Estimation and prediction in the random effects model with AR() remainder disturbances

Baltagi, Badi H.; Liu, Long

doi:10.1016/j.ijforecast.2012.07.001

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…This paper derives the BLUP for the unbalanced panel data model and the unequally spaced panel data model with AR(1) remainder disturbances and illustrates these with an earnings equation using the NLS young women data over the period 1968-1988 employed by Drukker (2003) using Stata. These results can be extended to the unbalanced panel data model with AR(p) remainder disturbances, see Baltagi and Liu (2013a) for the corresponding balanced panel data case. Also, the unbalanced panel data model with MA(q) remainder disturbances, see Baltagi and Liu (2013b) for the corresponding balanced panel data case.…”

Section: Resultsmentioning

confidence: 82%

Forecasting with unbalanced panel data

Baltagi

Liu

2020

Journal of Forecasting

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This paper derives the best linear unbiased prediction (BLUP) for an unbalanced panel data model. Starting with a simple error component regression model with unbalanced panel data and random effects, it generalizes the BLUP derived by Taub (1979) to unbalanced panels. Next it derives the BLUP for an unequally spaced panel data model with serial correlation of the AR(1) type in the remainder disturbances considered by Baltagi and Wu (1999). This in turn extends the BLUP for a panel data model with AR(1) type remainder disturbances derived by Baltagi and Li (1992) from the balanced to the unequally spaced panel data case. The derivations are easily implemented and reduce to tractable expressions using an extension of the Fuller and Battese (1974) transformation from the balanced to the unbalanced panel data case.

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Section: Resultsmentioning

confidence: 82%

Forecasting with unbalanced panel data

Baltagi

Liu

2020

Journal of Forecasting

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“…Since the latter model encompasses many of the spatial panel data models considered in the literature, this in turn provides valuable BLUP for several spatial panel models as Special Cases. Extensions of this BLUP should be applied to dynamic spatial panel models (see Baltagi et al , ), and to panel data models with a spatial lag, as well as higher‐order autoregressive and moving average processes (see Baltagi and Liu, ,b). Furthermore, applied researchers may be interested in confidence intervals for serially dependent data (see Lahiri and Yang (), for an example).…”

Section: Resultsmentioning

confidence: 99%

Prediction in a Generalized Spatial Panel Data Model with Serial Correlation

Baltagi

Liu

2016

Journal of Forecasting

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This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (Spatial panels: random components versus fixed effects. International Economic Review 2012; 53: 1369–1412.), which encompasses many of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as Special Cases. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Panel data defined by Arellano and Bond [39] is the pooling of observations on a cross-section of units of observation over a period. This overcomes some limitations of using strictly cross-sectional or time series data [39][40][41]. The panel regressions usually take the form of the following relationship:…”

Section: Tax Concessionmentioning

confidence: 99%

Impact of Tax Incentives on Foreign Direct Investment: Evidence from Africa

et al. 2021

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African countries have faced competition and several challenges to attract foreign direct investment given the role that FDIs play in the development process. Several efforts made have been futile because of numerous factors that play against the business environment for foreign investments. Our paper analyses the influence of tax incentives on foreign direct investment in African economies based on data from 2000–2018. We utilized panel data on forty (40) African countries and an econometric model of four proxies of tax incentives, after controlling other variables, with robust Random Effect as our discussion estimator. Our results revealed that FDI responds to lower corporate income tax (CTR). Furthermore, foreign direct investment predominates in African economies with longer tax holidays and withholding tax. However, tax concession is insignificant to the inflows of FDIs in Africa. Summarizing, our results recommend that without proper restructuring of the tax incentives to deal with policy lapses by the governments of Africa, achieving the four main goals, i.e., poverty eradication, sustainable growth and development, African integration in the competitive global economy, and women empowerment, will be hindered.

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Estimation and prediction in the random effects model with AR() remainder disturbances

Cited by 9 publications

References 15 publications

Forecasting with unbalanced panel data

Forecasting with unbalanced panel data

Prediction in a Generalized Spatial Panel Data Model with Serial Correlation

Impact of Tax Incentives on Foreign Direct Investment: Evidence from Africa

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