nep-for New Economics Papers
on Forecasting
Issue of 2014‒11‒01
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Bagging Constrained Equity Premium Predictors By Tae-Hwy Lee; Eric Hillebrand; Marcelo Medeiros
  2. Evaluating Conditional Forecasts from Vector Autoregressions By Clark, Todd E.; McCracken, Michael W.
  3. Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance By Marco Del Negro; Raiden B. Hasegawa; Frank Schorfheide
  4. Density forecasts with MIDAS models By Knut Are Aastveit; Claudia Foroni; Francesco Ravazzolo
  5. Real-time forecasting us GDP from small-scale factor models By Máximo Camacho; Jaime Martínez-Martín
  6. Inflation Forecasts and Forecaster Herding: Evidence from South African Survey Data By Christian Pierdzioch; Monique B. Reid; Rangan Gupta
  7. Gold Price Forecasts in a Dynamic Model Averaging Framework – Have the Determinants Changed Over Time? By Dirk G. Baur; Joscha Beckmann; Robert Czudaj
  8. Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions By Guillén, Osmani; Hecq, Alain; Issler, João Victor; Saraiva, Diogo
  9. Rationality and Momentum in Real Estate Investment Forecasts By Dimitrios Papastamos; Fotis Mouzakis; Simon Stevenson
  10. Real Estate Returns Predictability Revisited: Novel Evidence from the US REITs Market By Kola Akinsomi; Goodness C. Aye; Vassilios Babalos; Fotini Economou; Rangan Gupta
  11. Analysing and forecasting price dynamics across euro area countries and sectors: A panel VAR approach By Stéphane Dées; Jochen Güntner

  1. By: Tae-Hwy Lee (Department of Economics, University of California Riverside); Eric Hillebrand (Aarhus University); Marcelo Medeiros (Pontifical Catholic University of Rio de Janeiro)
    Abstract: The literature on excess return prediction has considered a wide array of estimation schemes, among them unrestricted and restricted regression coefficients. We consider bootstrap aggregation (bagging) to smooth parameter restrictions. Two types of restrictions are considered: positivity of the regression coefficient and positivity of the forecast. Bagging constrained estimators can have smaller asymptotic mean-squared prediction errors than forecasts from a restricted model without bagging. Monte Carlo simulations show that forecast gains can be achieved in realistic sample sizes for the stock return problem. In an empirical application using the data set of Campbell, J., and S. Thompson (2008): "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?", Review of Financial Studies 21, 1511-1531, we show that we can improve the forecast performance further by smoothing the restriction through bagging.
    Keywords: Constraints on predictive regression function; Bagging; Asymptotic MSE; Equity premium; Out-of-sample forecasting; Economic value functions.
    JEL: C5 E4 G1
    Date: 2014–09
  2. By: Clark, Todd E. (Federal Reserve Bank of St. Louis); McCracken, Michael W. (Federal Reserve Bank of St. Louis)
    Abstract: Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout the analysis, we focus on tests of bias, efficiency, and equal accuracy applied to conditional forecasts from VAR models.
    Keywords: Prediction; forecastingf out-of-sample
    JEL: C12 C32 C52 C53
    Date: 2014–09–01
  3. By: Marco Del Negro (Federal Reserve Bank of New York); Raiden B. Hasegawa (Wharton School, University of Pennsylvania); Frank Schorfheide (Department of Economics, University of Pennsylvania)
    Abstract: We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call Dynamic Pools, and use it to investigate the relative forecasting performance of DSGE models with and without financial frictions for output growth and inflation from 1992 to 2011. We find strong evidence of time variation in the pool's weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but does not perform as well in tranquil periods. The dynamic pool's weights react in a timely fashion to changes in the environment, leading to real-time forecast improvements relative to other methods of density forecast combination, such as Bayesian Model Averaging, optimal (static) pools, and equal weights. We show how a policymaker dealing with model uncertainty could have used a dynamic pools to perform a counterfactual exercise (responding to the gap in labor market conditions) in the immediate aftermath of the Lehman crisis.
    Keywords: Bayesian estimation, DSGE Models, Financial Frictions, Forecasting, Great Recession, Linear Prediction Pools
    JEL: C53 E31 E32 E37
    Date: 2014–10–03
  4. By: Knut Are Aastveit; Claudia Foroni; Francesco Ravazzolo
    Abstract: In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions with and without an autoregressive component. First, we compare the forecasting performance of the different MIDAS models in Monte Carlo simulation experiments. We find that the results in terms of point and density forecasts are coherent. Moreover, the results do not clearly indicate a superior performance of one of the models under scrutiny when the persistence of the low frequency variable is low. Some differences are instead more evident when the persistence is high, for which the AR- MIDAS and the AR-U-MIDAS produce better forecasts. Second, in an empirical exercise we evaluate density forecasts for quarterly US output growth, exploiting information from typical monthly series. We find that MIDAS models provide accurate and timely density forecasts.
    Keywords: Mixed Data Sampling, Density Forecasts, Nowcasting
    JEL: C10 C53 E37
    Date: 2014–09
  5. By: Máximo Camacho (Universidad de Murcia); Jaime Martínez-Martín (Banco de España)
    Abstract: We show that the single-index dynamic factor model developed by Aruoba and Diebold (Am Econ Rev, 100:20-24, 2010) to construct an index of US business cycle conditions is also very useful for forecasting US GDP growth in real time. In addition, we adapt the model to include survey data and financial indicators. We find that our extension is unequivocally the preferred alternative for computing backcasts. In nowcasting and forecasting, our model is able to forecast growth as well as AD and better than several baseline alternatives. Finally, we show that our extension could also be used to infer US business cycles with great accuracy.
    Keywords: real-time forecasting, economic indicators, business cycles.
    JEL: E32 C22 E27
    Date: 2014–10
  6. By: Christian Pierdzioch (Department of Economics, Helmut-Schmidt-University); Monique B. Reid (Department of Economics, Stellenbosch University); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: We use South African survey data to study whether short-term inflation forecasts are unbiased. Depending on how we model a forecaster’s information set, we find that forecasts are biased due to forecaster herding. Evidence of forecaster herding is strong when we assume that the information set contains no information on the contemporaneous forecasts of others. When we randomly allocate forecasters into a group of early forecasters who can only observe the past forecasts of others and late forecasters who can observe the contemporaneous forecasters of their predecessors, then evidence of forecaster herding weakens. Further, evidence of forecaster herding is strong and significant in times of high inflation volatility. In time of low inflation volatility, in contrast, forecaster anti-herding seems to dominate.
    Keywords: Inflation rate, Forecasting, Forecaster Herding
    JEL: C53 D82 E37
    Date: 2014–10
  7. By: Dirk G. Baur; Joscha Beckmann; Robert Czudaj
    Abstract: The price of gold is influenced by a wide range of local and global factors such as commodity prices, interest rates, inflation expectations, exchange rate changes and stock market volatility among others. Hence, forecasting the price of gold is a notoriously difficult task and the main problem a researcher faces is to select the relevant regressors at each point in time. This combination of model and parameter uncertainty is explicitly accounted for by Dynamic Model Averaging which allows both the forecasting model and the coefficients to change over time. Based on this framework, we systematically evaluate a large set of possible gold price determinants and use both the predictive likelihood and the mean squared error as a measure of the forecasting performance. We carefully assess which predictors are relevant for forecasting at different points in time through the posterior probability. Our findings show that (1) DMA improves forecasts compared to other frameworks and (2) provides clear evidence for the time-variation of gold price predictors.
    Keywords: Bayesian econometrics; dynamic model averaging; forecasting; gold
    JEL: C32 G10 G15 F37
    Date: 2014–10
  8. By: Guillén, Osmani; Hecq, Alain; Issler, João Victor; Saraiva, Diogo
    Abstract: This paper has two original contributions. First, we show that the presentvalue model (PVM hereafter), which has a wide application in macroeconomicsand fi nance, entails common cyclical feature restrictions in the dynamics of thevector error-correction representation (Vahid and Engle, 1993); something thathas been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. Wealso provide the present value reduced rank constraints to be tested within thelog-linear model. Our second contribution relates to forecasting time seriesthat are subject to those long and short-run reduced rank restrictions. Thereason why appropriate common cyclical feature restrictions might improveforecasting is because it finds natural exclusion restrictions preventing theestimation of useless parameters, which would otherwise contribute to theincrease of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to besubject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes thelevels of interest rates with long and short maturities, the second includes thelevel of real price and dividend for the S&P composite index, and the thirdincludes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that betterforecasts can be achieved when restrictions are applied to them. Moreover,imposing short-run restrictions produce forecast winners 70% of the time fortarget variables of PVMs and 63.33% of the time when all variables in thesystem are considered.
    Date: 2014–06–02
  9. By: Dimitrios Papastamos (Eurobank EFG Property Services S.A.); Fotis Mouzakis (Frynon Consulting); Simon Stevenson (School of Real Estate & Planning, Henley Business School, University of Reading)
    Abstract: This study examines the rationality and momentum in forecasts for rental, capital value and total returns for the real estate investment market in the United Kingdom. In order to investigate if forecasters are affected by the general economic conditions present at the time of forecast we incorporate into the analysis Gross Domestic Product (GDP) and the Default Spread (DS). The empirical findings show high levels of momentum in the forecasts, with highly persistent forecast errors. The results also indicate that forecasters are affected by adverse conditions. This is consistent with the finding that they tend to exhibit greater forecast error when the property market is underperforming and vice-versa.
    Keywords: C10, C53, L80
    Date: 2014–05
  10. By: Kola Akinsomi (School of Construction Economics and Management, University of Witwatersrand, Johannesburg, South Africa.); Goodness C. Aye (Department of Economics, University of Pretoria); Vassilios Babalos (Department of Accounting & Finance, Technological Educational Institute of Peloponnese, Greece; Department of Banking & Financial Management, University of Piraeus, Greece.); Fotini Economou (Centre of Planning and Economic Research, Greece & Open University of Cyprus, Cyprus.); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: In this paper we examine the real estate returns predictability employing US REITs and a set of possible predictors for the period January 1991 to September 2013. To this end we employ several forecasting models to test for REITs predictability under a flexible framework that captures parameter instability. Apart from the traditional factors examined in relevant studies, we also account for a series of sentiment and uncertainty indicators that may be significant predictors of REITs returns, especially during turbulent times when sentiment determines investment decisions to a greater extent. The empirical results indicate that the good predictors of REITs returns vary over time and over the forecast horizons. Our results suggest that economy-wide indicators, monetary policy instrument and sentiment indicators are among the most powerful predictors of REITs returns. The issue of the most suitable forecasting method is also discussed in detail. Our results might entail implications for investors and market authorities.
    Keywords: Real estate investment trusts, return predictability, dynamic model averaging, uncertainty indicator
    JEL: C22 C32 E52 R31
    Date: 2014–10
  11. By: Stéphane Dées; Jochen Güntner
    Abstract: This paper uses a panel VAR (PVAR) approach to estimating, analysing and forecasting price dynamics in four different sectors - industry, services, construction, and agriculture - across the four largest euro area economies - Germany, France, Italy and Spain - and the euro area as a whole. By modelling prices together with real activity, employment and wages, we can disentangle the role of unit labour costs and profit margins as the factors affecting price pressures on the supply side. In out-of-sample forecast exercises, the PVAR model fares comparatively well against common alternatives, although short-horizon forecast errors tend to be large when we consider only the period of the recent financial crisis. The second part of the paper focuses on Spain, for which prediction errors during the crisis are particularly large. Given that its economy faced dramatic sectoral changes due to the burst of a housing bubble, we use the PVAR model for studying the transmission of shocks originating from the Spanish construction sector to other sectors. In a multi-country extension of the model, we also allow for spillovers to the other euro area countries in our sample.
    Keywords: Cost pressures, forecasting, impulse response analysis, panel VAR models
    JEL: C33 C53 E31 E37
    Date: 2014–09

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