This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model speci…cation in the presence of mixed-frequency data, e.g., monthly and quarterly series. MIDAS leads to parsimonious models based on exponential lag polynomials for the coe¢ cients, whereas MF-VAR does not restrict the dynamics and therefore can su¤er from the curse of dimensionality. But if the restrictions imposed by MIDAS are too stringent, the MF-VAR can perform better. Hence, it is di¢ cult to rank MIDAS and MF-VAR a priori, and their relative ranking is better evaluated empirically. In this paper, we compare their performance in a relevant case for policy making, i.e., nowcasting and forecasting quarterly GDP growth in the euro area, on a monthly basis and using a set of 20 monthly indicators. It turns out that the two approaches are more complementary than substitutes, since MF-VAR tends to perform better for longer horizons, whereas MIDAS for shorter horizons.
An integration test against fractional alternatives is suggested for univariate time series+ The new test is a completely regression-based, lag augmented version of the Lagrange multiplier~LM! test by Robinson~1991, Journal of Econometrics 47, 67-84!+ Our main contributions, however, are the following+ First, we let the short memory component follow a general linear process+ Second, the innovations driving this process are martingale differences with eventual conditional heteroskedasticity that is accounted for by means of White's standard errors+ Third, we assume the number of lags to grow with the sample size, thus approximating the general linear process+ Under these assumptions, limiting normality of the test statistic is retained+ The usefulness of the asymptotic results for finite samples is established in Monte Carlo experiments+ In particular, several strategies of model selection are studied+
SUMMARY This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low‐frequency variable with a large number of high‐frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed‐data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright © 2012 John Wiley & Sons, Ltd.
We investigate convergence in European price level, unit labor cost, income, and productivity data over the period of 1960-2006 using the non-linear time-varying coefficients factor model proposed by Phillips and Sul (2007). This approach is extremely flexible on order to model a large number of transition paths to convergence. We find regional clusters in consumer price level data. GDP deflator data and unit labor cost data are far less clustered than CPI data. Income per capita data indicate the existence of three convergence clubs without strong regional linkages; Italy and Germany are not converging to any of those clubs. Total factor productivity data indicate the existence of a small club including fast-growing countries and a club consisting of all other countries.
This paper applies the Phillips and Sul (2007) method to test for convergence in stock returns to an extensive dataset including monthly stock price indices for five EU countries (Germany, France, the Netherlands, Ireland and the UK) as well as the US over the period . We carry out the analysis on both sectors and individual industries within sectors. As a first step, we use the Stock and Watson (1998) procedure to filter the data in order to extract the long-run component of the series; then, following Phillips and Sul (2007), we estimate the relative transition parameters. In the case of sectoral indices we find convergence in the middle of the sample period, followed by divergence, and detect four (two large and two small) clusters. The analysis at a disaggregate, industry level again points to convergence in the middle of the sample, and subsequent divergence, but a much larger number of clusters is now found. Splitting the cross-section into two subgroups including Euro area countries, the UK and the US respectively, provides evidence of a global convergence/divergence process not obviously influenced by EU policies. JEL classification codes: C32, C33, G11, G15
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