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on Econometric Time Series |
| By: | Chudik , A.; Pesaran, M.H. |
| Abstract: | This paper introduces a novel approach for dealing with the .curse of dimensionality.in the case of large linear dynamic systems. Restrictions on the coefficients of an unrestricted VAR are proposed that are binding only in a limit as the number of endogenous variables tends to infinity. It is shown that under such restrictions, an infinite-dimensional VAR (or IVAR) can be arbitrarily well characterized by a large number of finite-dimensional models in the spirit of the global VAR model proposed in Pesaran et al. (JBES, 2004). The paper also considers IVAR models with dominant individual units and shows that this will lead to a dynamic factor model with the dominant unit acting as the factor. The problems of estimation and inference in a stationary IVAR with unknown number of unobserved common factors are also investigated. A cross section augmented least squares estimator is proposed and its asymptotic distribution is derived. Satisfactory small sample properties are documented by Monte Carlo experiments. An empirical application to modelling of real GDP growth and investment-output ratios provides an illustration of the proposed approach. Considerable heterogeneities across countries and signi.cant presence of dominant effects are found. The results also suggest that increase in investment as a share of GDP predict higher growth rate of GDP per capita for non-negligible fraction of countries and vice versa. |
| Keywords: | Large N and T Panels, Weak and Strong Cross Section Dependence, VAR, Global VAR, Factor Models, Capital Accumulation and Growth. |
| JEL: | C10 C33 C51 O40 |
| Date: | 2007–11 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:0757&r=ets |
| By: | Cizek, P. (Tilburg University, Center for Economic Research) |
| Abstract: | Abstract. This paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust fit. Just like many existing two-step robust methods, the proposed 2S-LWS estimator preserves robust properties of the initial robust estimate. However contrary to existing methods, the first-order asymptotic behavior of 2S-LWS is fully independent of the initial estimate under mild conditions. We propose data-adaptive weighting schemes that perform well both in the cross-section and time-series data and prove the asymptotic normality and efficiency of the resulting procedure. A simulation study documents these theoretical properties in finite samples. |
| Keywords: | Asymptotic efficiency;least weighted squares;robust regression;time series |
| JEL: | C13 C21 C22 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:kubcen:200795&r=ets |
| By: | Chevillon, Guillaume (ESSEC Business School) |
| Abstract: | The focus of this paper is inference about stochastic and deterministic trends when both types are present. We show that, contrary to asymptotic theory and the existing literature, the parameters of the deterministic components must be taken into account in finite samples. We analyze the ubiquitous Likelihood Ratio test for the rank of cointegration in vector processes. Here, we directly control the parameters of the data generating process so that a local-asymptotic framework accounts for small sample interactions between stochastic and deterministic trends. We show that the usual corrections are invalid as they take no account of the relative magnitudes of these two types of trends. Block-local models provide an embedding framework which provides a rationale for consistent estimation and testing of the whole set of parameters. In an empirical application to European GDP series, we show that using usual corrections leads to underestimating the number of stochastic trends. |
| Keywords: | Block Local Models; Cointegration; Finite Samples; Likelihood Ratio; Weak Trends |
| JEL: | C12 C32 C51 |
| Date: | 2007–08 |
| URL: | http://d.repec.org/n?u=RePEc:ebg:essewp:dr-07021&r=ets |
| By: | Enzo Weber |
| Abstract: | This paper seeks to disentangle the sources of correlations between high-, mid- and lowcap stock indexes from the German prime standard. In principle, such comovement can arise from direct spillover between the variables or due to common factors. By standard means, these different components are obviously not identifiable. As a solution, the underlying study proposes specifying ARCH-type models for both the idiosyncratic innovations and a common factor, so that the model structure can be identified through heteroscedasticity. The seemingly surprising result that smaller caps have higher influence than larger ones is explained by asymmetric information processing in financial markets. Broad macroeconomic information is shown to enter the common factor rather than the segment-specific shocks. |
| Keywords: | Identification, Spillover, Common Factor, Structural EGARCH, DAX |
| JEL: | C32 G10 |
| Date: | 2007–12 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2007-064&r=ets |