nep-for New Economics Papers
on Forecasting
Issue of 2010‒03‒28
nineteen papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Inflation in Mexico Using Factor Models: Do Disaggregated CPI Data Improve Forecast Accuracy? By Raúl Ibarra-Ramírez
  2. What do we know about comparing aggregate and disaggregate forecasts? By SBRANA, Giacomo; SILVESTRINI, Andrea
  3. Simulating Inflation Forecasting in Real-Time: How Useful Is a Simple Phillips Curve in Germany, the UK, and the US? By Jens R. Clausen; Bianca Clausen
  4. The Use of GARCH Models in VaR Estimation By Timotheos Angelidis; Alexandros Benos; Stavros Degiannakis
  5. Interest rate pass-through in the major European economies - the role of expectations By Anindya Banerjee; Victor Bystrov; Paul Mizen
  6. Long memory and nonlinearities in realized volatility: a Markov switching approach. By S. Bordignon; D. Raggi
  7. Predicting chaos with Lyapunov exponents : Zero plays no role in forecasting chaotic systems By Dominique Guegan; Justin Leroux
  8. An In-Sample and Out-of-Sample Empirical Investigation of the Nonlinearity in House Prices of South Africa By Mehmet Balcilar; Rangan Gupta; Zahra Shah
  9. Forecasting Monetary Rules in South Africa By Ruthira Naraidoo; Ivan Paya
  10. Structural Models in Real Time By Jaromir Benes; Marianne Johnson; Kevin Clinton; Troy Matheson; Douglas Laxton
  11. Systemic Risks and the Macroeconomy By Gianni De Nicoló; Marcella Lucchetta
  12. The Predictive Content of Commodity Futures By Menzie D. Chinn; Olivier Coibion
  13. Bayesian option pricing using mixed normal heteroskedasticity models By ROMBOUTS, Jeroen V.K.; STENTOFT, Lars
  14. The Global Financial Crisis and Workers' Remittances to Africa: What's the Damage? By Anjali Garg; Adolfo Barajas; Ralph Chami; Connel Fullenkamp
  15. Econometric Models of Forecasting Money Supply in India By Das, Rituparna
  16. Short-Term Inflation Projections: a Bayesian Vector Autoregressive Approach By Domenico Giannone; Michele Lenza; Daphne Momferatu; Luca Onorante
  17. Estimation of a volatility model and portfolio allocation By Adam Clements; Annastiina Silvennoinen
  18. On marginal likelihood computation in change-point models By BAUWENS, Luc; ROMBOUTS, Jeroen
  19. Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model By HEINEN, AndrŽas; VALDESOGO, Alfonso

  1. By: Raúl Ibarra-Ramírez
    Abstract: In this paper we apply a dynamic factor model to generate out of sample forecasts for the inflation rate in Mexico. We evaluate the role of using a wide range of macroeconomic variables with particular interest on the importance of using CPI disaggregated data to forecast inflation. Our data set contains 54 macroeconomic series and 243 CPI subcomponents from 1988 to 2008. Our results indicate that: i) Factor models outperform the benchmark autoregressive model at horizons of one, two, four and six quarters, ii) Using disaggregated price data improves forecasting performance, and iii) The factors are related to key variables in the economy such as output growth and inflation.
    Keywords: Factor models, Inflation forecasting, Disaggregate information, Principal components, Forecast evaluation.
    JEL: C22 C53 E37
    Date: 2010–03
  2. By: SBRANA, Giacomo; SILVESTRINI, Andrea
    Keywords: contemporaneous aggregation, forecasting
    JEL: C10 C32 C43 C52
    Date: 2009–03–01
  3. By: Jens R. Clausen; Bianca Clausen
    Abstract: This paper simulates out-of-sample inflation forecasting for Germany, the UK, and the US. In contrast to other studies, we use output gaps estimated with unrevised real-time GDP data. This exercise assumes an information set similar to that available to a policymaker at a given point in time since GDP data is subject to sometimes substantial revisions. In addition to using real-time datasets for the UK and the US, we employ a dataset for real-time German GDP data not used before. We find that Phillips curves based on ex post output gaps generally improve the accuracy of inflation forecasts compared to an AR(1) forecast but that real-time output gaps often do not help forecasting inflation. This raises the question how operationally useful certain output gap estimates are for forecasting inflation.
    Keywords: Cross country analysis , Economic forecasting , Economic growth , Germany , Gross domestic product , Inflation , United Kingdom , United States ,
    Date: 2010–02–26
  4. By: Timotheos Angelidis; Alexandros Benos; Stavros Degiannakis
    Abstract: We evaluate the performance of an extensive family of ARCH models in modelling daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce better one-step-ahead VaR forecasts; second, the choice of sample size is important for the accuracy of the forecast, whereas the specification of the conditional mean is indifferent. Finally, the ARCH structure producing the most accurate forecasts is different for every portfolio and specific to each equity index.
    Keywords: Value at Risk, GARCH estimation, Backtesting, Volatility forecasting, Quantile Loss Function.
    Date: 2010
  5. By: Anindya Banerjee; Victor Bystrov; Paul Mizen
    Abstract: Much of the literature on interest rate pass through assumes banks set retail rates in relation to contemporary market rates. We argue that future rates also matter, and if forecasts of future rates are included, the empirical specifications of many previous studies are misspecified. Including forecasts rquires careful choice of the data and models used to make forecasts: a large number of variables could influence future market rates, suggesting that factor forecasts method may be an appropriate method to consider. We evaluate forecasts before including them in a model of retail rate adjustment for five interest rates in five European countries and the euro area as a whole. We find a significant role for forecasts of future interest rates in determining short- and long-run pass through, and we show that models which do not include future rates do not provide accurate estimates.
    Keywords: forecasting, factor models, interest rate pass-through
    JEL: C32 C53 E43 E47
    Date: 2010–02
  6. By: S. Bordignon; D. Raggi
    Abstract: Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and predictive densities. The insample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample results at several forecast horizons, show that introducing these nonlinearities produces superior forecasts over those obtained from nested models.
    Date: 2010–02
  7. By: Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Justin Leroux (Institute for Applied Economics - HEC MONTRÉAL)
    Abstract: We propose a nouvel methodology for forecasting chaotic systems which uses information on local Lyapunov exponents (LLEs) to improve upon existing predictors by correcting for their inevitable bias. Using simulations of the Rössler, Lorenz and Chua attractors, we find that accuracy gains can be substantial. Also, we show that the candidate selection problem identified in Guégan and Leroux (2009a,b) can be solved irrespective of the value of LLEs. An important corrolary follows : the focal value of zero, which traditionally distinguishes order from chaos, plays no role whatsoever when forecasting deterministic systems.
    Keywords: Chaos theory, forecasting, Lyapunov exponent, Lorenz attractor, Rössler attractor, Chua attractor, Monte Carlo simulations.
    Date: 2010–01
  8. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, North Cyprus,via Mersin 10, Turkey); Rangan Gupta (Department of Economics, University of Pretoria); Zahra Shah (Department of Economics, University of Pretoria)
    Abstract: This paper first tests if housing prices in the five segments of the South African housing market, namely, large-middle, medium-middle, small-middle, luxury and affordable, exhibits non-linearity based on smooth transition autoregressive (STAR) models estimated using quarterly data covering the period of 1970:Q2 to 2009:Q3. We find overwhelming evidence of non-linearity in these five segments based on in-sample evaluation of the linear and non-linear models. We then provide further support for non-linearity by comparing one- to four-quarters-ahead out-of-sample forecasts of the non-linear time series model with those of the classical and Bayesian versions of the linear autoregressive (AR) models for each of these segments, over an out-of-sample horizon of 2001:Q1 to 2009:Q3, using an in-sample period from 1970:Q2 to 2000:Q4. Our results indicate that barring the one-, two and four-step(s)-ahead forecasts of the small-middle-segment the non-linear model always outperforms the linear models.
    Keywords: Bayesian autoregressive models, Housing market, smooth transition autoregressive models, Forecast accuracy
    JEL: C12 C13 C22 C52 C53 R31
    Date: 2010–03
  9. By: Ruthira Naraidoo (Department of Economics, University of Pretoria); Ivan Paya (Department of Economics, Lancaster University)
    Abstract: This paper is the ?rst one to analyze the ability of linear and nonlinear monetary policy rule specifications as well as nonparametric and semiparametric models in forecasting the nominal interest rate setting that describes the South African Reserve Bank (SARB) policy decisions. We augment the traditional Taylor rule with indicators of asset prices in order to account for potential financial stability targets implicitly considered by the SARB. Using an in-sample period of 1986:01 to 2004:12, we compare the out-of-sample forecasting ability of the models over the period 2005:01 to 2008:12. Our results indicate that the semiparametric models perform particularly well relative to the Taylor rule models currently dominating the monetary policy literature, and that nonlinear Taylor rules improve their performance under the new monetary regime.
    Keywords: Monetary policy, Taylor rules, nonlinearity, nonparametric, semiparametric, forecasting
    JEL: C14 C51 C52 C53 E52 E58
    Date: 2010–03
  10. By: Jaromir Benes; Marianne Johnson; Kevin Clinton; Troy Matheson; Douglas Laxton
    Abstract: This paper outlines a simple approach for incorporating extraneous predictions into structural models. The method allows the forecaster to combine predictions derived from any source in a way that is consistent with the underlying structure of the model. The method is flexible enough that predictions can be up-weighted or down-weighted on a case-by-case basis. We illustrate the approach using a small quarterly structural and real-time data for the United States.
    Keywords: Economic forecasting , Economic indicators , Economic models , Monetary policy ,
    Date: 2010–03–09
  11. By: Gianni De Nicoló; Marcella Lucchetta
    Abstract: This paper presents a modeling framework that delivers joint forecasts of indicators of systemic real risk and systemic financial risk, as well as stress-tests of these indicators as impulse responses to structural shocks identified by standard macroeconomic and banking theory. This framework is implemented using large sets of quarterly time series of indicators of financial and real activity for the G-7 economies for the 1980Q1-2009Q3 period. We obtain two main results. First, there is evidence of out-of sample forecasting power for tail risk realizations of real activity for several countries, suggesting the usefulness of the model as a risk monitoring tool. Second, in all countries aggregate demand shocks are the main drivers of the real cycle, and bank credit demand shocks are the main drivers of the bank lending cycle. These results challenge the common wisdom that constraints in the aggregate supply of credit have been a key driver of the sharp downturn in real activity experienced by the G-7 economies in 2008Q4- 2009Q1.
    Keywords: Banking sector , Capital markets , Economic forecasting , Economic indicators , Economic models , External shocks , Financial risk , Group of seven , International financial system , Time series ,
    Date: 2010–02–04
  12. By: Menzie D. Chinn (Department of Economics, University of Wisconsin); Olivier Coibion (Department of Economics, College of William and Mary)
    Abstract: This paper examines the relationship between spot and futures prices for a broad range of commodities, including energy, precious and base metals, and agricultural commodities. In particular, we examine whether futures prices are (1) an unbiased and/or (2) accurate predictor of subsequent spot prices. While energy futures prices are generally unbiased predictors of future spot prices, there is much stronger evidence against the null for other commodity markets. This difference appears to be driven in part by the depth of each market. We find that over the last five years, it is much harder to reject the null of futures prices being unbiased predictors of future spot prices than in earlier periods for almost all commodities. In addition, futures prices do approximately as well as a random walk in forecasting future spot prices, and vastly outperform a reduced form empirical model.
    Keywords: futures, energy, petroleum, natural gas, heating oil, gasoline, precious metals, base metals, agricultural commodities, forecasting, efficient markets hypothesis.
    JEL: G13 Q43
    Date: 2010–03–15
  13. By: ROMBOUTS, Jeroen V.K.; STENTOFT, Lars
    Keywords: Bayesian inference, option pricing, finite mixture models, out-of-sample prediction, GARCH models
    JEL: C11 C15 C22 G13
    Date: 2009–03–01
  14. By: Anjali Garg; Adolfo Barajas; Ralph Chami; Connel Fullenkamp
    Abstract: Using data on the distribution of migrants from Africa, GDP growth forecasts for host countries, and after estimating remittance multipliers in recipient countries, this paper estimates the impact of the global economic crisis on African GDP via the remittance channel during 2009-2010. It forecasts remittance declines into African countries of between 3 and 14 percentage points, with migrants to Europe hardest hit while migrants within Africa relatively unaffected by the crisis. The estimated impact on GDP for relatively remittance-dependent countries is 2 percent for 2009, but will likely be short-lived, as host country income is projected to rise in 2010.
    Keywords: Africa , Capital flows , Cross country analysis , Economic forecasting , Economic growth , Financial crisis , Global Financial Crisis 2008-2009 , Migration , Workers remittances ,
    Date: 2010–01–29
  15. By: Das, Rituparna
    Abstract: Monetary policy is a very important factor influencing the working of the financial sector of the economy. Forecasting money supply is a part and parcel of designing monetary policy. This paper reviews the econometric models of forecasting money supply in India for the entire post independence period, points out their gaps and tries to fill these gaps. Following are the findings of the paper: (a) Money stock appears as an important determining factor of the economic variables like exchange rate and export volume, which in turn determine the external balance. (b) RBI’s operations in the foreign exchange market affect the exchange rate not immediately, but at 12 months lag. (c) The exchange rate movement may affect the RBI decision to interfere in the foreign exchange market in the immediate short run, but not in the long run. (d) In short run export performance may in some cases give incentives to banks to offer loans, but not in long run. Exercising of discretionary power by bank managers in matter of extending credit facilities is a short-term and not much frequent phenomenon. (e) In absence of bank credit to commercial sector export would be negative or there will be net import.
    Keywords: interest rate; forecasting; money supply; bank credit
    JEL: E51
    Date: 2010–03–10
  16. By: Domenico Giannone; Michele Lenza; Daphne Momferatu; Luca Onorante
    Abstract: In this paper, we construct a large Bayesian Vector Autoregressive model (BVAR) for the Euro Area that captures the complex dynamic inter-relationships between the main components of the Harmonized Index of Consumer Price (HICP) and their determinants. The model is estimated using Bayesian shrinkage. We evaluate the model in real time and find that it produces accurate forecasts. We use the model to study the pass-through of an oil shock and to study the evolution of inflation during the global financial crisis.
    Date: 2010
  17. By: Adam Clements (QUT); Annastiina Silvennoinen (QUT)
    Abstract: Volatility forecasts are important inputs into financial decisions such as portfolio allocation. While the forecasts are often used in such economic applications, the parameters of these models are traditionally estimated within a statistical framework. This leads to an inconsistency between the loss functions under which the model is estimated and under which it is applied. This paper examines the impact of the choice of loss function on model performance in a portfolio allocation setting. It is found that employing a utility based estimation criteria is preferred over likelihood estimation, however a simple mean squared error criteria performs in a similar manner. These finding have obvious implications for the manner in which volatility models are estimated when one wishes to inform the portfolio allocation decision.
    Keywords: Volatility, utility, portfolio allocation, realized volatility, MIDAS
    JEL: C22 G11
    Date: 2010–03–10
  18. By: BAUWENS, Luc (UniversitŽ catholique de Louvain (UCL). Center for Operations Research and Econometrics (CORE)); ROMBOUTS, Jeroen (Institute of Applied Economics at HEC MontrŽal)
    Abstract: Change-point models are useful for modeling time series subject to structural breaks. For interpretation and forecasting, it is essential to estimate correctly the number of change points in this class of models. In Bayesian inference, the number of change points is typically chosen by the marginal likelihood criterion, computed by Chib's method. This method requires to select a value in the parameter space at which the computation is done. We explain in detail how to perform Bayesian inference for a change-point dynamic regression model and how to compute its marginal likelihood. Motivated by our results from three empirical illustrations, a simulation study shows that Chib's method is robust with respect to the choice of the parameter value used in the computations, among posterior mean, mode and quartiles. Furthermore, the performance of the Bayesian information criterion, which is based on maximum likelihood estimates, in selecting the correct model is comparable to that of the marginal likelihood.
    Keywords: BIC, change-point model, Chib's method, marginal likelihood
    JEL: C11 C22 C53
    Date: 2009–10–01
  19. By: HEINEN, AndrŽas (Departamento de Estadistica, Universidad Carlos III de Madrid, Spain); VALDESOGO, Alfonso (CREA, University of Luxembourg, Luxembourg)
    Abstract: We propose a new dynamic model for volatility and dependence in high dimensions, that allows for departures from the normal distribution, both in the marginals and in the dependence. The dependence is modeled with a dynamic canonical vine copula, which can be decomposed into a cascade of bivariate conditional copulas. Due to this decomposition, the model does not suffer from the curse of dimensionality. The canonical vine autoregressive (CAVA) captures asymmetries in the dependence structure. The model is applied to 95 S&P500 stocks. For the marginal distributions, we use non-Gaussian GARCH models, that are designed to capture skewness and kurtosis. By conditioning on the market index and on sector indexes, the dependence structure is much simplified and the model can be considered as a non-linear version of the CAPM or of a market model with sector effects. The model is shown to deliver good forecasts of Value-at-Risk.
    Keywords: asymmetric dependence, high dimension, multivariate copula, multivariate GARCH, Value-at-Risk
    JEL: C32 C53 G10
    Date: 2009–11–01

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