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We consider models defined by a set of moment restrictions that may be subject to weak identification. We propose a testing procedure to assess whether instruments are ”too weak” for standard (Gaussian) asymptotic theory to be reliable. Since the validity of standard asymptotics for GMM rests upon a Taylor expansion of the first order conditions, we distinguish two cases: (i) models that are either linear or separable in the parameters of interest; (ii) general models that are neither linear nor separable. Our testing procedure is similar in both cases, but our null hypothesis of weak identification for a nonlinear model is broader than the popular one. Our test is straightforward to apply and allows to test the null hypothesis of weak identification of specific subvectors without assuming identification of the components not under test. In the linear case, it can be seen as a generalization of the popular first-stage F-test but allows us to fix its shortcomings in case of heteroskedasticity. In simulations, our test is well behaved when compared to contenders, both in terms of size and power. In particular, the focus on subvectors allows us to have power to reject the null of weak identification on some components of interest. This observation may explain why, when applied to the estimation of the Elasticity of Intertemporal Substitution, our test is the only one to find matching results for every country under the two symmetric popular specifications: the intercept parameter is always found strongly identified, whereas the slope parameter is always found weakly identified.\ud

This paper examines real exchange rate responses to shocks in exchange rate determinants for fourteen Asian developing countries. The analysis is based on a panel structural vector error correction model, and the shocks are identified using sign and zero restrictions. We find that trade liberalization generates permanent depreciation, and higher government consumption causes persistent appreciation. Traded-sector productivity gains induce appreciation but their effects are not immediate and last only for a few years. Real exchange rate responses to unexpected monetary tightening are consistent with the long-run neutrality of money. The evidence suggests that trade liberalization and government consumption have a strong effect on real exchange rates, while the effects of traded-sector productivity shocks are much weaker.

In this paper, we consider a partially linear panel data model with cross-sectional dependence and non-stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then established some consistent closed-form estimates for both the unknown parameters and the unknown function for the case where N and T go jointly to infinity. Rates of convergence and asymptotic normality results are established for the proposed estimators. Both the finite-sample performance and the empirical applications show that the proposed estimation method works well when the cross-sectional dependence exists in the data set.

In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant spatial dynamics have been uncovered, which lead to some interesting policy implications.

In There is evidence that exponential smoothing methods as well as time varying parameter models perform relatively well in forecasting comparisons. The aim of this paper is to introduce a new forecasting technique by integrating the exponential smoothing model with regressors whose coefficients are time varying. In doing this, we construct an exponential smoothing model with regressors by extending Holt's linear exponential smoothing model. We then translate it into an equivalent state space structure so that the parameters can be estimated via the maximum likely-hood estimation procedure. Due to the potential problem in the updating equation for the regressor coefficients when the change in regressor is too small, we propose an alternative structure of the state space model which allows the updating process to be put on hold until sufficient information is available. An empirical study of forecast accuracy shows that the new model performs better than the existing exponential smoothing model as well as the linear regression model.

This paper investigates international responses of key macroeconomic variables, particularly real exchange rates, to simultaneous shocks to productivity in the traded sector in eight Asian emerging and developing countries. We use panel estimation techniques to construct component submodels in a thirty country global vector autoregressive (GVAR) model. The GVAR approach can account for interaction among all countries and capture many potential international transmission channels. We identify the shocks by using sign restricted impulse responses. We find that increases in traded-sector productivity in Asian developing countries lead to a real appreciation of the domestic currencies, in line with the Balassa-Samuelson hypothesis. Inflation also increases in many Asian developing countries. After the shocks, nontraded sector productivity in the US and other developed countries increases, suggesting that there is a compositional shift in their production, away from the traded goods toward the nontraded goods. This allows productivity in the nontraded sector to increase. Further, the traded sector productivity shocks in Asia stimulate international trade in most countries.

Trygve Haavelmo’sProbability Approachaimed to implement economic theories, but he later recognized their incompleteness. Although he did not explicitly consider model selection, we apply it when theory-relevant variables, {Xt}, are retained without selection while selecting other candidate variables, {Wt}. Under the null that the {Wt} are irrelevant, by orthogonalizing with respect to the {Xt}, the estimator distributions of theXt’s parameters are unaffected by selection even for more variables than observations and for endogenous variables. Under the alternative, when the joint model nests the generating process, an improved outcome results from selection. This implements Haavelmo’s program relatively costlessly.

Unpredictability arises from intrinsic stochastic variation, unexpected instances of outliers, and unanticipated extrinsic shifts of distributions. We analyze their properties, relationships, and different effects on the three arenas in the title, which suggests considering three associated information sets. The implications of unanticipated shifts for forecasting, economic analyses of efficient markets, conditional expectations, and inter-temporal derivations are described. The potential success of general-to-specific model selection in tackling location shifts by impulse-indicator saturation is contrasted with the major difficulties confronting forecasting.

This paper investigates the structure and degree of extreme dependence in international equity markets using carefully selected tools from the theory of copulas. We examine both the static and dynamic dependence via unconditional and conditional copulas. We Önd signiÖcant asymmetric tail dependence in equity markets, with the overall larger lower tail dependence than upper tail dependence. Moreover, in Europe and East Asia but not in North America, the extreme dependence is time-varying in both its structure and degree. Our results also indicate a higher intra-continental than inter-continental tail dependence. Our Öndings have important implications in global risk management strategies.

We introduce a canonical representation of call options, and propose a solution to two open problems in option pricing theory. The first problem was posed by (Kassouf, 1969, pg. 694) seeking “theoretical substantiation” for his robust option pricing power law which eschewed assumptions about risk attitudes, rejected risk neutrality, and made no assumptions about stock price distribution. The second problem was posed by (Scott, 1987, pp. 423-424) who could not find a unique solution to the call option price in his option pricing model with stochastic volatility–without appealing to an equilibrium asset pricing model by Hull and White (1987), and concluded: “[w]e cannot determine the price of a call option without knowing the price of another call on the same stock”. First, we show that under certain conditions derivative assets are superstructures of the underlying. Hence any option pricing or derivative pricing model in a given number field, based on an anticipating variable in an extended field, with coefficients in a subfield containing the underlying, is admissible for market timing. For the anticipating variable is an algebraic number that generates the subfield in which it is the root of an equation. Accordingly, any polynomial which satisfies those criteria is admissible for price discovery and or market timing. Therefore, at least for empirical purposes, elaborate models of mathematical physics or otherwise are unnecessary for pricing derivatives because much simpler adaptive polynomials in suitable algebraic numbers are functionally equivalent. Second, we prove, analytically, that Kassouf (1969) power law specification for option pricing is functionally equivalent to Black and Scholes (1973); Merton (1973) in an algebraic number field containing the underlying. In fact, we introduce a canonical polynomial representation theory of call option pricing convex in time to maturity, and algebraic number of the underlying–with coefficients based on observables in a subfield. Thus, paving the way for Wold decomposition of option prices, and subsequently laying a theoretical foundation for a GARCH option pricing model. Third, our canonical representation theory has an inherent regenerative multifactor decomposition of call option price that (1) induces a duality theorem for call option prices, and (2) permits estimation of risk factor exposure for Greeks by standard [polynomial] regression procedures. Thereby providing a theoretical (a) basis for option pricing of Greeks, and (b) solving Scott’s dual call option problem a fortiori with our duality theory in tandem with Riesz representation theory. Fourth, when the Wold decomposition procedure is applied we are able to construct an empirical pricing kernel for call option based on residuals from a model of risk exposure to persistent and transient risk factors. Keywords: number theory; price discovery; derivatives pricing; asset pricing; canonical representation; Wold decomposition; empirical pricing kernel; option Greeks; dual option pricing

In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the asymptotic hedging error for options under a generalised jump-diffusion model with kernel bias, which nests a number of very important processes in finance. We then obtain an estimation for the distribution of hedging error by maximising Shannon’s entropy subject to a set of moment constraints, which in turn yields the value-at-risk and expected shortfall of the hedging error. The significance of this approach lies in the fact that the maximum entropy estimator allows us to obtain a consistent estimate of the asymptotic distribution of hedging error, despite the non-normality of the underlying distribution of returns.

A new test for non-linearity is developed using weighted combinations of regressor powers based on the eigenvectors of the variance-covariance matrix. The test extends the ingenious test for heteroskedasticity proposed by White (1980), but both circumvents problems of high dimensionality and collinearity, and allows inclusion of cubic functions to ensure power against asymmetry or skewness. A Monte Carlo analysis compares the performance of the test to the optimal infeasible test and to a variant of White's test. The relative performance of the test is encouraging: the test has the appropriate size and has high power in many situations. Furthermore, collinearity between regressors can increase the power of the test.

© 2012 Springer-Verlag London Limited.Our strategy for automatic selection in potentially non-linear processes is: test for non-linearity in the unrestricted linear formulation; if that test rejects, specify a general model using polynomials, to be simplified to a minimal congruent representation; finally select by encompassing tests of specific non-linear forms against the selected model. Non-linearity poses many problems: extreme observations leading to non-normal (fat-tailed) distributions; collinearity between non-linear functions; usually more variables than observations when approximating the non-linearity; and excess retention of irrelevant variables; but solutions are proposed. A returns-to-education empirical application demonstrates the feasibility of the non-linear automatic model selection algorithm Autometrics.

Functional Signal plus Noise (FSN) models are proposed for analysing the dynamics of a large cross-section of yields or asset prices in which contemporaneous observations are functionally related. The FSN models are used to forecast high dimensional yield curves for US Treasury bonds at the one month ahead horizon. The models achieve large reductions in mean square forecast errors relative to a random walk for yields and readily dominate both the Diebold and Li (2006) and random walk forecasts across all maturities studied. We show that the Expectations Theory (ET) of the term structure completely determines the conditional mean of any zero-coupon yield curve. This enables a novel evaluation of the ET in which its 1-step ahead forecasts are compared with those of rival methods such as the FSN models, with the results strongly supporting the growing body of empirical evidence against the ET. Yield spreads do provide important information for forecasting the yield curve, especially in the case of shorter maturities, but not in the manner prescribed by the Expectations Theory.

In many countries, house prices are subject to boom/bust cycles and in some these are linked to severe economic and financial instability. Overheating can have both a price and a quantity dimension, but it is likely that they are linked by common drivers. However, much depends on the land-use planning regime which profoundly affects the supply response. It is helpful to make the distinction between overshooting of house prices due to extrapolative expectations and 'frenzy', given fundamentals, and shifts in possibly fragile fundamentals. The contribution of careful econometric modelling to estimating the effects of the former is demonstrated: central banks or other policy makers should institute quarterly surveys of house price expectations of potential housing market participants to help assess the first type of overshooting. Assessing the fragility or otherwise of the economic fundamentals is more complex. Credit supply conditions in the mortgage market are the 'elephant in the room'. Without taking a credit conditions measure into account, one simply cannot understand the behaviour of house prices, housing debt and consumption in countries such as Australia, the UK, the US, South Africa or France or understand vulnerability of some economies to high levels of household debt. Other financial and economic indicators of vulnerability are discussed, including high bank leverage ratios, high ratios of loans to deposits, debt, deficit and current account of ratios. Models of early warning of financial and economic crises estimated on large country panels need to be quite complex, for example, including some important interaction effects since shock transmission is very institution dependent.

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.

Using an extension of general-to-specific modelling, based on the recent developments of impulse-indicator saturation (IIS), we consider selecting significant step indicators from a saturating set to capture location shifts. The approximate non-centrality of the test is derived for a variety of shifts using a 'split-half' analysis, the simplest specialization of a multiple-block search algorithm. Monte Carlo simulations confirm the accuracy of the nominal significance levels under the null, and show rejections when location shifts occur, improving in non-null rejection frequency compared to the corresponding IIS-based and to Chow (1960) tests.

Latin America experienced a decline in household income inequality in the 2000s, in sharp contrast to growing inequality in other regions of the world. This has been attributed to macroeconomic policy, social spending, and increased returns to education. This paper explores this issue from a gender perspective by econometrically evaluating how changes in economic structure and policy have impacted gendered employment and unemployment rates, as well as gender inequality in these variables, using country-level panel data for a set of 18 Latin American countries between 1990 and 2010. Three variables stand out as having consistent gender-equalizing effects in the labor market: social spending, minimum wages, and public investment. Less important or consistent were the effects of external factors (such as terms of trade), economic structure, and GDP growth.

International risk sharing has been among the most actively researched areas of macroeconomics for the last two decades. Empirical contributions in this field make extensive use of so called "consumption insurance" tests evaluating the extent to which idiosyncratic shocks in income get transferred to consumption. A prerequisite of such a test is the isolation of country specific variation in the data. We show that the cross-sectional demeaning technique frequently used in the literature is in general inadequate to eliminate global factors from a panel data set, and can lead to misleading inference. We argue that international risk sharing tests should instead be based on a method that more reliably deals with global factors. We claim and illustrate in our empirical application that the fairly simple common correlated e ects estimator for cross-sectionally dependent panels introduced by Pesaran (2006), and Kapetanios et al. (2010) is a tool that satisfies this requirement.

"This is the peer reviewed version of the following article: [Boug, P, Cappelen, Å. & Swensen, A. R. (2017). Inflation dynamics in a small open economy. Scandinavian Journal of Economics, 119 (4), 1010-1039, which has been published in final form at https://doi.org/10.1111/sjoe.12194. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions." We evaluate the empirical performance of forward-looking models for inflation dynamicsin a small open economy. Using likelihood-based testing procedures, we find that the exactformulation is at odds with Norwegian data. Moreover, some of the parameters in themodel are not well identified. We also find that the inexact formulation is not rejectedstatistically using a test based on a minimum distance method. However, confidence regionsalso reveal an identification problem with this model. Instead, we find a well-specifiedbackward-looking model with imperfect competition underlying the price setting, which isa model that outperforms an alternative forward-looking model in-sample. The backward-looking model also forecasts somewhat better than the alternative forward-looking model,during and after the recent financial crisis. Keywords: Backward-looking; cointegrated vector autoregressive models; equilibrium correc-tion models; forward-looking; likelihood-based methods; minimum distance method

This paper investigates volatility spillovers between UK regional job finding, job separation and vacancy rates. Employing a logistic smooth transition vector autoregression (VAR) to model the large nonlinear dynamic system, we use the methods of Diebold and Yilmaz (2012) to decompose the forecast error variances. Our approach is Bayesian. More specifically, we extend doubly adaptive elastic-net Lasso (DAELasso) methods for VAR parameter shrinkage into a nonlinear framework to allow for the possible regime changes. We find that for each variable, both the volatility spillovers to and from other variables are high, providing clear evidence for the close interdependence between UK regional labour markets. The pivotal role of London in generating and spreading changes in volatility is highlighted. Analysis of net spillovers shows that, in general, shocks to job separation rates tend to spread into job finding and vacancy rates. By contrast, vacancy rates are usually at the receiving ends of shocks transmitted from the job separation and finding rates. We further examine the shock propagation mechanism in more detail, such as the differences in spillovers between regions within the same regime, and that of the same region but in different regimes. Finally, we draw inferences that are of economic and policy importance.

We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and LM statistics. Due to the boundary problem, these large-sample properties become nonstandard. The size and power properties of the tests are investigated in a simulation study. As an empirical illustration we test for (no) volatility spillovers between foreign exchange rates.

This paper deals with the identification and estimation of dynamic games when players' beliefs about other players' actions are biased, i.e., beliefs do not represent the probability distribution of the actual behavior of other players conditional on the information available. First, we show that a exclusion restriction, typically used to identify empirical games, provides testable nonparametric restrictions of the null hypothesis of equilibrium beliefs. Second, we prove that this exclusion restriction, together with consistent estimates of beliefs at several points in the support of the special state variable (i.e., the variable involved in the exclusion restriction), is sufficient for nonparametric point-identification of players' payoffs and belief functions. The consistent estimates of beliefs at some points of support may come either from an assumption of unbiased beliefs at these points in the state space, or from available data on elicited beliefs for some values of the state variables. Third, we propose a simple two-step estimation method. We illustrate our model and methods using both Monte Carlo experiments and an empirical application of a dynamic game of store location by retail chains. The key conditions for the identification of beliefs and payoffs in our application are the following: (a) the previous year's network of stores of the competitor does not have a direct effect on the profit of a firm, but the firm's own network of stores at previous year does affect its profit because the existence of sunk entry costs and economies of density in these costs; and (b) firms' beliefs are unbiased in those markets that are close, in a geographic sense, to the opponent's network of stores, though beliefs are unrestricted, and potentially biased, for unexplored markets which are farther away from the competitors' network. Our estimates show significant evidence of biased beliefs. Furthermore, imposing the restriction of unbiased beliefs generates a substantial attenuation bias in the estimate of competition effects.

This paper analyzes the patent propensity as an outcome of innovative activities of regional SMEs. To achieve the aims, we apply robust regression analysis to estimate the models to test 5 research hypotheses using 263 firm level data located at Gwangju region in Korea. Our empirical results show that a firm’s industry characteristics, such as machinery and automotive parts industry, is negatively related with propensity to patent innovation. Also, unlike expectations, the InnoBiz firms designated as innovative SMEs by the government are not performing differently than general firms. Only the CEO’s academic credentials are positively related with propensity to patent. From the findings, we can conclude that patenting propensity is not directly related with a firm’s characteristics but mainly to CEO’s managerial strategy. Also, we cannot find evidence for policy effectiveness from public support given to InnoBiz firms as part of the state policy to nurture photonic industry to boost regional economic development. Given the lack of strong policy effects, a new industry policy should be considered to actively promote SMEs innovativeness.

This paper analyzes the dynamic behavior of day-ahead spot prices in the German electricity spot market due to positive structural shocks in wind and solar power. It uses a dynamic structural vector autoregressive model to estimate the related structural impulse response functions. The estimates suggest that wind power shocks have a more prolonged negative effect on spot prices than solar power shocks. Significant autocorrelations of wind power for larger lags indicate these results. The total negative merit order effect of a solar power shock is larger when using a reasonable model specification. One reason might be that solar power shocks coincide with demand peaks. However, results regarding the relation of the effects of wind and solar should be dealt with carefully because these results do not hold for all specifications. Past static estimates show differences in the total average negative merit order effects. These estimates might be overstated if wind and solar power dynamics are transferred to the merit order effects.

Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, nonlinear models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are extended by means of nonlinear transformations (quadratic, cubic, sine, exponential functions) of lagged time series observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and financial time series for 10 European countries. Findings suggest that these models, including nonlinear transformation of lagged autoregression residuals, are able to provide better forecasting results than simple linear models. Thus, it may be possible to improve the forecasting accuracy of linear models by including nonlinear components. This is especially true for time series that are positively tested for nonlinear characteristics and longer forecast horizons.

In this paper we 'update' the option implied probability of default (option iPoD) approach recently suggested in the literature. First, a numerically more stable objective function for the estimation of the risk neutral density is derived whose integrals can be solved analytically. Second, it is reasoned that the originally proposed approach for the estimation of the PoD produces arbitrary results and hence an alternative procedure is suggested that is based on the Lagrange multipliers. Based on numerical evaluations and an illustrative empirical application we conclude that the framework provides very promising results.

This paper evaluates the effect of an anti-poverty program, Chile Solidario, during its first two years of operation. We find that the program tends to increases significantly their take-up of cash assistance programs and of social programs for housing and employment, and to improve education and health outcomes for participating households. There is no evidence that the participation to employment program translates into improved employment or income outcomes in the short term. Finally, we provide suggestive evidence of the key role that the psycho-social support had in enabling this change, by increasing awareness of social services in the community as well as households’ orientation towards the future., En este estudio se evalúa el efecto de un programa de lucha contra la pobreza, Chile Solidario, durante sus primeros dos años de funcionamiento. Encontramos que el programa tiende a aumentar significativamente su asimilación para los programas de asistencia en efectivo y los programas de vivienda sociales y empleo, así como a mejorar los resultados de la educación y la salud de los hogares participantes. No hay evidencia de que la participación en programas de empleo se traduzca en mejoras en el empleo o los ingresos en el corto plazo. Finalmente, la evidencia sugiere que el apoyo psicosocial ha tenido un rol fundamental en generar este cambio, tanto por medio de aumentar el conocimiento acerca de los servicios sociales en la comunidad como a través de la orientación de los hogares hacia el futuro.

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose is to combine the need for interpretability and efficiency faced by model users with the computational problems that may emerge when the number of assets can be very large. We contribute to this strand of the literature by proposing a block-type parameterization for multivariate stochastic volatility models. The empirical analysis on stock returns on the US market shows that 1% and 5 % Value-at-Risk thresholds based on one-step-ahead forecasts of covariances by the new specification are satisfactory for the period including the Global Financial Crisis.

This paper proposes a recursive procedure, called the extended local rank factorization (elrf), that characterizes the order of the pole and the coefficients of the Laurent series representation of the inverse of a regular analytic matrix function around a given point. The elrf consists in performing a finite sequence of rank factorizations of matrices of nonincreasing dimension, at most equal to the dimension of the original matrix function. Each step of the sequence is associated with a reduced rank condition, while the termination of the elrf corresponds to a full rank condition; this last step reveals the order of the pole. The Laurent coefficients $B_{n}$ are calculated recursively as $B_{n} = C_{n} + \sum_{k=1}^{n} D_{k} B_{n-k}$, where $C_{n}$, $D_{k}$ have simple closed form expressions in terms of the quantities generated by the elrf. It is also shown that the elrf characterizes the structure of Jordan pairs, Jordan chains, and the local Smith form. The procedure is easily cast in an algorithmic ...

Although a general unrestricted model may under-specify the data generation process, especially when breaks occur, model selection can still improve over estimating a prior specification. Impulse-indicator saturation (IIS) can 'correct' non-constant intercepts induced by location shifts in omitted variables, which surprisingly leave slope parameters unaltered even when correlated with included variables. However, location shifts in included variables do induce changes in slopes where there are correlated omitted variables. IIS acts as a 'robust method' when models are mis-specified, and helps mitigate the adverse impacts of induced location shifts on non-constant intercepts and equation standard errors.

This paper studies the determinants of software piracy losses along five major macro­economic dimensions: Technological, Educational, Institutional, Access to Information and Labor force. The study was conducted based on a large dataset available from 1995 to 2010 and comprising 81 countries. As for the Technological dimension, more patents by residents increases piracy losses while the effect of R&D is the opposite (decreases piracy losses). In terms of the Educational dimension, the results show that more spending on education increase the piracy losses but, at the same time, more schooling years have the contrary effect. Concerning the Institutional dimension, nations with less corruption have lower piracy levels. Regarding the Access to Information, it seems that access to Internet diminishes the losses while the share of Internet broadband subscriptions has no effect. The results also show that, regarding the Labor dimension, employment in services has a deterrent effect while labor force with higher education and youth unemployment increases piracy losses. First published online 20October 2015&nbsp

The paper analyses the contagion channels of the European financial system through the stochastic block model (SBM). The model groups homogeneous connectivity patterns among the financial institutions and describes the shock transmission mechanisms of the financial networks in a compact way. We analyse the global financial crisis and European sovereign debt crisis and show that the network exhibits a strong community structure with two main blocks acting as shock spreader and receiver, respectively. Moreover, we provide evidence of the prominent role played by insurances in the spread of systemic risk in both crises. Finally, we demonstrate that policy interventions focused on institutions with inter-community linkages (community bridges) are more effective than the ones based on the classical connectedness measures and represents consequently, a better early warning indicator in predicting future financial losses.

The existing literature on binary games with incomplete information assumes that either payoff functions or the distribution of private information are finitely parameterized to obtain point identification. In contrast, we show that, given excluded regressors, payoff functions and the distribution of private information can both be nonparametrically point identified. An excluded regressor for player i is a sufficiently varying state variable that does not affect other players utility and is additively separable from other components in i's payoff. We show how excluded regressors satisfying these conditions arise in contexts such as entry games between firms, as variation in observed components of fixed costs. Our identification proofs are constructive, so consistent nonparametric estimators can be readily based on them. For a semiparametric model with linear payoffs, we propose root-N consistent and asymptotically normal estimators for parameters in players payoffs. Finally, we extend our approach to accommodate the existence of multiple Bayesian Nash equilibria in the data-generating process without assuming equilibrium selection rules.

This paper proposes a method of averaging generalized least squares (GLS) estimators for linear regression models with heteroskedastic errors. We derive two kinds of Mallows' Cp criteria, calculated from the estimates of the mean of the squared errors of the tted value based on the averaged GLS estimators, for this class of models. The averaging weights are chosen by minimizing Mallows' Cp criterion. We show that this method achieves asymptotic optimality. It is also shown that the asymptotic optimality holds even when the variances of the error terms are estimated and the feasible generalized least squares (FGLS) estimators are averaged. Monte Carlo simulations demonstrate that averaging FGLS estimators yields an estimate that has a remarkably lower level of risk compared with averaging least squares estimators in the presence of heteroskedasticity, and it also works when heteroskedasticity is not present, in nite samples.

The empirically most relevant stylized facts when it comes to modeling time varying financial volatility are the asymmetric response to return shocks and the long memory property. Up till now, these have largely been modeled in isolation though. To more flexibly capture asymmetry also with respect to the memory structure we introduce a new model and apply it to stock market index data. We find that, although the effect on volatility of negative return shocks is higher than for positive ones, the latter are more persistent and relatively quickly dominate negative ones.

The vector autoregressive approach for testing present value models is applied to a heterogeneous-agent asset pricing model using historical observations of the S&P 500 index. Besides fundamentalists, who value assets according to expected dividends, the model features rational and contrarian speculators. Agents choose their strategy based on evolutionary considerations. Supplementing the standard present value model with speculative agents dramatically improves the model's ability to replicate observed market dynamics. In particular, the existence of contrarians can explain some of the most volatile episodes including the 1990s bubble, suggesting this was not a rational bubble. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1937 . This paper was accepted by Itay Goldstein, finance.

The paper proposes a general asymmetric multifactor Wishart stochastic volatility (AMWSV) di usion process which accommodates leverage, feedback e ects and mul- tifactor for the covariance process. The paper gives the closed-form solution for the conditional and unconditional Laplace transform of the AMWSV models. The paper also suggests estimating the AMWSV model by the generalized method of moments using information not only of stock prices but also of realized volatilities and co- volatilities. The empirical results for the bivariate data of the NASDAQ 100 and S&P 500 indices show that the general AMWSV model is preferred among several nested models.

The paper aims to analyze the relationship between the size of Romanian shadow economy expressed as % of official GDP and active ageing in order to see if the shadow economy represents a social buffer for active ageing phenomena using Granger causality analysis. The size of Romanian shadow economy is estimated previously using a revised version of the currency demand approach based on autoregressive distributed lag (ARDL) approach to cointegration analysis. Using Granger causality tests, we examine the relationship between employment rate for older people and the size of Romanian shadow economy covering the period between 2000 and 2010. The empirical results revealed the existence of a long-run unidirectional causality that runs shadow economy to employment rate for elderly and do not support the existence of any short-run relationship between employment rate for older people and shadow economy.

Economic growth and financial development are closely related. The interaction between them is crucial and has attracted great attention of researchers. This study attempts to examine the relationship between economic growth and financial development in Nepal between 1975 and 2012. The paper has used Augmented Dickey-Fuller and Philips-Perron tests to test for the existence of unit root, Co-integration test to examine long run relationship and Granger Causality test to find out causal relationship. In addition, vector error correction method has been applied to find out the speed of adjustment and the dynamics of relationship. The empirical evidence confirms that the financial development causes economic growth. In fact, financial development is the cause for economic growth in terms of short-term dynamics, while economic growth sustains financial development in the long-run. Based on the empirical findings, this study recommends that it is necessary to launch the reform programs in the financial system to consolidate and improve the efficiency and effectiveness of the financial system as well as to cope with the emerging changes. Thus, it asks for the consolidation of the system not only for the positive reinforcement between economic growth and financial development but also for the post crisis resilience and sustainability.

En este artículo se propone una nueva extensión de la metodología multivariada de desagregación temporal de Di-Fonzo (1990), la cual supone que los errores de las series de alta frecuencia siguen un modelo VAR(1) en lugar de un proceso ruido blanco. Adicionalmente, se realiza una reseña de diferentes métodos de desagregación, tanto univariados como multivariados, incluyendo sus principales ventajas y desventajas. Finalmente, se lleva a cabo una aplicación multivariada para obtener las cuentas nacionales colombianas mensuales a partir de datos trimestrales. Los resultados bajo la metodología propuesta son similares a los obtenidos por el método de Di-Fonzo, pero son menos volátiles. In this paper we propose a new extension of Di-Fonzo (1990)'s methodology for multivariate temporal disaggregation. We assume that the errors of the high-frequency series follow a VAR(1) model instead of a white noise process. Additionally, an extensive review of different univariate and multivariate disaggregation methods is presented. Finally, we carry out a multivariate application to obtain Colombia's monthly national accounts from quarterly data. The results obtained using the proposed methodology are similar to those with Di-Fonzo's method. However, our resulting series are less volatile. Cet article présente une nouvelle extension de la méthodologie désagrégation temporelle multivariée proposée par Di-Fonzo(1990). Cette méthodologie suppose que les erreurs de la série à haute fréquence suivent un modèle VAR(1), au lieu d'un processus bruit blanc. Nous examinons les différentes méthodes de désagrégation aussi bien univariée que multivariée, ainsi que leurs principaux avantages et inconvénients. Enfin, nous avons appliqué la méthodologie multivariée afin d'obtenir les comptes nationaux colombiens mensuels à partir de données trimestrielles. Les résultats obtenus sont similaires à ceux obtenus par la méthode de Di-Fonzo mais ils sont moins volatiles

Tail dependence plays important role in portfolio optimization. The higher tail dependence among assets, the higher risk of simultaneous high loss in their prices. In this paper the choice of pair-copulas in pair-copula construction model is done by minimizing the distances between theoretical and empirical tail dependence functions. This method is believed to provide better approximation of tails of joint distribution (compared to maximal spanning tree methods), yet hold all advantages of pair-copula constructions as models of multivariate dependence.

Tourism is the act of travelling for a person or group of people from their own locality to a specific destination in a short term or long term period either for leisure or business purposes. Tourism is an important sector in the Malaysian economy where tourism development will lead to the positive economic development of the country and in general improve the quality of life for all citizens. Therefore, forecasting tourist arrivals with high accuracy becomes important since it may ensure the development and the readiness of all tourism related industries such as hotels, transportation, food and services industries and their best shape. This study focuses on tourist arrivals in Langkawi Island as one of the major tourist attractions situated in the northerly region of Peninsular Malaysia. Importantly, this paper attempts to measure and compare the performance of forecasting with Exponential Smoothing, ARIMA and ARFIMA models using the R software package.

We study how unconventional monetary policy affects the shape of the yield curve and, conversely, the predictive power of short-run yield curve dynamics for the policy event. Two types of unconventional monetary policy measures are analysed: i) announcements and actions related to extension of central bank liquidity and ii) secondary market purchases. We find that ex-post effects on the shape of the yield curve differ substantially both in size and direction. Specifically, sovereign debt purchases are found to instantaneously lower and flatten the curve, while liquidity related actions steepen the curve, albeit mildly and for a very limited time. Further to that, the predictive power of yield curve dynamics in relation to policy implementation is supportive only for the latter signal. These findings broadly align both with the design of unconventional policy measures and with the horizon in which they are deemed to become effective.

We propose the realized systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the realized systemic risk beta as the total time-varying marginal effect of a firm’s Value-at-risk (VaR) on the system’s VaR. Suitable statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential regulation.

Based on a micro-level approach and using data from the European Working Conditions Survey, covering 27 countries, we analyse the determinants of job quality. With cluster analysis applied to 11 dimensional indices, we form three homogeneous country groups and identify, by estimating twice-censored Tobit models, the main determinant factors affecting the individual level of job quality in each group. We verify the relevance of variables related to worker characteristics, firm characteristics, and the country in which the individual works. Among worker characteristics, education and employment status are the factors with the highest impact on job quality, while the economic sector is the most important firm characteristic. The results suggest the existence of important differences among groups regarding the magnitude of the impact of some factors. The highest dissimilarities are found between the group with better jobs (Nordic countries plus Belgium) and the group with lower quality jobs (Central and Eastern European countries plus Portugal and Greece). Variables related to age, education, dimension of the firm, and economic sector are those in which more heterogeneity is found among the groups.

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.

This paper estimates per capita demand for diesel by agricultural sector in Iran using Auto-Regressive Distributed Lags (ARDL) method over 1973-2012. The findings indicate that the demand for diesel is relatively inelastic in response to changes in its price, electricity acts as substitute input for diesel, diesel is a necessary input in agriculture, and mechanization index, despite having expected positive sign, fails to explain variations in diesel use. In addition, there is a long-run relationship among variables. According to Error Correction Model, the speed of adjustment in short-run distortions towards long-run equilibrium is moderate. The findings show that government policy on cutting energy subsidies is not enough for reducing diesel consumption in Iranian agricultural sector. Thus, non-price measures such as innovations in inter-fuel substitution technologies and applying efficient machineries are recommended to manage energy uses in agriculture.

One of the necessities of public life and one of the basic elements of the state aiming to fulfill the demands and requirements of individuals which form the community is sovereignty. Sovereignty is separated into two as internal and external. This means, state should provide internal security and peace and also should be able to protect itself against the external threats. In order for a state to achieve these, it has to fulfill the defense services. Due to the unease in Arab regions after Arab spring and as Mediterranean region has strategic importance, 10 countries in Mediterranean region were selected and analysis with panel data was performed for years 2005-2012, in order to examine the effect of military spending of these countries on economic growth and unemployment.

The main goal of this research is to model and predict the fiscal pressure indicator and the real GDP rate in the European Union during 1996-2013 using the vectorial autoregressive approach. According to Granger test for causality, only the real GDP rate is a cause of the weight tax in GDP, the relationship not being reciprocal. The fiscal pressure volatility is due mainly to the evolution of this indicator, but the influence decreases in time, not descending under 82%. More than 41% of the variation in real growth is explained by the fiscal pressure volatility starting with the 6th lag. The static and deterministic simulation generated the best predictions of the fiscal pressure indicator on the horizon 2011-2013.