Linear Dynamic Panel Data Models Research Articles

Determinants of Strategic Management Accounting Information Disclosure under a Multi-Theoretical Framework: Evidence of Listed Firms on Ghana Stock Exchange

Sharing information is vital for decision-making as it helps mitigate risks by providing a wealth of relevant information. When the perceived risk is low, it ultimately reduces the costs associated with making a decision. Strategic Management Accounting Information is an important resource for decision-making. This study sought to identify the determinants of strategic management accounting information disclosure (SMAID) from multiple perspectives. The study used a non-experimental, quantitative correlational design to analyze 35 out of 36 firms listed on the Ghana Stock Exchange for a period between 2010 and 2019. The study employed a linear dynamic panel data model with a one-step system GMM. The model included lagged values of the dependent variable and accounted for unobserved panel-level effects. It was discovered that Board subcommittees have non-linear relationships with SMAID. The previous year's subcommittee negatively impacts SMAID, while the current year's has a positive influence. Past disclosures of strategic management and competitor accounting information have a positive impact on current SMAID. Past customer accounting information disclosures also positively influenced the current SMAID. The study reveals that past disclosures of strategic costing information positively and statistically affect current aggregate SMAID. It is recommended that firms establish effective board sub-committees that can provide oversight and guidance to the management team. Policymakers should consider enforcing the presence of such sub-committees to ensure high-quality and sufficient disclosure of SMAID. Maintaining a high level of SMAID from the previous year can positively impact the current year's SMAID, enabling firms to make better-informed decisions and enhance overall performance.

The impact of international trade on the price of solar photovoltaic modules: empirical evidence

PurposeOver the past decades, the global solar photovoltaic (PV) market has experienced an unprecedented development associated with a substantial decline in solar PV module prices. A body of literature has attempted to identify and evaluate the different sources of price variation. However, the impact of international trade on the price of solar PV modules has not yet been empirically examined. This paper contributes to filling this gap in the literature by providing a comprehensive empirical examination on the relationship between international trade and solar PV module prices.Design/methodology/approachThe author uses a sample of 15 countries over the period 2006–2015 and proposes a linear dynamic panel data model based on a new specification, including a number of relevant factors influencing solar PV module prices.FindingsThe empirical analysis reveals that an increase in imports of solar PV cells and modules is associated with a decline in solar PV module prices. This finding suggests that international trade could lead to further price reductions, thus fostering the deployment of solar PV technology. The study reveals several other important findings. Market and technological development are key factors explaining the decline in solar PV module prices. Moreover, government policies such as public budget for R&D in PV and feed-in tariff for solar PV are effective in reducing the price of solar PV modules.Originality/valueThis paper examines the influence of international trade, government policies, market development and technological development on solar PV module prices. The results may be of interest to both academic research and policy analysis.

Income and democracy: a semiparametric approach

We examine heterogeneous nonlinear effects of income on democracy using country-level data from 1960 to 2000. Existing studies mainly focused on a linear relationship or restricted nonlinear ones and find mixed findings about the effects of income on democracy. The strong positive cross-country correlation between income and democracy is often found to disappear after controlling country specific fixed effects, although the result varies with different estimation methods and samples. In contrast to previous studies, we apply a flexible semiparametric additive partially linear dynamic panel data model to explore the heterogeneous effects of income on democracy. We assume income is endogenous and it enters in the regression model nonparametrically. Our model specification also allows for different democracy equilibria and adjustment speeds toward equilibria. We propose a nonlinearity test for our model and a penalized sieve minimum distance estimator to solve the ill-posed inverse problem in the semiparametric instrumental variable estimator. The finite sample performance of the proposed test and estimator are evaluated by simulations. In the empirical model, we find that the relationship between income and democracy is nonlinear and it is more complex than a simple inverted U-shape. Specifically, depending on the choice of the democracy measure, income may have positive effects on democracy for low-income countries, negative effects for middle-income countries, and no effects for high-income countries.

Resource curse hypothesis: fresh evidence from OPEC member countries

AbstractThis study investigated the impact of natural resources on economic growth among the Organization of the Petroleum Exporting Countries (OPEC) by controlling for institutional quality and financial development during the period 2008–2018. The study applied the recent generalised method of moment estimation method proposed by Kripfganz (2019, Generalized method of moments estimation of linear dynamic panel data models. London Stata Conference, pp. 1–128). The results revealed a negative and significant impact of natural resource rents on economic growth. However, oil rents showed a positive and statistically significant impact on economic growth. Similarly, institutional quality has been found to have positive and significant contribution to economic growth. Finally, financial development has also been found to have positive and significant impact on economic growth of the OPEC member countries.

Does increasing environmental policy stringency lead to accelerated environmental innovation? A research note

ABSTRACT This research note empirically investigates the effect of stringent environmental regulations on environmentally friendly technological innovation for 27 OECD countries from 1990 to 2015, using the two-step system-GMM estimation method for a linear dynamic panel data model. We further test the robustness of the empirical results by the method of correlated random effects for dynamic panel data estimated by Maximum Likelihood, the fixed effect with Driscoll–Kraay standard errors and the mixed-effect maximum-likelihood estimator. Overall, we find that increasing environmental policy stringency leads to accelerated environmental innovation. In addition, the non-market-based environmental policy instruments are found to stimulate more environmental innovation than market -based instruments. Thus, the instruments based on command and control policies are more effective than price mechanisms in inducing eco-innovation.

Partially linear functional-coefficient dynamic panel data models: sieve estimation and specification testing

We study the nonparametric estimation and specification testing for partially linear functional-coefficient dynamic panel data models, where the effects of some covariates on the dependent variable vary nonparametrically according to a set of low-dimensional variables. Based on the sieve approximation of unknown slope functions, we propose a sieve 2SLS procedure to estimate the model. The asymptotic properties of the estimators of both parametric and nonparametric components are established when sample size N and T tend to infinity jointly. A nonparametric specification test for the constancy of slopes is also proposed. We show that after being appropriately standardized, the test is asymptotically normally distributed under the null hypothesis. The asymptotic properties of the test is also studied under a sequence of local Pitman alternatives and global alternatives. A set of Monte Carlo simulations show that our sieve 2SLS estimators and specification test perform remarkably well in finite samples. We apply our method to study the impact of income on democracy, and find strong evidence of nonlinear/nonconstant effect of income on democracy.

Bias-corrected method of moments estimators for dynamic panel data models

A computationally simple bias correction for linear dynamic panel data models is proposed and its asymptotic properties are studied when the number of time periods is fixed or tends to infinity with the number of panel units. The approach can accommodate both fixed-effects and random-effects assumptions, heteroskedastic errors, as well as higher-order autoregressive models. Panel-corrected standard errors are proposed that allow for robust inference in dynamic models with cross-sectionally correlated errors. Monte Carlo experiments suggest that under the assumption of strictly exogenous regressors the bias-corrected method of moment estimator outperforms popular GMM estimators in terms of efficiency and correctly sized tests.

IDENTIFICATION ROBUST INFERENCE FOR MOMENTS-BASED ANALYSIS OF LINEAR DYNAMIC PANEL DATA MODELS

We use identification robust tests to show that difference (Dif), level (Lev), and nonlinear (NL) moment conditions, as proposed by Arellano and Bond (1991, Review of Economic Studies 58, 277–297), Ahn and Schmidt (1995, Journal of Econometrics 68, 5–27), Arellano and Bover (1995, Journal of Econometrics 68, 29–51), and Blundell and Bond (1998, Journal of Econometrics 87, 115–143) for the linear dynamic panel data model, do not separately identify the autoregressive parameter when its true value is close to one and the variance of the initial observations is large. We prove that combinations of these moment conditions, however, do so when there are more than three time series observations. This identification then solely results from a set of, so-called, robust moment conditions. These robust moments are spanned by the combined Dif, Lev, and NL moment conditions and only depend on differenced data. We show that, when only the robust moments contain identifying information on the autoregressive parameter, the discriminatory power of the Kleibergen (2005, Econometrica 73, 1103–1124) Lagrange multiplier (KLM) test using the combined moments is identical to the largest rejection frequencies that can be obtained from solely using the robust moments. This shows that the KLM test implicitly uses the robust moments when only they contain information on the autoregressive parameter.

Female participation in African labor markets: The role of information and communication technologies

This article investigates the effects of information and communication technologies (ICT) on female labor force participation in a sample of 48 African countries. We specify and estimate linear regression and dynamic panel data models with fixed effects (FE) and system-generalized method of moments (SYS-GMM) estimation over the period 2001–2017. The three main results are that ICT use (mobile phone and internet) significantly stimulates female labor force participation in Africa; this effect is enhanced by financial development and female education; the effect of ICT on female employment in Africa is strongest in the industrial sector. These results remain robust to the provision of social, cultural, and institutional variables.

Adversarial Inference Is Efficient

We study properties of the adversarial framework, introduced in Kaji, Manresa and Pouliot (2020). We show that the adversarial inference with an oracle classifier is statistically efficient. In addition, we study the finite sample properties of the adversarial estimation framework for the autoregressive parameter of a linear dynamic fixed effects panel data model with Gaussian errors. Unlike maximum likelihood, but similarly as other minimum distance estimators, the adversarial estimators do not suffer from the incidental parameter bias. In our simulations, using a one-hidden-layer neural network as discriminator delivers the estimates with smallest root mean squared error.

Foreign Direct Investment and World Pandemic Uncertainty Index: Do Health Pandemics Matter?

This paper explores the impacts of health pandemics on foreign direct investment (FDI) using the new world pandemic uncertainty index (WPUI). We investigate the effects of pandemics, including COVID-19, on FDI based on a sample of 142 economies and sub-samples (incomes and regions) from 1996 to 2019. The two-step system Generalised Method of Moments estimation of linear dynamic panel-data model (DPDGMM) is used in this study. The estimation results are robust with the results of the two-step sequential (two-stage) estimation of linear panel-data models (SELPDM) and the two-step system Generalised Method of Moments estimation (BBGMM). The results show that health pandemics have negative impacts on FDI. Significantly, the uncertainty caused by pandemics creates adverse shocks on FDI net inflows in Asia-Pacific countries and emerging economies.

Identification robust inference for moments based analysis of linear dynamic panel data models

We use identification robust tests to show that difference, level and non-linear moment conditions, as proposed by Arellano and Bond (1991), Arellano and Bover (1995), Blundell and Bond (1998) and Ahn and Schmidt (1995) for the linear dynamic panel data model, do not separately identify the autoregressive parameter when its true value is close to one and the variance of the initial observations is large. We prove that combinations of these moment conditions, however, do so when there are more than three time series observations. This identification then solely results from a set of, so-called, robust moment conditions. These robust moments are spanned by the combined difference, level and non-linear moment conditions and only depend on differenced data. We show that, when only the robust moments contain identifying information on the autoregressive parameter, the discriminatory power of the Kleibergen (2005) LM test using the combined moments is identical to the largest rejection frequencies that can be obtained from solely using the robust moments. This shows that the KLM test implicitly uses the robust moments when only they contain information on the autoregressive parameter.

Market Pricing of Fundamentals at the Shanghai Stock Exchange: Evidence from a Dividend Discount Model with Adaptive Expectations

We study market pricing of fundamentals at the Shanghai Stock Exchange, incorporating possible irrational pricing behavior with adaptive expectation. Using panel data of listed stocks to overcome the limited information in aggregate time series data, we estimated key parameters of the price elasticity of dividends and the expectation adjustment based on a linear dynamic panel data model. We use a major subset of stocks with stationary real prices and cash flows and apply methods that correct for incidental parameter bias. The resulting price elasticity of dividends is about 0.46 (0.35) based on annual (quarterly) data, which is sizable given high PD (PE) ratios in the market. Our results imply that slow expectation adjustment contributes to “bubble-like” price patterns. We also show prices significantly react to macro information related to the discount rate, but these effects are very sensitive to the information set used.

On the asymptotic distribution of the quadratic GMM estimator of a dynamic panel data model under a unit root

This paper considers the GMM estimator, αˆ, of the autoregressive parameter in linear dynamic panel data models with fixed effects when the data-generating process has a unit root. Previous literature has established that the limit distribution of n1∕4(αˆ−1) is degenerate and nondegenerate each with probability 1/2. We sharpen this result by showing that the limit distribution of n1∕2(αˆ−1) is nondegenerate when n1∕4(αˆ−1) converges in probability to 0, and we characterize the limit distribution which is nonstandard.

Remittances, real exchange rate and the Dutch disease in Asian developing countries

A huge amount of remittances as a source of capital flows can lead to the overshooting of a country’s real exchange rate and hurt its competitiveness, a phenomenon known as the Dutch disease. This problem concerns many recipient countries, especially the Asian developing ones as they have obtained a quite high value of remittances. In addition to this fact, in our perception, there have been no research to look into this impact for this group of countries. Therefore, the purpose of the paper is to investigate whether Asian developing countries have been facing Dutch disease or not. We thus apply System Generalized Methods of Moment (S-GMM) for the linear dynamic panel data (DPD) model from 32 countries during the period from 2006 to 2016 to examine the relationship between remittances and real effective exchange rate. Our finding indicates that as remittances per capita increase by 1%, the real effective exchange rate (REER) of these countries appreciates by 0.103% which undermines these countries’ competitiveness, supporting the existence of Dutch disease. Interestingly, we also discover that the Dutch disease only appears in countries with low ratio of remittances of GDP (less than or equal to 1%). Meanwhile, for countries having higher ratio, remittances result in REER depreciation. Moreover, as types of exchange rate regime of countries are considered, the finding supports the idea of Combes, Kinda, and Plane (2011) that floating exchange rate results in the dampening appreciation of the real exchange rate caused by capital flows. Besides, only for the countries with low levels of export (at the group of q25), Dutch disease appears due to the remittance inflows.

Level-Based Estimation of Dynamic Panel Models

Abstract This paper develops an alternative estimator for linear dynamic panel data models based on parameterizing the covariances between covariates and unobserved time-invariant effects. A GMM framework is used to derive an optimal estimator based on moment conditions in levels, with no efficiency loss compared to the classic alternatives like (Arellano, M., and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” Review of Economic Studies 58 (2): 277–297), (Ahn, S. C., and P. Schmidt. 1995. “Efficient Estimation of Models for Dynamic Panel Data.” Journal of Econometrics 68 (1): 5–27) and (Ahn, S. C., and P. Schmidt. 1997. “Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation.” Journal of Econometrics 76: 309–321). Still, we show analytically and by Monte Carlo simulations that the new procedure leads to efficiency improvements for certain data generating processes. The framework also leads to a very simple test for unobserved effects.

Estimation of linear dynamic panel data models with time‐invariant regressors

SummaryWe present a sequential approach to estimating a dynamic Hausman–Taylor model. We first estimate the coefficients of the time‐varying regressors and subsequently regress the first‐stage residuals on the time‐invariant regressors. In comparison to estimating all coefficients simultaneously, this two‐stage procedure is more robust against model misspecification, allows for a flexible choice of the first‐stage estimator, and enables simple testing of the overidentifying restrictions. For correct inference, we derive analytical standard error adjustments. We evaluate the finite‐sample properties with Monte Carlo simulations and apply the approach to a dynamic gravity equation for US outward foreign direct investment.

Robust estimation and moment selection in dynamic fixed-effects panel data models

Considering linear dynamic panel data models with fixed effects, existing outlier–robust estimators based on the median ratio of two consecutive pairs of first-differenced data are extended to higher-order differencing. The estimation procedure is thus based on many pairwise differences and their ratios and is designed to combine high precision and good robust properties. In particular, the proposed two-step GMM estimator based on the corresponding moment equations relies on an innovative weighting scheme reflecting both the variance and bias of those moment equations, where the bias is assumed to stem from data contamination. To estimate the bias, the influence function is derived and evaluated. The robust properties of the estimator are characterized both under contamination by independent additive outliers and the patches of additive outliers. The proposed estimator is additionally compared with existing methods by means of Monte Carlo simulations.

The economic growth and foreign direct investment nexus: Does democracy matter? Evidence from African countries

This article investigates the impact of democracy on the foreign direct investment (FDI)–economic growth nexus by considering both a country's current and past political regimes. We apply a linear dynamic panel data model to data from 53 African countries over the period 1989–2014. Standard errors of the estimates are Weidmeijer corrected, following an orthogonal deviations transformation. The results show that the direct impact of FDI on growth is positive and significant. Likewise, the stock of democracy plays a positive and significant role in the growth process. However, the positive impact of FDI on growth decreases with the improvement in the historical experience of a country with democracy. These findings imply that with contemporary efforts to expand political rights in Africa, it is critical to identify alternative channels that facilitate the transmission of the flow of FDI into further and sustainable growth.

Political Instability, Corruption, and Economic Growth: Evidence from a Panel of OECD Countries

Keywords: Political Instability, Corruption, Economic Growth, Political Conflict1.IntroductionThe major aim of this paper is to examine the empirical relations between economic growth and a broad group of political instability factors including corruption, government instability, internal and external conflicts, religious and ethnic tensions, democratic accountability and bureaucracy quality. Moreover, one of the main objectives of our paper is to explore the effects of serious problems such as political instability and corruption on economic growth for Organisation for Economic Co-operation and Development (OECD) countries during the period 1984-2012. Thus, most of the countries in our sample are developed countries of the world.The main contribution and distinctive characteristic of this article is to focus not only on the relationship between political stability and economic growth, but also on the relations between some specific categories of political instability and economic growth. It is generally accepted that corruption is an element of political instability as well. In this paper, we employ system GMM estimator for linear dynamic panel data models in order to overcome a potential endogeneity problem.The relationship between political instability and economic growth has been an issue of concern for long. Political instability is one of the conventional themes of the modern political economy theory. Modern theory of political economy suggests that political stability plays a significant role in economic growth of a country. Thus, an unstable political system could seriously hinder economic growth. Within the theoretical framework of modern political economy, a government is considered to be inefficient if policy objectives vary over a short period of time. Thus, coalition governments are a serious threat and to be more prone to the political stability. Moreover, modern political economy theory emphasizes that political instability also affects the level of economic growth in the country as the rates of economic growth are correlated with persistent policies of government and how government perform these policies (Barro, 2013).On the other hand, corruption is a widespread phenomenon in several countries around the world, which are regarded by economists as seriously harmful to economic growth (Aisen & Veiga, 2011). The majority of academic research reveals that corruption impedes economic growth, creates political instability, weakens the state's capacity to tax, undermines spending programs, increases the cost and lowers the quality of public investment (IMF, 2016). Some economists consider that corruption can also have distributional consequences. Corruption increases income inequality and poverty through lower economic growth, biased tax systems favoring the rich, and lower social spending (Gupta, Davoodi & Alonso-Terme, 2002). However, some researchers suggest that the impact of corruption on economic growth is related with factors such as the country's legal and institutional framework, quality of governance and political regime. Thus, in some highly regulated countries, corruption can compensate for red tape and institutional weaknesses and overcome the government failure in the economy (Campos, Dimova & Saleh, 2010). Since there is a large consensus that corruption hinders economic growth and increases socio-economic inequalities, international organizations such as the World Bank and OECD emphasize that corruption is among the greatest obstacles to economic and social development (OECD, 2013).The remaining part of this paper is organized as follows. Section 2 presents a brief literature review. Section 3 provides information about the data, empirical model, and empirical methodology. Section 4 contains empirical results. Section 5 includes a summary and concluding remarks.2.Brief Literature ReviewAlesina, Ozler, Roubini, & Swagel (1992) define political instability narrowly as the tendency of the change in cabinet either by constitutional or unconstitutional means. …