nep-for New Economics Papers
on Forecasting
Issue of 2015‒12‒12
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks By Jozef Barunik; Barbora Malinska
  2. Exploiting the monthly data flow in structural forecasting By Giannone, Domenico; Monti, Francesca; Reichlin, Lucrezia
  3. Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models? By Yue-Jun Zhang; Ting Yao; Ling-Yun He
  4. Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH By Syed Abul, Basher; Perry, Sadorsky
  5. The effectiveness of nonstandard monetary policy measures: evidence from survey data By Altavilla, Carlo; Giannone, Domenico
  6. Estimating Brazilian Monthly GDP: a State-Space Approach* By Issler, João Victor; Notini, Hilton Hostalacio
  7. Bending The Learning Curve By Jan Witajewski-Baltvilks; Elena Verdolini; Massimo Tavoni
  8. Price Scissors as a Dangerous Gap between the Price Projections of Supply and Demand By Vladimir Kossov
  9. Projecting a Range of Possible Results in the December 2015 Elections for National Assembly in Venezuela By David Rosnick

  1. By: Jozef Barunik (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic); Barbora Malinska (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)
    Abstract: The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.
    Keywords: term structure, Nelson-Siegel model, dynamic neural networks, crude oil futures
    JEL: C14 C32 C45 G02 G17
    Date: 2015–11
  2. By: Giannone, Domenico (Federal Reserve Bank of New York); Monti, Francesca (Bank of England); Reichlin, Lucrezia (London Business School)
    Abstract: This paper develops a framework that allows us to combine the tools provided by structural models for economic interpretation and policy analysis with those of reduced-form models designed for nowcasting. We show how to map a quarterly dynamic stochastic general equilibrium (DSGE) model into a higher frequency (monthly) version that maintains the same economic restrictions. Moreover, we show how to augment the monthly DSGE with auxiliary data that can enhance the analysis and the predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of the DSGE and the auxiliary variables offer help in real time for identifying the drivers of the dynamics of the economy.
    Keywords: DSGE models; forecasting; temporal aggregation; mixed-frequency data; large data sets
    JEL: C33 C53 E30
    Date: 2015–12–01
  3. By: Yue-Jun Zhang; Ting Yao; Ling-Yun He
    Abstract: In order to obtain a reasonable and reliable forecast method for crude oil price volatility, this paper evaluates the forecast performance of single-regime GARCH models (including the standard linear GARCH model and the nonlinear GJR-GARCH and EGARCH models) and the two-regime Markov Regime Switching GARCH (MRS-GARCH) model for crude oil price volatility at different data frequencies and time horizons. The results indicate that, first, the two-regime MRS-GARCH model beats other three single-regime GARCH type models in in-sample data estimation under most evaluation criteria, although it appears inferior under a few of other evaluation criteria. Second, the two-regime MRS-GARCH model overall provides more accurate volatility forecast for daily data but this superiority dies way for weekly and monthly data. Third, among the three single-regime GARCH type models, the volatility forecast of the nonlinear GARCH models exhibit greater accuracy than the linear GARCH model for daily data at longer time horizons. Finally, the linear single-regime GARCH model overall performs better than other three nonlinear GARCH type models in Value-at-Risk (VaR) forecast.
    Date: 2015–12
  4. By: Syed Abul, Basher; Perry, Sadorsky
    Abstract: While much research uses multivariate GARCH to model volatility dynamics and risk measures, one particular type of multivariate GARCH model, GO-GARCH, has been underutilized. This paper uses DCC, ADCC and GO-GARCH to model volatilities and conditional correlations between emerging market stock prices, oil prices, VIX, gold prices and bond prices. A rolling window analysis is used to construct out-of-sample onestep-ahead forecasts of dynamic conditional correlations and optimal hedge ratios. In most of the situations we study, oil is the best asset to hedge emerging market stock prices. Hedge ratios from the ADCC model are preferred (most effective) for hedging emerging market stock prices with oil, VIX, or bonds. Hedge ratios estimated from the GO-GARCH are most effective for hedging emerging market stock prices with gold in some instances. These results are reasonably robust to choice of model refits, forecast length and distributional assumptions.
    Keywords: Emerging market stock prices; DCC-GARCH, GO-GARCH; Oil prices; hedging
    JEL: G15 Q43
    Date: 2015–12–06
  5. By: Altavilla, Carlo (European Central Bank); Giannone, Domenico (Federal Reserve Bank of New York)
    Abstract: We assess the perception of professional forecasters regarding the effectiveness of unconventional monetary policy measures announced by the U.S. Federal Reserve after the collapse of Lehman Brothers. Using survey data collected at the individual level, we analyze the change in forecasts of Treasury and corporate bond yields around the announcement dates of nonstandard monetary policy measures. We find that professional forecasters expect bond yields to drop significantly for at least one year after the announcement of accommodative policies.
    Keywords: Survey of Professional Forecasters; large-scale asset purchases; quantitative easing; Operation Twist; forward guidance; tapering
    JEL: E58 E65
    Date: 2015–12–01
  6. By: Issler, João Victor; Notini, Hilton Hostalacio
    Abstract: The first contribution of this paper is to employ a superior interpolation method that enables to estimate, nowcast and forecast monthly Brazilian GDP for 1980-2012 in an integrated way; see Bernanke, Gertler and Watson (1997, Brookings Papers on Eco- nomic Activity). The second contribution, along the spirit of Mariano and Murasawa (2003, Journal of Applied Econometrics), is to propose and test a myriad of inter- polation models and interpolation auxiliary series all coincident with GDP from a business-cycle dating point of view. Based on these results, we finally choose the most appropriate monthly indicator for Brazilian GDP. Third, this monthly GDP estimate is compared to an economic activity indicator widely used by practitioners in Brazil - the Brazilian Economic Activity Index - (IBC-Br). We found that our monthly GDP tracks economic activity better than IBC-Br. This happens by construction, since our state- space approach imposes the restriction (discipline) that our monthly estimate must add up to the quarterly observed series in any given quarter, which may not hold regarding IBC-Br. Moreover, our method has the advantage to be easily implemented: it only requires conditioning on two observed series for estimation, while estimating IBC-Br requires the availability of hundreds of monthly series. The third contribution is to illustrate, in a nowcasting and forecasting exercise, the advantages of our integrated approach. Finally, we compare the chronology of recessions of our monthly estimate with those done elsewhere.
    Date: 2015–11–30
  7. By: Jan Witajewski-Baltvilks (Fondazione Eni Enrico Mattei (FEEM) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Elena Verdolini (Fondazione Eni Enrico Mattei (FEEM) and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Massimo Tavoni (Fondazione Eni Enrico Mattei (FEEM), Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) and Politecnico di Milano)
    Abstract: This paper aims at improving the application of the learning curve, a popular tool used for forecasting future costs of renewable technologies in integrated assessment models (IAMs). First, we formally discuss under what assumptions the traditional (OLS) estimates of the learning curve can deliver meaningful predictions in IAMs. We argue that the most problematic of them is the absence of any effect of technology cost on its demand (reverse causality). Next, we show that this assumption can be relaxed by modifying the traditional econometric method used to estimate the learning curve. The new estimation approach presented in this paper is robust to the reverse causality problem but preserves the reduced form character of the learning curve. Finally, we provide new estimates of learning curves for wind turbines and PV technologies which are tailored for use in IAMs. Our results suggest that the learning rate should be revised downward for wind power, but possibly upward for solar PV.
    Keywords: Learning Curve, Renewable Technologies, Integrated Assessment Models
    JEL: Q42 Q55 C26
    Date: 2015–07
  8. By: Vladimir Kossov (National Research University Higher School of Economics)
    Abstract: The forecast of commodity prices for the upcoming years yields two different prices: one for the producer (seller) and another for the consumer (buyer). Usually, an imperfect market gap between these two prices is not accidental. Strong differences between the views of buyers and sellers are analogous to a dialogue between the deaf and the blind. Thus, a third party, such as the government, is required in order to facilitate communication between the two parties
    Keywords: producer and seller price forecast; producer (seller) price – supply side; consumer (buyer) price – demand side
    JEL: D4 H2 L5
    Date: 2015
  9. By: David Rosnick
    Abstract: This paper finds that a wide range of outcomes are possible in the December 6 National Assembly elections, based on current polling data; and that there is potential for significant disparity between the popular vote and the distribution of seats among the opposing parties and coalitions. The paper simulates, based on the 2010 election results, the 2015 election under various assumptions regarding the government’s share of the vote and the degree to which the opposition is fractured among different coalitions. The projections look at the percent increase in votes the opposition would need in order to secure a simple majority, three-fifths, and two-thirds majority in the Assembly. These results are potentially important because of widespread misunderstanding of the Venezuelan electoral system, and the emphasis on national polls which may differ considerably from the election results for a legislative body under the current voting system. The paper also shows how the current system of disproportional representation for sparsely populated states — similar to a combination of the U.S. Senate and House into a single chamber — will favor the government.
    Keywords: Latin America, elections, electoral system, Venezuela
    JEL: N N4 N46
    Date: 2015–12

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