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on Econometrics |
| By: | Giuliano De Rossi; Andrew Harvey |
| Abstract: | A time-varying quantile can be fitted to a sequence of observations by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm. Quantiles estimated in this way provide information on various aspects of a time series, including dispersion, asymmetry and, for financial applications, value at risk. Tests for the constancy of quantiles, and associated contrasts, are constructed using indicator variables; these tests have a similar form to stationarity tests and, under the null hypothesis, their asymptotic distributions belong to the Cramér von Mises family. Estimates of the quantiles at the end of the series provide the basis for forecasting. As such they offer an alternative to conditional quantile autoregressions and, at the same time, give some insight into their structure and potential drawbacks. |
| Keywords: | Dispersion; quantile regression; signal extraction; state space smoother; stationarity tests; value at risk. |
| JEL: | C14 C22 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:0649&r=ecm |
| By: | Longhi, Simonetta (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics); Nijkamp, Peter |
| Abstract: | Because of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level, and the need for forecasts at the regional level is rapidly increasing. The data available to compute regional forecasts is usually based on a pseudo-panel of a limited number of observations over time, and a large number of areas (regions) strongly interacting with each other. The application of traditional time-series techniques to distinct time series of regional data is likely to be a suboptimal forecasting strategy. In the field of regional forecasting of socioeconomic variables, both linear and nonlinear models have recently been applied and evaluated. However, often such analyses ignore the spatial interactions among regions. We evaluate the ability of different statistical techniques - namely spatial error and spatial cross-regressive models - to correct for misspecifications due to neglected spatial correlation in the data. Our empirical application concerns short-term forecasts of employment in 326 West German regions; we find that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of non-spatial models. |
| Keywords: | Space-Time Data; Regional Forecasts; Spatial Heterogeneity; Spatial Correlation |
| JEL: | R12 C53 |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:vuarem:2006-15&r=ecm |
| By: | Raffaella Giacomini (Department of Economics, UCLA, Box 951477, Los Angeles, CA 90095-1477, USA.); Barbara Rossi (Department of Economics, Duke University, Durham NC27708, USA.) |
| Abstract: | We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss function, is significantly worse than its in-sample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data generating process and relate the properties of our forecast breakdown test to those of existing structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips’ curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed. JEL Classification: C22; C52; C53. |
| Keywords: | Structural change; forecast evaluation; forecast rationality testing; in-sample evaluation; out-of-sample evaluation. |
| Date: | 2006–06 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20060638&r=ecm |
| By: | Jonathan Lewellen; Stefan Nagel; Jay Shanken |
| Abstract: | It has become standard practice in the cross-sectional asset-pricing literature to evaluate models based on how well they explain average returns on size- and B/M-sorted portfolios, something many models seem to do remarkably well. In this paper, we review and critique the empirical methods used in the literature. We argue that asset-pricing tests are often highly misleading, in the sense that apparently strong explanatory power (high cross-sectional R2s and small pricing errors) in fact provides quite weak support for a model. We offer a number of suggestions for improving empirical tests and evidence that several proposed models don’t work as well as originally advertised. |
| JEL: | G12 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:12360&r=ecm |
| By: | David H. Small (Federal Reserve Board - Monetary Studies Section, 20th and C Streets, NW, Washington , DC 20551, United States.); Domenico Giannone (Free University of Brussels (VUB/ULB), European Center for Advanced Research in Economics and Statistics (ECARES), Ave. Franklin D Roosevelt, 50 - C.P. 114, B-1050 Brussels, Belgium.); Lucrezia Reichlin (Free University of Brussels (VUB/ULB), European Center for Advanced Research in Economics and Statistics (ECARES), Ave. Franklin D Roosevelt, 50 - C.P. 114, B-1050 Brussels, Belgium.) |
| Abstract: | This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing news on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real variables and this effect is larger than that of the Employment Report. When we control for timeliness of the releases, the effect of hard data becomes sizeable. Prices and quantities affect the precision of the estimates of inflation while GDP is only affected by real variables and interest rates. JEL Classification: E52; C33; C53. |
| Keywords: | Forecasting; monetary policy; factor model; real time data; large data sets; news. |
| Date: | 2006–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20060633&r=ecm |
| By: | Domenico Giannone (Free University of Brussels (VUB/ULB), European Center for Advanced Research in Economics and Statistics (ECARES), Ave. Franklin D Roosevelt, 50 - C.P. 114, B-1050 Brussels, Belgium.); Lucrezia Reichlin (Free University of Brussels (VUB/ULB), European Center for Advanced Research in Economics and Statistics (ECARES), Ave. Franklin D Roosevelt, 50 - C.P. 114, B-1050 Brussels, Belgium.) |
| Abstract: | This paper asks two questions. First, can we detect empirically whether the shocks recovered from the estimates of a structural VAR are truly structural? Second, can the problem of nonfundamentalness be solved by considering additional information? The answer to the first question is “yes” and that to the second is “under some conditions”. JEL Classification: C32; C33; E00; E32; O3. |
| Keywords: | Identification; information; invertibility; structural VAR. |
| Date: | 2006–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20060632&r=ecm |