有谁了解pooled mean group(pmg)估计啊？

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不知到那位了解动态面板数据中的pooled mean group(pmg)估计的（这一方法较新，1999年出现），如何在STATA中来实现这一估计。

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关键词：pooled Group mean OLED POOL Group pooled mean pmg

Pooled mean group estimation of dynamic heterogeneous panels

1. INTRODUCTION Recent years have brought increasing interest in dynamic panel data models, where the number of time series observations, T, is relatively large and of the same order of magnitude as N, the number of groups. Such panels arise particularly in cross-country analyses. In most applications of this type, the parameters of interest are the long-run effects and the speed of adjustment to the long run. An example is the large literature on testing purchasing power parity in panels, where according to economic theory the long-run coefficients of the logarithms of domestic prices, foreign prices, and exchange rates should be (1,-1,-1), with the speed of adjustment being of central policy concern. (A recent review of this literature was provided in Rogoff 1996; some of the related econometric issues were discussed in Boyd and Smith 1998.) Another prominent example is the Fisher equation, which postulates a unit coefficient for the long-run effect of (expected) inflation on the nominal rate of interest, but is silent as to the magnitude of the short-run effects of changes in inflation on interest rates. There are two procedures commonly used for such panels. At one extreme, one can estimate separate equations for each group and examine the distribution of the estimated coefficients across groups. Of particular interest will be the mean of the estimates, which we call the mean group (MG) estimator. In earlier work, Pesaran and Smith (1995) we showed that the MG estimator will produce consistent estimates of the average of the parameters. This estimator, however, does not take account of the fact that certain parameters may be the same across groups. At the other extreme are the traditional pooled estimators, such as the fixed and random effects estimators, where the intercepts are allowed to differ across groups while all other coefficients and error variances are constrained to be the same. In this paper, we consider an intermediate estimator, which we call the pooled mean group (PMG) estimator because it involves both pooling and averaging. This estimator allows the intercepts, short-run coefficients, and error variances to differ freely across groups, but constrains the long-run coefficients to be the same. There are often good reasons to expect the long-run equilibrium relationships between variables to be similar across groups, due to budget or solvency constraints, arbitrage conditions, or common technologies influencing all groups m a similar way. The reasons for assuming that short-run dynamics and error variances should be the same tend to be less compelling. Not imposing equality of short-run slope coefficients also allows the dynamic specification (e.g., the number of lags included) to differ across groups. We use two important empirical examples to compare the MG, PMG, and dynamic fixed effect (DFE) estimators. The first is the consumption function in Organization for Economic Cooperation and Development (OECD) countries. Determination of saving rates is an important policy issue and, as with the example of purchasing power parity mentioned earlier, the theory makes strong predictions: The long-run income elasticity of consumption should be unity in all countries, despite their important institutional and cultural differences. Otherwise, national (private) saving rates will be rising or falling indefinitely. The PMG estimator allows us to estimate this common long-run coefficient without making the less plausible assumption of identical dynamics in...

[此贴子已经被作者于2006-4-30 6:54:49编辑过]

In brief, Pesaran's method allows for a common cointegrating vector and
heterogeneous short-run dynamics. My code command, xtpmg, will estimate
these types of models.

I have a working paper currently being reviewed that details the
methodology.

If anyone is interested in the code and/or paper, feel free to contact
me.

-Ed Blackburne

blackburne@shsu.edu

Mariano,

It seems to me that you are referring to something like the DOLS estimators of Stock and Watson (1993) and (for panel data) Kao and Chiang (1998). Is that correct?

I have ado files for these estimators, but they may need more testing and polishing. The ado for the Stock and Watson estimator is based on a RATS program. The ado for the Kao and Chiang estimator is based on these authors' Gauss program. Other than that, I don't know of any ado for these estimators.

I'm curious: how exactly do you proceed in EViews?

Jean Salvati

References:

Kao, Chihwa and Min-Hsien Chiang (1998), "On the estimation and inference of a cointegrated regression in panel data", Working Paper, Syracuse University.

Stock, James and Mark Watson (1993), "A simple estimator of cointegrating vectors in higher order integrated systems", Econometrica, July.

Jean,

does your Kao&Chiang-Panel-DOLS ado implement the non-parametric
correction for heterogenous panels? I'm very interested in that one
(even if it does not), since I'm currently looking for possiblities to
implement estimators for cointegrated panels in STATA.

Christoph Birkel, M.A.
Martin-Luther-Universität Halle-Wittenberg
Institut für Soziologie
Emil-Abderhalden-Str.7
06108 Halle (Saale)
Tel.: 03 45 / 55 24 22 5
Fax.: 03 45 / 55 27 14 9

Pooled mean group estimation of an environmental Kuznets curve for CO2

Inmaculada Martínez-Zarzoso, and Aurelia Bengochea-Morancho

Department of Economics, Universitat Jaume I, 12071, Castellón, Spain

Received 10 February 2003; revised 23 June 2003; accepted 22 July 2003. ; Available online 10 December 2003.

Abstract
We apply the Pooled Mean Group Estimator to test for the existence of an environmental Kuznets curve for CO2 in 22 OECD countries. This approach allows for more flexible assumptions in a panel data framework. The period goes from 1975 to 1998.

Author Keywords: Pooled mean group estimation; Environmental Kuznets curve; CO2 emissions; Kyoto Protocol; OECD

Does Human Capital Matter for Growth in OECD Countries?

Evidence

from Pooled Mean-Group Estimates

ANDREA BASSANINI
Organization for Economic Co-Operation and Development (OECD); EPEE Universit?d' Evry
STEFANO SCARPETTA
World Bank - Social Protection Unit (HDNSP); Institute for the Study of Labor (IZA)
--------------------------------------------------------------------------------
January 2001

OECD Economics Working Paper No. 282


Abstract:
This paper presents empirical estimates of human-capital augmented growth equations for a panel of 21 OECD countries over the period 1971-98. It uses an improved dataset on human capital and a novel econometric technique that reconciles growth model assumptions with the needs of panel data regressions. Unlike several previous studies, our results point to a positive and significant impact of human capital accumulation to output per capita growth. The estimated long-run effect on output of one additional year of education (about 6 percent) is also consistent with microeconomic evidence on the private returns to schooling. We also found a significant growth effect from the accumulation of physical capital and a speed of convergence to the steady state of around 15 percent per year. Taken together these results are not consistent with the human capital augmented version of the Solow model, but rather they support an endogenous growth model a la Uzawa-Lucas, with constant returns to scale to "broad" (human and physical) capital.


Keywords: growth, human capital, panel data

JEL Classifications: O11, O15, O41

[此贴子已经被作者于2006-4-30 6:53:23编辑过]

Mean group tests for stationarity in heterogeneous panels

Yongcheol Shin and Andy Snell

Summary This paper proposes a panel-based mean group test for the null of stationarity against the alternative of unit roots in the presence of both heterogeneity across cross-section units and serial correlation across time periods. Using both sequential and joint asymptotic analyses the proposed test statistic is shown to be distributed as standard normal under the null for large N (number of groups) and large T (number of time periods). Monte Carlo results support the use of joint asymptotic limits (under the further condition that N/T→ 0) as a guide to finite sample performance, but also clearly indicate that the power of our suggested panel-based test is substantially higher than that of the single time-series-based test.

The Econometrics Journal Volume 9 Page 123 - March 2006 doi:10.1111/j.1368-423X.2006.00179.x

Volume 9 Issue 1

[此贴子已经被作者于2006-4-30 6:32:41编辑过]

Trade Unionism and Growth: A Panel Data Study

Using the recently developed mean group and pooled mean. group estimation techniques on cross-country panel data, the paper offers support to ... Pooled Mean Group (PMG) estimation. These methods are particularly suited to the

[此贴子已经被作者于2006-4-30 6:36:45编辑过]

Structural Policy Reform andExternal Imbalances

relationship between the variables is by using a dynamic fixed effects specification and the Pooled Mean Group ... b) Tests the null of equality of the mean group and pooled mean group estimates. Here the null is accepted, ...