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on Forecasting |
| By: | Tucker McElroy; Michael W. McCracken |
| Abstract: | This paper develops the theory of multi-step ahead forecasting for vector time series that exhibit temporal nonstationarity and co-integration. We treat the case of a semi-infinite past, developing the forecast filters and the forecast error filters explicitly, and also provide formulas for forecasting from a finite-sample of data. This latter application can be accomplished by the use of large matrices, which remains practicable when the total sample size is moderate. Expressions for Mean Square Error of forecasts are also derived, and can be implemented readily. Three diverse data applications illustrate the flexibility and generality of these formulas: forecasting Euro Area macroeconomic aggregates; backcasting fertility rates by racial category; and forecasting regional housing starts using a seasonally co-integrated model. |
| Keywords: | Econometric models ; Economic forecasting |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2012-060&r=for |
| By: | Domenico Giannone (Université Libre de Bruxelles; CEPR - Centre for Economic Policy Research); Michele Lenza (European Central Bank); Giorgio E. Primiceri (Northwestern University; CEPR - Centre for Economic Policy Research; NBER - the National Bureau of Economic Research) |
| Abstract: | Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious naïve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach is theoretically grounded, easy to implement, and greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well both in terms of out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions. JEL Classification: C11, C32, C53, E37 |
| Keywords: | Forecasting, Bayesian methods, Marginal Likelihood, Hierarchical modeling, impulse responses |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20121494&r=for |
| By: | Robert Rich; Joseph Song; Joseph Tracy |
| Abstract: | We use matched point and density forecasts of output growth and inflation from the ECB Survey of Professional Forecasters to derive measures of forecast uncertainty, forecast dispersion, and forecast accuracy. We construct uncertainty measures from aggregate density functions as well as from individual histograms. The uncertainty measures display countercyclical behavior, and there is evidence of increased uncertainty for output growth and inflation since 2007. The results also indicate that uncertainty displays a very weak relationship with forecast dispersion, corroborating the findings of other recent studies indicating that disagreement is not a valid proxy for uncertainty. In addition, we find no correspondence between movements in uncertainty and predictive accuracy, suggesting that time-varying conditional variance estimates may not provide a reliable proxy for uncertainty. Last, using a regression equation that can be interpreted as a (G)ARCH-M-type model, we find limited evidence of linkages between uncertainty and levels of inflation and output growth. |
| Keywords: | Uncertainty ; European Central Bank ; Economic forecasting ; Inflation (Finance) |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:588&r=for |
| By: | Marco J. Lombardi (Bank for International Settlements,); Francesco Ravazzolo (Norges Bank (Central Bank of Norway) and BI Norwegian Business School) |
| Abstract: | In the recent years several commentators hinted at an increase of the correlation between equity and commodity prices, and blamed investment in commodity-related products for this. First, this paper investigates such claims by looking at various measures of correlation. Next, we assess to what extent correlations between oil and equity prices can be exploited for asset allocation. We develop a time-varying Bayesian Dynamic Conditional Correlation model for volatilities and correlations and nd that joint modelling of oil and equity prices produces more accurate point and density forecasts for oil which lead to substantial bene ts in portfolio wealth. |
| Keywords: | Oil price, Stock price, Density forecasting, Correlation, Bayesian DCC. |
| JEL: | C11 C15 C33 E17 G17 |
| Date: | 2012–12–20 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2012_24&r=for |
| By: | António Afonso; Jorge Silva |
| Abstract: | This study aims at explaining the deviation between the budget balance ratio forecasts and the outcomes in the Portuguese official forecasts and in the European Commission (EC) vintage forecasts. Therefore, we used data from the EC for the period 1969-2011 and also the Portuguese official forecasts for 1977-2011. We explain the deviation of the budget balanceto- GDP through econometric estimations and present statistical decomposition about budget balance, revenue and spending-to-GDP deviations. The statistical significance of real GDP and inflation deviations reveals the effect of automatic stabilizers and the imperfect tax indexation system. The European panel reveals statistical significance (no significance) of investment (unemployment) deviations in the budget-to-GDP ratio. Countries with better fiscal rules seem to present favourable deviations (in the absence of fixed effects). In Portugal, there is evidence of unfavourable errors about the budget balance in nominal currency in most years, which has been offset (totally or partially) by a favourable nominal GDP effect deviation. JEL Classification: C23, E44, H68. |
| Keywords: | macro forecasts, fiscal forecasts, EU, Portugal. |
| Date: | 2012–10 |
| URL: | http://d.repec.org/n?u=RePEc:ise:isegwp:wp372012&r=for |
| By: | Andrea Stella; James H. Stock |
| Abstract: | We develop a parsimonious bivariate model of inflation and unemployment that allows for persistent variation in trend inflation and the NAIRU. The model, which consists of five unobserved components (including the trends) with stochastic volatility, implies a time-varying VAR for changes in the rates of inflation and unemployment. The implied backwards-looking Phillips curve has a time-varying slope that is steeper in the 1970s than in the 1990s. Pseudo out-of-sample forecasting experiments indicate improvements upon univariate benchmarks. Since 2008, the implied Phillips curve has become steeper and the NAIRU has increased. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:1062&r=for |
| By: | Michael Scholz; Stefan Sperlich (Université de Genéve; Karl-Franzens University of Graz); Jens Perch Nielsen (Cass Business School) |
| Abstract: | The question of whether empirical models are able to forecast the equity premium more accurately than the simple historical mean is intensively debated in the nancial literature. The low prediction power is disappointing, even when using nonparametric models that make use of typical predictor variables. Motivated by the co-movement of bond and stock returns, the inclusion of the current bond yield in a prediction model is proposed. This results in a notable improvement in the prediction of stock returns, as measured by the validated R2. Since the current bond yield is unknown, it is predicted in a prior step. The essential point is that the inclusion of the generated bond can be seen as a kind of dimension and complexity reduction that imposes more structure in an appropriate way that circumvents the curse of dimensionality and complexity. |
| Keywords: | Prediction, Stock returns, Bond yield, Cross validation, Generated regressors |
| JEL: | C58 G17 C14 |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:grz:wpaper:2012-10&r=for |
| By: | Makram El-Shagi; Gregor von Schweinitz |
| Abstract: | Due to the recent financial crisis, the interest in econometric models that allow to incorporate binary variables (such as the occurrence of a crisis) experienced a huge surge. This paper evaluates the performance of the Qual VAR, i.e. a VAR model including a latent variable that governs the behavior of an observable binary variable. While we find that the Qual VAR performs reasonably well in forecasting (outperforming a probit benchmark), there are substantial identification problems. Therefore, when the economic interpretation of the dynamic behavior of the latent variable and the chain of causality matter, the Qual VAR is inadvisable. |
| Keywords: | binary choice model, Gibbs sampling, latent variable, MCMC, method evaluation |
| JEL: | C15 C35 E37 |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:iwh:dispap:12-12&r=for |
| By: | Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244-1020); Bernard Fingleton (University of Cambridge); Alain Pirotte (Université Panthéon-Assas Paris II) |
| Abstract: | This paper focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial GMM estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the Spatial AutoRegressive (SAR) error model. The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography. Key Words: Forecasting, Spatial Correlation, Panel Data, Dynamic Models JEL No. C33 |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:max:cprwps:149&r=for |
| By: | B. Onur Tas |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:tob:wpaper:1207&r=for |
| By: | Eliasson, Jonas (KTH); Amelsfort, Dirk van (WSP Analysis & Strategy); Börjesson, Maria (KTH); Brundell-Freij, Karin (WSP Analysis & Strategy); Engelson, Leonid (WSP Analysis & Strategy) |
| Abstract: | This paper compares forecasted effects of the Stockholm congestion charges with actual outcomes. The most important concerns during the design of the congestion charging scheme were the traffic reduction in bottlenecks, the increase in public transport ridership, the decrease of vehicle kilometres in the city centre, and potential traffic effects on circumferential roads. Comparisons of forecasts and actual outcomes show that the transport model predicted all of these factors well enough to allow planners to draw correct conclusions regarding the design and preparations for the scheme. The one major shortcoming was that the static assignment network model was unable to predict the substantial reductions of queuing times. We conclude that the transport model worked well enough to be useful as decision support, performing considerably better than unaided “experts’ judgments”, but that results must be interpreted taking the model’s limitations into account. The positive experiences from the Stockholm congestion charges hence seem to be transferable to other cities in the sense that if a charging system is forecasted to have beneficial effects on congestion, then this is most likely true. |
| Keywords: | Forecast accuracy; Congestion pricing; Model validation; Policy transfer. |
| JEL: | R41 R48 |
| Date: | 2012–12–21 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:ctswps:2012_031&r=for |
| By: | Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244-1020); Long Liu (The University of Texas at San Antonio) |
| Abstract: | This paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(p) remainder disturbances. It utilizes a simple exact transformation for the AR(p) time series process derived by Baltagi and Li (1994) and obtains the generalized least squares estimator for this panel model as a least squares regression. This exact transformation is also used in conjunction with Goldberger’s (1962) result to derive an analytic expression for the best linear unbiased predictor. The performance of this predictor is investigated using Monte Carlo experiments and illustrated using an empirical example. Key Words: Prediction; Panel Data; Random Effects; Serial Correlation; AR(p) JEL Classification: C32 |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:max:cprwps:138&r=for |
| By: | Janet Koech; Mark A. Wynne |
| Abstract: | The cross-section distribution of U.S. import prices exhibits some of the fat-tailed characteristics that are well documented for the cross-section distribution of U.S. consumer prices. This suggests that limited-influence estimators of core import price inflation might outperform headline or traditional measures of core import price inflation. We examine whether limited influence estimators of core import price inflation help forecast overall import price inflation. They do not. However, limited influence estimators of core import price inflation do seem to have some predictive power for headline consumer price inflation in the medium term. |
| Keywords: | Price levels ; Forecasting |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:131&r=for |
| By: | Ginters Buss |
| Abstract: | The paper studies regularised direct filter approach as a tool for high-dimensional filtering and real-time signal extraction. It is shown that the regularised filter is able to process high-dimensional data sets by controlling for effective degrees of freedom and that it is computationally fast. The paper illustrates the features of the filter by tracking the medium-to-long-run component in GDP growth for the euro area, including replication of Eurocoin-type behavior as well as producing more timely indicators. A further robustness check is performed on a less homogeneous dataset for Latvia. The resulting real-time indicators are found to track economic activity in a timely and robust manner. The regularised direct filter approach can thus be considered a promising tool for both concurrent estimation and forecasting using high-dimensional datasets and a decent alternative to the dynamic factor methodology. |
| Keywords: | high-dimensional filtering, real-time estimation, coincident indicator, leading indicator, parameter shrinkage, business cycles, dynamic factor model |
| JEL: | C13 C32 E32 E37 |
| Date: | 2012–12–27 |
| URL: | http://d.repec.org/n?u=RePEc:ltv:wpaper:201206&r=for |
| By: | Tom Engsted (Aarhus University and CREATES); Thomas Q. Pedersen (Aarhus University and CREATES) |
| Abstract: | We investigate the predictive power of the rent-to-price ratio for future real estate returns and rent growth in 18 OECD countries over the period 1970 to 2011. First, we document that in most countries returns are signi?cantly predictable by the rent-price ratio. An increase (decrease) in the ratio signals a future increase (decrease) in returns. Second, there are large cross-country di¤erences in how the rent-price ratio predicts rent growth. For some countries the direction of predictability is negative, for other countries it is positive. Third, the predictive patterns are highly dependent on whether returns and rents are measured in nominal or real terms. Finally, there is some evidence of sub-sample instability in the predictive patterns, especially wrt. rent growth predictability. The predictability tests are conducted within a restricted VAR framework based on the dynamic Gordon growth model. This model implies restrictions across the VAR parameters that can be used to construct powerful tests of predictability. |
| Keywords: | Real estate predictability, dynamic Gordon growth model, rent-price ratio, VAR model, OECD countries |
| JEL: | C32 G12 R31 |
| Date: | 2012–12–17 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2012-58&r=for |
| By: | Gadea Rivas, Maria Dolores; Pérez-Quirós, Gabriel |
| Abstract: | Much has been written about why economists failed to predict the latest financial and real crisis. Reading the recent literature, it seems that the crisis was so obvious that economists must have been blind when looking at data not to see it coming. In this paper, we illustrate this failure by looking at one of the most cited and relevant variables in this analysis, the now infamous credit to GDP chart. We compare the conclusions reached in the literature after the crisis with the results that could have been drawn from an ex ante analysis. We show that, even though credit affects the business cycle in both the expansion and the recession phases, this effect is almost negligible and impossible to exploit from a policymaker’s point of view. |
| Keywords: | Business Cycles; Credit; Financial Crisis; Forecasting |
| JEL: | C22 E32 |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:9269&r=for |