Linear Dynamic Panel Data Models Research Articles

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. …

Modeling debt choice in agriculture: the effect of endogenous asset values

PurposeThe purpose of this paper is to investigate the endogeneity of asset values and how it relates to farm financial stress in US agriculture. The authors conceptualize an implied measure of farm financial stress as a function of debt position. The authors posit that there are variations in the asset values that are beyond the farmer’s control and therefore have implications on farm debt.Design/methodology/approachThe framework recognizes the endogeneity of return on assets (ROA). It uses a non-parametric technique to approximate the variance of expected ROA (VEROA). The authors model the rate of return on agricultural assets and interest rate with a formulation that focuses on macroeconomic policy. Further, the authors use a dynamic balanced panel data set from 1960 to 2011 for 15 US agricultural states from the Agricultural Resource Management Survey, and information from traditional state-level financial statements.FindingsEstimation of linear dynamic debt panel data models accounting for the endogeneity of ROA and VEROA is a challenging task. Estimated variances are unstable. Hence, the authors focus on variance specification that uses the residuals squared from the ARIMA specification and non-parametric estimators. Arellano-Bover/Blundell-Bond generalized method of moments estimation procedures, although may be biased, show that VEROA has a negative and significant effect on the total amount of debt in the agricultural sector.Research limitations/implicationsThe instruments used in this analysis are lagged regressors which may be weakly correlated with the relevant first-order condition, hence not properly identifying the parameters of interest. Future research could include the identification of better instruments, potentially use of sequential moment conditions.Originality/valueUnlike previous study, the authors use non-parametric approximation of VEROA. The authors model the rate of return on agricultural assets and interest rate with a formulation that focuses on macroeconomic policy. Second, the authors make use of a large dynamic balanced panel data set from 1960 to 2011 for 15 agricultural states in the USA. To the best of the authors’ knowledge, this study is one of the few that provides evidence on risk-balancing behavior at the agricultural sector level, of the USA.

Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation, the effects are examined in terms of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification and coefficient restrictions show serious deficiencies. The variance of the individual effects is shown to be a major determinant of the poor quality of most asymptotic approximations; therefore, the accurate estimation of this nuisance parameter is investigated. A modification of GMM is found to have some potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample is not too small. Finally, all techniques are employed to actual data and lead to insights which differ considerably from those published earlier.

Quasi–maximum Likelihood Estimation of Linear Dynamic Short-T panel-data Models

In this article, I describe the xtdpdqml command for the quasi–maximum likelihood estimation of linear dynamic panel-data models when the time horizon is short and the number of cross-sectional units is large. Based on the theoretical groundwork by Bhargava and Sargan (1983, Econometrica 51: 1635–1659) and Hsiao, Pesaran, and Tahmiscioglu (2002, Journal of Econometrics 109: 107–150), the marginal distribution of the initial observations is modeled as a function of the observed variables to circumvent a short- T dynamic panel-data bias. Both random-effects and fixed-effects versions are available.

Median-based estimation of dynamic panel models with fixed effects

Outlier-robust estimators are proposed for linear dynamic fixed-effect panel data models where the number of observations is large and the number of time periods is small. In the simple setting of estimating the AR(1) coefficient from stationary Gaussian panel data, the estimator is (a linear transformation of) the median ratio of adjacent first-differenced data pairs. Its influence function is bounded under contamination by independent or patched additive outliers. The influence function and the gross-error sensitivity are derived. When there are independent additive outliers, the estimator is asymptotically biased towards 0, but its sign remains correct and it has a reasonably high breakdown point. When there are patched additive outliers with point mass distribution, the asymptotic bias is upward in nearly all cases; breakdown towards 1 can occur; and the associated breakdown point increases with the patch length.

Profitability and Investment: Evidence from India's Organized Manufacturing Sector

AbstractUsing a state‐industry panel data set of 55 industries for 19 major Indian states over the period 1983–84 to 2007–08, we analyze the contemporaneous and long run impacts of the rate of profit and its components—profit share, capacity utilization rate and capacity‐capital ratio—on investment using linear dynamic panel data models. Our results show that: (a) the rate of profit has both short and long run positive impacts on investment; (b) the profit share and capacity‐capital ratio have mainly long run positive impacts, but the capacity utilization rate has a more complex pattern of impact on investment.

Moment Restrictions and Identification in Linear Dynamic Panel Data Models

This paper investigates the relationship between moment restrictions and identification in simple linear AR(1) dynamic panel data models with fixed effects under standard minimal assumptions. The number of time periods is assumed to be small. The assumptions imply linear and quadratic moment restrictions which can be used for GMM estimation. The paper makes three points. First, contrary to common belief, the linear moment restrictions may fail to identify the autoregressive parameter even when it is known to be less than 1. Second, the quadratic moment restrictions provide full or partial identification in many of the cases where the linear moment restrictions do not. Third, the first moment restrictions can also be important for identification. Practical implications of the findings are illustrated using Monte Carlo simulations.

Shrinkage estimation of dynamic panel data models with interactive fixed effects

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai (2009) and Moon and Weidner (2015), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to zero. Further, we demonstrate that our shrinkage estimators of the nonzero slope parameters exhibit some oracle property. We conduct Monte Carlo simulations to demonstrate the superb finite-sample performance of the proposed method. We apply our method to study the determinants of economic growth and find that in addition to three common unobserved factors selected by our method, government consumption share has negative effects, whereas investment share and lagged economic growth have positive effects on economic growth.

Bootstrap inference for linear dynamic panel data models with individual fixed effects

This paper’s main contribution is to propose and theoretically justify the application of bootstrap methods for inference in autoregressive panel data models with fixed effects. Whereas the focus of the existing literature has been on bias correcting the standard fixed effects OLS estimator (due to the well known incidental parameter bias), our focus here is on improving the quality of inference by relying on the bootstrap instead of the standard normal distribution when computing critical values for test statistics. In particular, we show by simulation that confidence intervals based on the normal distribution can be very distorted in finite samples. Instead, a bootstrap that resamples the residuals and generates the bootstrap observations recursively using the estimated autoregressive panel data model greatly reduces these distortions. We show that this recursive-design residual-based bootstrap fixed effects OLS estimator contains a built-in bias correction term that mimics the incidental parameter bias. Thus, this method can be used to approximate the bias (as well as the entire distribution) of the (biased) fixed effects OLS estimator. This is in contrast with two other methods we consider (a fixed-design residual-based bootstrap and a pairs bootstrap) whose distributions are incorrectly centered at zero. As it turns out, both the recursive-design and the pairs bootstrap are asymptotically valid when applied to the bias-corrected estimator, but the fixed-design bootstrap is not. In the simulations, the recursive-design bootstrap is the method that does best overall.

Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon and Weidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to zero. Further, we demonstrate that our shrinkage estimators of the nonzero slope parameters exhibit some oracle property. We conduct Monte Carlo simulations to demonstrate the superb finite-sample performance of the proposed method. We apply our method to study the determinants of economic growth and find that in addition to three common unobserved factors selected by our method, government consumption share has negative effects, whereas investment share and lagged economic growth have positive effects on economic growth.

Government size, composition of public expenditure, and economic development

This paper analyzes the effects of government size and of the composition of public expenditure on economic development. Using the system-GMM estimator for linear dynamic panel data models, on a sample covering up to 156 countries and 5-year periods from 1980 to 2010, we find that government size as a percentage of GDP has a quadratic (inverted U-shaped) effect on the growth rate of the Human Development Index (HDI). This effect is especially pronounced in developed and high-income countries. We also find that the composition of public expenditure affects development, with the share of five subcomponents exhibiting nonlinear relationships with HDI growth.

The Determinants of the Volatility of Fiscal Policy Discretion

AbstractThis paper investigates the determinants of the volatility of fiscal policy discretion. Using a linear dynamic panel data model for 113 countries from 1980 to 2006 and a system‐GMM estimator, we find that an increase in the number of episodes of government crisis, less democracy and presidentialist systems raise the volatility of the discretionary component of fiscal policy. Additionally, we show that countries with larger populations and less flexible exchange rate systems are more insured against uncertainty about the conduct of fiscal policy. Our results are robust to various regional dummy variables, different subsets of countries and the presence of high inflation and crisis episodes.

Testing a linear dynamic panel data model against nonlinear alternatives

The most popular econometric models in the panel data literature are the class of linear panel data models with unobserved individual- and/or time-specific effects. The consistency of parameter estimators and the validity of their economic interpretations as marginal effects depend crucially on the correct functional form specification of the linear panel data model. In this paper, a new class of residual-based tests is proposed for checking the validity of dynamic panel data models with both large cross-sectional units and time series dimensions. The individual and time effects can be fixed or random, and panel data can be balanced or unbalanced. The tests can detect a wide range of model misspecifications in the conditional mean of a dynamic panel data model, including functional form and lag misspecification. They check a large number of lags so that they can capture misspecification at any lag order asymptotically. No common alternative is assumed, thus allowing for heterogeneity in the degrees and directions of functional form misspecification across individuals. Thanks to the use of panel data with large N and T, the proposed nonparametric tests have an asymptotic normal distribution under the null hypothesis without requiring the smoothing parameters to grow with the sample sizes. This suggests better nonparametric asymptotic approximation for the panel data than for time series or cross sectional data. This is confirmed in a simulation study. We apply the new tests to test linear specification of cross-country growth equations and found significant nonlinearities in mean for OECD countries’ growth equation for annual and quintannual panel data.

Political, Institutional, and Economic Factors Underlying Deficit Volatility

AbstractIt is well known that fiscal policy can counter‐cyclically smooth out the effect of unexpected shocks and public deficit volatility may reflect the (optimal) policy response to them. However, the welfare losses associated to fiscal instability are also an important challenge for many countries, as it typically implies an inefficient allocation of resources, higher sovereign risk premium and an inadequate provision of public services. In this paper, we empirically analyze the political, institutional, and economic sources of public deficit volatility. Using the system‐generalized method‐of‐moments (GMM) estimator for linear dynamic panel data models and a sample of 125 countries analyzed from 1980 to 2006, we show that higher public deficit volatility is typically associated with higher levels of political instability and less democracy. In addition, public deficit volatility tends to be magnified for small countries, in the outcome of hyper‐inflation episodes and for countries with a high degree of openness.

Robust estimation of dynamic fixed-effects panel data models

This paper extends an existing outlier-robust estimator of linear dynamic panel data models with fixed effects, which is based on the median ratio of two consecutive pairs of first-order differenced data. To improve its precision and robustness properties, a general procedure based on higher-order pairwise differences and their ratios is designed. The asymptotic distribution of this class of estimators is derived. Further, the breakdown point properties are obtained under contamination by independent additive outliers and by the patches of additive outliers, and are used to select the pairwise differences that do not compromise the robustness properties of the procedure. The proposed estimator is additionally compared with existing methods by means of Monte Carlo simulations.

Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects

In this article, we consider semiparametric estimation in a partially linear single-index panel data model with fixed effects. Without taking the difference explicitly, we propose using a semiparametric minimum average variance estimation (SMAVE) based on a dummy variable method to remove the fixed effects and obtain consistent estimators for both the parameters and the unknown link function. As both the cross-section size and the time series length tend to infinity, we not only establish an asymptotically normal distribution for the estimators of the parameters in the single index and the linear component of the model, but also obtain an asymptotically normal distribution for the nonparametric local linear estimator of the unknown link function. The asymptotically normal distributions of the proposed estimators are similar to those obtained in the random effects case. In addition, we study several partially linear single-index dynamic panel data models. The methods and results are augmented by simulation studies and illustrated by application to two real data examples. This article has online supplementary materials.

Dynamic Panels, Cross Sectional Correlation, and Arbitrage in Equities Market

This paper studies estimation in linear dynamic panel data models with multiple interactive effects when both N and T are large. We derive the bias term in an order p dynamic panel data model and the limiting distribution of the estimator. Simulation results show good finite sample properties of the estimator. An application to arbitrage in the U.S. equities market between 2000 and 2011 shows dynamic panel data model is an effective method to produce trading signals and generate profit and loss.

How does political instability affect economic growth?

The purpose of this paper is to empirically determine the effects of political instability on economic growth. By using the system-GMM estimator for linear dynamic panel data models on a sample covering up to 169 countries, and 5-year periods from 1960 to 2004, we find that higher degrees of political instability are associated with lower growth rates of GDP per capita. Regarding the channels of transmission, we find that political instability adversely affects growth by lowering the rates of productivity growth and, to a smaller degree, physical and human capital accumulation. Finally, economic freedom and ethnic homogeneity are beneficial to growth, while democracy may have a small negative effect.

Detection of structural breaks in linear dynamic panel data models

A break detection testing procedure for the well-known AR(p) linear panel data model with exogenous or pre-determined regressors is developed. The proposed method can accommodate a structural break in the slope parameters as well as in the fixed effects. Breaks in the latter are not constrained by any type of cross-sectional homogeneity and are allowed to be correlated with all past information. Monte Carlo simulations indicate that the test performs satisfactorily even in the type of panel datasets with short time-dimension often encountered in practice. As an empirical illustration, the paper implements the test to detect the effects of the 1997 Asian crisis on the investment decisions of Asian companies.