Biases in Dynamic Models with Fixed Effects

Nickell, Stephen

doi:10.2307/1911408

Cited by 6,805 publications

(3,990 citation statements)

References 0 publications

Supporting

Mentioning

3,716

Contrasting

Unclassified

Order By: Relevance

“…Correspondingly, the original breakpoints of GDP are 9876, 17,625, and 32,378 Yuan. Introducing lagged variable into fixed-effect model would lead to biased estimate coefficient because of the correlation between stochastic error term and the lagged dependent variable (Nickell 1981). System GMM developed by Arellano and Bond would produce consistent estimated coefficient (Arellano and Bond 1991;Conley 1999).…”

Section: Estimation Resultsmentioning

confidence: 99%

Forecasting residential electricity demand in provincial China

Liao

Liu

Gao

et al. 2016

Environ Sci Pollut Res

View full text Add to dashboard Cite

In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.

show abstract

Section: Estimation Resultsmentioning

confidence: 99%

Forecasting residential electricity demand in provincial China

Liao

Liu

Gao

et al. 2016

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…In a dynamic panel data model;  Since it y is a function of i  the OLS estimator biased and inconsistent (Sevestre & Trognon, 1985). so the Fixed Effect estimator is biased (Nickell, 1981).  The random effects GLS estimator is also biased (Anderson & Hsiao, 1981).…”

Section: Methodsmentioning

confidence: 99%

The Macroeconomic Determinants of Stock Market Development in Selected European Countries: Dynamic Panel Data Analysis

Şükrüoğlu¹,

Nalın²

2014

IJEF

View full text Add to dashboard Cite

This paper is concerned with the effects of macroeconomic variables and their role in development of stock market in selected Europen countries by estimating a dynamic panel data for the period of 1995-2011. We found that income, monetazation ratio, liquidity ratio, saving rate and inflation effect on stock market development. Monetazation ratio and inflation have negative effects while income, liquidity ratio, saving rate have positive effects on stock market development. Liquidity ratio emphasizes that the stock market liquidity help to improve stock market development. Furthermore, income and saving rate are correlated with stock market growth.

show abstract

“…Nerlove (1971) and Nickell (1981) point at the bias of the fixed effects estimator when the lagged dependent variable is included in the RHS of the equation. However, this dynamic panel bias is declining with an increasing number of time periods.…”

Section: Robustness Analysismentioning

confidence: 99%

The convergence of health care financing structures: empirical evidence from OECD-countries

Leiter

Theurl

2010

Eur J Health Econ

View full text Add to dashboard Cite

In this paper we concentrate on the question whether the financing structure of the health care systems converges. In a world of increasing economic integration convergence in health care financing (HCF) and, hence, decreasing differences in HCF across countries enhance individuals' (labour) mobility and support harmonization processes. As an indicator for convergence we take the public financing ratio in % of total HCF and in % of GDP. The major finding is that HCF in the OECD countries converged in the time period 1970 -2005. This conclusion also holds when looking at smaller sub groups of countries and shorter time periods. However, we find evidence that countries do not move towards a common mean and that the rate of convergence is decreasing over time.

show abstract

Biases in Dynamic Models with Fixed Effects

Cited by 6,805 publications

References 0 publications

Forecasting residential electricity demand in provincial China

Forecasting residential electricity demand in provincial China

The Macroeconomic Determinants of Stock Market Development in Selected European Countries: Dynamic Panel Data Analysis

The convergence of health care financing structures: empirical evidence from OECD-countries

Contact Info

Product

Resources

About