Do energy efficiency building codes help minimize the efficiency gap in the U.S.? A dynamic panel data approach

2020 ◽  
pp. 0958305X2094388
Author(s):  
Bishwa S Koirala ◽  
Alok K Bohara
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Data Model ◽  
Unobserved Heterogeneity ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Residential Sector ◽  
Energy Efficiency Policy ◽  
Efficiency Gap

This study estimates the effects of energy efficiency policy in the residential sector using panel data of 48 contiguous states starting from 1970 to 2017. To avoid any unobserved heterogeneity and facilitate efficiency in estimation, this study employs a Dynamic Panel Data model with a two-step Generalized Method of Moments technique. The results suggest that energy efficiency policy for the residential sector has saved about 8.6 percent in energy consumption, which is about 22 percent of the total stated saving, leaving an energy efficiency gap of 1.5771 quadrillion Btu. Consistent with previous estimations, this study finds that theoretical saving amounts overestimate energy efficiency output and overinflate the increase in potential energy efficiency by about 32 percent. Since energy efficiency policy has failed to achieve the stated amount of saving in the residential sector, households have no incentive to adopt the energy efficiency policy, which has created an unusual gap in energy efficiency.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

scholarly journals Bank-Specific and Macroeconomic Determinants of Profitability: A Revisit of Pakistani Banking Sector under Dynamic Panel Data Approach

2020 ◽  
Vol 8 (3) ◽  
pp. 42 ◽  
Author(s):  
Habib-ur Rahman ◽  
Muhammad Waqas Yousaf ◽  
Nageena Tabassum
Keyword(s):  
Panel Data ◽  
Interest Rates ◽  
Economies Of Scale ◽  
Banking Sector ◽  
Unobserved Heterogeneity ◽  
Dynamic Panel Data ◽  
Capital Adequacy ◽  
Bank Failures ◽  
Dynamic Panel ◽  
Macroeconomic Determinants

This study aims to examine the effect of the bank-specific and macroeconomic determinants of profitability for the banking sector of Pakistan. To incorporate the issues of endogeneity, unobserved heterogeneity, and profit persistence, we apply a generalised method of moments (GMM) technique under the Arellano–Bond framework to a panel of Pakistani banks that covers the period 2003–2017. The results of a dynamic panel data approach reveal that capital adequacy accelerates the profitability of the banking sector in Pakistan. Capital adequacy helps the financial system to absorb any negative shock by reducing the number of bank failures and losses. Conversely, our empirical investigation reveals that the liquidity ratio, business mix indicators, interest rates, and industrial production deteriorates the bank profitability. Liquidity risks enhance the probability of default risks and transmit into the unpaid loans and hence the lower return. Our empirical evidence further reveals that Pakistani banks are not getting any benefit of the economies of scale in terms of financial performance.

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