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A model of financial asset price determination is proposed that incorporates flat trading features into an efficient price process. The model involves the superposition of a Brownian semimartingale process for the effcient price and a Bernoulli process that determines the extent of price trading. The approach is related to sticky price modeling and the Calvo pricing mechanism in macroeconomic dynamics. A limit theory for the conventional realized volatility (RV) measure of integrated volatility is developed. The results show that RV is still consistent but has an inflated asymptotic variance that depends on the probability of flat trading. Estimated quarticity is similarly affected, so that both the feasible central limit theorem and the inferential framework suggested in Barndorff-Nielson and Shephard (2002) remain valid under flat price trading even though there is information loss due to flat trading effects. The results are related to work by Jacod (1993) and Mykland and Zhang (2006) on realized volatility measures with random and intermittent sampling, and to ACD models for irregularly spaced transactions data. Extensions are given to include models with microstructure noise. Some simulation results are reported. Empirical evaluations with tick-by-tick data indicate that the effect of flat trading on the limit theory under microstructure noise is likely to be minor in most cases, thereby affirming the relevance of existing approaches.

Alpha-Beta pruning is one of the most powerful and fundamental MiniMax search improvements. It was designed for sequential two-player zero-sum perfect information games. In this paper we introduce an Alpha-Beta-like sound pruning method for the more general class of “stacked matrix games” that allow for simultaneous moves by both players. This is accomplished by maintaining upper and lower bounds for achievable payoffs in states with simultaneous actions and dominated action pruning based on the feasibility of certain linear programs. Empirical data shows considerable savings in terms of expanded nodes compared to naive depth-first move computation without pruning.

Kernel density estimation is an important technique for understanding the distributional properties of data. Some investigations have found that the estimation of a global bandwidth can be heavily affected by observations in the tail. We propose to categorize data into low- and high-density regions, to which we assign two different bandwidths called the low-density adaptive bandwidths. We derive the posterior of the bandwidth parameters through the Kullback-Leibler information. A Bayesian sampling algorithm is presented to estimate the bandwidths. Monte Carlo simulations are conducted to examine the performance of the proposed Bayesian sampling algorithm in comparison with the performance of the normal reference rule and a Bayesian sampling algorithm for estimating a global bandwidth. According to Kullback-Leibler information, the kernel density estimator with low-density adaptive bandwidths estimated through the proposed Bayesian sampling algorithm outperforms the density estimators with bandwidth estimated through the two competitors. We apply the low-density adaptive kernel density estimator to the estimation of the bivariate density of daily stock-index returns observed from the U.S. and Australian stock markets. The derived conditional distribution of the Australian stock-index return for a given daily return in the U.S. market enables market analysts to understand how the former market is associated with the latter.

We approximate the error density of a nonparametric regression model by a mixture of Gaussian densities with means being the individual error realizations and variance a constant parameter. We investigate the construction of a likelihood and posterior for bandwidth parameters under this Gaussian-component mixture density of errors in a nonparametric regression. A Markov chain Monte Carlo algorithm is presented to sample bandwidths for the kernel estimators of the regression function and error density. A simulation study shows that the proposed Gaussian-component mixture density of errors is clearly favored against wrong assumptions of the error density. We apply our sampling algorithm to a nonparametric regression model of the All Ordinaries daily return on the overnight FTSE and S&P 500 returns, where the error density is approximated by the proposed mixture density. With the estimated bandwidths, we estimate the density of the one-step-ahead point forecast of the All Ordinaries return, and therefore, a distribution-free value-at-risk is obtained. The proposed Gaussian component mixture density of regression errors is also validated through the nonparametric regression involved in the state-price density estimation proposed by Aït-Sahalia and Lo (1998).

The present work applies several advanced spectral methods to the analysis of macroeconomic fluctuations in three countries of the European Union: Italy, The Netherlands, and the United Kingdom. We focus here in particular on singular-spectrum analysis (SSA), which provides valuable spatial and frequency information of multivariate data and that goes far beyond a pure analysis in the time domain. The spectral methods discussed here are well established in the geosciences and life sciences, but not yet widespread in quantitative economics. In particular, they enable one to identify and describe nonlinear trends and dominant cycles | including seasonal and interannual components that characterize the deterministic behavior of each time series. These tools have already proven their robustness in the application on short and noisy data, and we demonstrate their usefulness in the analysis of the macroeconomic indicators of these three countries. We explore several fundamental indicators of the countries' real aggregate economy in a univariate, as well as a multivariate setting. Starting with individual single-channel analysis, we are able to identify similar spectral components among the analyzed indicators. Next, we consider combinations of indicators and countries, in order to take different effects of comovements into account. Since business cycles are cross-national phenomena, which show common characteristics across countries, our aim is to uncover hidden global behavior across the European economies. Results are compared with previous findings on the U.S. indicators (Groth et al., 2012). Finally, the analysis is extended to include several indicators from the U.S. economy, in order to examine its influence on the European market.

Using an equilibrium asset and option pricing model in a production economy under jump diffusion, we derive an analytical link between the equity premium, risk aversion and the systematic variance and skewness risk premium. In an empirical application of the model using more than 20 years of data on S&P500 index options, we find that, in line with theory, risk-averse investors demand risk-compensation for holding equity when the systematic skewness risk premium is high. However, when we differentiate between market conditions proxied by investor sentiment, we find that in up-markets (high sentiment) risk aversion is low, while in down-markets (low sentiment) risk aversion is high. We show that in line with theory, the skewness-risk-premium-return relationship only holds when risk aversion is high. In periods of low risk aversion, investors demand lower risk compensation, thus substantially weakening the skewness-risk premium-return trade off. Therefore, we also provide new evidence that helps to disentangle sentiment from risk aversion.

Despite some disagreements about specific timing, it is now widely accepted that France was the first European country to experience a systematic decline in fertility, a decline that took place in a very distinctive geographical pattern. Whereas two areas of low birth rates (the Seine valley and the Aquitaine region) kept spreading, two 'islands' of high fertility (Bretagne and the Massif Central) shrank until they more or less disappeared in the early 1900s. In an attempt to provide a sensible explanation of this pattern, we build an agent-based simulation model which incorporates both historical data on population characteristics and spatial informaton on the geography of France, and allows us to study the role of social influence in fertility decisions. We assess how different behavioural assumptions and network topologies cause variations in diffusion patterns, using quantitative data on the Ecclesiastical Oath of 1791 to proxy for the impact of the Revolution. Analysis of several simulations shows that a combination of both endogenous and exogenous factors help to explain the way in which the diffusion took place and suggests some of the mechanisms through which this was materialised.

In this paper we adapt the Hamiltonian Monte Carlo (HMC) estimator to DSGE models, a method presently used in various fields due to its superior sampling and diagnostic properties. We implement it into a state-of-theart, freely available high-performance software package, STAN. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model using US data. Our results and sampling diagnostics confirm the parameter estimates available in existing literature. In addition, we find bimodality in the Smets-Wouters model even if we estimate the model using the original tight priors. Finally, we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm to create a powerful tool which permits the estimation of DSGE models with ill-behaved posterior densities.

Mit einem um die Behandlungskapazität des Gesundheitssystems erweiterten epidemiologischen SIRD-Modell werden Mechanismen und Dynamik einer Virusepidemie wie Corona anhand von stilisierten politischen Reaktionsmustern (Ignore, Shutdown, Ignore-Shutdown-Relax) simuliert. Ferner werden aus dem Modell Lehren für die statistische Analyse von Corona gezogen, wie die Aussagekraft publizierter Verdopplungszeiten und Reproduktionszahlen. Die Dunkelziffer unbestätigter Fälle und die im Epidemieverlauf variable Genauigkeit von medizinischen Infektionstests werden diskutiert. Zur Messung der medizinischen Kosten von Corona sowie für regionale und internationale Vergleiche wird ein Schadensindex der verlorenen Lebenszeit vorgeschlagen. Zuletzt geht die Arbeit kurz auf die ökonomischen Kosten von Corona in Deutschland ein. Using an epidemiological SIRD model, extended by treatment capacity of the health care system, mechanisms and dynamics of a virus epidemic such as Corona are simulated by means of stylized political reaction patterns (Ignore, Shutdown, Ignore-Shutdown-Relax). Moreover, lessons will be drawn from the model for statistical analysis of corona, such as the significance of published doubling times and reproduction rates. The (dark) figure of unconfirmed cases and the accuracy of medical infection tests, which varies during the course of an epidemic, will be addressed. To measure the medical costs of corona, as well as for regional and international comparisons, an index of lost lifetime is proposed. Finally, the paper briefly discusses the economic costs of corona in Germany.

Effective hedging strategies on oil spot and future markets are relevant in reducing price volatility for investors, energy traders and companies operating in the oil markets. A new Bayesian multi-chain Markov-switching GARCH model for dynamic hedging in energy futures markets is developed. It builds on the construction of a system of simultaneous equations for the return dynamics on the hedged portfolio and the futures. The implication of our model for portfolios allocation and energy trading are manyfold. First, our formulation allows for an easy identification of the different states of the discrete processes as volatility regimes. Secondly, the use of regime-switching models with multiple chains allows for an effective way to reduce risk. Furthermore, correlated chains are more flexible than single chain models since they account for co-movement in the volatility regimes of different markets thus they should be preferred in turbulent periods (e.g. financial crisis). Finally, the combination of the expected utility framework with the regime-switching structure allows us to define a robust minimum variance hedging strategy. Changes in the optimal hedging strategies before and after the financial crisis are evidenced when the proposed robust hedging strategy is applied to crude oil spot and futures markets. It is therefore recommended that many and different models should be used in place of a single one in energy risk hedging since they could perform differently in various phases of the market.

This paper weakens the size and moment conditions needed for typical block bootstrap methods (i.e., the moving blocks, circular blocks, and stationary bootstraps) to be valid for the sample mean of Near-Epoch-Dependent (NED) functions of mixing processes; they are consistent under the weakest conditions that ensure the original NED process obeys a central limit theorem (CLT), established by De Jong (1997, Econometric Theory 13(3), 353–367). In doing so, this paper extends De Jong’s method of proof, a blocking argument, to hold with random and unequal block lengths. This paper also proves that bootstrapped partial sums satisfy a functional CLT (FCLT) under the same conditions.

This paper presents Rtadf (right-tail augmented Dickey-Fuller), an EViews add-in that facilitates the performance of time series based tests that help detect and date-stamp asset price bubbles. The detection strategy is based on a right-tail variation of the standard augmented Dickey-Fuller (ADF) test where the alternative hypothesis is of a mildly explosive process. Rejection of the null in each of these tests may serve as empirical evidence for an asset price bubble. The add-in implements four types of tests: standard ADF, rolling window ADF, supremum ADF (SADF; Phillips, Wu, and Yu 2011) and generalized SADF (GSADF; Phillips, Shi, and Yu 2015). It calculates the test statistics for each of the above four tests, simulates the corresponding exact finite sample critical values and p values via Monte Carlo methods, under the assumption of Gaussian innovations, and produces a graphical display of the date stamping procedure.

This article studies the pruned state-space system for higher-order perturbation approximations to dynamic stochastic general equilibrium (DSGE) models.We show the stability of the pruned approximation up to third order and provide closed-form expressions for first and second unconditional moments and impulse response functions. Our results introduce generalized method of moments (GMM) estimation and impulse-response matching for DSGE models approximated up to third order and provide a foundation for indirect inference and simulated method of moments (SMM). As an application, we consider a New Keynesian model with Epstein-Zin preferences and two novel feedback effects from long-term bonds to the real economy, allowing us to match the level and variability of the 10-year term premium in the U.S. with a low relative risk aversion of 5.

In this paper, we discuss the key conditions for the identification and estimation of the local average treatment effect (LATE, Imbens and Angrist, 1994): the valid instrument assumption (LI) and the monotonicity assumption (LM). We show that the joint assumptions of LI and LM have a testable implication that can be summarized by a sign restriction defined by a set of intersection bounds. We propose an easy-to-implement testing procedure that can be analyzed in the framework of Chernozhukov, Lee, and Rosen (2013) and implemented using the Stata package of Chernozhukov, Kim, Lee, and Rosen (2013). We apply the proposed tests to the "draft eligibility" instrument in Angrist (1991), the "college proximity" instrument in Card (1993) and the "same sex" instrument in Angrist and Evans (1998).

We use a factor model with stochastic volatility to decompose the time-varying variance of macroeconomic and financial variables into contributions from country-specific uncertainty and uncertainty common to all countries. We find that the common component plays an important role in driving the time-varying volatility of nominal and financial variables. The cross-country co-movement in volatility of real and financial variables has increased over time with the common component becoming more important over the last decade. Simulations from a two-country DSGE model featuring Epstein-Zin preferences suggest that increased globalisation and trade openness may be the driving force behind the increased cross-country correlation in volatility.

We propose nonparametric estimators for conditional value-at-risk (CVaR) and conditional expected shortfall (CES) associated with conditional distributions of a series of returns on a financial asset. The return series and the conditioning covariates, which may include lagged returns and other exogenous variables, are assumed to be strong mixing and follow a nonparametric conditional location-scale model. First stage nonparametric estimators for location and scale are combined with a generalized Pareto approximation for distribution tails proposed by Pickands (1975) to give final estimators for CVaR and CES. We provide consistency and asymptotic normality of the proposed estimators under suitable normalization. We also present the results of a Monte Carlo study that sheds light on their finite sample performance. Empirical viability of the model and estimators is investigated through a backtesting exercise using returns on future contracts for five agricultural commodities., Comment: 45 pages, Econometric Theory, 2017

We present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the topical and challenging context of multistep macroeconomic forecasting in a monetary policy setting. BPS evaluates—sequentially and adaptively over time—varying forecast biases and facets of miscalibration of individual forecast densities for multiple time series, and—critically—their time-varying inter-dependencies. We define BPS methodology for a new class of dynamic multivariate latent factor models implied by BPS theory. Structured dynamic latent factor BPS is here motivated by the application context—sequential forecasting of multiple U.S. macroeconomic time series with forecasts generated from several traditional econometric time series models. The case study highlights the potential of BPS to improve of forecasts of multiple series at multiple forecast horizons, and its use in learning dynamic relationships among forecasting models or agents. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

The recent increase in the breath of computational methodologies has been matched with a corresponding increase in the difficulty of comparing the relative explanatory power of models from different methodological lineages. In order to help address this problem a Markovian information criterion (MIC) is developed that is analogous to the Akaike information criterion (AIC) in its theoretical derivation and yet can be applied to any model able to generate simulated or predicted data, regardless of its methodology. Both the AIC and proposed MIC rely on the Kullback---Leibler (KL) distance between model predictions and real data as a measure of prediction accuracy. Instead of using the maximum likelihood approach like the AIC, the proposed MIC relies instead on the literal interpretation of the KL distance as the inefficiency of compressing real data using modelled probabilities, and therefore uses the output of a universal compression algorithm to obtain an estimate of the KL distance. Several Monte Carlo tests are carried out in order to (a) confirm the performance of the algorithm and (b) evaluate the ability of the MIC to identify the true data-generating process from a set of alternative models.

The deviance information criterion (DIC) has been widely used for Bayesian model comparison. However, recent studies have cautioned against the use of certain variants of the DIC for comparing latent variable models. For example, it has been argued that the conditional DIC-based on the conditional likelihood obtained by conditioning on the latent variables-is sensitive to transformations of latent variables and distributions. Further, in a Monte Carlo study that compares various Poisson models, the conditional DIC almost always prefers an incorrect model. In contrast, the observed-data DIC-calculated using the observed-data likelihood obtained by integrating out the latent variables-seems to perform well. It is also the case that the conditional DIC based on the maximum a posteriori (MAP) estimate might not even exist, whereas the observed-data DIC does not suffer from this problem. In view of these considerations, fast algorithms for computing the observed-data DIC for a variety of high-dimensional latent variable models are developed. Through three empirical applications it is demonstrated that the observed-data DICs have much smaller numerical standard errors compared to the conditional DICs. The corresponding Matlab code is available upon request.

We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to existing literature is manifold. First, we discuss different multi-move sampling techniques for Markov Switching (MS) state space models with particular attention to MS-GARCH models. Our multi-move sampling strategy is based on the Forward Filtering Backward Sampling (FFBS) applied to an approximation of MS-GARCH. Another important contribution is the use of multi-point samplers, such as the Multiple-Try Metropolis (MTM) and the Multiple trial Metropolize Independent Sampler, in combination with FFBS for the MS-GARCH process. In this sense we ex- tend to the MS state space models the work of So [2006] on efficient MTM sampler for continuous state space models. Finally, we suggest to further improve the sampler efficiency by introducing the antithetic sampling of Craiu and Meng [2005] and Craiu and Lemieux [2007] within the FFBS. Our simulation experiments on MS-GARCH model show that our multi-point and multi-move strategies allow the sampler to gain efficiency when compared with single-move Gibbs sampling., 38 pages, 7 figures

We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995{2014. and compare these to the tail indexes produced by simulating GARCH models. Our results suggest that actual and simulated values differ greatly for GARCH models with normal conditional distributions, which underestimate the tail risk. By contrast, the GARCH models with Student's t conditional distributions capture the tail shape more accurately, with GARCH and GJR-GARCH being the top performers.

I propose a nonparametric iid bootstrap procedure for the empirical likelihood, the exponential tilting, and the exponentially tilted empirical likelihood estimators that achieves asymptotic refinements for t tests and confidence intervals, and Wald tests and confidence regions based on such estimators. Furthermore, the proposed bootstrap is robust to model misspecification, i.e., it achieves asymptotic refinements regardless of whether the assumed moment condition model is correctly specified or not. This result is new, because asymptotic refinements of the bootstrap based on these estimators have not been established in the literature even under correct model specification. Monte Carlo experiments are conducted in dynamic panel data setting to support the theoretical finding. As an application, bootstrap confidence intervals for the returns to schooling of Hellerstein and Imbens (1999) are calculated. The result suggests that the returns to schooling may be higher.

Little attention has been paid to the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. We study several such tests in models estimated by instrumental variables (IV) and limited-information maximum likelihood (LIML). Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The distributions of the statistics are shown to have an ill-defined limit as the parameter that determines the strength of the instruments tends to zero and as the correlation between the disturbances of the structural and reduced-form equations tends to plus or minus one. Simulation experiments demonstrate that this makes it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are not too weak. We also study the power properties of the bootstrap tests.

In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the sequence of bootstrap P values obtained by iterating the bootstrap. The main idea of the paper is that, if this sequence converges to a random variable that follows the uniform U(0; 1) distribution, then the bootstrap is valid. The idea is studied by making the model under test discrete and finite, so that it is characterised by a finite three-dimensional array of probabilities. This device, when available, renders bootstrap iteration to any desired order feasible. It is used for studying a unit-root test for a process driven by a stationary MA(1) process, where it is known that the unit-root test, even when bootstrapped, becomes quite unreliable when the MA(1) parameter is in the vicinity of -1. Iteration of the bootstrap P value to convergence achieves reliable inference except for a parameter value very close to -1. The paper then endeavours to see these specific results in a wider context, and tries to cast new light on where bootstrap theory may be going.

A growing empirical literature has considered the impact of uncertainty using SVAR models that include proxies for uncertainty shocks as endogenous variables. In this paper we consider the possible impact of measurement error in the uncertainty shock proxies on the estimated impulse responses from these SVAR models. We show via a Monte Carlo experiment that measurement error can result in attenuation bias in the SVAR impulse responses. In contrast, the proxy SVAR that uses the uncertainty shock proxy as an instrument to identify the underlying shock does not suffer from this bias. Applying this proxy SVAR method to the Bloom (2009) data set results in estimated impulse responses to uncertainty shocks that are larger in magnitude and persistence than those obtained from a standard recursive SVAR.

In this paper, we analyze the development of technical efficiency in the Tunisian manufacturing sector using advanced analysis methods. The technical efficiency of the industrial sectors is measured on the basis of panel data through the bias of a classical approach and a Bayesian one, which makes the inefficiency terms change over time. This exercise helps to assess the robustness of the estimated technical efficiency compared to the choice of the estimation technique. The mean efficiency score is found to be of 77 percent and there is no evidence of a continuous increase in efficiency.

In this study, we verify the existence of predictability in the Brazilian equity market. Unlike other studies in the same sense, which evaluate original series for each stock, we evaluate synthetic series created on the basis of linear models of stocks. Following Burgess (1999), we use the "stepwise regression" model for the formation of models of each stock. We then use the variance ratio profile together with a Monte Carlo simulation for the selection of models with potential predictability. Unlike Burgess (1999), we carry out White's Reality Check (2000) in order to verify the existence of positive returns for the period outside the sample. We use the strategies proposed by Sullivan, Timmermann & White (1999) and Hsu & Kuan (2005) amounting to 26,410 simulated strategies. Finally, using the bootstrap methodology, with 1,000 simulations, we find strong evidence of predictability in the models, including transaction costs.

A number of recently published papers have focused on the problem of testing for a unit root in the case where the driving shocks may be unconditionally heteroskedastic. These papers have, however, assumed that the lag length in the unit root test regression is a deterministic function of the sample size, rather than data-determined, the latter being standard empirical practice. In this paper we investigate the finite sample impact of unconditional heteroskedasticity on conventional data-dependent methods of lag selection in augmented Dickey-Fuller type unit root test regressions and propose new lag selection criteria which allow for the presence of heteroskedasticity in the shocks. We show that standard lag selection methods show a tendency to over-fit the lag order under heteroskedasticity, which results in significant power losses in the (wild bootstrap implementation of the) augmented Dickey-Fuller tests under the alternative. The new lag selection criteria we propose are shown to avoid this problem yet deliver unit roots with almost identical finite sample size and power properties as the corresponding tests based on conventional lag selection methods when the shocks are homoskedastic.

International audience; This paper proposes an indirect inference (Gourieroux, Monfort and Renault, 1993; Smith, 1993) estimation method for a large class of dynamic equilibrium models. Our approach is based on the observation that the econometric structure of these systems naturally generates auxiliary equilibria that can serve as building blocks for estimation. We use this insight to develop an accurate estimator for the long-run risk model of Bansal and Yaron (2004). We demonstrate the accuracy of our method by Monte Carlo simulation and estimate the long-run risk model on U.S. data. We also illustrate the good performance of the methodology on an asset pricing model with investor learning.

The evolution of financial sector stability risks is a major cause of concern for central banks in democratic countries, the macroprudential approach being a top priority nowadays, amid efforts to overcome the current global financial crisis. The systemic risk management approach has a major impact on economic growth. It refers to financial, economic and social cycles as well. The analysis that reveals this evolution is based on the correlations between risk indicators and economic indicators. It is worth mentioning that the banking sector is the largest part of the financial system in Romania, while the equity stock market is relatively small. In this context, the paper presents the correlations between the main banking sector risk indicators, namely the solvency indicator, leverage and total risks, and key and derived economic and financial indicators. In addition, this paper presents a stress test that predicts the impact of macroeconomic shocks on financial soundness indicators, such as the domestic credit and its main components – governmental and, respectively, non-governmental credit – each of them having a different impact on economic growth. The stress test is based on an econometric model, i.e. the historical scenario. Finally, conclusions are drawn, emphasizing the essential role of prudential regulation, which diminished before the financial crisis that emerged in the United States in 2007. As a result, toxic financial assets could no longer be kept off markets, leading to the destabilization of the financial system and of economy of democratic countries.

This paper tests a simple market fraction asset pricing model with heterogeneous agents. By selecting a set of structural parameters of the model through a systematic procedure, we show that the autocorrelations (of returns, absolute returns and squared returns) of the market fraction model share the same pattern as those of the DAX 30. By conducting econometric analysis via Monte Carlo simulations, we characterize these power-law behaviours and find that estimates of the power-law decay indices, the (FI)GARCH parameters, and the tail index of the selected market fraction model closely match those of the DAX 30. The results strongly support the explanatory power of the heterogeneous agent models.

Two difficulties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, (ii) the aggregate properties of the model cannot be analytically understood. In this paper we show how to circumvent these difficulties and under which conditions ergodic models can be consistently estimated by simulated minimum distance techniques, both in a long-run equilibrium and during an adjustment phase.

This discussion paper led to a publication in the Journal of Business & Economic Statistics , 2015, 33(2), 241-254. We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists in estimating the parameters of the model under the null hypothesis of the cointegration rank r=1,2,…,p-1. This step provides consistent estimates of the order of fractional cointegration, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup-likelihood ratio test of no-cointegration on the estimated p-r common trends that are not cointegrated under the null. The order of fractional cointegration is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step to adapt to the representation of the common trends estimated in the first step. The critical values of the proposed tests depend only on the number of common trends under the null, p-r, and on the interval of the orders of fractional cointegration b allowed in the estimation, but not on the order of fractional cointegration of already identified relationships. Hence this reduces the set of simulations required to approximate the critical values, making this procedure convenient for practical purposes. In a Monte Carlo study we analyze the finite sample properties of our procedure and compare with alternative methods. We finally apply these methods to study the term structure of interest rates.

Estimated profit is extremely important for any business. Tourism companies can be focused on weekend or medium stay tourism. This paper aims to highlight advantages and disadvantages of each. The study aims also to highlight the usefulness of the Monte Carlo method for estimating profit and the probability of obtaining it. The paper presents a simplified example to estimate profit. It makes an analysis on a single hotel unit, once considered for weekend tourism, then considered for medium stay tourism. In both cases, the profit will be associated with the probability of achieving it.

Guyana was a proving ground for both the capitalist and socialist development ideas in the latter half of the 20th century. We analyze the problems and solutions for those experiment from the point of views of appraising their performances. Traditional theories such as input-output models, sectorial models, and growth models form the windows for the appraisal. When the subject matter is judgmental, Analytic Network Process and (ANP) and Data Envelop Analysis (DEA) are engaged. When the subject matter is structural, the time series of gross investment to GDP is considered as a driver of growth. We subject that ratio to spectral and cross-spectral analysis for a half a century of data for Guyana vs. neighboring countries. The results indicate that Guyana has stepped into the long-run cumulative growth process after its struggles with development.Keywords: Development, Planning, Sectorial Models, Analytic Network Process, Data Envelop Analysis, Spectral Analysis.JEL Class.:C00 (Quantitative Analysis); C15 (Simulation); C88 (Data Analysis)

The paper considers the modeling and estimation of the stochastic frontier model where the error components are assumed to be correlated and the inefficiency error is assumed to be autocorrelated. The multivariate Farlie-Gumble-Morgenstern (FGM) and normal copula are used to capture both the contemporaneous and the temporal dependence between, and among, the noise and the inefficiency components. The intractable multiple integrals that appear in the likelihood function of the model are evaluated using the Halton sequence based Monte Carlo (MC) simulation technique. The consistency and the asymptotic efficiency of the resulting simulated maximum likelihood (SML) estimators of the present model parameters are established. Finally, the application of model using the SML method to the real life US airline data shows significant noise-inefficiency dependence and temporal dependence of inefficiency.

En este trabajo se generan estrategias especulativas en volatilidad con opciones europeas sobre veintiún componentes del Índice de Precios y Cotizaciones (IPC) y sobre este mismo índice, bajo el supuesto de que la volatilidad del activo subyacente es conducida por un proceso GARCH-M (1,1) calibrado con datos históricos, el precio de la opción se obtiene por simulación Monte Carlo. Con las estrategias de volatilidad construidas con los precios de las opciones simuladas se determinó que la estrategia cono corto es adecuada al realizar operaciones en el mercado mexicano (IPC) para plazos de 45 y 60 días, pero no para aquellas a 90 días. Asimismo se observó una relación directa entre el plazo y la estrategia cono corto en cuanto a ganancias al emplear ésta en el componente KIMBERA en el corto plazo, es decir, al aumentar el plazo, se obtuvo una mayor ganancia generada por dicha estrategia / In this paper speculative volatility strategies are generated with European options on twenty one components of the Mexican Stock Exchange (IPC) and on this same index, under the assumption that the volatility of the underlying asset is driven by a GARCH-M (1,1) process calibrated with historical data, the option price is obtained by Monte Carlo simulation. With volatility strategies built with simulated option prices, it was determined that the short cone strategy is adequate to trade in the Mexican market (IPC) for periods of 45 to 60 days, but not for 90 days periods. Moreover, a direct relationship between the term and the short cone strategy in terms of profits was observed when using this strategy in the KIMBERA component in the short term, i.e. by increasing the length a higher profit generated by such a strategy was obtained

In this paper we estimate average and individual countries’ exchange rate pass-through to producer and consumer price indices in four Central and Eastern Europe countries (Romania, the Czech Republic, Hungary and Poland) within a Panel Bayesian VAR model. This method makes it possible to efficiently combine static and dynamic interrelations with any possible heterogeneity within the units. The resulting average long-run pass-through coefficients are about 0.5 for producer prices and 0.3 for consumer prices (with around 0.25 in the short run for producer prices and no immediate pass-through for consumer prices), with the estimates for Hungary and the Czech Republic generally situated above the group average and Romania and Poland below the average. Subsample estimates showed that starting in 2008 pass-through enlarged slightly, given the increased macroeconomic distress during this period, while the countries in the study display a higher endogenously determined degree of homogeneity.

In this paper, we first provide an empirical evidence of the existence of intraday jumps in the crude oil price series. We then show that these jumps, in conjunction with realized volatility measures, are important in modeling the convenience yield over the 2001–2010 period. Our empirical results indicate that lagged jump mean only explains around 16% of the weekly convenience yield. Our best specification, including variation in inventories, 8-week realized variance and the 250-day jump mean is able to explain around 61% of the weekly convenience yield. Importantly, our results are not driven by the simultaneous determination of the various variables at work as we only use lagged variables in all regressions.

In 1993 Engle and Kozicki proposed the notion of common features of which one example is a serial correlation common feature. We say that stationary, non-innovation processes exhibit common serial correlation when there exists at least one linear combination of them which is an innovation. Later on in 1993 Vahid and Engle combined the notions of cointegration among I(1) processes with common serial correlation within their first differences. It is commonly known that cointegrated time series have vector error correction (VEC) representation. The existence of common serial correlation leads to an additional reduced rank restriction imposed on the VEC model’s parameters. This type of restriction was later termed a strong form (SF) reduced rank structure, as opposed to a weak one introduced in 2006 by Hecq, Palm and Urbain. The main aim of the present paper is to construct the Bayesian vector error correction model with these additional strong form restrictions. The empirical validity of investigating both the short- and long-run co-movements between macroeconomic time series will be illustrated by the analysis of the price-wage nexus in the Polish economy.

The paper addresses the issue of long-term forecasting of the global nuclear power market and opportunities for studying its individual sections. Given the current state of the market and related industries, a scientific approach to strategic forecasting based on evaluating dispersions of forecasts acquires particular significance. The authors first developed and applied a probabilistic forecasting technique to a number of market indicators of the global nuclear power industry in real terms for the period up to 2035. In particular, the calculations concern the number and electric capacity of input-output nuclear power plants; and the demand for natural and enriched uranium and enrichment services. The forecasting is based on stochastic modeling of NPP lifecycles and exploitation parameters, uranium enrichment rate, and nuclear energy planes in the different regions. The proposed model, as opposed to scenario approaches, means that the probability distributions of mentioned values can be calculated. This is of crucial importance in assessing the economic risks for various economic agents operating in the world nuclear technology market. The results of modeling the main indicators of nuclear power markets (the dynamics of the net electric capacity of nuclear power plants worldwide and across the largest regions) are consistent with the scenario forecasts provided by WNA and IAEA (2013), which are based on data provided by the members of these organizations. This fact indicates the correct choice of the model for describing the frequency distributions of the key stages of reactor ‘arbor vitae’. The authors modeled the likely volumes of the market for constructing new NPP and taking the stopped ones off the road for the next 15 year period in the different regions, Russia and worldwide. Finally, the paper estimates the likely share of the new Russian designed NPP construction in the world market for the period up to 2030.

We provide a comprehensive study of the liquidity of spot foreign exchange (FX) rates over more than two decades and a large cross-section of currencies. First, we show that FX liquidity can be accurately measured with daily and readily available data. Second, we demonstrate that FX liquidity declines with funding constraints and global risk, supporting theoretical models relating funding and market liquidity. In these distressed circumstances, liquidity tends to evaporate more for developed and riskier currencies. Finally, we show stronger comovements of FX liquidities in distressed markets, especially when funding is constrained, volatility is high, and FX speculators incur losses.

The paper investigates the households’ preference for electricity service reliability. Using a contingent valuation survey; we elicit Beninese urban households’ willingness to pay (WTP) to avoid power outages. In the study respondents are asked to state their WTP for avoiding six different unplanned outages. We therefore apply a random parameter Tobit model on a temporal panel data since there is cross-sectional heterogeneity and a proportion of zero responses. Based on the estimations, we find that the preference for electricity service reliability among household is higher during night time and weekend days and depends positively on the duration of the outages. These results tend to validate the thesis that households preference for electricity service reliability strongly depends on leisure time

This study utilizes results from an agent-based simulation model to conduct public policy simulation of firms’ networking and cooperation in innovation. The simulation game investigates the differences in sector responses to internal and external changes, including cross-sector spillovers, when applying three different policy strategies to promote cooperation in innovation. The public policy strategies include clustering to develop certain industries, incentives to encourage cooperative R&D and spin-off policies to foster entrepreneurship among R&D personnel. These policies are compared with the no-policy alternative evolving from the initial state serving as a benchmark to verify the gains (or loses) in the number of firms cooperating and networking. Firms’ behavior is defined according to empirical findings from analysis of determinants of firms’ participation in cooperation in innovation with other organizations using the Korean Innovation Survey. The analysis based on manufacturing sector data shows that firms’ decision to cooperate with partners is primarily affected positively by firm’s size and the share of employees involved in R&D activities. Then, each cooperative partnership is affected by a different set of determinants. The agent-based models are found to have a great potential to be used in decision support systems for policy makers. The findings indicate possible appropriate policy strategies to be applied depending on the target industries. We have applied few examples and showed how the results may be interpreted. Guidelines are provided on how to generalize the model to include a number of extensions that can serve as an optimal direction for future research in this area.

If before the outbreak of the great recession in the year 2008 the European labour markets were added almost 30 million new jobs, during the crisis the same markets eliminated six million jobs, and unemployment reached a peak of 11% in 2013, the highest rate in more than a decade.At European level, even though political and economic decision factors took firm measures for alleviating the negative effects of the economic crisis, many economies are still faced with the actual perspective of an extended period of tempered economic growth or even economic decline. The employment of vulnerable labour force and employed population poverty are very important issues needing to be tackled and solved. The statistical methods and techniques of quantification, the factor analysis for estimating and testing are both represented by a wide and varied multitude of procedures and statistic-mathematical instruments. For deepened analysis of the impact of adopting active measures on the evolution of the main macroeconomic indicators which highlight the employment of labour force, the paper used the method of principal components

We propose a new method to assess sovereign risk in Eurozone countries using an approach that relies on consistent tests for stochastic dominance e¢ ciency. The test statistics and the estimators are computed using mixed integer programming methods. This paper’s analysis is based on macroeconomic fundamentals and their importance in accounting for sovereign risk. The results suggest that the net international investment position/GDP and public debt/GDP are the main contributors to country risk in the Eurozone. We also conduct ranking analysis of countries for …scal and external trade risk. We …nd a positive correlation between our rankings of the most vulnerable countries and the S&P’s ratings, whereas the correlation for other countries is weaker.

Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large out-of-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces., 32 pages, 19 figures

Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation, the effects are examined in terms of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification and coefficient restrictions show serious deficiencies. The variance of the individual effects is shown to be a major determinant of the poor quality of most asymptotic approximations; therefore, the accurate estimation of this nuisance parameter is investigated. A modification of GMM is found to have some potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample is not too small. Finally, all techniques are employed to actual data and lead to insights which differ considerably from those published earlier.

Among the goals of statistical matching, a very important one is the estimation of the joint distribution of variables not jointly observed in a sample survey but separately available from independent sample surveys. The absence of joint information on the variables of interest leads to uncertainty about the data generating model since the available sample information is unable to discriminate among a set of plausible joint distributions. In the present paper a short review of the concept of uncertainty in statistical matching under logical constraints, as well as how to measure uncertainty for continuous variables is presented. The notion of matching error is related to an appropriate measure of uncertainty and a criterion of selecting matching variables by choosing the variables minimizing such an uncertainty measure is introduced. Finally, a method to choose a plausible joint distribution for the variables of interest via iterative proportional fitting algorithm is described. The proposed methodology is then applied to household income and expenditure data when extra sample information regarding the average propensity to consume is available. This leads to a reconstructed complete dataset where each record includes measures on income and expenditure.

We present a local linear estimator with variable bandwidth for multivariate non-parametric regression. We prove its consistency and asymptotic normality in the interior of the observed data and obtain its rates of convergence. This result is used to obtain practical direct plug-in bandwidth selectors for heteroscedastic regression in one and two dimensions. We show that the local linear estimator with variable bandwidth has better goodness-of-fit properties than the local linear estimator with constant bandwidth, in the presence of heteroscedasticity.