In the context of the “dual carbon goals” and intensified international manufacturing competition, the green and high-end transformation of manufacturing is the direction for the industry’s future growth in China. The study discusses the effect of producer service industry co-agglomeration and manufacturing on the transformation of manufacturing into being green and high-end. Firstly, we systematically elaborate on the mechanism of the collaborative promotion of high-end manufacturing by the service and manufacturing industries and propose research hypotheses. Based on the 2010 to 2020 Hunan Provincial Statistical Yearbook data, we used the coupling coordination model and entropy method to calculate the level of collaborative development between the manufacturing and service industry, as well as the level of green high-end development in the manufacturing industry. Lastly, the specific impact of the synergistic effect of the two industries on the green high-end transformation of the manufacturing industry was analyzed using the dynamic panel regression model. Results found that service industry manufacturing synergy has a noteworthy positive driving effect on the green and high-end transformation of manufacturing. However, the impact varies across different service industries and manufacturing sectors with different technological levels. We also provide some implications for improving transformation efficiency in the green and high-end manufacturing industry.

We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively weak assumptions about errors and permits avoiding the weak instruments problem associated with differencing. We also propose a simple test for selection bias that is based on the addition of a selection term to the first-difference equation and subsequent testing for significance of this term. The methods are applied to estimating dynamic earnings equations for women.

Dynamic panel regression results.

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T <= 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization transformed-likelihood estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one.

This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross-sectional dependence. A new estimator is defined that uses cross-sectional dependence for efficiency while being robust to the misspecification of the form of the cross-sectional dependence. We show that using cross-sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses nuisance parameters parsimoniously so that it exhibits good small- and large-sample properties even when the number of time periods is large. As an empirical application, we estimate the effect of attending private school on student achievement using a value-added model.

This dataset contains panel data for a sample of 15 countries (Australia, Austria, Canada, China, Denmark, France, Germany, Israel, Italy, Japan, Republic of Korea, Spain, Sweden, Switzerland and United States) over the period 2006-2015. The series used are available for a small number of developed countries and for a relatively short time period. Solar PV module prices, imports of solar PV panels and public budget for R&D in PV are in real terms and were obtained by dividing them by the United States GDP deflator. The series are obtained from five main sources. Imports value of solar PV panels series are taken from Commodity Trade Statistics database (COMTRADE). PV panels (cells and modules) are a part of the category HS 854140, "Photosensitive Semiconductor Devices, Photovoltaic Cells and Light-Emitting Diodes". Solar PV module prices, cumulative installed PV capacity and public budget for R&D in PV series are constructed from the PVPS report Trends in Photovoltaic Applications of the International Energy Agency (IEA). Population density, political stability index, renewable energy consumption and per capita carbon dioxide emissions series are all obtained from the World Bank (WB). Real GDP per capita series is taken from Federal Reserve Bank of St. Louis (FRED). Technological development in PV and crude oil import price series are drawn from the Organisation for Economic Co-operation and Development (OECD) database. Since crude oil import price series are not available for China and Israel, we use the West Texas Intermediate spot crude oil price as a proxy. The dummy for presence of feed-in tariff is constructed from the OECD database.

These are the replication files for MS# 28927-3 “Moment Conditions for Dynamic Panel Logit Models with Fixed Effects”

This note offers methodological comments on a recent (November 2014) Economic Journal article. The comments consider its use of a dynamic model – the inclusion of a lagged dependent variable – and its approach to estimation. By way of critique, the authors highlight general issues regarding dynamic panel analysis that are still less fully appreciated in the economics of happiness literature than elsewhere in economics and other quantitative social sciences. This discussion of methodological issues arising from dynamic estimation may be of practical assistance to researchers new to the field and/or to dynamic modelling.

This file describes and lists the Stata and data files that are used to produce the results of the paper titled "Revisiting Neoclassical Growth Theory: A Primary Role for Inflation and Capacity Utilization". A step-by-step instructions are found in the Readme.pdf.

Whether democratic and nondemocratic regimes perform differently in social provision policy is an important issue to social scientists and policy makers. Since political regimes are rarely changing, their long-term or dynamic effects on the outcome are of concern to researchers when they evaluate how political regimes affect social policy. However, estimating the dynamic effects of rarely changing variables in the analysis of time-series cross-sectional (TSCS) data by conventional estimators may be problematic when the unit effects are included in the model specification. This article proposes a model to account for and estimate the correlation between the unit effects and explanatory variables. Applying the proposed model to 18 Latin American countries, this article finds evidence that democracy has a positive effect on social spending both in the short and long term.

In this paper, a unified M-estimation method in Yang (2018) is extended to the matrix exponential spatial dynamic panel specification (MESDPS) with fixed effects in short panels. Similar to the STLE model which includes the spatial lag effect, the space-time effect and the spatial error effect in Yang (2018), the quasi-maximum likelihood (QML) estimation for MESDPS also has the initial condition specification problem. The initial-condition free M-estimator in this paper solves this problem and is proved to be consistent and asymptotically normal. An outer product of martingale difference (OPMD) estimator for the variance-covariance (VC) matrix of the M-estimator is also derived and proved to be consistent. The finite sample property of the M-estimator is studied through an extensive Monte Carlo study. The method is applied to US outward FDI data to show its validity.

This article considers a consistent test for serial correlation of unknown form in the residual of panel data models with interactive fixed effects and possibly lagged dependent variables. Following the spirit of Hong, we construct a test statistic based on the comparison of a kernel-based spectral density estimator and the null spectral density. Under the null hypothesis, our test statistic is asymptotically N(0, 1) as both N and T tend to infinity. In contrast to existing tests for serial correlation, there is no need to specify the order of serial correlation about the alternative. We further examine the local and global power properties of test. A simulation study shows that our test performs well in finite samples. In the empirical application, we apply the test to study the impact of the divorce law reform on divorce rate. We find strong evidence of serial correlation in the residual, and our results show that the divorce law reform has permanent positive effects on divorce rates.

Dataset for a research paper entitled "The Growth Effects of Import and Export Restrictions: Evidence from Dynamic Panel Data".

We find that the empirical results reported in Chang (Journal of Applied Econometrics 2011; 26(5): 854-871) are contingent on the specification of the model. The use of Heckman's initial conditions combined with observed and not latent lagged dependent variables leads to a counter-intuitive estimation of the true state dependence. The use of Wooldridge's initial conditions together with the observed lagged dependent variable and a proper modelling of censoring provides a much more natural estimate of the true state dependence parameters together with a clearer interpretation of the decision to participate in the labour market in the two-tiered model.

Abstract: This empirical study analyses the potential determinants of GDP growth in selected European countries. The study is conducted on the data for 19 countries from Central, Eastern and South-Eastern Europe within 2014 to 2020 time - framework. The influence of possible drivers of economic growth are investigated by employing dynamic panel data modeling, specifically System GMM method. The insights made by the study reveal that fiscal responsibility, initial development, inflation rate, EU membership are the main GDP growth drivers. In addition, we control for the institutional determinants of economic growth, as well as the role of R&D. These results provide further support for the hypothesis that macroeconomic policies conducted in a responsible and sustainable way can significantly improve countries growth perspectives. These findings may help us to understand that trinity between policies, institutions and technology is conditio sine qua non of economic growth.

The Panel Study of Income Dynamics (PSID) began in 1968 with a nationally representative sample of over 18,000 individuals living in 5,000 families in the United States. Information on these individuals and their descendants has been collected continuously, including data covering employment, income, wealth, expenditures, health, marriage, childbearing, child development, philanthropy, education, and numerous other topics.

This research aims to test the nexus between real effective exchange rates and absolute cost advantage for the North American Free Trade Agreement (NAFTA) between 1995–2014. By using the dynamic panel generalised method of moments (GMM), the findings show that the manufacturing sectors’ competitiveness is positively associated with the decrease in unit production costs and negatively related to the increase in the intrasectoral profitability gap.

This paper extends the Common Correlated Effects Pooled (CCEP) estimator to homogeneous dynamic panels. In this setting CCEP suffers from a large bias when the time span (T) of the dataset is fixed. We develop a bias-corrected CCEP estimator that is consistent as the number of cross-sectional units (N) tends to infinity, for T fixed or growing large, provided that the specification is augmented with a sufficient number of cross-sectional averages, and lags thereof. Monte Carlo experiments show that the correction offers strong improvements in terms of bias and variance. We apply our approach to estimate the dynamic impact of temperature shocks on aggregate output growth.

This panel dataset was used for the purpose of economic analyses and interpretations in the paper titled "Institutional threshold in the nexus between financial openness and TFP in Africa: A dynamic panel analysis."

The files contain the general database used for the paper, the database used for the spatial dynamic panel data and the Stata do-file with the code for all tests and estimations.