Looking for a Needle in a Haystack? A Structural Time Series Model of the Relationship Between Teenage Employment and Minimum Wages in the United States

The work of Card and Krueger has cast doubt on the nature of the relationship between the minimum wage and teenage employment. The earlier "consensus" finding of a small but statistically significant negative effect was based on time series data whereas Card and Krueger's findings are based mainly on cross section data. In this article, we re-examine the time series relationship between minimum wage and teenage employment. We find that previous models break down due to their inability to capture changes in the trend, cyclical and seasonal components of teenage employment. We propose a structural time series model in which these components are treated as stochastic components and which contains the traditional approach as a special case. The model when estimated up to 1979 accurately predicts what happens to teenage employment subsequently, when the minimum wage was frozen after 1981 and then increased quite substantially in the early 1990s. Moreover, we find that there is a significant, negative effect of the minimum wage on teenage employment and its size and significance have hardly changed during the 1980s and early 1990s. Finally, the model remains robust in an out-of-sample test for 1993-99 containing two further minimum wage hikes.

Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model.

This paper presents a new multivariate GARCH model with time-varying conditional correlation structure which is a generalization of the Regime Switching Dynamic Correlation (RSDC) of Pelletier (2006). This model, which we name Hierarchical RSDC, is building with the hierarchical generalization of the hidden Markov model introduced by Fine et al. (1998). This can be viewed graphically as a tree-structure with different types of states. The first are called production states and they can emit observations, as in the classical Markov-Switching approach. The second are called abstract states. They can't emit observations but establish vertical and horizontal probabilities that define the dynamic of the hidden hierarchical structure. The main gain of this approach compared to the classical Markov-Switching model is to increase the granularity of the regimes. Our model is also compared to the new Double Smooth Transition Conditional Correlation GARCH model (DSTCC), a STAR approach for dynamic correlations proposed by Silvennoinen and Teräsvirta (2007). The reason is that under certain assumptions, the DSTCC and our model represent two classical competing approaches to modeling regime switching. We also perform Monte-Carlo simulations and we apply the model to two empirical applications studying the conditional correlations of selected stock returns. Results show that the Hierarchical RSDC provides a good measure of the correlations and also has an interesting explanatory power.Multivariate GARCH; Dynamic correlations; Regime switching; Markov chain; Hidden Markov models; Hierarchical Hidden Markov models

Extreme Value Theory and Value at Risk : Application to Oil Market

Recent increases in energy prices, especially oil prices, have become a principal concern for consumers, corporations, and governments. Most analysts believe that oil price fluctuations have considerable consequences on economic activity. Oil markets have become relatively free, resulting in a high degree of oil-price volatility and generating radical changes to world energy and oil industries. As a result oil markets are naturally vulnerable to significant negative volatility. An example of such a case is the oil embargo crisis of 1973. In this newly created climate, protection against market risk has become a necessity. Value at Risk (VaR) measures risk exposure at a given probability level and is very important for risk management. Appealing aspects of Extreme Value Theory (EVT) have made convincing arguments for its use in managing energy price risks. In this paper, we apply both unconditional and conditional EVT models to forecast Value at Risk. These models are compared to the performances of other well-known modelling techniques, such as GARCH, historical simulation and Filtered Historical Simulation. Both conditional EVT and Filtered Historical Simulation procedures offer a major improvement over the parametric methods. Furthermore, GARCH(1, 1)-t model may provide equally good results, as well as the combining of the two procedures.Extreme Value Theory, Value at Risk, oil price volatility, GARCH, Historical Simulation, Filtered Historical Simulation.

Moorings: Indian Ocean Creolizations

In this essay written in 2004, Françoise Vergès and Carpanin Marimoutou explore the ways in which processes and practices of creolization occurred in Réunion Island. They argue that creolization must be analyzed within the historical, political and cultural context in which they emerge. Vergès and Marimoutou reflect on these processes -- frictions, conflicts, and exchanges among slaves, settlers, migrants, and indentured workers from Madagascar, Mozambique, Gujarat, Bengal, France, Tamil Nadu, Southern China, Malaysia, Vietnam..., who were brought or came on the uninhabited island, colonized by the French in the 17th century. The authors also looked at the post-colonial moment, the French policies of assimilation and repression in the 1960s-1970s. For them, vernacular cultural practices and memories of struggle continue to work as counter strategies against local and national reactionary politics. In their conclusion, Vergès and Marimoutou look at the current form of globalization and its consequences on processes of creolization

The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

In this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade.Gravity Equation; Flexible Least Squares; Geographical Distance

The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

In this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade.Gravity Equation, Flexible Least Squares, Geographical Distance

The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

In this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade.Gravity Equation; Flexible Least Squares; Geographical Distance

On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree

This study examines the volatility and correlation and their relationships among the euro/US dollar exchange rates, the S&P500 equity indices, and the prices of WTI crude oil and the precious metals (gold, silver, and platinum) over the period 2005 to 2012. Our model links the univariate volatilities with the correlations via a hidden stochastic decision tree. The ensuing Hidden Markov Decision Tree (HMDT) model is in fact an extension of the Hidden Markov Model (HMM) introduced by Jordan et al. (1997). The architecture of this model is the opposite that of the classical deterministic approach based on a binary decision tree and, it allows a probabilistic vision of the relationship between univariate volatility and correlation. Our results are categorized into three groups, namely (1) exchange rates and oil, (2) S&P500 indices, and (3) precious metals. A switching dynamics is seen to characterize the volatilities, while, in the case of the correlations, the series switch from one regime to another, this movement touching a peak during the period of the Subprime crisis in the US, and again during the days following the Tohoku earthquake in Japan. Our findings show that the relationships between volatility and correlation are dependent upon the nature of the series considered, sometimes corresponding to those found in econometric studies, according to which correlation increases in bear markets, at other times differing from them

The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

International audienceIn this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade

Extreme Value Theory and Value at Risk : Application to Oil Market

Recent increases in energy prices, especially oil prices, have become a principal concern for consumers, corporations, and governments. Most analysts believe that oil price fluctuations have considerable consequences on economic activity. Oil markets have become relatively free, resulting in a high degree of oil-price volatility and generating radical changes to world energy and oil industries. As a result oil markets are naturally vulnerable to significant negative volatility. An example of such a case is the oil embargo crisis of 1973. In this newly created climate, protection against market risk has become a necessity. Value at Risk (VaR) measures risk exposure at a given probability level and is very important for risk management. Appealing aspects of Extreme Value Theory (EVT) have made convincing arguments for its use in managing energy price risks. In this paper, we apply both unconditional and conditional EVT models to forecast Value at Risk. These models are compared to the performances of other well-known modelling techniques, such as GARCH, historical simulation and Filtered Historical Simulation. Both conditional EVT and Filtered Historical Simulation procedures offer a major improvement over the parametric methods. Furthermore, GARCH(1, 1)-t model may provide equally good results, as well as the combining of the two procedures