nep-for New Economics Papers
on Forecasting
Issue of 2014‒05‒04
fourteen papers chosen by
Rob J Hyndman
Monash University

  1. Forecasts and Reactivity By Reto Cueni; Bruno S. Frey
  2. A Note on the Representative Adaptive Learning Algorithm By Jaqueson Galimberti; Michele Berardi
  3. Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation By Pejman Bahramian; Mehmet Balcilar; Rangan Gupta; Patrick T. kanda
  4. Forecasting the U.S. Real House Price Index By Vasilios Plakandaras; Rangan Gupta; Periklis Gogas; Theophilos Papadimitriou
  5. Forecasting Chilean Inflation with International Factors By Pablo Pincheira; Andrés Gatty
  6. Modelling Return and Volatility of Oil Price using Dual Long Memory Models By Heni BOUBAKER; Nadia SGHAIER
  7. Forecasting the Volatility of the Dow Jones Islamic Stock Market Index: Long Memory vs. Regime Switching By Adnen Ben Nasr; Thomas Lux; Ahdi Noomen Ajmi; Rangan Gupta
  8. The FRBNY staff underlying inflation gauge: UIG By Amstad, Marlene; Potter, Simon M.; Rich, Robert W.
  9. Trade Misinvoicing and Macroeconomic Outcomes in India By Raghbendra Jha; Truong Duc Nguyen
  10. The Elusive Predictive Ability of Global Inflation By Carlos Medel; Michael Pedersen; Pablo Pincheira
  11. Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks By Nonejad, Nima
  12. Editorial for the Special Issue on 'Computational Methods for Russian Economic and Financial Modelling' By Fantazzini, Dean
  13. Directional Volatility Spillovers between Agricultural, Crude Oil, Real Estate and other Financial Markets By Grosche, Stephanie; Heckelei, Thomas
  14. External Balances, Trade Flows and Financial Conditions By Evans, Martin

  1. By: Reto Cueni; Bruno S. Frey
    Abstract: Forecasters’ estimates influence peoples’ expectations, their decisions and thus also actual market outcomes. Such reactivity to forecasts induces externalities which harm the ex-post assessment of the forecasters’ accuracy and in turn the improvement of forecasting accuracy and market outcomes. To empirically analyze the impact of reactivity on forecast accuracy, we compare the development of the forecast error in a non-reactive system, such as the market for weather forecasts, to reactive systems, such as the stock and the art market. The evidence strongly supports our proposition that in reactive systems, despite the vast increase of data and processing techniques, forecast accuracy did not improve, while forecast accuracy in non-reactive systems did.
    Date: 2014–03
  2. By: Jaqueson Galimberti (KOF Swiss Economic Institute, ETH Zurich, Switzerland); Michele Berardi (University of Manchester, United Kingdom)
    Abstract: We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting accuracy and in matching (future) survey forecasts.
    Keywords: expectations, learning algorithms, forecasting, learning-to-forecast, least squares, stochastic gradient
    JEL: C53 D83 D84 E37
    Date: 2014–04
  3. By: Pejman Bahramian (Department of Economics, Eastern Mediterranean University, Famagusta, Turkish Republic of Northern Cyprus, via Mersin 10, Turkey); Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, Turkish Republic of Northern Cyprus, via Mersin 10, Turkey); Rangan Gupta (Department of Economics, University of Pretoria); Patrick T. kanda (Department of Economics, University of Pretoria)
    Abstract: 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.
    Keywords: Inflation, forecasting, non-linear models, weighted loss function, South Africa
    JEL: C32 E31 E52
    Date: 2014–04
  4. By: Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Greece); Rangan Gupta (Department of Economics, University of Pretoria); Periklis Gogas (Department of Economics, Democritus University of Thrace, Greece); Theophilos Papadimitriou (Department of Economics, Democritus University of Thrace, Greece)
    Abstract: The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.
    Keywords: House prices, Forecasting, Machine learning, Support vector regression
    JEL: C32 C53 R31
    Date: 2014–05
  5. By: Pablo Pincheira; Andrés Gatty
    Abstract: In this paper we build forecasts for Chilean year-on-year inflation using simple time-series models augmented with different measures of international inflation. Broadly speaking, we construct two families of international inflation factors. The first family is built using year-on-year inflation of 18 Latin American (LA) countries (excluding Chile). The second family is built using year-on-year inflation of 30 OECD countries (excluding Chile). We show sound in-sample and pseudo out-ofsample evidence indicating that these international factors do help forecast Chilean inflation at several horizons. Incorporating the international factors reduce the Root Mean Squared Prediction Error of pure univariate SARIMA models statistically speaking. We also show that the predictive pass-through from international to local inflation has increased in the recent years. As a final exercise we construct another international inflation factor as an average of the inflation of fifteen countries from which Chile gets a high percentage of its imports. With the aid of this factor the models outperform our univariate benchmarks but also underperform the results obtained with the broader factors built with LA or OECD countries, suggesting that imported inflation is not the only channel explaining our findings.
    Date: 2014–02
  6. By: Heni BOUBAKER; Nadia SGHAIER
    Abstract: This paper investigates the dynamic properties of both return and volatility of the oil price. The analysis is carried out using a set of double long memory specifications incorporating several features such as long range dependence, asymmetry in conditional variances and time varying correlations. The in-sample diagnostic tests as well as the out-of-sample forecasting results show the performance of the ARFIMA-FIAPARCH model.
    Keywords: Oil price, return, volatility, dual long memory.
    Date: 2014–04–29
  7. By: Adnen Ben Nasr; Thomas Lux; Ahdi Noomen Ajmi; Rangan Gupta
    Abstract: The financial crisis has fueled interest in alternatives to traditional asset classes that might be less affected by large market gyrations and, thus, provide for a less volatile development of a portfolio. One attempt at selecting stocks that are less prone to extreme risks, is obeyance of Islamic Sharia rules. In this light, we investigate the statistical properties of the DJIM index and explore its volatility dynamics using a number of up-to-date statistical models allowing for long memory and regime-switching dynamics. We find that the DJIM shares all stylized facts of traditional asset classes, and estimation results and forecasting performance for various volatility models are also in line with prevalent findings in the literature. Overall, the relatively new Markov-switching multifractal model performs best under the majority of time horizons and loss criteria. Long memory GARCH-type models always improve upon the short-memory GARCH specification and additionally allowing for regime changes can further improve their performance.
    Keywords: Islamic finance, volatility dynamics, long memory, multifractals. Tals.
    JEL: G15 G17 G23
    Date: 2014–04–28
  8. By: Amstad, Marlene (Federal Reserve Bank of New York); Potter, Simon M. (Federal Reserve Bank of New York); Rich, Robert W. (Federal Reserve Bank of New York)
    Abstract: Monetary policymakers and long-term investors would benefit greatly from a measure of underlying inflation that uses all relevant information, is available in real time, and forecasts inflation better than traditional underlying inflation measures such as core inflation measures. This paper presents the “FRBNY Staff Underlying Inflation Gauge (UIG)” for CPI and PCE. Using a dynamic factor model approach, the UIG is derived from a broad data set that extends beyond price series to include a wide range of nominal, real, and financial variables. It also considers the specific and time-varying persistence of individual subcomponents of an inflation series. An attractive feature of the UIG is that it can be updated on a daily basis, which allows for a close monitoring of changes in underlying inflation. This capability can be very useful when large and sudden economic fluctuations occur, as at the end of 2008. In addition, the UIG displays greater forecast accuracy than traditional measures of core inflation.
    Keywords: expectations; survey forecasts; imperfect information; term structure of disagreement
    JEL: C13 C33 C43 E31
    Date: 2014–04–22
  9. By: Raghbendra Jha; Truong Duc Nguyen
    Abstract: This paper has two main objectives. First, it computes capital flight (CF) through trade misinvoicing from India using data from UNCOMTRADE, MIT Observatory of Economic Complexity and IMF E-library. India's trade with 17 countries over the period 1988-2012 is considered. We find that CF has accelerated since 2004 and particularly sharply since 2007. It peaked at nearly $40 billion in 2008 with the total outflow between 1988-2012 exceeding $186 billion. Second, we model the mutual dependence of GDP growth, CF, and various risk factors in a VAR framework. We find that the VAR models chosen fit the data well. We conduct impulse response function analysis, forecast the key variables until 2020 and forecast error variance decomposition. Broadly we find that, if left undisturbed, CF through trade misinvoicing will continue to be high and play a significant macroeconomic role. Thus, CF needs to be checked urgently not only because it is a drain of the country's resources but also because it continues to have a significant and, by its very nature, uncontrollable effect on the economy. At least some of the failures of current macroeconomic policy in India could be attributed to CF.
    Keywords: Trade Misinvocing, VAR, Impulse Response, Forecasting, India
    JEL: E17 F32 F47 K42
    Date: 2014–04
  10. By: Carlos Medel; Michael Pedersen; Pablo Pincheira
    Abstract: In this paper we analyze the contribution of international measures of inflation to predict local ones. To that end, we consider the set of current thirty one OECD economies for which inflation data is available at a monthly frequency. By considering this set of countries, a span of time including the post-crisis period and measures of both core and headline inflation, we are extending in three important dimensions the previous literature on this topic. Our main results indicate that on average there is a non-negligible predictive pass-through from international to local inflation both at the core and headline levels. This predictive pass-through has increased in the last period of our sample. Nevertheless, there is heterogeneity in the size and statistical significance of this pass-through which is especially important at the core level. Finally, important reductions in the Root Mean Squared Prediction Error are obtained only for a handful of countries
    Date: 2014–03
  11. By: Nonejad, Nima
    Abstract: Particle Gibbs with ancestor sampling (PG-AS) is a new tool in the family of sequential Monte Carlo methods. We apply PG-AS to the challenging class of unobserved component time series models and demonstrate its flexibility under different circumstances. We also combine discrete structural breaks within the unobserved component model framework. We do this by modeling and forecasting time series characteristics of postwar US inflation using a long memory autoregressive fractionally integrated moving average model with stochastic volatility where we allow for structural breaks in the level, long and short memory parameters contemporaneously with breaks in the level, persistence and the conditional volatility of the volatility of inflation.
    Keywords: Ancestor sampling, Bayes, Particle filtering, Structural breaks
    JEL: C11 C22 C52 C63
    Date: 2014–05–01
  12. By: Fantazzini, Dean
    Abstract: This double-issue contains 11 papers invited for the first special issue on “Computational methods for Russian economic and financial modelling”. It was an attempt to explore and bring together practical, state-of-the-art applications of computational techniques with a particular focus on Russia and the Commonwealth of Independent States. The response was beyond expectations and managed to cover a wide range of issues, so that a double-issue was considered: the first dealing with Finance and the second with Economics.
    Keywords: Forecasting; oil price; Google; Russian stock market; T-distribution with vector degrees of freedom; portfolio management; Fund manager; Russian banking sector; Credit Risk; DSGE; Russia; Immigrants; Intertemporal general equilibrium model; Intertemporal equilibrium; Inflation; Inflation expectations;
    JEL: C02 C11 C22 C32 C61 C68 E5 G1 G2 J0 J1
    Date: 2014
  13. By: Grosche, Stephanie; Heckelei, Thomas
    Abstract: The addition of commodities to financial portfolios and resulting weight adjustments may create volatility linkages between commodity and financial markets, especially during financial crises. Also, biofuel mandates are suspected to integrate agricultural and energy markets. We calculate directional pairwise range-based volatility spillover indices (Diebold and Yilmaz, 2012) for corn, wheat, soybeans, crude oil, equity, real estate, treasury notes and U.S. dollar exchange rates between 06/1998 and 12/2013. During the recent financial crisis, volatility spillovers from equity and real estate to commodities rise to unprecedented levels. Yet, we find no indication of a parallel increase of volatility linkages between agricultural and crude oil markets.
    Keywords: Volatility spillovers, financialization, generalized forecast error variance decomposition, VAR, Agricultural and Food Policy, Agricultural Finance, Financial Economics, Research Methods/ Statistical Methods, Q13, C32, G11, G01,
    Date: 2014–04–17
  14. By: Evans, Martin
    Abstract: This paper studies how changing expectations concerning future trade and financial con- ditions are reflected in international external positions. In the absence of Ponzi schemes and arbitrage opportunities, the net foreign asset position of any country must, as a matter of theory, equal the expected present discounted value of future trade deficits, discounted at the cumulated world stochastic discount factor (SDF) that prices all freely traded financial assets. I study the forecasting implications of this theoretical link in 12 countries (Australia, Canada, China, France, Germany, India, Italy, Japan, South Korea, Thailand, The United States and The United Kingdom) between 1970 and 2011. I find that variations in the ex- ternal positions of most countries reflect changing expectations about trade conditions far into the future. I also find the changing forecasts for the future path of the world SDF is reflected in the dynamics of the U.S. external position.
    Keywords: Global Imbalances, Foreign Asset Positions, Current Accounts, Trade Flows, International Asset Pricing
    JEL: F3 F31 F32 F34
    Date: 2014–05–01

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