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on Forecasting |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); Yundong Tu (Peking University, Beijing, China); Aman Ullah (University of California, Riverside) |
| Abstract: | This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang (2001). Asymptotic properties of our proposed estimators and forecasts are established. Monte Carlo simulation is conducted to show their finite sample performance. An application to predicting equity premium is taken for illustration. We introduce a new forecasting evaluation criterion based on the second order stochastic dominance in the size of forecast errors and compare models over different sizes of forecast errors. Imposing monotonicity constraint can mitigate the chance of making large size forecast errors. |
| Keywords: | Local monotonicity, Bagging, Asymptotic mean squared errors, Second order stochastic dominance, Equity premium prediction. |
| JEL: | C14 C50 C53 G17 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:201404&r=for |
| By: | Atsushi Inoue; Lu Jin; Barbara Rossi |
| Abstract: | While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast in ation in 2014, for example, should we use the last 30 years of data or the last 10 years of data? There is strong evidence of structural changes in economic time series, and the forecasting performance is often quite sensitive to the choice of such window size". In this paper, we develop a novel method for selecting the estimation window size for forecasting. Specically, we propose to choose the optimal window size that minimizes the forecaster's quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs quite well under various types of structural changes. When applied to forecasting US real output growth and in ation, the proposed method tends to improve upon conventional methods. |
| Keywords: | Macroeconomic forecasting; parameter instability; nonparametric estimation; band-width selection. |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1435&r=for |
| By: | Tara M. Sinclair (Department of Economics/Institute for International Economic Policy, George Washington University); Jeff Messina (Department of Economics/Institute for International Economic Policy, George Washington University); Herman Stekler (Department of Economics, George Washington University) |
| Abstract: | Although there have been many evaluations of the Fed Greenbook forecasts, we analyze them in a different dimension. We examine the revisions of these forecasts in the context of fixed event predictions to determine how new information is incorporated in the forecasting process. This analysis permits us to determine whether there was an underutilization of information. There is no evidence of forecast smoothing, but rather that the revisions were sometimes in the wrong direction. |
| Keywords: | Federal Reserve; Forecast Evaluation; Forecast Revisions |
| JEL: | C5 E2 E3 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:gwi:wpaper:2014-14&r=for |
| By: | Pingfan Hong; Zhibo Tan |
| Abstract: | This article evaluates and compares the forecasting performance of three international organizations: the United Nations, the International Monetary Fund and the World Bank. The annual forecasts made by the United Nations in the period of 1981-2011 are found to be fairly robust, in terms of bias and efficiency. In comparison, the forecasting performance of the United Nations is found to be marginally better than the other two organizations during the period of 2000-2012. However, the forecasts of all these organizations missed the Great Recession of 2009 by a large margin. |
| Keywords: | evaluation of forecasts; forecasting errors; macroeconomic forecasting; financial crisis |
| JEL: | C30 C80 |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:une:wpaper:133&r=for |
| By: | Tara M. Sinclair (Department of Economics/Institute for International Economic Policy, George Washington University); Hans Christian Müller-Dröge (Handelsblatt Newspaper); Herman Stekler (Department of Economics, George Washington University) |
| Abstract: | In this paper we present an evaluation of forecasts of a vector of variables of the German economy made by different institutions. Our method permits one to evaluate the forecasts for each year and then if one is interested to combine the years. We use our method to determine an overall winner for a forecasting competition across twenty-five different institutions for a single time period using a vector of eight key economic variables. Typically forecasting competitions are judged on a variable-by-variable basis, but our methodology allows us to determine how each competitor performed overall. We find that the Bundesbank was the overall winner for 2013. |
| Keywords: | Mahalanobis Distance, forecasting competition, GDP components, German macroeconomic data |
| JEL: | C5 E2 E3 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:gwi:wpaper:2014-15&r=for |
| By: | Knut Are Aastveit (Norges Bank (Central Bank of Norway)); Andrea Carriero (Queen Mary, University of London); Todd E. Clark (Federal Reserve Bank of Cleveland); Massimiliano Marcellino (Bocconi University, IGIER and CEPR) |
| Abstract: | Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier literature focused on whether there were sizable parameter changes in the early 1980s, in either the conditional mean or variance parameters, and in the subsequent period till the beginning of the new century. In this paper we conduct a similar analysis but focus on the e ects of the recent crisis. Using a range of techniques, we provide substantial evidence against parameter stability. The evolution of the unemployment rate seems particularly different relative to its past behavior. We then discuss and evaluate alternative methods to handle parameter instability in a forecasting context. While none of the methods clearly emerges as best, some techniques turn out to be useful to improve the forecasting performance. |
| Keywords: | Bayesian VAR, Forecasting, Time-varying parameters, Stochastic volatility |
| JEL: | E17 C11 C33 C53 |
| Date: | 2014–09–11 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2014_13&r=for |
| By: | Leschinski, Christian; Sibbertsen, Philipp |
| Abstract: | We propose an automatic model order selection procedure for k-factor GARMA processes. The procedure is based on sequential tests of the maximum of the periodogram and semiparametric estimators of the model parameters. As a byproduct, we introduce a generalized version of Walker's large sample g-test that allows to test for persistent periodicity in stationary ARMA processes. Our simulation studies show that the procedure performs well in identifying the correct model order under various circumstances. An application to Californian electricity load data illustrates its value in empirical analyses and allows new insights into the periodicity of this process that has been subject of several forecasting exercises. |
| Keywords: | seasonal long memory, k-factor GARMA, model selection, electricity loads |
| JEL: | C22 C52 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-535&r=for |
| By: | Anjeza Kadilli |
| Abstract: | We investigate the predictability of stock returns in the financial market for a large panel of developed countries using investor sentiment, business-cycle variables and financial indicators within two panel regime-switching models, with threshold and smooth transition between regimes. We find strong evidence of predictability of long-term returns following the business cycles, but much weaker results for the short-run returns. During crisis times, investor sentiment and inflation become key factors in predicting stock returns. Different tests and goodness of fit measures point out that the use of regime-switching models is more appropriate than linear models. To our knowledge, this study is the first to examine the impact of investor sentiment on future returns for a large number of countries, the existing literature being mainly focused on the U.S. stock market. |
| Keywords: | Financial stock returns predictability, Investor sentiment, Regime switching, Panel data, Financial crisis |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:gen:geneem:14083&r=for |