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on Forecasting |
| By: | Ralph D. Snyder (Department of Econometrics and Business Statistics, Monash University); J. Keith Ord (McDonough School of Business, Georgetown University); Adrian Beaumont (Department of Econometrics and Business Statistics, Monash University) |
| Abstract: | Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine different approaches to forecasting for such products, paying particular attention to the need for inventory planning over a multi-period lead-time when the underlying process may be nonstationary. This emphasis leads to consideration of prediction distributions for processes with time-dependent parameters. A wide range of possible distributions could be considered but we focus upon the Poisson (as a widely used benchmark), the negative binomial (as a popular extension of the Poisson) and a hurdle shifted Poisson (which retains Croston’s notion of a Bernoulli process for times between orders). We also develop performance measures related to the entire predictive distribution, rather than focusing exclusively upon point predictions. The three models are compared using data on the monthly demand for 1,046 automobile parts, provided by a US automobile manufacturer. We conclude that inventory planning should be based upon dynamic models using distributions that are more flexible than the traditional Poisson scheme. |
| Keywords: | Croston's method; Exponential smoothing; Hurdle shifted Poisson distribution; Intermittent demand; Inventory control; Prediction likelihood; State space models |
| JEL: | C25 C53 M21 |
| Date: | 2010–05 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2010-003&r=for |
| By: | Pami Dua (Department of Economics, Delhi School of Economics, Delhi, India); Rajiv Ranjan (Reserve Bank of India, India) |
| Abstract: | This paper develops vector autoregressive and Bayesian vector autoregressive models to forecast the Indian Re/US dollar exchange rate which is governed by a managed floating exchange rate regime. It considers extensions of the monetary model that include the forward premium, capital inflows, volatility of capital flows, order flows and central bank intervention. The study finds that the monetary model generally outperforms the naïve model. It also finds that forecast accuracy can be improved by extending the monetary model to include forward premium, volatility of capital inflows and order flow. Information on intervention by the central bank also helps to improve forecasts at the longer end. The study also reports that the Bayesian vector autoregressive models generally outperform their corresponding VAR variants. |
| Keywords: | exchange rate; monetary model; VAR and Bayesian VAR models |
| JEL: | C11 C32 C53 F31 F47 |
| Date: | 2011–02 |
| URL: | http://d.repec.org/n?u=RePEc:cde:cdewps:197&r=for |
| By: | Yu-chin Chen (University of Washington); Wen-Jen Tsay (Institute of Economics, Academia Sinica, Taipei, Taiwan) |
| Abstract: | This paper presents a generalized autoregressive distributed lag (GADL) model for conducting regression estimations that involve mixed-frequency data. As an example, we show that daily asset market information - currency and equity market movements - can produce forecasts of quarterly commodity price changes that are superior to those in the previous literature. Following the traditional ADL literature, our estimation strategy relies on a Vandermonde matrix to pa-rameterize the weighting functions for higher-frequency observations. Accord-ingly, inferences can be obtained under ordinary least squares principles without Kalman filtering or non-linear optimizations. Our findings provide an easy-to-use method for conducting mixed data-sampling analysis as well as for forecasting world commodity price movements. |
| Keywords: | Mixed frequency data, autoregressive distributed lag, commodity prices, forecasting |
| JEL: | C22 C53 F31 F47 |
| Date: | 2011–03 |
| URL: | http://d.repec.org/n?u=RePEc:sin:wpaper:11-a001&r=for |
| By: | Matthew S. Yiu (Hong Kong Monetary Authority); Kenneth K. Chow (Hong Kong Institute for Monetary Research) |
| Abstract: | This paper applies the factor model proposed by Giannone, Reichlin, and Small (2005) on a large data set to nowcast (i.e. current-quarter forecast) the annual growth rate of China¡¦s quarterly GDP. The data set contains 189 indicator series of several categories, such as prices, industrial production, fixed asset investment, external sector, money market and financial market. This paper also applies Bai and Ng¡¦s criteria (2002) to determine the number of common factors in the factor model. The identified model generates out-of-sample nowcasts for China's GDP with smaller mean squared forecast errors than those of the Random Walk benchmark. Moreover, using the factor model, we find that interest rate data is the single most important block in estimating current-quarter GDP in China. Other important blocks are consumer and retail prices data and fixed asset investment indicators. |
| Keywords: | Large Data Set, Pseudo Real Time Estimates, Factor Model, Kalman Filtering, Nowcasting, Information Content |
| JEL: | C33 C53 E32 E37 |
| Date: | 2011–02 |
| URL: | http://d.repec.org/n?u=RePEc:hkm:wpaper:042011&r=for |
| By: | Gonzalo, Jesus; Pitarakis, Jean-Yves |
| Abstract: | Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a more persistent regressor with the aim of assessing the presence of predictability. Key complications that arise are the potential presence of endogeneity and the poor adequacy of asymptotic approximations. In this paper we develop tests for uncovering the presence of predictability in such models when the strength or direction of predictability may alternate across different economically meaningful episodes. An empirical application reconsiders the Dividend Yield based return predictability and documents a strong predictability that is countercyclical, occurring solely during bad economic times |
| Keywords: | Endogeneity; Persistence; Return Predictability; Threshold Models |
| JEL: | C50 C22 |
| Date: | 2010–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:29190&r=for |