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on Forecasting |
| By: | Ramazan Gencay (Simon Fraser University); Ege Yazgan (Istanbul Bilgi University) |
| Abstract: | When data exhibit high volatility and jumps, which are common features in most high frequency financial time series, forecasting becomes even more challenging. Using high frequency exchange rate data, we show that wavelets, which are robust to high volatility and jumps, are useful forecasters in high frequency settings when high volatility is a dominant feature that affects estimation zones, forecasting zones or both. The results indicate that decomposing the time series into homogeneous components that can then be used in time series forecast models is critical. Different components become more useful than others for different data features associated with a volatility regime. We cover a wide range of linear and nonlinear time series models for forecasting high frequency exchange rate return series. Our results indicate that when data display nonstandard features with high volatility, nonlinear models outperform linear alternatives. However, when data are in low volatility ranges for both estimations and forecasts, simple linear autoregressive models prevail, although considerable denoising of the data via wavelets is required. |
| Keywords: | Wavelets; Forecasting; High Frequency Data; Nonlinear Models; Maximum Overlap; Discrete Wavelet Transformation |
| JEL: | C12 C22 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:bli:wpaper:1704&r=for |
| By: | Gómez-Zamudio Luis M.; Ibarra-Ramírez Raúl |
| Abstract: | This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. In particular, the mixed data sampling (MIDAS) regression model is employed to incorporate both quarterly and daily frequencies while remaining parsimonious. To preserve parsimony, factor analysis and forecast combination techniques are used to summarize the information contained in a dataset containing 392 daily financial series. Our findings suggest that the MIDAS model that incorporates daily financial data lead to improvements for quarterly forecasts of GDP growth over traditional models that either rely only on quarterly macroeconomic data or average daily financial data. Furthermore, we explore the ability of the MIDAS model to provide forecast updates for GDP growth (nowcasting). |
| Keywords: | GDP Forecasting;Mixed Frequency Data;Daily Financial Data;Nowcasting |
| JEL: | C22 C53 E37 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:bdm:wpaper:2017-17&r=for |
| By: | Prüser, Jan |
| Abstract: | This study considers Bayesian variable selection in the Phillips curve context by using the Bernoulli approach of Korobilis (2013a). The Bernoulli model, however, is unable to account for model change over time, which is important if the set of relevant predictors changes over time. To tackle this problem, this paper extends the Bernoulli model by introducing a novel modeling approach called Markov Dimension Switching (MDS). MDS allows the set of predictors to change over time. The MDS and Bernoulli model reveal that the unemployment rate, the Treasury bill rate and the number of newly built houses are the most important variables in the generalized Phillips curve. Furthermore, these three predictors exhibit a sizeable degree of time variation for which the Bernoulli approach is not able to account, stressing the importance and benefit of the MDS approach. In a forecasting exercise the MDS model compares favorably to the Bernoulli model for one quarter and one year ahead inflation. In addition, it turns out that the performance of MDS model forecasting is competitive in comparison with other models found to be useful in the inflation forecasting literature. |
| Keywords: | Phillips Curve,fat data,variable selection,model change |
| JEL: | C11 C32 C53 E37 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:710&r=for |
| By: | Garnitz, Johanna; Lehmann, Robert; Wohlrabe, Klaus |
| Abstract: | Comprehensive and international comparable leading indicators across countries and continents are rare. In this paper, we use a free and fast available source of leading indicators, the World Economic Survey (WES) conducted by the ifo Institute, to forecast growth of Gross Domestic Product (GDP) in 44 countries and three country aggregates separately. We come up with three major results. First, for 35 countries as well as the three aggregates a model containing one of the major WES indicators produces on average lower forecast errors compared to an autoregressive benchmark model. Second, the most important WES indicators are either the economic climate or the expectations on future economic development for the next six months. And last, 70% of all country-specific models contain WES information from at least one of the main trading partners. Thus, by allowing WES indicators from economic important partners to forecast GDP of the country under consideration, increases forecast accuracy. |
| Keywords: | World Economic Survey, Economic Climate, Forecasting GDP |
| JEL: | E17 E27 E37 |
| Date: | 2017–10–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:81772&r=for |
| By: | Jianqing Fan; Yuan Ke; Yuan Liao |
| Abstract: | We provide an econometric analysis for the factor models when the latent factors can be explained partially by several observed explanatory proxies. In financial factor models for instance, the unknown factors can be reasonably well predicted by a few observable proxies, such as the Fama-French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. To incorporate the explanatory power of these observed characteristics, we propose a new two-step estimation procedure: (i) regress the data onto the observables, and (ii) take the principal components of the fitted data to estimate the loadings and factors. The proposed estimator is robust to possibly heavy-tailed distributions, which are encountered by many macroeconomic and financial time series. With those proxies, the factors can be estimated accurately even if the cross-sectional dimension is mild. Empirically, we apply the model to forecast US bond risk premia, and find that the observed macroeconomic characteristics contain strong explanatory powers of the factors. The gain of forecast is more substantial when these characteristics are incorporated to estimate the common factors than directly used for forecasts. |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1603.07041&r=for |
| By: | Heinrich, Markus; Carstensen, Kai; Reif, Magnus; Wolters, Maik |
| Abstract: | We use a Markow-switching dynamic factor model with three states for Germany with indicators selected by the Elastic Net. The states represent expansions, normal - and severe recessions. Adding a third state helps to identify all business cycle turning points in-sample and in real-time. Combining the factor and the recession probabilities with a GDP forecasting model yields accurate nowcasts and a correct prediction of the timing of the Great Recession and its recovery one quarter in advance. |
| JEL: | C53 E32 E37 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc17:168206&r=for |
| By: | Laura Liu; Hyungsik Roger Moon; Frank Schorfheide |
| Abstract: | This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a non-parametric estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated-random-effects distribution as known (ratio-optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions. |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1709.10193&r=for |
| By: | Roy Verbaan; Wilko Bolt; Carin van der Cruijsen |
| Abstract: | In this paper we analyse whether the use of debit card payments data improves the accuracy of one-quarter ahead forecasts and nowcasts (current-quarter forecasts) of Dutch private household consumption. Since debit card payments data are timely available, they may be a valuable indicator of economic activity. We study a variety of models with payments data and find that a combination of models provides the most accurate nowcast. The best combined model reduces the root mean squared prediction error (RMSPE) by 18% relative to the macroeconomic policy model (DELFI) that is used by the Dutch central bank (DNB). Based on these results for the Netherlands, we conclude that debit card payments data are useful in modelling household consumption. |
| Keywords: | Nowcasting; debit card payments; household consumption; Midas |
| JEL: | C53 E27 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:571&r=for |