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on Forecasting |
| By: | Philip Hans Franses (Econometric Institute, Erasmus University Rotterdam); Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Rianne Legerstee (Econometric Institute and Tinbergen Institute, Erasmus University Rotterdam) |
| Abstract: | The primary purpose of the paper is to answer the following two questions regarding the performance of the influential Federal Open Market Committee (FOMC) of the Federal Reserve System, in comparison with the forecasts contained in the "Greenbooks" of the professional staff of the Board of Governors: Does the FOMC have expertise, and can it forecast better than the staff? The FOMC forecasts that are analyzed in practice are nonreplicable forecasts. In order to evaluate such forecasts, this paper develops a model to generate replicable FOMC forecasts, and compares the staff forecasts, non-replicable FOMC forecasts, and replicable FOMC forecasts, considers optimal forecasts and efficient estimation methods, and presents a direct test of FOMC expertise on nonreplicable FOMC forecasts. The empirical analysis of Romer and Romer (2008) is reexamined to evaluate whether their criticisms of the FOMC's forecasting performance should be accepted unreservedly, or might be open to alternative interpretations. |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2009cf648&r=for |
| By: | Chia-Lin Chang (Department of Applied Economics, National Chung Hsing University); Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Roengchai Tansuchat (Faculty of Economics, Maejo University and Faculty of Economics, Chiang Mai University) |
| Abstract: | Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at- Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia- Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover effects across and within the four markets, using three multivariate GARCH models, namely the CCC, VARMA-GARCH and VARMA-AGARCH models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecasted conditional correlations between pairs of crude oil returns have both positive and negative trends. |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2009cf641&r=for |
| By: | Chia-Lin Chang (Department of Applied Economics, National Chung Hsing University); Philip Hans Franses (Econometric Institute, Erasmus School of Economics); Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo) |
| Abstract: | A government's ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or, expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate. In this paper, we develop a methodology to evaluate non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that it is the undocumented knowledge of the Taiwanese government that reduces forecast errors substantially. |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2009cf637&r=for |
| By: | Kim, Hyeongwoo; Durmaz, Nazif |
| Abstract: | The least squares (LS) estimator suffers from signicant downward bias in autoregressive models that include an intercept. By construction, the LS estimator yields the best in-sample fit among a class of linear estimators notwithstanding its bias. Then, why do we need to correct for the bias? To answer this question, we evaluate the usefulness of the two popular bias correction methods, proposed by Hansen (1999) and So and Shin (1999), by comparing their out-of-sample forecast performances with that of the LS estimator. We find that bias-corrected estimators overall outperform the LS estimator. Especially, Hansen's grid bootstrap estimator combined with a rolling window method performs the best. |
| Keywords: | Small-Sample Bias; Grid Bootstrap; Recursive Mean Adjustment; Out-of-Sample Forecast; Diebold-Mariano Test |
| JEL: | C53 |
| Date: | 2009–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:16780&r=for |
| By: | Michael McAleer (Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Juan-Angel Jimenez-Martin (Department of Quantitative Economics, Complutense University of Madrid); Teodosio Perez-Amaral (Department of Quantitative Economics, Complutense University of Madrid) |
| Abstract: | When dealing with market risk under the Basel II Accord, variation pays in the form of lower capital requirements and higher profits. Typically, GARCH type models are chosen to forecast Value-at-Risk (VaR) using a single risk model. In this paper we illustrate two useful variations to the standard mechanism for choosing forecasts, namely: (i) combining different forecast models for each period, such as a daily model that forecasts the supremum or infinum value for the VaR; (ii) alternatively, select a single model to forecast VaR, and then modify the daily forecast, depending on the recent history of violations under the Basel II Accord. We illustrate these points using the Standard and Poor's 500 Composite Index. In many cases we find significant decreases in the capital requirements, while incurring a number of violations that stays within the Basel II Accord limits. |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2009cf636&r=for |
| By: | Shripad Tuljapurkar; Ryan D. Edwards |
| Abstract: | Entropy, or the gradual decline through age in the survivorship function, reflects the considerable amount of variance in length of life found in any human population. Part is due to the well-known variation in life expectancy between groups: large differences according to race, sex, socioeconomic status, or other covariates. But within-group variance is very large even in narrowly defined groups, and it varies strongly and inversely with the group average length of life. We show that variance in length of life is inversely related to the Gompertz slope of log mortality through age, and we reveal its relationship to variance in a multiplicative frailty index. Our findings bear a variety of implications for modeling and forecasting mortality. In particular, we examine how the assumption of proportional hazards fails to account adequately for differences in subgroup variance, and we discuss how several common forecasting models treat the variance along the temporal dimension. |
| JEL: | I1 J11 N3 |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:15288&r=for |