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on Forecasting |
| By: | Onur Ince; Tanya Molodtsova; David H. Papell |
| Abstract: | The Taylor rule has become the dominant model for academic evaluation of out-of-sample exchange rate predictability. Two versions of the Taylor rule model are the Taylor rule fundamentals model, where the variables that enter the Taylor rule are used to forecast exchange rate changes, and the Taylor rule differentials model, where a Taylor rule with postulated coefficients is used in the forecasting regression. We use data from 1973 to 2014 to evaluate short-run out-of-sample predictability for eight exchange rates vis-à-vis the U.S. dollar, and find strong evidence in favor of the Taylor rule fundamentals model alternative against the random walk null. The evidence of predictability is weaker with the Taylor rule differentials model, and still weaker with the traditional interest rate differential, purchasing power parity, and monetary models. The evidence of predictability for the fundamentals model is not related to deviations from the original Taylor rule for the U.S., but is related to deviations from a modified Taylor rule for the U.S. with a higher coefficient on the output gap. The evidence of predictability is also unrelated to deviations from Taylor rules for the foreign countries and adherence to the Taylor principle for the U.S. Key Words: |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:apl:wpaper:15-02&r=for |
| By: | A. Benchimol; Irene Albarrán; J. Miguel Marín; Pablo J. Alonso |
| Abstract: | Some groups of countries are connected not only economically, but also social and even demographically. This last fact can be exploited when trying to forecast the death rates of their populations. In this paper we propose a hierarchical specification of the Lee-Carter model and we assume that there is a common latent mortality factor for all of them. We introduce an estimation procedure for this kind of structures by means of a data cloning methodology. To our knowledge, this is the first time that this methodology is used in the actuarial field. It allows approximating the maximum likelihood estimates, which are not affected by the prior distributions assumed for the calculus. Finally, we apply the methodology to some France, Italy, Portugal and Spain data. The forecasts obtained using this methodology can be considered as very satisfactory. |
| Keywords: | Bayesian inference , Data cloning , Hierarchical model , Lee-Carter model , Longevity risk , Projected life tables |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws1510&r=for |
| By: | Mazzeu Gonçalves; Henrique Joao; Esther Ruiz; Helena Veiga |
| Abstract: | The objective of this paper is to survey the literature on the effects of model uncertainty on the forecast accuracy of linear univariate ARMA models. We consider three specific uncertainties: parameter estimation, error distribution and lag order. We also survey the procedures proposed to deal with each of these sources of uncertainty. The results are illustrated with simulated data. |
| Keywords: | Bayesian forecast, Bootstrap, Model misspecification, Parameter uncertainty , Bootstrap , Model misspecification , Parameter uncertainty |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws1508&r=for |
| By: | Katarzyna Maciejowska; Jakub Nowotarski |
| Abstract: | This paper provides detailed information on Team Poland’s approach in the electricity price forecasting track of GEFCom2014. A new hybrid model is proposed, consisting of four major blocks: point forecasting, pre-filtering, quantile regression modeling and post-processing. This universal model structure enables independent development of a single block, without affecting performance of the remaining ones. The four-block model design in complemented by including expert judgements, which may be of great importance in periods of unusually high or low electricity demand. |
| Keywords: | Probabilistic forecasting; Hybrid model; Quantile regression; Electricity spot price; Forecasts combination; Pinball function |
| JEL: | C22 C32 C53 Q47 |
| Date: | 2015–05–11 |
| URL: | http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1506&r=for |
| By: | Knut Are Aastveit (Norges Bank (Central Bank of Norway)); Anne Sofie Jore (Norges Bank (Central Bank of Norway)); Francesco Ravazzolo (Norges Bank (Central Bank of Norway) and BI Norwegian Business School) |
| Abstract: | We define and forecast classical business cycle turning points for the Norwegian economy. When defining reference business cycles, we compare a univariate and a multivariate Bry-Boschan approach with univariate Markov-switching models and Markov-switching factor models. On the basis of a receiver operating characteristic curve methodology and a comparison of business cycle turning points with Norway's main trading partners, we find that a Markov-switching factor model provides the most reasonable definition of Norwegian business cycles for the sample 1978Q1-2011Q4. In a real-time out-of-sample forecasting exercise, focusing on the last recession, we show that univariate Markov-switching models applied to surveys and a financial conditions index are timely and accurate in calling the last peak in real time. The models are less accurate and timely in calling the trough in real time. |
| Keywords: | Business cycle, Dating rules, Turning Points, Real-time data |
| JEL: | C32 C52 C53 E37 E52 |
| Date: | 2015–05–09 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2015_09&r=for |
| By: | Francesco Ravazzolo (Norges Bank (Central Bank of Norway) and BI Norwegian Business School); Joaquin L. Vespignani (University of Tasmania, Tasmanian School of Business and Economics and Centre for Applied Macroeconomic Analysis, Australia) |
| Abstract: | In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian's index of global real economic activity and the index of OECD World industrial production. We develop an econometric approach based on desirable econometric properties in relation to the quarterly measure of World or global gross domestic product to evaluate and to choose across different alternatives. The method is designed to evaluate short-term, long-term and predictability properties of the indicators. World steel production is proven to be the best monthly indicator of global economic activity in terms of our econometric properties. Kilian's index of global real economic activity also accurately predicts World GDP growth rates. When extending the analysis to an out-of-sample exercise, both Kilian's index of global real economic activity and the World steel production produce accurate forecasts for World GDP, confirming evidence provided by the econometric properties. Specifically, a forecast combination of the three indices produces statistically significant gains up to 40% at nowcast and more than 10% at longer horizons relative to an autoregressive benchmark. |
| Keywords: | Global real economic activity, World steel production, Forecasting |
| JEL: | E1 E3 C1 C5 C8 |
| Date: | 2015–04–13 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2015_06&r=for |
| By: | Monica Billio (Department of Economics, University Of Venice Cà Foscari); Roberto Casarin (Department of Economics, University Of Venice Cà Foscari); Michele Costola (Department of Economics, University Of Venice Cà Foscari); Andrea Pasqualini (Department of Economics, University Of Venice Cà Foscari) |
| Abstract: | The purpose of this paper is the construction of an early warning indicator for systemic risk using entropy measures. The analysis is based on the cross-sectional distribution of marginal systemic risk measures such as Marginal Expected Shortfall, Delta CoVaR and network connectedness. These measures are conceived at a single institution for the financial industry in the Euro area. We estimate entropy on these measures by considering different definitions (Shannon, Tsallis and Renyi). Finally, we test if these entropy indicators show forecasting abilities in predicting banking crises. In this regard, we use the variable presented in Babeck? et al. (2012) and Alessi and Detken (2011) from European Central Bank. Entropy indicators show promising forecast abilities to predict financial and banking crisis. The proposed early warning signals reveal to be effective in forecasting financial distress conditions. |
| Keywords: | Entropy, systemic risk measures, early warning indicators, aggregation. |
| JEL: | C10 C11 G12 G29 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2015:09&r=for |
| By: | Shiqing Ling (Department of Mathematics Hong Kong University of Science and Technology Hong Kong, China); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of Economic Research, Kyoto University.); Howell Tong (Emeritus Professor Department of Statistics. London School of Economics) |
| Abstract: | Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contributed significantly to time series and financial econometrics, including forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance, prediction of Lévy-driven CARMA processes, functional index coefficient models with variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, toward optimal model averaging in regression models with time series errors, high dimensional dynamic stochastic copula models, a misspecification test for multiplicative error models of non-negative time series processes, sample quantile analysis for long-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic simultaneous equations models, specification tests of calibrated option pricing models, asymptotic inference in multiple-threshold double autoregressive models, a new hyperbolic GARCH model, intraday value-at-risk: an asymmetric autoregressive conditional duration approach, refinements in maximum likelihood inference on spatial autocorrelation in panel data, statistical inference of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process, threshold models in time series analysis - some reflections, and generalized ARMA models with martingale difference errors. |
| Keywords: | Time series, Financial econometrics, Threshold models, Conditional volatility, Stochastic volatility, Copulas, Conditional duration |
| JEL: | C22 C52 C58 G32 |
| Date: | 2015–02 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1504&r=for |
| By: | Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Wei Lin (Capital University of Economics and Business) |
| Abstract: | The current regression models for interval-valued data ignore the extreme nature of the lower and upper bounds of intervals. We propose a new estimation approach that considers the bounds of the interval as realizations of the max/min order statistics coming from a sample of n_t random draws from the conditional density of an underlying stochastic process {Y_t}. This approach is important for data sets for which the relevant information is only available in interval format, e.g., low/high prices. We are interested in the characterization of the latent process as well as in the modeling of the bounds themselves. We estimate a dynamic model for the conditional mean and conditional variance of the latent process, which is assumed to be normally distributed, and for the conditional intensity of the discrete process {n_t}, which follows a negative binomial density function. Under these assumptions, together with the densities of order statistics, we obtain maximum likelihood estimates of the parameters of the model, which are needed to estimate the expected value of the bounds of the interval. We implement this approach with the time series of livestock prices, of which only low/high prices are recorded making the price process itself a latent process. We find that the proposed model provides an excellent fit of the intervals of low/high returns with an average coverage rate of 83%. We also offer a comparison with current models for interval-valued data. |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:201505&r=for |
| By: | Anna Pestova (National Research University Higher School of Economics) |
| Abstract: | In this paper, I develop the leading indicators of the business cycle turning points exploiting the quarterly panel dataset comprising OECD countries and Russia over the 1980-2013 period. Contrasting to the previous studies, I combine data on OECD countries and Russia into a single dataset and develop universal models suitable for the entire sample with a quality of predictions comparable to the analogues of single-country models. On the basis of conventional dynamic discrete dependent variable framework I estimate the business cycle leading indicator models at different forecasting horizons (from one to four quarters). The results demonstrate that there is a trade-off between forecasting accuracy and the earliness of the recession signal. Best predictions are achieved for the model with one quarter lag (approximately 94% of the observations were correctly classified with a noise-to-signal ratio of 7%). However, even the model with the four quarter lags correctly predicts more than 80% of recessions with the noise-to-signal ratio of 25% can be useful for the policy analysis. I also reveal significant gains of accounting for the credit market variables when forecasting recessions at the long horizons (four quarter lag) as their use leads to a significant reduction of the noise-to-signal ratio of the model. I propose using the “optimal” cut-off threshold of the binary models based on the minimization of regulator loss function arising from different types of wrong classification. I show that this optimal threshold improves model forecasts as compared to other exogenous thresholds. |
| Keywords: | business cycles, leading indicators, turning points, dynamic logit models, recession forecast. |
| JEL: | E32 E37 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:94/ec/2015&r=for |