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This paper examines economic growth in 52 African countries for 1961–2016 and seeks to find if there is common growth. As all African countries have their particular features, concerning climate, harvest, industry, size, politics, and infrastructure, and more, it seems best to rely on a non-parametric method. Dynamic Time Warping is such a convenient method, also as it allows leads and lags across countries to vary over time, and as it can easily be incorporated into a clustering technique. Five clusters are found, two of which concern Equatorial Guinea and Botswana, and the three other clusters have common growth rates of about 0, 2 and 4 over more than five decades.

This paper examines economic growth in 52 African countries for 1961-2016 and seeks to find if there is common growth. As all African countries have their particular features, concerning climate, harvest, industry, size, politics, and infrastructure, and more, it seems best to rely on a non-parametric method. Dynamic Time Warping is such a convenient method, also as it allows leads and lags across countries to vary over time, and as it can easily be incorporated into a clustering technique. Five clusters are found, two of which concern Equatorial Guinea and Botswana, and the three other clusters have common growth rates of about 0, 2 and 4 over more than five decades.

We present various stylized facts about annual CPI based inflation in 47 African countries. Some stylized facts concern time series properties for each of the series but also across series. To achieve a useful and relevant dataset, we impute all missing values in the sample 1960- 2015 using a new method based on postage stamps prices. This results in a balanced panel of annual figures from 1960 to and including 2015 for 47 countries. The key conclusion from our tour around various data properties is that differences across the country-specific series are substantially larger than their common properties. These differences concern features like peak inflation rates, years of peak inflation, correlation with worldwide inflation figures and country-specific persistence. In one word, there is no such thing as "African inflation", and we recommend that models for inflation in an African country should be designed one by one. When we correlate inflation features in a cross section with country-specific conditions, we see that more democracy, less corruption, and less religious fractionalization associate with lower inflation rates.

Pre-election polls can suffer from survey effects. For example, surveyed individuals can become more aware of the upcoming election so that they become more inclined to vote. These effects may depend on factors like political orientation and prior intention to vote, and this may cause biases in forecasts of election outcomes. We advocate a simple methodology to estimate the magnitude of these survey effects, which can be taken into account when translating future poll results into predicted election outcomes. The survey effects are estimated by collecting survey data both before an

Real GDP growth in China follows a random walk. Also, it has often been suggested that China "cooks its books", that is to say that governmental officials in China manipulate economic statistics such as GDP growth rate to present the outside world a rosy picture (Foreign Policy, September 3, 2009). If such unreliability is known to stock traders, news on GDP should not impact stock market fluctuations or their volatility. We test this hypothesis for 12 series with daily stock market returns for the years 2006 to and including 2009.

This paper investigates convergence in left-right ideological positions in The Netherlands using cointegration techniques. Our sample consists of 765 weekly observations on those positions as well as on the corresponding political party preference. The time series data display nonstationary patterns in the sense that their means are not constant over time. Therefore, we rely on recently developed techniques in the analysis of multivariate nonstationary time series to study convergence. One of our results is that the ideological positions, when considered relative to a benchmark, can be described by trend-stationary processes. This means that we cannot reject the presence of convergence. Implications of this result are discussed.

This manuscript uses a global and comprehensive approach based on 1.14 million observations to investigate whether the effect of corruption can be measured towards safety qualities of vessels. Since safety qualities of vessels are influenced by many factors and the effect of corruption can be confounded by their interactions, a multi-step approach in used at ship. The findings confirm the hypothesis that port states with higher perceived corruption are less likely to detain vessels and that flag states or ship owners located in countries associated with higher perceived corruption are more likely to have very serious incidents as the operating environment might facilitate substandard shipping and weaker enforcement of international conventions or increased operating costs that can influence safety qualities of vessels in economic hardship. The findings also revealed the degree of underreporting for serious incidents by flag states. The results support the establishment of accountability frameworks and current efforts at the International Maritime Organization (IMO) to address corruption to support sustainable development goal 16 (SDG 16). Relevant policy implications could be to strengthen the fight against corruption via IMO's Member State Audit Scheme and Facilitation Committee in general and specifically by strengthening training and enforcement of the Code of Good Conduct for Port State Control Inspectors. To enhance transparency, the Global Integrated Ship Information System could be adjusted to include an option to report corruption directly by ship owners and operators to IMO and flag states and mandatory reporting requirements should be revised to capture all serious incidents to GISIS

A time series (t=921) of weekly survey data on vote intentions in the Netherlands for the period 1978-1995 shows that the percentage of undecided voters follows a cyclical pattern over the election calendar. The otherwise substantial percentage of undecided voters decreases sharply in weeks leading up to an election and gradually increases afterwards. This paper models the dynamics of this asymmetric electoral cycle using artificial neural networks, with the purpose of estimating when the undecided voters start making up their minds. We find that they begin to decide which party to vote for nine weeks before a first order national parliamentary election and one to four weeks before a second order election, depending on the type of election (European Parliament, Provincial States, City-councils). The effect of political campaigns and the implications for political analysis are discussed.

Macro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average, as the actual value is approached. Otherwise, forecast updates would be neutral. The paper proposes a methodology to test whether forecast updates are progressive and whether econometric models are useful in updating forecasts. The data set for the empirical analysis are for Taiwan, where we have three decades of quarterly data available of forecasts and updates of the inflation rate and real GDP growth rate. The actual series for both the inflation rate and the real GDP growth rate are always released by the government one quarter after the release of the revised forecast, and the actual values are not revised after they have been released. Our empirical results suggest that the forecast updates for Taiwan are progressive, and can be explained predominantly by intuition. Additionally, the one-, two- and three-quarter forecast errors are predictable using publicly available information for both the inflation rate and real GDP growth rate, which suggests that the forecasts can be improved.

A government's ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or, expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate. In this paper, we develop a methodology to evaluate non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that it is the undocumented knowledge of the Taiwanese government that reduces forecast errors substantially.

The business cycle is a concept of key interest for all economic actors alike. One can think of consumption and savings decisions of individual consumers, production decisions of private sector firms, and monetary and fiscal policy decisions of central bankers and the government. All can benefit from accurate forecasts of the future development of economic variables, which to a large extent depend on the business cycle. The characterization of the business cycle, and the analysis of its properties, has been the subject of innumerable studies. It is widely recognized that business cycle research started with, or at least was boosted by, the seminal work of Burns and Mitchell (1946) and the preceding work on statistical testing of business cycles by Jan Tinbergen (1939). Copyright