throw light on sectoral sources of inflation, useful to monetary policy. Data for 1979 to 2003 are used for model selection, and pseudo out of sample forecasting performance to the end of 2007 is examined. Aggregating the weighted sub-component forecasts indicates gains are made over forecasting the overall index using these methods, and also substantial gains over forecasting using benchmark naïve models. To extend this work, including sectoral information such as an explicit treatment of tax policy, regulatory information and announced administered price rises, should further enhance these forecasting methods.

Forecasting economic activity with higher frequency targeted predictors

In this paper we explore the performance of bridge and factor models in forecasting quarterly aggregates in the very short-term subject to a pre-selection of monthly indicators. Starting from a large information set, we select a subset of targeted predictors using data reduction techniques as in Bai and Ng (2008). We then compare a Diffusion Index forecasting model as in Stock and Watson (2002), with a Bridge model specified with an automated General-To-Specific routine. We apply these techniques to forecasting Italian GDP growth and its main components from the demand side and find that Bridge models outperform naive forecasts and compare favorably against factor models. Results for France, Germany, Spain and the euro area confirm these findings.short-term GDP forecast, factor models, bridge models, General To Specific

Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?

Monitoring and forecasting price developments in the euro area is essential in the light of the second pillar of the ECB's monetary policy strategy. This study analyses whether the forecasting accuracy of forecasting aggregate euro area inflation can be improved by aggregating forecasts of subindices of the Harmonized Index of Consumer Prices (HICP) as opposed to forecasting the aggregate HICP directly. The analysis includes univariate and multivariate linear time series models and distinguishes between different forecast horizons, HICP components and inflation measures. Various model selection procedures are employed to select models for the aggregate and the disaggregate components. The results indicate that aggregating forecasts by component does not necessarily help forecast year-on-year inflation twelve months ahead. JEL Classification: E31, E37, C53, C32Euro Area Inflation, HICP subindex forecast aggregation, linear time series models

Optimal model-free prediction from multivariate time series

Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal pre-selection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used sub-optimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Ni\~no Southern Oscillation.Comment: 14 pages, 9 figure

A Simple Expected Volatility (SEV) Index: Application to SET50 Index Options

In 2003, the Chicago Board Options Exchange (CBOE) made two key enhancements to the volatility index (VIX) methodology based on S&P options. The new VIX methodology seems to be based on a complicated formula to calculate expected volatility. In this paper, with the use of Thailand’s SET50 Index Options data, we modify the VIX formula to a very simple relationship, which has a higher negative correlation between the VIX for Thailand (TVIX) and SET50 Index Options. We show that TVIX provides more accurate forecasts of option prices than the simple expected volatility (SEV) index, but the SEV index outperforms TVIX in forecasting expected volatility. Therefore, the SEV index would seem to be a superior tool as a hedging diversification tool because of the high negative correlation with the volatility index.Financial markets; model selection; new products; price forecasting; time series; volatility forecasting

Indeks Harga Komsumen (IHK) di Lampung Menggunakan Autoregressive Integrated Moving Average (ARIMA)

The Consumer Price Index (CPI) is an indicator that influences economic growth. CPI is an index that calculates the average of price change of a group of goods and services consumed by households in a certain period of time. CPI is also used to measure inflation in a country. Inflation is described by changes in the CPI from time to time. To anticipate and minimize economic risks caused by inflation, forecasting will be carried out on CPI data. In this study, the CPI will be predicted for the next 6 months using the ARIMA (Autoregressive Integrated Moving Average) model. The result of this research shows that the ARIMA models that can be used to predict CPI are ARIMA (0,2,0), ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (1,2,1) . The selection of the best model is carried out based on the model that has the smallest AIC value. Based on this, the best model used to predict CPI is the ARIMA model (0,2,1) with an AIC value of 83.21. In addition, this model fulfills diagnostics with white noise residuals, so that forecasting results using this model will be more accurate

When can the Planck satellite measure spectral index running?

We use model selection forecasting to assess the ability of the Planck satellite to make a positive detection of spectral index running. We simulate Planck data for a range of assumed cosmological parameter values, and carry out a three-way Bayesian model comparison of a Harrison-Zel'dovich model, a power-law model, and a model including running. We find that Planck will be able to strongly support running only if its true value satisfies |dn/d ln k| > 0.02.Comment: 5 pages with 7 figures included. Full resolution PDF at http://astronomy.susx.ac.uk/~andrewl/planckev2D.pdf Minor updates to match version accepted by MNRA

Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

During recent decades, the use of probabilistic forecasting methods has increased markedly. However, these predictions still need improvement in uncertainty quantification and predictability analysis. For this reason, the main aim of this paper is to develop tools for quantifying uncertainty and predictability of wind speed over the Iberian Peninsula. To achieve this goal, several spread indexes extracted from an ensemble prediction system are defined in this paper. Subsequently, these indexes were evaluated with the aim of selecting the most appropriate for the characterization of uncertainty associated to the forecasting. Selection is based on comparison of the average magnitude of ensemble spread (ES) and mean absolute percentage error (MAPE). MAPE is estimated by comparing the ensemble mean with wind speed values from different databases. Later, correlation between MAPE and ES was evaluated. Furthermore, probability distribution functions (PDFs) of spread indexes are analyzed to select the index with greater similarity to MAPE PDFs. Then, the spread index selected as optimal is used to carry out a spatiotemporal analysis of model uncertainty in wind forecasting. The results indicate that mountainous regions and the Mediterranean coast are characterized by strong uncertainty, and the spread increases more rapidly in areas affected by strong winds. Finally, a predictability index is proposed for obtaining a tool capable of providing information on whether the predictability is higher or lower than average. The applications developed may be useful in the forecasting of wind potential several days in advance, with substantial importance for estimating wind energy production

Forecasting inflation: An art as well as a science!

In this study we build two forecasting models to predict inflation for the Netherlands and for the euro area. Inflation is the yearly change of the Harmonised Index of Consumer Prices (HICP). The models provide point forecasts and prediction intervals for both the components of the HICP and the aggregated HICP-index itself. Both models are small-scale linear time series models allowing for long run equilibrium relationships between HICP components and other variables, notably the hourly wage rate and the import or producer prices. The model for the Netherlands is used to generate the Dutch inflation projections over a horizon of 11-15 months ahead for the eurosystem’s Narrow Inflation Projection Exercise (NIPE). The recursive forecast errors for several forecast horizons are evaluated for all models, and are found to outperform a naive forecast and optimal AR models. Moreover, the same result holds for the Dutch NIPE projections, which have been provided quarterly since 1999. The direct and aggregation methods to predict total HICP inflation perform about equally goodmodel selection, time series models, aggregation

Prediction and performance evaluation of BDI forecasting models : Cross efficiency, the directional distance function and the AVS utility function

Workshop 2015 -Advances in DEA Theory and Applications (December 1-2, 2015)In the study, we propose a nonparametric efficiency measurement approach for the forecasting model selection problem. Three autoregressive models and three fuzzy time series approaches are employed for the calibration of data structure to depict the trend. The directional distance function and portfolio theory are further used to evaluate the performance of BDI predictions. A directional distance function is defined that looks for possible increases in accuracy and skewness, and decreases in variance obtained by cross efficiencies of those forecasting models. We also establish a link to proper indirect accuracy- variance -skewness (AVS) utility function for various users in various utilities. An empirical section on a set of forecasting Baltic Dry Index (BDI) forecasting models serves as an illustration.The workshop is supported by JSPS (Japan Society for the Promotion of Science), Grant-in-Aid for Scientific Research (B), #25282090, titled “Studies in Theory and Applications of DEA for Forecasting Purpose.本研究はJSPS科研費 基盤研究(B) 25282090の助成を受けたものです