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on Econometric Time Series |
| By: | Marco Del Negro; Christopher Otrok |
| Abstract: | We develop a dynamic factor model with time-varying factor loadings and stochastic volatility in both the latent factors and idiosyncratic components. We employ this new measurement tool to study the evolution of international business cycles in the post-Bretton Woods period, using a panel of output growth rates for nineteen countries. We find 1) statistical evidence of a decline in volatility for most countries, with the timing, magnitude, and source (international or domestic) of the decline differing across countries; 2) some evidence of a decline in business cycle synchronization for Group of Seven (G-7) countries, but otherwise no evidence of changes in synchronization for the sample countries, including European and euro-area countries; and 3) convergence in the volatility of business cycles across countries. |
| Keywords: | Time-series analysis ; International economic integration ; Business cycles ; Group of Seven countries |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:326&r=ets |
| By: | Jan J. J. Groen; George Kapetanios |
| Abstract: | This paper revisits a number of data-rich prediction methods that are widely used in macroeconomic forecasting, such as factor models, Bayesian ridge regression, and forecast combinations, and compares these methods with a lesser known alternative: partial least squares regression. In this method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the linear combinations maximize the covariance between the target variable and each of the common components constructed from the predictor variables. We provide a theorem that shows that when the data comply with a factor structure, principal components and partial least squares regressions provide asymptotically similar results. We also argue that forecast combinations can be interpreted as a restricted form of partial least squares regression. Monte Carlo experiments confirm our theoretical results that principal components and partial least squares regressions are asymptotically similar when the data has a factor structure. These experiments also indicate that when there is no factor structure in the data, partial least square regression outperforms both principal components and Bayesian ridge regressions. Finally, we apply partial least squares, principal components, and Bayesian ridge regressions on a large panel of monthly U.S. macroeconomic and financial data to forecast CPI inflation, core CPI inflation, industrial production, unemployment, and the federal funds rate across different subperiods. The results indicate that partial least squares regression usually has the best out-of-sample performance when compared with the two other data-rich prediction methods. ; These experiments also indicate that when there is no factor structure in the data, partial least square regression outperforms both principal components and Bayesian ridge regressions. Finally, we apply partial least squares, principal components, and Bayesian ridge regressions on a large panel of monthly U.S. macroeconomic and financial data to forecast CPI inflation, core CPI inflation, industrial production, unemployment, and the federal funds rate across different subperiods. The results indicate that partial least squares regression usually has the best out-of-sample performance when compared with the two other data-rich prediction methods. |
| Keywords: | Time-series analysis ; Economic forecasting ; Business cycles ; Econometric models |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:327&r=ets |
| By: | Hammad Qureshi (Department of Economics, Ohio State University) |
| Abstract: | Level vector autoregressive (VAR) models are used extensively in empirical macroeconomic research. However, estimated level VAR models may contain explosive roots, which is at odds with the widespread consensus among macroeconomists that roots are at most unity. This paper investigates the frequency of explosive roots in estimated level VAR models in the presence of stationary and nonstationary variables. Monte Carlo simulations based on datasets from Christiano, Eichenbaum, & Evans (1999,2005) and Eichenbaum & Evans (1995) reveal that the frequency of explosive roots exceeds 40% in the presence of unit roots. Even when all the variables are stationary, the frequency of explosive roots is substantial. Furthermore, explosion increases significantly, to as much as 100% when the estimated level VAR coefficients are corrected for small-sample bias. |
| Keywords: | Level VAR Models, Explosive Roots, Bias Correction |
| JEL: | F31 |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:osu:osuewp:08-02&r=ets |
| By: | Luati, Alessandra; Proietti, Tommaso |
| Abstract: | The paper establishes the conditions under which the generalised least squares estimator of the regression parameters is equivalent to the weighted least squares estimator. The equivalence conditions have interesting applications in local polynomial regression and kernel smoothing. Specifically, they enable to derive the optimal kernel associated with a particular covariance structure of the measurement error, where optimality has to be intended in the Gauss-Markov sense. For local polynomial regression it is shown that there is a class of covariance structures, associated with non-invertible moving average processes of given orders which yield the the Epanechnikov and the Henderson kernels as the optimal kernels. |
| Keywords: | Local polynomial regression; Epanechnikov Kernel; Non-invertible Moving average processes. |
| JEL: | C13 C14 C22 |
| Date: | 2008–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:8910&r=ets |