nep-for New Economics Papers
on Forecasting
Issue of 2015‒08‒01
nine papers chosen by
Rob J Hyndman
Monash University

  1. Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models By Cesar Carrera; Alan Ledesma
  2. Efficiency Gains in Commodity Forecasting with High Volatility in Prices using Different Levels of Data Aggregation By Pena-Levano, Luis M.; Ramirez, Octavio; Renteria-Pinon, Mario
  3. Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance By Roberto Casarin; Stefano Grassi; Francesco Ravazzolo; Herman K. van Dijk
  4. Financialization of Agricultural Commodity Markets: Do Financial Data Help to Forecast Agricultural Prices? By Etienne, Xiaoli L.
  5. What is the Expected Return on the Market? By Martin, Ian
  6. Inflation forecasting models for Uganda: is mobile money relevant? By Aron, Janine; Muellbauer, John; Sebudde, Rachel
  7. Does Spatial Correlation Matter in Econometric Models of Crop Yield Response and Weather? By Yun, Seong Do; Gramig, Benjamin M; Delgado, Michael S.; Florax, Raymond J.G.M.
  8. The Public R&D and Productivity Growth in Australian Broadacre Agriculture: A Cointegration and Causality Approach By Khan, Farid; Salim, Ruhul
  9. How to predict the consequences of a tick value change? Evidence from the Tokyo Stock Exchange pilot program By Weibing Huang; Charles-Albert Lehalle; Mathieu Rosenbaum

  1. By: Cesar Carrera (Banco Central de Reserva del Perú); Alan Ledesma (UC Santa Cruz)
    Abstract: We forecast 18 groups of individual components of the Consumer Price Index (CPI) using a large Bayesian vector autoregressive model (BVAR) and then aggregate those forecasts in order to obtain a headline inflation forecast (bottom-up approach). De Mol et al. (2006) and Banbura et al. (2010) show that BVAR's forecasts can be significantly improved by the appropriate selection of the shrinkage hyperparameter. We follow Banbura et al. (2010)’s strategy of “mixed priors," estimate the shrinkage parameter, and forecast inflation. Our findings suggest that this strategy for modeling outperform the benchmark random walk as well as other strategies for forecasting inflation.
    Keywords: Inflation forecasting, aggregate forecast, Bayesian VAR
    JEL: C22 C52 C53 E37
    Date: 2015–07
  2. By: Pena-Levano, Luis M.; Ramirez, Octavio; Renteria-Pinon, Mario
    Keywords: efficiency, forecast, ARMA models, GARCH model, volatility, price, commodity, cotton, livestock, coffee, dissagregation, Agribusiness, Research Methods/ Statistical Methods, Risk and Uncertainty, C53, E17, C10,
    Date: 2015
  3. By: Roberto Casarin (University Ca’ Foscari of Venice, Italy); Stefano Grassi (University of Kent, United Kingdom); Francesco Ravazzolo (Norges Bank and Centre for Applied Macro and Petroleum Economics, Norway); Herman K. van Dijk (Erasmus University Rotterdam, VU University Amsterdam, the Netherlands)
    Abstract: A Bayesian nonparametric predictive model is introduced to construct time-varying weighted combinations of a large set of predictive densities. A clustering mechanism allocates these densities into a smaller number of mutually exclusive subsets. Using properties of Aitchinson's geometry of the simplex, combination weights are defined with a probabilistic interpretation. The class-preserving property of the logistic-normal distribution is used to define a compositional dynamic factor model for the weight dynamics with latent factors defined on a reduced dimension simplex. Groups of predictive models with combination weights are updated with parallel clustering and sequential Monte Carlo filters. The procedure is applied to predict Standard & Poor's 500 index using more than 7000 predictive densities based on US individual stocks and finds substantial forecast and econ omic gains. Similar forecast gains are obtained in point and density forecasting of US real GDP, Inflation, Treasury Bill yield and employment using a large data set.
    Keywords: Density Combination; Large Set of Predictive Densities; Compositional Factor Models; Nonlinear State Space; Bayesian Inference; GPU Computing
    JEL: C11 C15 C53 E37
    Date: 2015–07–20
  4. By: Etienne, Xiaoli L.
    Abstract: The dramatic rise in commodity index investment have made many market analysts and researchers believe that commodity markets have undergone a financialization process that forged a closer link between commodity and financial markets. I empirically test whether this hypothesis is true in a forecasting context by using high-frequency financial data to forecast monthly US corn prices. Specific financial series examined include the Baltic Dry Index, the US exchange rate, the Standard and Poor’s 500 market index, the 3-month US Treasury bill interest rate, and crude oil futures prices. Using a recently developed statistical model that deals with mixed-frequency data, I find that while some improvements may be made when including high-frequency financial data in the forecasting model, the improvements in mean-squared prediction error and directional accuracy using such models are minimal, and that models generated from random walk and autoregressive models perform satisfactory well compared to more complicated models.
    Keywords: mixed-frequency data, corn prices, volatility, price forecasting, mean-squared prediction error, directional accuracy, commodity index funds, financial market, financialization, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Demand and Price Analysis, Marketing, Risk and Uncertainty, Q11, Q14, O13, C5, C00,
    Date: 2015–07
  5. By: Martin, Ian
    Abstract: This paper presents a new lower bound on the equity premium in terms of a volatility index, SVIX, that can be calculated from index option prices. This bound, which relies only on very weak assumptions, implies that the equity premium is extremely volatile, and that it rose above 20% at the height of the crisis in 2008. More aggressively, I argue that the lower bound---whose time-series average is about 5%---is approximately tight and that the high equity premia available at times of stress largely reflect high expected returns over the very short run. Under a stronger assumption, I show how to use option prices to measure the probability that the market goes up (or down) over some given horizon, and to compute the expected excess return on the market conditional on the market going up (or down).
    Keywords: equity premium; expected return; index options; predictive regression; return forecasting; SVIX; VIX
    JEL: G00 G1
    Date: 2015–07
  6. By: Aron, Janine; Muellbauer, John; Sebudde, Rachel
    Abstract: Forecasting inflation is challenging in emerging markets, where trade and monetary regimes have shifted, and the exchange rate, energy and food prices are highly volatile. Mobile money is a recent financial innovation giving financial transaction services via a mobile phone, including to the unbanked. Stable models for the 1-month and 3-month-ahead rates of inflation in Uganda, measured by the consumer price index for food and non-food, and for the domestic fuel price, are estimated over 1994-2013. Key features are the use of multivariate models with equilibrium-correction terms in relative prices; introducing non-linearities to proxy state dependence in the inflation process; and applying a ‘parsimonious longer lags’ (PLL) parameterisation to feature lags up to 12 months. International influences through foreign prices and the exchange rate (including food prices in Kenya after regional integration) have an important influence on the dependent variables, as does the growth of domestic credit. Rainfall deviation from the long-run mean is an important driver for all, most dramatically for food. The domestic money stock is irrelevant for food and fuel inflation, but has a small effect on non-food inflation. Other drivers include the trade and current account balances, fiscal balance, terms of trade and trade openness, and the international interest rate differential. Parameter stability tests suggest the models could be useful for short-term forecasting of inflation. There is no serious evidence of a link between mobile money and inflation.
    Keywords: error correction models; mobile money; model selection; modelling inflation
    JEL: C22 C51 C52 C53 E31 E37 E52
    Date: 2015–07
  7. By: Yun, Seong Do; Gramig, Benjamin M; Delgado, Michael S.; Florax, Raymond J.G.M.
    Abstract: Due to the rapidly growing availability and accessibility of spatially gridded weather data products, significant effort has been devoted to handling weather and climate variables properly in econometric models. It is, however, noteworthy that relatively less econometric attention is paid to how spatial correlation in weather variables and econometric models can be specified and performed. To fill this gap, this study scrutinizes the main source spatial correlation in econometric models of weather and climate variables, and implements in-sample and out-of-sample prediction analyses with spatial panel model specifications of crop yield response function. First, this paper theoretically and empirically demonstrates that the aggregation bias is a main source of spatial correlation rather than omitted weather variables. With soil variables, we specify six competing specifications of crop yield response function with pooled, fixed effects and random effects with spatially robust standard errors. From the results of prediction performances, we demonstrate that the choice of predictor (prediction models) can be motivated from the purpose of models rather than a better prediction performance. In addition, we empirically argue that the omitted socio-economic variables are not a serious econometric concern in crop yield response function of this study.
    Keywords: Spatial Correlation, Panel Estimation Approach, Crop Yield Response Function, Weather Variables, Climate Change, Crop Production/Industries, Environmental Economics and Policy, Food Security and Poverty, Research and Development/Tech Change/Emerging Technologies, Resource /Energy Economics and Policy, C33, C53, Q51, Q54,
    Date: 2015
  8. By: Khan, Farid; Salim, Ruhul
    Abstract: This study investigates the nexus between research and development expenditure and productivity growth in Australian broadacre agriculture using country-level time-series data for the period 1953 to 2009. Using standard time-series econometrics data are analysed to examine the dynamic relationships between research and development expenditure (R&D) and total factor productivity (TFP) growth. Findings here provide econometric evidence of a co-integrating relationship between R&D and productivity growth, and a unidirectional causality emergent from R&D to TFP growth. Moreover, employing variance decomposition and impulse response function the dynamic properties of the model are explored beyond the sample periods. Findings suggest that R&D can be readily linked to the variation in productivity growth beyond the sample periods. Further, forecasting result suggests a significant out-of-sample relationship exists between the public R&D and productivity in broadacre agriculture. We used a novel method MIRR which is conceptually superior than the conventional IRR to obtain a credible estimate of returns on public research investment. We found MIRR of 10.06% per year for the reinvestment rate of 3% per year. Therefore, results establishing long run relationship between productivity and R&D in Australian agriculture shed light on the future policies in R&D investments in Australia.
    Keywords: Public Research & Development (R&D), Productivity, Australian Broadacre Agriculture, Cointegration, Internal Rates of Return, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,
    Date: 2015–02
  9. By: Weibing Huang; Charles-Albert Lehalle; Mathieu Rosenbaum
    Abstract: The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the consequences of a tick value change on the microstructure of an asset. To that purpose, we analyze the pilot program on tick value modifications started in 2014 by the Tokyo Stock Exchange in light of this methodology. We focus on forecasting the future cost of market and limit orders after a tick value change and show that our predictions are very accurate. Furthermore, for each asset involved in the pilot program, we are able to define (ex ante) an optimal tick value. This enables us to classify the stocks according to the relevance of their tick value, before and after its modification.
    Date: 2015–07

This nep-for issue is ©2015 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.