Missing data, imputation, and endogeneity

McDonough, Ian K.; Millimet, Daniel L.

doi:10.1016/j.jeconom.2017.05.006

Cited by 18 publications

(18 citation statements)

References 55 publications

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“…Objective imputation implies generating a regression equation based on the data set containing complete records of the variable to be subjected to the imputation process [30]. The equation may take the form y=…”

Section: Transparency International [29]mentioning

confidence: 99%

China-Angola Investment Model

Begu

Vasilescu

Stanila³

et al. 2018

Sustainability

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Abstract:In the aftermath of Angola's civil war, strong economic relations developed between the country and the People's Republic of China. Our study addresses China's investment risks in Angola, considering an infrastructure-for-petroleum partnership between these two countries. The main working hypothesis is that the recovery of Chinese investments made in Angola is has translated into thousands of barrels of petroleum being imported daily from Angola. We analyzed the main economic, social, and political indicators that describe the situation in Angola that could impact the recovery of Chinese loans in the form of oil exports. Data processing implied involved regression-based imputation, MinMax data normalization, the use of the Analytical Hierarchy Process (AHP), and econometric analysis, next to the construction of a composite risk indicator. The results of the econometric analysis highlighted that an increase in the composite risk indicator of 1% leads to a decrease in the quantity of petroleum exported by almost 6377 barrels per day. Because, at least in the short run, the economic diversification in Angola is weak, and the most important asset is its oil, the partnership with China will continue to exist. This cooperation model represents a source of economic growth and infrastructure development for Angola and a source of energy that fuels China-one of the most powerful economies in the world.

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Section: Transparency International [29]mentioning

confidence: 99%

China-Angola Investment Model

Begu

Vasilescu

Stanila³

et al. 2018

Sustainability

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“…For this reason, traditionally economists have resorted to dummy variable and switching regressions (Kennedy, 1992;Lim, Narrisetty, and Cheon, 2017), empirical functions like bootstrapping (Efron, 1994), and cointegration approaches (Harris, 1995) to circumvent the missing value trap. As McDonough and Millimet (2016) show these methods are lacking, and in our judgment the DLNN offer some advantage. The first step to try to prepare the data for the application the DLNN is to impute the 30.2% of missing values.…”

Section: Imputation Of the Missing Valuesmentioning

confidence: 83%

Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Andrés

Otero

Amavilah

2021

Expert Systems with Applications

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Missing values and the inconsistency of the measures of the knowledge economy remain vexing problems that hamper policy-making and future research in developing and emerging economies. This paper contributes to the new and evolving literature that seeks to advance better understanding of the importance of the knowledge economy for policy and further research in developing and emerging economies. In this paper we use a supervised machine deep learning neural network (DLNN) approach to predict the knowledge economy index of 71 developing and emerging economies during the 1995-2017 period. Applied in combination with a data imputation procedure based on the K-closest neighbor algorithm, DLNN is capable of handling missing data problems better than alternative methods. A 10-fold validation of the DLNN yielded low quadratic and absolute error (0,382 +-0,065). The results are robust and efficient, and the model's predictive power is high. There is a difference in the predictive power when we disaggregate countries in all emerging economies versus emerging Central European countries. We explain this result and leave the rest to future endeavors. Overall, this research has filled in gaps due to missing data thereby allowing for effective policy strategies. At the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from our approach until substitutes come along.

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“…Specifically, the 2SLS estimators enable us to take advantage of having more instrumental variables than endogenous regressors, contributing to the tests for over-identifying restrictions (McDonough and Millimet, 2017). In this way, as inventory stickiness in the previous year goes hand in hand with that in the current year, but does not have a direct impact on productivity in the current year, we use the lagged inventory stickiness as an IV in our model so as to correct for the endogenous problem (Zhu et al, 2021).…”

Section: Correcting For Endogenous Problemsmentioning

confidence: 99%

Does sticky inventory management improve productivity?

Wang

Hou

Zhu

2021

JMTM

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PurposeThe purpose of this study is to demonstrate the nonlinear relationship between inventory stickiness and productivity, with investment efficiency being a mediator and environmental dynamism being a moderator.Design/methodology/approachUsing a large panel data collected from 1,479 Chinese listed manufacturing enterprises over the period from 2010 to 2020, this research employs the instrumental variable method combined with two-stage least squares estimators to explore the inverted-U-shaped relationship between inventory stickiness and productivity. Furthermore, the mediating role of investment efficiency and the moderating role of environmental dynamism are demonstrated via two three-model systems.FindingsAs its core, productivity initially increases with inventory stickiness until a turning point at the end of the sample, beyond which the incremental effect of inventory stickiness on productivity become negative. That is, an inverted U-shaped relationship between inventory stickiness and productivity is found to exist. Moreover, further mediated moderation analysis highlights that investment efficiency is a key mediator of this relationship, whereas environmental dynamism is a key moderator.Practical implicationsManagers ought to gauge carefully against the tradeoffs between inventory stickiness and productivity. In general, over 90% of manufacturing enterprises have great potential to increase productivity by implementing sticky inventory management. In addition, managers are suggested to place emphasis on investment management and environmental strategy.Originality/valueThis paper contributes to the current understanding about productivity by illustrating and verifying the nonlinear effect of sticky inventory management. It may be the first study to empirically demonstrate the mediating effect of investment efficiency and the moderating effect of environmental dynamism on the relationship between inventory stickiness and productivity.

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Missing data, imputation, and endogeneity

Cited by 18 publications

References 55 publications

China-Angola Investment Model

China-Angola Investment Model

Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Does sticky inventory management improve productivity?

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