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Full-Text Articles in Econometrics

Sources Of Productivity Spillovers: Panel Data Evidence From China, Badi H. Baltagi, Peter H. Egger, Michaela Kesina Dec 2014

Sources Of Productivity Spillovers: Panel Data Evidence From China, Badi H. Baltagi, Peter H. Egger, Michaela Kesina

Center for Policy Research

This paper assesses sources of productivity spillovers in China's electric and electronic manufacturing industry using a rich panel data-set of 25,360 firms observed over the period 2004-2007. This industry is characterized by its important reliance on technology. In particular, the paper focuses on the role of other firms' productivity as well as productivity shifters in affecting own firm-level total factor productivity. In addition, this paper examines the possible difference between spillovers from foreign-owned units and from units which participate at global markets through exporting in comparison to domestically-owned and non-exporting units. We find evidence of stronger spillovers from exporting firms …


Firm-Level Productivity Spillovers In China’S Chemical Industry: A Spatial Hausman-Taylor Approach, Peter H. Egger, Badi H. Baltagi, Michaela Kesina Dec 2014

Firm-Level Productivity Spillovers In China’S Chemical Industry: A Spatial Hausman-Taylor Approach, Peter H. Egger, Badi H. Baltagi, Michaela Kesina

Center for Policy Research

This paper assesses the role of intra-sectoral spillovers in total factor productivity across Chinese producers in the chemical industry. We use a rich panel data-set of 12,552 firms observed over the period 2004-2006 and model output by the firm as a function of skilled and unskilled labor, capital, materials, and total factor productivity, which is broadly defined. The latter is a composite of observable factors such as export market participation, foreign as well as public ownership, the extent of accumulated intangible assets, and unobservable total factor productivity. Despite the richness of our data-set, it suffers from the lack of time …


Random Effects, Fixed Effects And Hausman’S Test For The Generalized Mixed Regressive Spatial Autoregressive Panel, Badi Baltagi, Long Liu Dec 2014

Random Effects, Fixed Effects And Hausman’S Test For The Generalized Mixed Regressive Spatial Autoregressive Panel, Badi Baltagi, Long Liu

Center for Policy Research

This paper suggests random and fixed effects spatial two-stage least squares estimators for the generalized mixed regressive spatial autoregressive panel data model. This extends the generalized spatial panel model of Baltagi, Egger and Pfaffermayr (2013) by the inclusion of a spatial lag dependent variable. The estimation method utilizes the Generalized Moments method suggested by Kapoor, Kelejian, and Prucha (2007) for a spatial autoregressive panel data model. We derive the asymptotic distributions of these estimators and suggest a Hausman test a la Mutl and Pfaffermayr (2011) based on the difference between these estimators. Monte Carlo experiments are performed to investigate the …


Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu Jul 2014

Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu

Center for Policy Research

This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.


Endogenous Network Production Functions With Selectivity, William C. Horrace, Xiaodong Liu, Eleonora Patacchini May 2014

Endogenous Network Production Functions With Selectivity, William C. Horrace, Xiaodong Liu, Eleonora Patacchini

Center for Policy Research

We consider a production function model that transforms worker inputs into outputs through peer effect networks. The distinguishing features of this production model are that the network is formal and observable through worker scheduling, and selection into the network is done by a manager. We discuss identification and suggest a variety of estimation techniques. In particular, we tackle endogeneity issues arising from selection into groups and exposure to common group factors by employing a polychotomous Heckman-type selection correction. We illustrate our method using data from the Syracuse University Men’s Basketball team, where at any point in time the coach selects …


New York Camp Econometrics Ix Program, Center For Policy Research Apr 2014

New York Camp Econometrics Ix Program, Center For Policy Research

Camp Econometrics-Programs

No abstract provided.