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This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a second-order local polynomial to estimate the bias of the local linear RD estimator; the bias is then subtracted from the original estimator. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals (CIs). The CIs generated by this procedure are valid under conditions similar to Calonico, Cattaneo, and Titiunik’s (2014) analytical correction – that is, when the bias of the naive RD estimator would otherwise prevent valid inference. This chapter also provides simulation evidence that our method is as accurate as the analytical corrections and we demonstrate its use through a reanalysis of Ludwig and Miller’s (2007) Head Start dataset.

This study aims to provide one of the first empirical investigations of market efficiency for developed markets during the recent global financial crisis.

Using the Morgan Stanley Capital International (MSCI) country indices as proxies for national stock markets, the study conducts a battery of econometric tests in assessing weak-form market efficiency for the developed markets.

The inferential outcomes are consistent among the different tests. Specifically, the study finds that the majority of developed markets are weak-form efficient while the USA is the sole equity market to be commonly diagnosed as weak-form inefficient across the different tests when using full period data spanning the January 2008-November 2011 period. However, when basing the analysis on one-year subsamples over the identical time period, this study fails to reject weak-form market efficiency for all of the developed markets and presents evidence consistent with the Adaptive Market Hypothesis as described by Urquhart and Hudson (2013). When applying technical analysis for the case of the USA over the full study period, the results indicate that the return predictabilities can be exploited for some horizon of variable length moving average (VMA) trading rules.

This study provides one of the first empirical investigations of market efficiency for developed markets during the recent global financial crisis using an extended set of econometric tests. The study contributes to the existing body of empirical research that formally assesses the impact of a financial crisis on stock market efficiency and underlines the significance and relevance of examining market efficiency through subsample analysis.

The aim of this paper is to test the existence of bubbles for the daily prices of cryptocurrencies Bitcoin and Ethereum and verify if there is a relationship between bubbles and volatility regimes.

The authors test the presence of bubbles with the generalized supremum augmented Dickey–Fuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and Schütte (2020). Also, the authors estimate Markov regime switching generalized autoregressive conditional heteroskedasticity model for these cryptocurrencies.

The GSADF test result indicates the presence of bubbles for both cryptocurrencies. Simulating critical values by wild-bootstrap, which is robust to non-stationary volatility, leads to the highest number of bubbles in both cryptocurrencies. In addition, based on the estimates of conditional variance models with regime changes, the authors find that the bubbles identified are associated with a regime of low returns volatility, indicating a change in the trade-off between risk and return when the prices of cryptocurrencies differ from their fundamental values.

To the best of the authors knowledge, there are no studies that test the explosive behavior for cryptocurrencies by the GSADF test using the bootstrap method to simulate critical values from the procedures of Harvey et al. (2016) or Pedersen and Schütte (2020). These bootstrapping procedures are robust to heteroscedasticity and avoid the detection of false bubbles. Further, the advantage of Harvey et al. (2016) procedure is the robustness to non-stationary volatility.

This paper aims to test the finite sample properties of the automatic variance ratio (AVR) test and suggest suitable measure to improve its small sample properties under conditional heteroskedasticity and apply it to test the martingale hypothesis in the stock prices of the Portugal, Ireland, Italy, Greece and Spain (PIIGS economies) markets. This paper also seeks to investigate that “If the time series is not martingale, then what else?”

Monte Carlo experiments have been undertaken to test the small sample properties of automatic variance ratio (AVR) test. The study uses AVR test on daily and weekly data of the indices to investigate their martingale behaviour. It uses detrended fluctuation analysis (DFA) and BDS test statistics to answer, “If not martingale, then what else?”. The study also applies moving subsample approach to examine the dynamic behavior of stock prices and to obtain inferential findings robust to possible structural changes and presence of influential outliers.

The author finds that weighted bootstrap procedure significantly improves the small sample properties of AVR tests under conditional heteroskedasticity. The results provide evidence in support of the weak‐form efficiency of Italy and Spain. But Portugal, Ireland and Greece exhibit signs of long memory in the stock prices. All indices also exhibit chaotic characteristics.

This paper has both methodological and empirical originality. On the methodological aspect, the author proposes weighted bootstrap procedure on AVR test to improve its small sample properties. On the empirical side, the study finds that all stocks exhibit dynamic behavioral characteristics which change over time.

The purpose of this paper is to examine how major changes in an industry may differentially affect firms based on their organizational structure. The authors examine midstream oil and gas firms, comparing master limited partnerships (MLPs or uncorporates) with more traditional midstream corporate firms when the industry changed from one that was considered mature to a more rapid growth industry.

Non-parametric comparisons of returns, distributions, and operating ratios are presented across the two organizational forms and across two distinct industry activity periods. The risk-adjusted return analysis, including Fama and French factors, incorporates a wild bootstrap to address heteroscedasticity in the data.

In the industry’s mature market period, partnerships provided a significantly greater payout, return, return on equity (ROE), cash flow, and lower leverage, while exhibiting lower levels of systematic risk than corporations. In the later growth period, midstream corporations and partnerships are no longer significantly different in their returns, ROE or margins. MLPs now have significantly higher leverage levels, while continuing to provide significantly higher dividend payouts.

The paper contributes to the literature with an analysis of the effects of a changing industry environment on two different organizational types across a common industry. The authors find that the optimal organizational structure may be dependent on the environment. The findings during the initial period are consistent with prior research comparing publicly traded partnerships and corporations. During the growth phase, the findings lend support to the seminal literature with respect to corporations potentially best-suited to “growth” industries, while highlighting specific results by organizational form.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

This study aims to test the dynamic impact of public debt and economic growth on newly democratized African countries (South Africa and Namibia) and compare the findings with those of newly democratized European countries (Germany and Ukraine) during the period 1990–2022.

The methodology involves three stages: identifying the appropriate transition variable, assessing the linearity between public debt and economic growth and selecting the order m of the transition function. The linearity test helps identify the nature of relationships between public debt and economic growth. The wild cluster bootstrap-Lagrange Multiplier test is used to evaluate the model’s appropriateness. All these tests would be executed using the Lagrange Multiplier type of test.

The results signify the policy switch, as the authors find that the relationship between public debt and economic growth is characterized by two transitions that symbolize that the current stage of the relationship is beyond the U-shape; however, an S-shape. The results show that for newly democratized African countries, the threshold during the first waves was 50% of GDP, represented by a U-shape, which then transits to an inverted U-shape with a threshold of 65% of GDP. Then, for the European case, it was 60% of GDP, which is now 72% of GDP.

The findings suggest that an escalating level of public debt has a negative impact on economic growth; therefore, it is important to implement fiscal discipline, prioritize government spending and reduce reliance on debt financing. This can be achieved by focusing on revenue generation, implementing effective taxation policies, reducing wasteful expenditures and promoting investment and productivity-enhancing measures.

The authors investigate the growth–inequality relationship, using panel data from 13 Southern African Development Community (SADC) countries over the period 1990–2015, to test the validity of the Kuznets and Tribble theories. Furthermore, the authors seek to determine the threshold level at which excessive growth hampers inequality.

The panel smooth transition regression (PSTR) model has several stages. The authors applied the Lagrange multiplier (LM) test to find the appropriate transition variable amongst all candidate variables, to assess the linearity between economic growth and income inequality and to find the sequence for selecting the order m of the transition function. The authors then estimated the PSTR model, but before facilitating the results, the authors first used the wild cluster bootstrap (WCB)–LM-type test to assess the appropriateness of the selected transition.

The authors found that at lower growth, income inequality tends to be lower, while if growth increases above US$8,969, inequality tends to increase in the SADC region. The findings combine into a U-shaped relationship, contradicting the Kuznets and Tribble theories.

The contribution of this paper is that it becomes the first to provide the threshold level at which excessive growth increases inequality in the selected countries. This study proposes that policymakers should focus on activities aimed at stimulating growth, in other words, activities such as spending more on infrastructure, drawing up a suitable investment portfolio and spending on technological investment for countries that are below US$8,969. An improvement in these activities will create job opportunities, which in turn will add to economic growth and thus lead to lower income inequality and better social cohesion.

This study reexamines the sustainability of fiscal policy in Sweden.

To test the sustainability of fiscal policy, two approaches are used; the methodology of Kejriwal and Perron (2010), testing for multiple structural changes in a cointegrated regression model and time-varying cointegration test of Bierens and Martins (2010), and Martins (2015).

Using the first approach of testing for multiple structural changes in a cointegrated regression model, the results indicate that government spending and revenue are cointegrated with two breaks. An estimation of a two-break long-run model shows that the slope coefficient increases from 0.678 to 0.892 from the first to the second regime, implying that fiscal deficits were weakly sustainable in the first two regimes, from 1800 to 1943, and from 1944 to 1974. Further, results from time-varying cointegration test indicate that cointegration between spending and revenue in Sweden is time-varying. Fiscal deficits were found to be unsustainable for the periods 1801–1811, 1831–1838, 1853–1860 , 1872–1882, 1897–1902, 1929–1940 and 1976–1982 and weakly sustainable over the rest of the study period.

A number of implications arise from this study: (1) Accounting for breaks in cointegration analysis and in the estimation of the level relationship between spending and revenue is very important because ignoring breaks may lead to an overestimated slope coefficient and hence a bias on the magnitude of fiscal deficit sustainability. (2) In testing for cointegration between spending and revenue, assuming a constant cointegrating slope when it is actually time-varying can also be misleading because deficits can be sustainable for a period of time and unsustainable over another period.

The contribution of this study is three-fold; first, the study uses a long series of annual data spanning over a period of two centuries, from 1800 to 2011. Second, because of the importance of structural change in economics, to examine the existence of a level relationship between spending and revenue, the study uses the methodology of Kejriwal and Perron (2010) to test for multiple structural changes in a cointegrated regression model, as well as time-varying cointegration of Bierens and Martins (2010) and Martins (2015).

This study reexamines fiscal deficit sustainability in South Africa.

The study applies three cointegration testing approaches, namely testing for multiple structural changes in a cointegrated regression model, time-varying cointegration test and asymmetric cointegration test.

The results point to the existence of a level relationship between government revenue and spending. In addition, the long-run equilibrium relationship between government revenue and spending in South Africa is found to be characterized by breaks. As such, assuming a constant cointegrating slope may be misleading. Results from time-varying cointegration and an estimation of a cointegrated two-break model indicate that cointegrating coefficient has been time-varying but has remained less than 1 for the entire study period, indicating that fiscal deficits have been weakly sustainable. This finding is also confirmed by the results from an estimated asymmetric error correction model.

In view of the findings, authorities should put in place policies to improve the fiscal budgetary stance and reinforce the sustainability of the fiscal deficits in South Africa. Among other things, South Africa could undertake reforms to state-owned companies to reduce their reliance on public funds, slow down the pace of the public sector wage growth and devise effective economic measures to boost long-term growth. In addition, tax compliance and other revenue collection measures should be enhanced for additional tax revenue.

The contribution of this study is twofold; first, the study uses a long series of annual data spanning over a century, from 1913 to 2020. Indeed, cointegration is better modeled using long spans of time series data. Second, to examine the existence of a level relationship between spending and revenue, the study uses cointegration tests which allow capturing time-variation in the cointegrating slope coefficient, and accounting for asymmetries in the relationship between government spending and revenue. It is important to allow for time-variation in the cointegrating slope coefficient, especially when it has been hardly treated in the empirical literature on fiscal deficit sustainability. Allowing for time-variation in the cointegrating slope coefficient helps us to analyze fiscal deficit sustainability by periods of time. Indeed, the degree of fiscal sustainability can change from one time period to another.