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on Forecasting |
| By: | Koop, Gary; Korobilis, Dimitris |
| Abstract: | Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods. |
| Keywords: | Bayesian, state space model, factor model, dynamic model averaging, |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:edn:sirdps:280&r=for |
| By: | Koop, Gary |
| Abstract: | This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts. |
| Keywords: | Bayesian, Minnesota prior, stochastic search variable selection, predictive likelihood, |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:edn:sirdps:279&r=for |
| By: | Koop, Gary; Tole, Lise |
| Abstract: | In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated nancial futures). As a consequence, the carbon market has properties that are quite different from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire fore- casting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical bene ts with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market. |
| Keywords: | Bayesian, carbon permit trading, fi nancial markets, state space model, model averaging, |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:edn:sirdps:261&r=for |
| By: | Koop, Gary; Korobilis, Dimitris |
| Abstract: | We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period. |
| Keywords: | Bayesian, State space model, Phillips curve, |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:edn:sirdps:281&r=for |
| By: | Koop, Gary; Onorante, Luca |
| Abstract: | This paper uses forecasts from the European Central Bank's Survey of Professional Forecasters to investigate the relationship between inflation and inflation expectations in the euro area. We use theoretical structures based on the New Keynesian and Neoclassical Phillips curves to inform our empirical work. Given the relatively short data span of the Survey of Professional Forecasters and the need to control for many explanatory variables, we use dynamic model averaging in order to ensure a parsimonious econometric speci cation. We use both regression-based and VAR-based methods. We find no support for the backward looking behavior embedded in the Neo-classical Phillips curve. Much more support is found for the forward looking behavior of the New Keynesian Phillips curve, but most of this support is found after the beginning of the financial crisis. |
| Keywords: | inflation expectations, survey of professional forecasters, Phillips curve, Bayesian, |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:edn:sirdps:260&r=for |
| By: | Bauwens, Luc; Korobilis, Dimitris; Koop, Gary |
| Abstract: | This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well. |
| Keywords: | Forecasting, change-points, Markov switching, Bayesian inference, |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:edn:sirdps:266&r=for |
| By: | BAUWENS, LUC; KOOP, GARY; KOROBILIS, DIMITRIS; ROMBOUTS, JEROEN V.K. |
| Abstract: | This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well. |
| Keywords: | Forecasting, change-points, Markov switching, Bayesian inference, |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:edn:sirdps:274&r=for |
| By: | Jiangyu Ji (VU University Amsterdam); Andre Lucas (VU University Amsterdam, and Duisenberg school of finance) |
| Abstract: | We propose a new semiparametric observation-driven volatility model where the form of the error density directly influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link between the estimated error density and the volatility dynamics follows from the application of the generalized autoregressive score framework of Creal, Koopman, and Lucas (2012). We provide simulated evidence for the estimation efficiency and forecast accuracy of the new model, particularly if errors are fat-tailed and possibly skewed. In an application to equity return data we find that the model also does well in density forecasting. |
| Keywords: | volatility clustering; Generalized Autoregressive Score model; kernel density estimation; density forecast evaluation |
| JEL: | C10 C14 C22 |
| Date: | 2012–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20120055&r=for |
| By: | Barnett, Alina (Bank of England); Mumtaz, Haroon (Bank of England); Theodoridis, Konstantinos (Bank of England) |
| Abstract: | Evidence from a large and growing empirical literature strongly suggests that there have been changes in inflation and output dynamics in the United Kingdom. This is largely based on a class of econometric models that allow for time-variation in coefficients and volatilities of shocks. While these have been used extensively to study evolving dynamics and for structural analysis, there is little evidence on their usefulness in forecasting UK output growth, inflation and the short-term interest rate. This paper attempts to fill this gap by comparing the performance of a wide variety of time-varying parameter models in forecasting output growth, inflation and a short rate. We find that allowing for time-varying parameters can lead to large and statistically significant gains in forecast accuracy. |
| Keywords: | Time-varying parameters; stochastic volatility; VAR; FAVAR; forecasting; Bayesian estimation |
| JEL: | C32 E37 E47 |
| Date: | 2012–05–18 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0450&r=for |
| By: | Ramirez, Octavio A. |
| Abstract: | Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised. |
| Keywords: | Data Aggregation, Efficient Forecasting, Research Methods/ Statistical Methods, |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea12:123470&r=for |
| By: | Ornelas, Jose Renato Haas; Barbachan, Jose Santiago Fajardo; Farias, Aquiles Rocha de |
| Abstract: | Building Risk-Neutral Densities (RND) from options data can provide market-impliedexpectations about the future behavior of a financial variable. And market expectations onfinancial variables may influence macroeconomic policy decisions. It can be useful also forcorporate and financial institutions decision making. This paper uses the Liu et all (2007)approach to estimate the option-implied Risk-neutral densities from the Brazilian Real/USDollar exchange rate distribution. We then compare the RND with actual exchange rates, ona monthly basis, in order to estimate the relative risk-aversion of investors and also obtain aReal-world density for the exchange rate. We are the first to calculate relative risk-aversionand the option-implied Real World Density for an emerging market currency. Our empiricalapplication uses a sample of Brazilian Real/US Dollar options traded at BM&F-Bovespafrom 1999 to 2011. The RND is estimated using a Mixture of Two Log-Normals distributionand then the real-world density is obtained by means of the Liu et al. (2007) parametric risktransformations.The relative risk aversion is calculated for the full sample. Our estimatedvalue of the relative risk aversion parameter is around 2.7, which is in line with other articlesthat have estimated this parameter for the Brazilian Economy, such as Araújo (2005) andIssler and Piqueira (2000). Our out-of-sample evaluation results showed that the RND hassome ability to forecast the Brazilian Real exchange rate. Abe et all (2007) found also mixedresults in the out-of-sample analysis of the RND forecast ability for exchange rate options.However, when we incorporate the risk aversion into RND in order to obtain a Real-worlddensity, the out-of-sample performance improves substantially, with satisfactory results inboth Kolmogorov and Berkowitz tests. Therefore, we would suggest not using the “pure”RND, but rather taking into account risk aversion in order to forecast the Brazilian Realexchange rate. |
| Date: | 2012–04–12 |
| URL: | http://d.repec.org/n?u=RePEc:fgv:ebapwp:1&r=for |
| By: | Hart, Rob (Department of Economics, Swedish University of Agricultural Sciences) |
| Abstract: | We develop a dynamic model of prices and quantities of non-renewable resources, carefully justifying our assumptions. Resource stocks are inhomogeneous, and there is endogenous directed technological change both in extraction and final-good production. The model explains stylized facts while simultaneously providing a framework for prediction; it yields analytical results in the baseline case, and may be developed to make empirical predictions about real resources. In the baseline case the economy passes through a series of phases: initially resource consumption is low; as technology improves, resource consumption rises and real resource price is constant; in the long run there is a transition to a b.g.p. on which resource consumption is constant and resource price tracks the wage. |
| Keywords: | Directed technological change; Natural resources; Hotelling rule. |
| JEL: | O13 O33 |
| Date: | 2012–05–04 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:slueko:2012_001&r=for |