Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects

2007 ◽  
Vol 140 (2) ◽  
pp. 670-694 ◽  
Author(s):  
Christian B. Hansen
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Serial Correlation ◽  
Multilevel Models ◽  
Generalized Least Squares

scholarly journals Pengaruh Sektor Tersier terhadap Pertumbuhan Ekonomi Kawasan Barat dan Timur Indonesia

2021 ◽  
Vol 2021 (1) ◽  
pp. 782-791
Author(s):  
Pramudya Kusuma ◽  
Aisyah Fitri Yuniasih
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Generalized Least Squares ◽  
Fixed Effects Model ◽  
Panel Fixed Effects

Sektor tersier merupakan sektor lapangan usaha yang menghasilkan produk berupa jasa. Sektor tersier sendiri telah mendominasi perekonomian di Indonesia. Perubahan struktural ekonomi menuju sektor tersier diperkirakan mampu memengaruhi pertumbuhan ekonomi sehingga dilakukan penelitian untuk menganalisis pengaruh sektor tersier terhadap pertumbuhan ekonomi provinsi-provinsi di Indonesia. Analisis yang dilakukan membagi provinsi-provinsi di Indonesia menjadi Kawasan Barat Indonesia (KBI) dan Kawasan Timur Indonesia (KTI). Metode yang digunakan adalah analisis data panel Fixed Effects Model dengan estimasi Feasible Generalized Least Squares. Hasil penelitian menunjukkan bahwa pada KBI maupun KTI, sektor tersier yang dijelaskan oleh produktivitas tenaga kerja dan share tenaga kerja memiliki pengaruh positif terhadap pertumbuhan ekonomi. Variabel lain yang digunakan yaitu belanja langsung pemerintah memiliki pengaruh positif terhadap pertumbuhan ekonomi serta laju pertumbuhan penduduk memiliki pengaruh negatif terhadap pertumbuhan ekonomi. Peningkatan produktivitas sektor tersier dan tenaga kerja sektor tersier dapat dilakukan sebagai upaya peningkatan pertumbuhan ekonomi.

Estimating an Autoregressive Current Effects Model of Sales Response when Observations are Aggregated over Time: Least Squares versus Maximum Likelihood

1988 ◽  
Vol 25 (3) ◽  
pp. 301-307
Author(s):  
Wilfried R. Vanhonacker
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Serial Correlation ◽  
Mean Squared Error ◽  
Generalized Least Squares ◽  
Parameter Estimates ◽  
Squared Error ◽  
Positive Serial Correlation ◽  
Serial Correlation Coefficient ◽  
Over Time

Estimating autoregressive current effects models is not straightforward when observations are aggregated over time. The author evaluates a familiar iterative generalized least squares (IGLS) approach and contrasts it to a maximum likelihood (ML) approach. Analytic and numerical results suggest that (1) IGLS and ML provide good estimates for the response parameters in instances of positive serial correlation, (2) ML provides superior (in mean squared error) estimates for the serial correlation coefficient, and (3) IGLS might have difficulty in deriving parameter estimates in instances of negative serial correlation.

scholarly journals Deriving the GLS Transformation Parameter in Elementary Panel Data Models

2005 ◽  
Vol 49 (1) ◽  
pp. 45-48 ◽  
Author(s):  
Philip N. Jefferson
Keyword(s):  
Fixed Effects ◽  
Serial Correlation ◽  
Generalized Least Squares ◽  
Panel Data Models ◽  
Complete Understanding ◽  
Quadratic Formula ◽  
Error Terms ◽  
Elementary Statistics ◽  
The Relationship ◽  
Mean Variance

The Generalized Least Squares (GLS) transformation that eliminates serial correlation in the error terms is central to a complete understanding of the relationship between the pooled OLS, random effects, and fixed effects estimators. A significant hurdle to attainment of that understanding is the calculation of the parameter that delivers the desired transformation. This paper derives this critical parameter in the benchmark case typically used to introduce these estimators using nothing more than elementary statistics (mean, variance, and covariance) and the quadratic formula.

IMPROVED AND EXTENDED END-OF-SAMPLE INSTABILITY TESTS USING A FEASIBLE QUASI-GENERALIZED LEAST SQUARES PROCEDURE

2009 ◽  
Vol 26 (4) ◽  
pp. 994-1031 ◽  
Author(s):  
Dukpa Kim
Keyword(s):  
Least Squares ◽  
Serial Correlation ◽  
Linear Time ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Consistent Estimate ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Error Process ◽  
Economic Statistics

This paper extends the Andrews (2002, Econometrica 71, 1661–1694) and Andrews and Kim (2006, Journal of Business & Economic Statistics 24, 379–394) ordinary least squares–based end-of-sample instability tests for linear regression models. The author proposes to quasi-difference the data first using a consistent estimate of the sum of the autoregressive coefficients of the error process and then test for the end-of-sample instability. For the cointegration model, the feasible quasi-generalized least squares (FQGLS) version of the Andrews and Kim (2006) P test is considered and is shown, by simulations, to be more robust to serial correlation in the error process and to have power no less than Andrews and Kim’s original test. For the linear time trend model, the FQGLS version of the Andrews (2002) S test is considered with the error process allowed to be nonstationary up to one unit root, and the new test is shown to be robust to potentially nonstationary serial correlation. A simulation study also shows that the finite-sample properties of the proposed test can be further improved when the Andrews (1993, Econometrica 61,139–165) or Andrews and Chen (1994, Journal of Business & Economic Statistics 12, 187–204) median unbiased estimate of the sum of the autoregressive coefficients is used.

scholarly journals Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity

2019 ◽  
Vol 101 (3) ◽  
pp. 452-467 ◽  
Author(s):  
Bruno Ferman ◽  
Cristine Pinto
Keyword(s):  
Least Squares ◽  
Empirical Evidence ◽  
Serial Correlation ◽  
Generalized Least Squares ◽  
Data Sets ◽  
Inference Method ◽  
Control Groups ◽  
Theoretical Justification ◽  
Inference Methods

We derive an inference method that works in differences-in-differences settings with few treated and many control groups in the presence of heteroskedasticity. As a leading example, we provide theoretical justification and empirical evidence that heteroskedasticity generated by variation in group sizes can invalidate existing inference methods, even in data sets with a large number of observations per group. In contrast, our inference method remains valid in this case. Our test can also be combined with feasible generalized least squares, providing a safeguard against misspecification of the serial correlation.

UNEQUALLY SPACED PANEL DATA REGRESSIONS WITH AR(1) DISTURBANCES

1999 ◽  
Vol 15 (6) ◽  
pp. 814-823 ◽  
Author(s):  
Badi H. Baltagi ◽  
Ping X. Wu
Keyword(s):  
Panel Data ◽  
Least Squares ◽  
Serial Correlation ◽  
Weighted Least Squares ◽  
Generalized Least Squares ◽  
Unequally Spaced ◽  
Panel Data Regression ◽  
Data Regression ◽  
Wide Range ◽  
Invariant Test

This paper deals with the estimation of unequally spaced panel data regression models with AR(1) remainder disturbances. A feasible generalized least squares (GLS) procedure is proposed as a weighted least squares that can handle a wide range of unequally spaced panel data patterns. This procedure is simple to compute and provides natural estimates of the serial correlation and variance components parameters. The paper also provides a locally best invariant test for zero first-order serial correlation against positive or negative serial correlation in case of unequally spaced panel data.

scholarly journals The Impact of Trade Credit Investment on Manufacturing Firms' Profitability: Evidence from Vietnam

2020 ◽  
Vol 68 (4) ◽  
pp. 775-796
Author(s):  
Duong Phuong Thao Pham ◽  
Thi Cam Ha Huynh
Keyword(s):  
Firm Performance ◽  
Panel Data ◽  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Trade Credit ◽  
Generalized Least Squares ◽  
Manufacturing Firms ◽  
Return On Assets ◽  
The Impact

The aim of this study is to examine the effect that trade credit investment has on firms' profitability. The characteristics of this relationship have not been dealt with in depth for manufacturing firms. We use panel data for a total of 227 Vietnamese publicly listed manufacturing firms for the period 2005–2017. Different econometric estimation techniques such as the feasible generalized least squares, fixed effects and random effects and different calculation of firm performance such as non market-based measure (return on assets) and market-based measure (Tobin's q) are employed to validate the consistent results. The robust results confirm a statistically significant inverted U-shaped relationship between trade credit investment and profitability.

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