2 edition of **he heterogeneity bias of pooled estimators in stationary VAR specifications** found in the catalog.

he heterogeneity bias of pooled estimators in stationary VAR specifications

Alessandro Rebucci

- 53 Want to read
- 5 Currently reading

Published
**2003**
by International Monetary Fund, Research Department in [Washington, D.C.]
.

Written in English

- Estimation theory.,
- Autoregression (Statistics).,
- Time-series analysis.,
- Probabilities.,
- Risk -- Econometric models.

**Edition Notes**

Statement | Alessandro Rebucci. |

Series | IMF working paper -- WP/03/73 |

Contributions | International Monetary Fund. Research Dept. |

The Physical Object | |
---|---|

Pagination | 44 p. ; |

Number of Pages | 44 |

ID Numbers | |

Open Library | OL20682910M |

In terms of variance however, the beam of predictions is narrower, which suggests that the variance is lower. Indeed, as the lower right figure confirms, the variance term (in green) is lower than for single decision trees. Overall, the bias- variance decomposition is therefore no longer the same. Bias and correlation squared difference scores, D2, and the number of pairs, n, is made possible by the fact that the variances of the two sets of ranks are equal and are given by (n2 – 1)/For present purposes, it is important to note the following properties.

Testing for Slope Heterogeneity Bias in Panel Data Models Murillo Campello, Antonio F. Galvao, and Ted Juhl the xed e ects estimation is subject to heterogeneity bias. The procedure tests the suggest a \Hausman-type" test in the context of stationary rst-order autoregressive panel models, where the cross-section, n, is xed as the time. properties of bias-corrected estimators that are based on our analytic results, as well as the corresponding bootstrap bias-corrected MLE’s. Some concluding remarks appear in section 5. 2. First-order biases of maximum likelihood estimators For some arbitrary distribution, let l() be the (total) log-likelihood based on a sample of n.

expressions for the first-order biases of the MLEs of the parameters of the generalized Pareto distribution in section 3. Section 4 reports the results of a simulation experiment that evaluates the properties of bias-corrected estimators that are based on our analytic results, as well as the corresponding bootstrap bias . Pooled variance is an estimate when there is a correlation between pooled data sets or the average of the data sets is not identical. Pooled variation is less precise the more non-zero the correlation or distant the averages between data sets. The variation of data for non-overlapping data sets is.

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"On the Heterogeneity Bias of Pooled Estimators in Stationary VAR Specifications" published on by INTERNATIONAL MONETARY FUND. The main results are: (i) asymptotically, the heterogeneity bias of the FE may be more or less severe in VAR specifications than in standard dynamic panel data specifications; (ii) in Monte Carlo simulations, slope heterogeneity must be relatively high to be a source of concern for pooled estimators; (iii) when this happens, the panel must be longer than a typical macro dataset for the MG Cited by: The main results are: (i) asymptotically, the heterogeneity bias of the FE may be more or less severe in VAR specifications than in standard dynamic panel data specifications; (ii) in Monte Carlo simulations, slope heterogeneity must be relatively high to be a source of concern for pooled estimators; (iii) when this happens, the panel must be longer than a typical macro dataset for the MG to be a viable solution.

Title: On the Heterogeneity Bias of Pooled Estimators in Stationary VAR Specifi cations - WP/03/73 Created Date: 4/11/ AMCited by: Baltagi, Griffin, and Xiong (). It is confirmed that pooled estimators are generally better than non-pooled estimators in terms of forecast performance, but the difference between two is not very large.

This implies that, whereas there is some merit in pooling the data, the OLS or SUR methods at industry level perform relatively well. coe¢ cient heterogeneity, but provides results on the estimation of cross-section speci–c coe¢ cients only. This paper provides an alternative CCE type estimation approach to Song™s extension of the 1See Everaert and Groote () who derive asymptotic bias of CCE pooled estimators in the case of dynamic homogeneous panels.

Machine Learning Basics: Estimators, Bias and Variance Estimators, Bias and Variance 5. Maximum Likelihood Estimation 6. – Referred to as function estimation • Here we predict a variable y given input x – We assume f(x) is the relationship between x and yFile Size: KB.

Pooled mean group (PMG) estimators provide an alternative to extremes of pooling the data assuming slope homogeneity and estimating individual states assuming complete heterogeneity. followed the development of several estimators for panel time series including Mean Group OLS, Pooled Mean Group (Pesaran, Shin and Smith ()), Panel Dynamic OLS (Pedroni ()), and Panel Fully Modi ed OLS (Pedroni ()).

These estimators are all robust to slope heterogeneity File Size: KB. Bias, Variance, and MSE of Estimators Guy Lebanon September 4, = Var(^) + Bias 2(^) + E((^ E(^))(E(^))) = Var Since the MSE decomposes into a sum of the bias and variance of the estimator, both quantities are important and need to be as small as possible to achieve good estimation performance.

It is common toFile Size: KB. "On the Heterogeneity Bias of Pooled Estimators in Stationary VAR Specifications," IMF Working Papers 03/73, International Monetary Fund. Badi H. Baltagi, " Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol.

27(2), pages Then, as Pesaran and Smith () pointed out, under slope heterogeneity, estimated coefficients will be affected by an heterogeneity bias. At the other extreme of the SFE and DFE estimators, we find the mean group approach (MG) that consists of estimating separate regressions for each country and calculating averages of the country-specific Cited by: variable bias in the parameter estimates of the regression model, and thus can lead to an erroneous inference about the extent of and trend in accounting conservatism.

Our study is motived by recent studies that call for controlling for firm heterogeneity in estimating measures of conservatism. properties but the exact small-sample distribution of competing estimators. In our evaluation, we find that PD imputation, ML imputation, and observed-data ML estimation all have potential for bias and inefficiency in small samples.

The biases are limited to estimation of the variance ê 6 and standard deviation ê; estimates of the mean äCited by: 7. To test the hypothesis of a difference stationary time series against a trend stationary alternative, Levin & Lin () and Im, Pesaran & Shin () suggest bias adjusted : Christoph Birkel.

a brief review of alternative panel data estimators and dis-cusses how the PMG estimator is related to them. Section 3 sets out the model and its underlying assumptions, and derives the log-likelihood function.

Section 4 develops the general theory of PMG estimation, in both stationary re-gressors and unit root processes. Section 5 discusses a num.

The pooled effect estimate under FEM is also used to calculate Q statistic, to test absence of the heterogeneity in the meta-analysis. The Q statistic is defined as the weighted sum of square of deviation of individual effect size from pooled effect size computed by : Mona Pathak, Mona Pathak, Sada Nand Dwivedi, Bhaskar Thakur, Sreenivas Vishnubhatla.

Note on the bias in the estimation of the serial correlation coeﬃcient of AR(1) processes ied for a stationary AR(1) process with known mean. We use the second order Taylor expansion of a ratio, and employ the can be used for bias correction, whereas var.

the variable notation that will be used. X random variable x i ith sample or realisation of the random variable X Ef g expectation operator population mean ˙2 population variance ^ sample mean ˙^2 sample variance 3 The concept of bias in estimators It is common place for us to estimate the value of a quantity that is related to a random.

In doing so, there is evidence of a bias in the pooled estimates which fail to account for unobserved heterogeneity. This result corresponds to Cornwell and Trumbull (), however, this study extends the analysis and finds that the bias varies significantly across crime types. Rebucci, A.

(). On the Heterogeneity Bias of Pooled Estimators in Stationary VAR Specifications. IMF Working Papers 03/ Google Scholar. Wieladek T. () Financial Regulation and the Current Account. In: Arestis P., Sawyer M. (eds) Financial Liberalisation. International Papers in Political Economy. Buy this book on publisher's Cited by: 7.Lin and Ng: Estimation of Panel Data Models 43 coefficients as a function of observed characteristics, but the results necessarily depend on the specifications used.

While one can assume complete parameter heterogeneity, this would reduce the problem to time series estimation on a unit by unit basis which does not take advantage of.All else being equal, an unbiased estimator is preferable to a biased estimator, but in practice biased estimators are frequently used, generally with small bias.

When a biased estimator is used, bounds of the bias are calculated.