Using VARs and TVP-VARs with Many Macroeconomic Variables

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

News Shocks and Business Cycles: Bridging the Gap from Different Methodologies

An important disconnect in the news driven view of the business cycle formalized by Beaudry and Portier (2004), is the lack of agreement between different—VAR and DSGE—methodologies over the empirical plausibility of this view. We argue that this disconnect can be largely resolved once we augment a standard DSGE model with a financial channel that provides amplification to news shocks. Both methodologies suggest news shocks to the future growth prospects of the economy to be significant drivers of U.S. business cycles in the post-Greenspan era (1990-2011), explaining as much as 50% of the forecast error variance in hours worked in cyclical frequencies

Data-based priors for vector autoregressions with drifting coefficients

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas

We study the asymmetric and dynamic dependence between financial assets and demonstrate, from the perspective of risk management, the economic significance of dynamic copula models. First, we construct stock and currency portfolios sorted on different characteristics (ex ante beta, coskewness, cokurtosis and order flows), and find substantial evidence of dynamic evolution between the high beta (respectively, coskewness, cokurtosis and order flow) portfolios and the low beta (coskewness, cokurtosis and order flow) portfolios. Second, using three different dependence measures, we show the presence of asymmetric dependence between these characteristic-sorted portfolios. Third, we use a dynamic copula framework based on Creal et al. (2013) and Patton (2012) to forecast the portfolio Value-at-Risk of long-short (high minus low) equity and FX portfolios. We use several widely used univariate and multivariate VaR models for the purpose of comparison. Backtesting our methodology, we find that the asymmetric dynamic copula models provide more accurate forecasts, in general, and, in particular, perform much better during the recent financial crises, indicating the economic significance of incorporating dynamic and asymmetric dependence in risk management.

Primary Commodity Prices: Co-movements, Common Factors and Fundamentals

The behavior of commodities is critical for developing and developed countries alike. This paper contributes to the empirical evidence on the co-movement and determinants of commodity prices. Using nonstationary panel methods, we document a statistically significant degree of co-movement due to a common factor. Within a Factor Augmented VAR approach, real interest rate and uncertainty, as postulated by a simple asset pricing model, are both found to be negatively related to this common factor. This evidence is robust to the inclusion of demand and supply shocks, which both positively impact on the co-movement of commodity prices.

Prior selection for panel vector autoregressions

There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage in large dimensions, but also allow for soft clustering of variables or countries which are homogeneous. I discuss the implications of these new priors for modelling interdependencies and heterogeneities among different countries in a panel VAR setting. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains of the new priors compared to existing popular priors for VARs and PVARs.

Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula

We investigate the dynamic and asymmetric dependence structure between equity portfolios from the US and UK. We demonstrate the statistical significance of dynamic asymmetric copula models in modelling and forecasting market risk. First, we construct “high-minus-low" equity portfolios sorted on beta, coskewness, and cokurtosis. We find substantial evidence of dynamic and asymmetric dependence between characteristic-sorted portfolios. Second, we consider a dynamic asymmetric copula model by combining the generalized hyperbolic skewed t copula with the generalized autoregressive score (GAS) model to capture both the multivariate non-normality and the dynamic and asymmetric dependence between equity portfolios. We demonstrate its usefulness by evaluating the forecasting performance of Value-at-Risk and Expected Shortfall for the high-minus-low portfolios. From back-testing, e find consistent and robust evidence that our dynamic asymmetric copula model provides the most accurate forecasts, indicating the importance of incorporating the dynamic and asymmetric dependence structure in risk management.

Development of a cyanobacterial biolarvicide

Results of studies on a larvicidal cyanobacterium that expresses a Bti cryIVD gene fusion are reported. Genetically altered Agmenellum quadruplicatum PR-6 is shown to be toxic to larvae of three major genera of disease-bearing mosquitos. Factors affecting expression of Bti genes in cyanobacteria are discussed.

Desarrollo de una colección de Líneas Casi Isogénicas (NIL) para el estudio de caracteres de interés agronómico en especies del género Fragaria

Fragaria vesca posee varias características que la hacen interesante como especie modelo en la familia Rosaceae. Además, su naturaleza diploide permite sortear la complejidad genética de la fresa cultivada. Considerando que los genomas de las especies diploides y octoploides de Fragaria mantienen una relación de sintenia y colinearidad, estamos desarrollando una colección de Líneas Casi Isogénicas (NIL) en Fragaria diploide, usando Fragaria bucharica, una variedad asiática, como donante de introgresiones y Fragaria vesca var. Reine des Vallées, una variedad francesa comúnmente cultivada en España para usos industriales, como parental recurrente. Para obtener introgresiones de F. bucharica en un fondo genético homogéneo de F. vesca, se desarrolló un retrocruzamiento y se obtuvo una población BC1 que fue analizada para la presencia de alelos de F. bucharica a lo largo de los 7 grupos de ligamiento (LG) del mapa de referencia de Fragaria. Los individuos con bajo número de introgresiones de gran tamaño, que en conjunto abarcaron todo el genoma de F. bucharica, fueron seleccionados y retrocruzados con el parental recurrente. La descendencia fue analizada para los loci segregantes y los individuos BC2 con 1 ó 2 introgresiones fueron seleccionados como líneas donadoras de introgresiones de las NIL. Hasta el momento se han descrito tres fenotipos dominantes diferentes bajo condiciones de invernadero en varias NIL heterozigóticas: Las plantas con introgresiones en LG2 presentan estolones. Las plantas con introgresiones en LG1 y LG3 presentan floración temprana. Las plantas con introgresiones en LG6 presentan floración estacional. Actualmente se está trabajando en la selección y caracterización de introgresiones de pequeño tamaño mediante la autofecundación de las líneas seleccionadas. La caracterización fenotípica de los recombinantes seleccionados permitirá localizar y estimar los patrones de herencia de los genes implicados en los caracteres descritos, además de la identificación de nuevos caracteres recesivos que aparecerán en homozigosis.

Loss risk through fraud in car insurance

Our objective is to analyse fraud as an operational risk for the insurance company. We study the effect of a fraud detection policy on the insurer's results account, quantifying the loss risk from the perspective of claims auditing. From the point of view of operational risk, the study aims to analyse the effect of failing to detect fraudulent claims after investigation. We have chosen VAR as the risk measure with a non-parametric estimation of the loss risk involved in the detection or non-detection of fraudulent claims. The most relevant conclusion is that auditing claims reduces loss risk in the insurance company.

Fiscal foresight and the effects of government spending

We study the effects of government spending by using a structural, large dimensional, dynamic factor model. We find that the government spending shock is non-fundamental for the variables commonly used in the structural VAR literature, so that its impulse response functions cannot be consistently estimated by means of a VAR. Government spending raises both consumption and investment, with no evidence of crowding out. The impact multiplier is 1.7 and the long run multiplier is 0.6.

Fiscal policy, foresight and the trade balance in the U.S.

This paper investigates the effects of fiscal policy on the trade balance using a structural factor model. A fiscal policy shock worsens the trade balance and produces an appreciation of the domestic currency but the effects are quantitatively small. The findings match the theoretical predictions of the standard Mundell-Fleming model, although fiscal policy should not be considered one of the main causes of the large US external deficit. My conclusions differ from those reached using VAR models since the fiscal shock, possibly due to fiscal foresight, is nonfundamental for the variables typically used in open economy VARs.

Approche biochimique et morphologique des espèces du genre Trichia (Helicidae, Gastropoda) de Suisse occidentale

The highly polymorphic so called "Trichia hispida group" is taxonomically problematic. According to different authors, one to about one hundred species are recognized in this group. Some recent publications admit 8 species for Central Euorpe. The present study gives arguments from biochemical data (12 enzymatic loci are electrophoretically analysed) for regrouping 5 morphological types into 2 species : Trichia sericea (Müller) var. sericea, plebeia, montana and striolata, and T.hispida(L.). A multivariate analysis of morphological measurments, and patterns of coexistence seem to corroborate this interpretation. T. sericea presents a wider ecological and morphological range, and both species need further investigations on their ecological and taxonomical relationship.

Testing for sufficient information in structural VARs

We derive necessary and sufficient conditions under which a set of variables is informationally sufficient, i.e. it contains enough information to estimate the structural shocks with a VAR model. Based on such conditions, we suggest a procedure to test for informational sufficiency. Moreover, we show how to amend the VAR if informational sufficiency is rejected. We apply our procedure to a VAR including TFP, unemployment and per-capita hours worked. We find that the three variables are not informationally sufficient. When adding missing information, the effects of technology shocks change dramatically.

Haar wavelets-based approach for quantifying credit portfolio losses

This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is specially suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. WA is an accurate, robust and fast method, allowing to estimate VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability.