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A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is considerably faster and easier to use than the leading methods recommended in the statistics literature. It shows through a set of simulations that the Lerp works well, whereas other existing methods fail to perform properly, when the panel data contain a high degree of missingness and/or a strong correlation across cross-sectional units. As an illustration, the method is applied to study the cost-of-living-index dataset with missing values. The test on the imputed panel data provides the supporting evidence for the U.S. economy convergence that depends on the state physical spatial proximities and the state industrial development similarities.

This paper proposes a new class of estimators for the autoregressive coefficient of a dynamic panel data model with random individual effects and nonstationary initial condition. The new estimators we introduce are weighted averages of the well-known first difference (FD) GMM/IV estimator and the pooled ordinary least squares (POLS) estimator. The proposed procedure seeks to exploit the differing strengths of the FD GMM/IV estimator relative to the pooled OLS estimator. In particular, the latter is inconsistent in the stationary case but is consistent and asymptotically normal with a faster rate of convergence than the former when the underlying panel autoregressive process has a unit root. By averaging the two estimators in an appropriate way, we are able to construct a class of estimators which are consistent and asymptotically standard normal, when suitably standardized, in both the stationary and the unit root case. The results of our simulation study also show that our proposed estimator has favorable finite sample properties when compared to a number of existing estimators.

This paper provides a selective survey of the panel macroeconometric techniques that focus on controlling the impact of “unobserved heterogeneity” across individuals and over time to obtain valid inference for “structures” that are common across individuals and over time. We consider issues of (i) estimating vector autoregressive models; (ii) testing of unit root or cointegration; (iii) statistical inference for dynamic simultaneous equations models; (iv) policy evaluation; and (v) aggregation and prediction.

This is a survey paper of the recent literature on the application of semiparametric–econometric advances to testing for functional form of the environmental Kuznets curve (EKC). The EKC postulates that there is an inverted U-shaped relationship between economic growth (typically measured by income) and pollution; that is, as economic growth expands, pollution increases up to a maximum and then starts declining after a threshold level of income. This hypothesized relationship is simple to visualize but has eluded many empirical investigations. A typical application of the EKC uses panel data models, which allows for heterogeneity, serial correlation, heteroskedasticity, data pooling, and smooth coefficients. This vast literature is reviewed in the context of semiparametric model specification tests. Additionally, recent developments in semiparametric econometrics, such as Bayesian methods, generalized time-varying coefficient models, and nonstationary panels are discussed as fruitful areas of future research. The cited literature is fairly complete and should prove useful to applied researchers at large.

Provides the first empirical time series analysis of commercial mortgage‐backed securities (CMBS) prices using a proprietary data set of 15 senior tranche securities. Postulates and tests the hypothesis that nonstationary CMBS and corporate bond prices are cointegrated since CMBS are priced analogous to corporate bonds. States that given the emerging status of the CMBS market, price data is limited to less than three years. To overcome the low power of unit root and cointegration methodology for short data sets, appeals to the concept of cointegration in heterogeneous panels advanced by Pedroni (1995). Claims the presence of cointegration between CMBS and corporate bond prices confirms that the stationary first difference in CMBS and corporate bond prices must be modelled in an error correction framework (ECM). Further states the sensitivity of CMBS price changes to changes in the default probability, proxied by the market value of loans to property value, is tested in a simple first order approximation ECM framework. The results suggest that senior tranche CMBS which comprise no more than 70 per cent are immune to the risk from default loss and supports the predictions in Childs et al. (1996).

This chapter provides an overview of topics in nonstationary panels: panel unit root tests, panel cointegration tests, and estimation of panel cointegration models. In addition it surveys recent developments in dynamic panel data models.

The purpose of this paper is to empirically analyse how different exchange rate regimes affect the links between monetary fundamentals and exchange rates in Sub-Saharan Africa.

Using the Pedroni method for panel cointegration, mean group and pooled mean group and the panel vector autoregressive technique, this study empirically investigates whether monetary fundamentals impact exchange rates similarly in both regimes. Thus, the author acquires needed and credible empirical data.

The result suggests that the impact is dissimilar. In the floating regime, an increase in relative money supply and relative real output depreciates and appreciates the nominal exchange rate in the long run whereas in the non-floating regime, the evidence is mixed. Thus, exchange rates bear a theoretically consistent relationship with monetary fundamentals across SSA countries with floating regimes but fails under non-floating regimes. This provides evidence that regime choice is important if the relationship between monetary fundamentals and exchange rates in SSA are to be theoretically consistent.

This study empirically incorporates the dissimilarities in exchange rate regimes in a panel framework and study the links between exchange rates and monetary fundamentals. The focus on how exchange rate regimes might alter the equilibrium relationships between exchange rates and monetary fundamentals in SSA is a pioneering experiment.

This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.

The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.

The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.

The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

In this chapter we extend the concept of serial correlation common features to panel data models. This analysis is motivated both by the need to develop a methodology to systematically study and test for common structures and comovements in panel data with autocorrelation present and by an increase in efficiency coming from pooling procedures. We propose sequential testing procedures and study their properties in a small scale Monte Carlo analysis. Finally, we apply the framework to the well known permanent income hypothesis for 22 OECD countries, 1950–1992.