Forecasting
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Forecasting2015-08-30Rob J HyndmanForecasting Core Inflation: The Case of South Africa
http://d.repec.org/n?u=RePEc:emu:wpaper:15-08.pdf&r=all
Forecasting and estimating core inflation has recently gained attention, especially for inflation targeting countries, following research showing that targeting headline inflation may not be optimal; a Central Bank can miss the signal due to the noise. Despite its importance there is sparse literature on estimating and forecasting core inflation in South Africa, with the focus still on measuring core inflation. This paper emphasises predicting core inflation using large time-varying parameter vector autoregressive models (TVP-VARs), factor augmented VAR, and structural break models using quarterly data from 1981Q1 to 2013Q4. We use mean squared forecast errors (MSFE) and predictive likelihoods to evaluate the forecasts. In general, we find that (i) small TVP-VARs consistently outperform all other models; (ii) models where the errors are heteroscedastic do better than models with homoscedastic errors; (iii) models assuming that the forgetting factor remains 0.99 throughout the forecast period outperforms models that allow for the forgetting factors to change with time; and (iv) allowing for structural break does not improve the predictability of core inflation. Overall, our results imply that additional information on the growth rate of the economy and interest rate is sufficient to forecast core inflation accurately, but the relationship between these three variables needs to be modelled in a time-varying (nonlinear) fashion.Franz Ruch, Mehmet Balcilar Author-Name-First Mehmet, Mampho P. Modise, Rangan Gupta2015Core inflation; forecasting; small- and large-scale vector autoregressive models; constant and time-varying parametersOil Price Forecasts for the Long-Term: Expert Outlooks, Models, or Both?
http://d.repec.org/n?u=RePEc:lvl:creacr:2015-3&r=all
Expert outlooks on the future path of oil prices are often relied on by industry participants and policymaking bodies for their forecasting needs. Yet little attention has been paid to the extent to which these area accurate. Using the regular publications by the Energy Information Administration (EIA), we examine the accuracy of annual recursive oil price forecasts generated by the National Energy Modeling System model of the Agency for forecast horizons of up to 15 years. Our results reveal that the EIA model is quite successful at beating the benchmark random walk model, but only at either end of the forecast horizons. We also show that, for the longer horizons, simple econometric forecasting models often produce similar if not better accuracy than the EIA model. Among these, time-varying specifications generally also exhibit stability in their forecast performance. Finally, while combining forecasts does not change the overall patterns, some additional accuracy gains are obtained at intermediate horizons, and in some cases forecast performance stability is also achieved.Jean-Thomas Bernard, Lynda Khalaf, Maral Kichian, Clement Yelou2015Oil price, expert outlooks, long run forecasting, forecast combinationsThe Accuracy of Forecasts Prepared for the Federal Open Market Committee
http://d.repec.org/n?u=RePEc:fip:fedgfe:2015-62&r=all
We analyze forecasts of consumption, nonresidential investment, residential investment, government spending, exports, imports, inventories, gross domestic product, inflation, and unemployment prepared by the staff of the Board of Governors of the Federal Reserve System for meetings of the Federal Open Market Committee from 1997 to 2008, called the Greenbooks. We compare the root mean squared error, mean absolute error, and the proportion of directional errors of Greenbook forecasts of these macroeconomic indicators to the errors from three forecasting benchmarks: a random walk, a first-order autoregressive model, and a Bayesian model averaged forecast from a suite of univariate time-series models commonly taught to first-year economics graduate students. We estimate our forecasting benchmarks both on end-of-sample vintage and real-time vintage data. We find find that Greenbook forecasts significantly outperform our benchmark forecasts for horizons less than one quarter ahead. However, by the one-year forecast horizon, typically at least one of our forecasting benchmarks performs as well as Greenbook forecasts. Greenbook forecasts of the personal consumption expenditures and unemployment tend to do relatively well, while Greenbook forecasts of inventory investment, government expenditures, and inflation tend to do poorly.Chang, Andrew C., Hanson, Tyler J.2015-07-09Bayesian model averaging; Federal Open Market Committee; forecast accuracy; Greenbook; NIPA; national income and product accounts; real-time dataRevisiting the greenbook's relative forecasting performance
http://d.repec.org/n?u=RePEc:hal:journl:hal-01087522&r=all
Since Romer and Romer (2000), a large literature has dealt with the relative forecasting performance of Greenbook macroeconomic forecasts of the Federal Reserve. This paper empirically reviews the existing results by comparing the different methods, data and samples used previously. The sample period is extended compared to previous studies and both real-time and final data are considered. We confirm that the Fed has a superior forecasting performance on inflation but not on output. In addition, we show that the longer the horizon, the more pronounced the advantage of Fed on inflation and that this superi- ority seems to decrease but remains prominent in the more recent period. The second objective of this paper is to underline the potential sources of this supe- riority. It appears that it may stem from better information rather than from a better model of the economy.Paul Hubert2014-10The Role of Economic Policy Uncertainty in Forecasting US Inflation Using a VARFIMA Model
http://d.repec.org/n?u=RePEc:emu:wpaper:15-12.pdf&r=all
We compare inflation forecasts of a vector fractionally integrated autoregressive moving average (VARFIMA) model against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models.Mehmet Balcilar, Rangan Gupta, Charl Jooste2014Inflation; long-range dependency; economic policy uncertainty;Forecasting the oil price using house prices Mechanism and the Business Cycle
http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2015-041&r=all
We show that house prices from Aberdeen in the UK improve in- and out-of-sample oil price forecasts. The improvements are of a similar magnitude to those attained using macroeconomic indicators. We ex- plain these forecast improvements with the dominant role of the oil industry in Aberdeen. House prices aggregate the dispersed knowl- edge of the future oil price that exists in the city. We obtain similar empirical evidence for Houston, another city dominated by the oil in- dustry. Consistent with our explanation, we nd that house prices from economically more diversied areas in the UK and the US do not improve oil price forecasts.Rainer Schulz, Martin Wersing, , 2015-08oil price forecasting, house prices, knowledge spilloverComparing the Forecasting Ability of Financial Conditions Indices: The Case of South Africa
http://d.repec.org/n?u=RePEc:emu:wpaper:15-06.pdf&r=all
In this paper we test the forecasting ability of three estimated financial conditions indices (FCIs) with respect to key macroeconomic variables of output growth, inflation and interest rates. We do this by forecasting the aforementioned macroeconomic variables based on the information contained in the three alternative FCIs using a Bayesian VAR (BVAR), nonlinear logistic vector smooth transition autoregression (VSTAR) and nonparametric (NP) and semi-parametric (SP) regressions, and compare the results with the standard benchmarks of random-walk, univariate autoregressive and classical VAR models. The three FCIs are constructed using rolling-window principal component analysis (PCA), dynamic model averaging (DMA) in the context of a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model, and a time-varying parameter vector autoregressive (TVP-VAR) model with constant factor loadings. Our results suggest that the VSTAR model performs best in the case of forecasting manufacturing production and inflation, while a SP specification proves to be the best for forecasting the interest rate. More importantly, statistics testing for significant differences in forecast errors across models corroborate the finding of superior predictive ability of the nonlinear models.Mehmet Balcilar, Rangan Gupta, Renee van Eyden, Kirsten Thompson2015Financial conditions index; dynamic model averaging; nonlinear logistic smooth transition vector autoregressive model;Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation
http://d.repec.org/n?u=RePEc:emu:wpaper:15-19.pdf&r=all
The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African infl ation by means of non-linear models and using a long historical dataset of seasonally-adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models' forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02-1966:09 and evaluate 24 step-ahead forecasts over an out-of-sample period of 1966:10-2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT's performance in forecasting South African inflation.Pejman Bahramian, Mehmet Balcilar, Rangan Gupta, Patrick T. kanda2014Inflation; forecasting; non-linear models; weighted loss function; South AfricaDo We Need Ultra-High Frequency Data to Forecast Variances?
http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01078158&r=all
In this paper we study various MIDAS models in which the future daily variance is directly related to past observations of intraday predictors. Our goal is to determine if there exists an optimal sampling frequency in terms of volatility prediction. Via Monte Carlo simulations we show that in a world without microstructure noise, the best model is the one using the highest available frequency for the predictors. However, in the presence of microstructure noise, the use of ultra high-frequency predictors may be problematic, leading to poor volatility forecasts. In the application, we consider two highly liquid assets (i.e., Microsoft and S&P 500). We show that, when using raw intraday squared log-returns for the explanatory variable, there is a "high-frequency wall" or frequency limit above which MIDAS-RV forecasts deteriorate. We also show that an improvement can be obtained when using intraday squared log-returns sampled at a higher frequency, provided they are pre-filtered to account for the presence of jumps, intraday periodicity and/or microstructure noise. Finally, we compare the MIDAS model to other competing variance models including GARCH, GAS, HAR-RV and HAR-RV-J models. We find that the MIDAS model provides equivalent or even better variance forecasts than these models, when it is applied on filtered data.Georgiana-Denisa Banulescu, Bertrand Candelon, Christophe Hurlin, Sébastien Laurent2014-10-26Nowcasting prices using Google trends : an application to Central America
http://d.repec.org/n?u=RePEc:wbk:wbrwps:7398&r=all
The objective of this study is to assess the possibility of using Internet search keyword data for forecasting price series in Central America, focusing on Costa Rica, El Salvador, and Honduras. The Internet search data comes from Google Trends. The paper introduces these data and discusses some of the challenges inherent in working with it in the context of developing countries. A new index is introduced for consumer search behavior for these countries using Google Trends data covering a two-week period during a single month. For each country, the study estimates one-step-ahead forecasts for several dozen price series for food and consumer goods categories. The study finds that the addition of the Internet search index improves forecasting over benchmark models in about 20 percent of the series. The paper discusses the reasons for the varied success and potential avenues for future research.Seabold,Skipper, Coppola,Andrea2015-08-19E-Business,Economic Theory&Research,Statistical&Mathematical Sciences,Information and Communication TechnologiesEuropean economic sentiment indicator: An empirical reappraisal
http://d.repec.org/n?u=RePEc:zag:wpaper:1505&r=all
In the last five decades the European Economic Sentiment Indicator (ESI) has positioned itself as a high-quality leading indicator of overall economic activity. Relying on data from five distinct business and consumer survey sectors (industry, retail trade, services, construction and the consumer sector), ESI is conceptualized as a weighted average of the chosen 15 response balances. However, the official methodology of calculating ESI is quite flawed because of the arbitrarily chosen balance response weights. This paper proposes two alternative methods for obtaining novel weights aimed at enhancing ESI's forecasting power. Specifically, the weights are determined by minimizing the root mean square error in simple GDP forecasting regression equations; and by maximizing the correlation coefficient between ESI and GDP growth for various lead lengths (up to 12 months). Both employed methods seem to considerably increase ESI's forecasting accuracy in 26 individual European Union countries. The obtained results are quite robust across specifications.Petar Sorić, Ivana Lolić, Mirjana Čižmešija2015-08-18Business and Consumer Surveys, Economic Sentiment Indicator, Nonlinear Optimization with Constraints, Leading IndicatorMultivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation
http://d.repec.org/n?u=RePEc:usg:sfwpfi:2015:13&r=all
This paper introduces multivariate dynamic copula models to account for the time-varying dependence structure in asset portfolios. We firstly enhance the flexibility of this structure by modeling regimes with multivariate mixture copulas. In our second approach, we derive dynamic elliptical copulas by applying the dynamic conditional correlation model (DCC) to multivariate elliptical copulas. The best-ranked copulas according to both in-sample fit and out-of-sample forecast performance indicate the importance of accounting for time-variation. The superiority of multivariate dynamic Clayton and Student-t models further highlight that positive tail dependence as well as the capability of capturing asymmetries in the dependence structure are crucial features of a well-fitting model for an equity portfolio.Aepli, Matthias D., Frauendorfer, Karl, Fuess, Roland, Paraschiv, Florentina2015-07Multivariate dynamic copulas, regime-switching copulas, dynamic conditional correlation (DCC) model, forecast performance, tail dependence.Direct Evidence on Sticky Information from the Revision Behavior of Professional Forecasters
http://d.repec.org/n?u=RePEc:pra:mprapa:66172&r=all
We provide direct evidence on the sticky information model of Mankiw and Reis (2002) by examining how frequently individual professional forecasters revise their forecasts. We draw interest rate and unemployment rate forecasts from the monthly Wall Street Journal surveys conducted between 2003 and 2013. Consistent with the sticky information model we find that forecasters frequently leave their forecasts unrevised but find evidence that revision frequency increases following larger changes in the information set. We also find revision frequencies became more sensitive to new information after the 2008 financial crisis but only weak evidence that frequent revisers forecast more accurately.Mitchell, Karlyn, Pearce, Douglas2015-07Expectations, Sticky Information, Survey ForecastsLong-Term Water Demand Forecasting
http://d.repec.org/n?u=RePEc:hal:journl:hal-01183853&r=all
This chapter reviews existing long term water demand forecasting methodologies. Based on an extensive literature review, it shows that the domain has benefited from contributions by economists, engineers and system modelers, producing a wide range of tools, many of which have been tested and adopted by practitioners. It illustrates, via three detailed case studies in the USA, the UK and Australia, how different tools can be used depending on the regulatory context, the water scarcity level, the geographic scale at which they are deployed and the technical background of water utilities and their consultants. The chapter reviews how practitioners address three main challenges, namely the integration of land use planning with demand forecasting; accounting for climate change; and dealing with forecast uncertainty. It concludes with a discussion of research perspectives in that domain.Jean-Daniel Rinaudo2015Rainfall Forecasts, Weather and Wages over the Agricultural Production Cycle
http://d.repec.org/n?u=RePEc:ecl:yaleco:128&r=all
We look at the effects of rainfall forecasts and realized rainfall on equilibrium agricultural wages over the course of the agricultural production cycle. We show theoretically that a forecast of good weather can lower wages in the planting stage, by lowering ex ante out-migration, and can exacerbate the negative impact of adverse weather on harvest-stage wages. Using Indian household panel data describing early-season migration and district-level planting- and harvest-stage wages over the period 2005-2010, we find results consistent with the model, indicating that rainfall forecasts improve labor allocations on average but exacerbate wage volatility because they are imperfect.Roswenzweig, Mark R., Udry, Christopher2014-01Copula-Based Factor Model for Credit Risk Analysis
http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2015-042&r=all
A standard quantitative method to access credit risk employs a factor model based on joint multi- variate normal distribution properties. By extending a one-factor Gaussian copula model to make a more accurate default forecast, this paper proposes to incorporate a state-dependent recovery rate into the con- ditional factor loading, and model them by sharing a unique common factor. The common factor governs the default rate and recovery rate simultaneously and creates their association implicitly. In accordance with Basel III, this paper shows that the tendency of default is more governed by systematic risk rather than idiosyncratic risk during a hectic period. Among the models considered, the one with random fac- tor loading and a state-dependent recovery rate turns out to be the most superior on the default prediction.Lu, Meng-Jou, Chen, Cathy Yi-Hsuan, Härdle, Karl Wolfgang, Härdle2015-08Factor Model, Conditional Factor Loading, State-Dependent Recovery RateInternational Stock Return Predictability: Is the Role of U.S. Time-Varying?
http://d.repec.org/n?u=RePEc:emu:wpaper:15-07.pdf&r=all
This study investigates the predictability of 11 industrialized stock returns with emphasis on the role of U.S. returns. Using monthly data spanning 1980:2 to 2014:12, we show that there exist multiple structural breaks and nonlinearities in the data. Therefore, we employ methods that are capable of accounting for these and at the same time date stamping the periods of causal relationship between the U.S. returns and those of the other countries. First we implement a subsample analysis which relies on the set of models, data set and sample range as in Rapach et al. (2013). Our results show that while the U.S. returns played a strong predictive role based on the OLS pairwise Granger causality predictive regression and news-diffusion models, it played no role based on the pooled version of the OLS model and its role based on the adaptive elastic net model is weak relative to Switzerland. Second, we implement our preferred model: a bootstrap rolling window approach using our newly updated data on stock returns for each countries, and find that U.S. stock return has significant predictive ability for all the countries at certain sub-periods. Given these results, it would be misleading to rely on results based on constant-parameter linear models that assume that the relationship between the U.S. returns and those of other industrialized countries are permanent, since the relationship is, in fact, time-varying, and holds only at specific periods.Goodness C. Aye, Mehmet Balcilar, Rangan Gupta2015Stock returns; predictability; structural breaks; nonlinearity; time varying causality;Correlated Beliefs, Returns, and Stock Market Volatility
http://d.repec.org/n?u=RePEc:nbr:nberwo:21480&r=all
Firm-level stock returns exhibit comovement above that in fundamentals, and the gap tends to be higher in developing countries. We investigate whether correlated beliefs among sophisticated, but imperfectly informed, traders can account for the patterns of return correlations across countries. We take a unique approach by turning to direct data on market participants’ information - namely, real-time firm-level earnings forecasts made by equity market analysts. The correlations of firm-level forecasts exceed those of fundamentals and are strongly related to return correlations across countries. A calibrated information-based model demonstrates that the correlation of beliefs implied by analyst forecasts leads to return correlations broadly in line with the data, both in levels and across countries - the correlation between predicted and actual is 0.63. Our findings have implications for market-wide volatility - the model-implied correlations alone can explain 44% of the cross-section of aggregate volatility. The results are robust to controlling for a number of alternative factors put forth by the existing literature.Joel M. David, Ina Simonovska2015-08Comparing Indirect Inference and Likelihood testing: asymptotic and small sample results
http://d.repec.org/n?u=RePEc:cdf:wpaper:2015/8&r=all
Indirect Inference has been found to have much greater power than the Likelihood Ratio in small samples for testing DSGE models. We look at asymptotic and large sample properties of these tests to understand why this might be the case. We find that the power of the LR test is undermined when reestimation of the error parameters is permitted; this offsets the effect of the falseness of structural parameters on the overall forecast error. Even when the two tests are done on a like-for-like basis Indirect Inference has more power because it uses the distribution restricted by the DSGE model being tested.Meenagh, David, Minford, Patrick, Wickens, Michael, Xu, Yongdeng2015-07Indirect Inference; Likelihood Ratio; DSGE model; structural parameters; error processesLIBOR troubles: anomalous movements detection based on Maximum Entropy
http://d.repec.org/n?u=RePEc:arx:papers:1508.04512&r=all
According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. In 2012, several newspapers' articles began to cast doubt on LIBOR integrity, leading surveillance authorities to conduct investigations on banks' behavior. Such procedures resulted in severe fines imposed to involved banks, who recognized their financial inappropriate conduct. In this paper, we uncover such unfair behavior by using a forecasting method based on the Maximum Entropy principle. Our results are robust against changes in parameter settings and could be of great help for market surveillance.Aurelio F. Bariviera, M. T. Martin, A. Plastino, V. Vampa2015-08Disentangling qualitative and quantitative central bank influence
http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01098464&r=all
We aim at investigating how two different types of central bank communication affect the private inflation expectations formation process. The effects of ECB inflation projections and Governing Council members’ speeches on private inflation forecasts are identified through an Instrumental-Variables estimation using a Principal Component Analysis to generate valid instruments. We find that ECB projections have an effect on private current-year forecasts, while ECB speeches and the ECB rate impact next-year forecasts. When both communication types are interacted and go in the same direction, the inflation outlook signal tends to outweigh the policy path signal conveyed to private agents (and vice-versa).Paul Hubert2014-12Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions
http://d.repec.org/n?u=RePEc:hal:journl:hal-01127790&r=all
We consider an inference method for prediction based on belief functions in quantile regression with an asymmetric Laplace distribution. We apply this method to the capital asset pricing model to estimate the beta coefficient and measure volatility under various market conditions at given quantiles. Likelihood-based belief functions are constructed from historical data of the securities in the S&P500 market. The results give us evidence on the systematic risk, in the form of a consonant belief function specified from the asymmetric Laplace distribution likelihood function given recorded data. Finally, we use the method to forecast the return of an individual stock.K Autchariyapanitkul, S Chanaim, S Sriboonchitta, T Denoeux2014-09-26