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The Central Limit Theorem (CLT) is an important result in statistics and econometrics and econometricians often rely on the CLT for inference in practice. Even though, different conditions apply to different kinds of data, the CLT results are believed to be generally available for a range of situations. This paper illustrates the use of the Kullback-Leibler Information (KLI) measure to assess how close an approximating distribution is to a true distribution in the context of investigating how different population distributions affect convergence in the CLT. For this purpose, three different non-parametric methods for estimating the KLI are proposed and investigated. The main findings of this paper are 1) the distribution of the sample means better approximates the normal distribution as the sample size increases, as expected, 2) for any fixed sample size, the distribution of means of samples from skewed distributions converges faster to the normal distribution as the kurtosis increases, 3) at least in the range of values of kurtosis considered, the distribution of means of small samples generated from symmetric distributions is well approximated by the normal distribution, and 4) among the nonparametric methods used, Vasicek's (1976) estimator seems to be the best for the purpose of assessing asymptotic approximations. Based on the results of the paper, recommendations on minimum sample sizes required for an accurate normal approximation of the true distribution of sample means are made.

Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non‐identification of models does not necessarily imply the nonidentification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non‐additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry/exit problem numerically, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and nonidentified counterfactuals of interest.

This paper investigates whether economic activity dynamics predict GDP revisions using panel data from 15 OECD countries. We find that economic activity predicts GDP revisions: early releases tend to overestimate GDP growth during slowdowns — and vice-versa. We also find that the source of the predictability could be related to the sampling of information collection. Finally, the predictability comes from short-term economic activity dynamics rather than business cycle position.

This paper proposes a methodology for analyzing nonlinear pricing data with an illustration on cellular phone. The model incorporates consumer exclusion. Assuming a known tariff, we establish identification of the model primitives using the first-order conditions of both the firm and the consumer up to a cost parameterization. Next, we propose a new one-step quantile-based nonparametric method to estimate the consumers’ inverse demand and their type distribution. We show that our nonparametric estimator is root-N-consistent. We then introduce unobserved product heterogeneity with an unknown tariff. We show how our identification and estimation results extend. Our analysis of cellular phone consumption data assesses the performance of alternative pricing strategies relative to nonlinear pricing.

We propose and solve a small-scale New-Keynesian model with Markov sunspot shocks that move the economy between a targeted-inflation regime and a deflation regime and fit it to data from the U.S. and Japan. For the U.S. we find that adverse demand shocks have moved the economy to the zero lower bound (ZLB) in 2009 and an expansive monetary policy has kept it there subsequently. In contrast, Japan has experienced a switch to the deflation regime in 1999 and remained there since then, except for a short period. The two scenarios have drastically different implications for macroeconomic policies. Fiscal multipliers are about 20% smaller in the deflationary regime, despite the economy remaining at the ZLB. While a commitment by the central bank to keep rates near the ZLB doubles the fiscal multipliers in the targeted-inflation regime (U.S.), it has no effect in the deflation regime (Japan).

We suggest using "realized volatility" as a volatility proxy to aid in model-based multivariate bond yield density forecasting. To do so, we develop a general estimation approach to incorporate volatility proxy information into dynamic factor models with stochastic volatility. The resulting model parameter estimates are highly efficient, which one hopes would translate into superior predictive performance. We explore this conjecture in the context of density prediction of U.S. bond yields by incorporating realized volatility into a dynamic Nelson-Siegel (DNS) model with stochastic volatility. The results clearly indicate that using realized volatility improves density forecasts relative to popular specifications in the DNS literature that neglect realized volatility.

This paper provides an assessment of the comparative effectiveness of four econometric methods in estimating the optimal hedge ratio in an emerging equity market, particularly the South African equity and futures markets. The paper bases the effectiveness of hedging on volatility reduction and minimisation of the coefficient of variation of hedged returns as well as risk-aversion based utility maximisation. The empirical analysis shows that the single equation method estimated by ordinary least squares is the most effective over daily hedging periods. However, the vector error-correction method and multivariate GARCH methods are most effective over weekly and monthly hedging periods.

Although there have been many evaluations of the Federal Reserve’s Greenbook forecasts, we analyze them in a different dimension. We examine the revisions of these forecasts in the context of fixed event predictions to determine how new information is incorporated in the forecasting process. This analysis permits us to determine if there was an inefficient use of information in the sense that the forecast revision has predictive power for the forecast error. Research on forecast smoothing suggests that we might find a positive relationship between the forecast error and the forecast revision. Although we do find for some variables and horizons the Fed’s forecast errors are predictable from its forecast revisions, there is no evidence of forecast smoothing. Instead the revisions sometimes have a negative relationship with the forecast error, suggesting in these cases that the Fed may be over-responsive to new information.

This paper introduces a complement statistical test for distinguishing between the predictive accuracy of two sets of forecasts. We propose a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. We perform a simulation study for the size and power of the proposed test and report the results for different noise distributions, sample sizes and forecasting horizons. The simulation results indicate that the KSPA test is correctly sized, and robust in the face of varying forecasting horizons and sample sizes along with significant accuracy gains reported especially in the case of small sample sizes. Real world applications are also considered to illustrate the applicability of the proposed KSPA test in practice.

Several graphical methods for testing univariate composite normality from an i.i.d. sample are presented. They are endowed with correct simultaneous error bounds and yield size-correct tests. As all are based on the empirical CDF, they are also consistent for all alternatives. For one test, called the modified stabilized probability test, or MSP, a highly simplified computational method is derived, which delivers the test statistic and also a highly accurate p-value approximation, essentially instantaneously. The MSP test is demonstrated to have higher power against asymmetric alternatives than the well-known and powerful Jarque-Bera test. A further size-correct test, based on combining two test statistics, is shown to have yet higher power. The methodology employed is fully general and can be applied to any i.i.d. univariate continuous distribution setting.

We examine the relationship between consistent parameter estimation and\ud model selection for autoregressive panel data models with fixed effects. We find that the\ud transformation of fixed effects proposed by Lancaster (2002) does not necessarily lead to\ud consistent estimation of common parameters when some true exogenous regressors are\ud excluded. We propose a data dependent way to specify the prior of the autoregressive\ud coefficient and argue for comparing different model specifications before parameter\ud estimation. Model selection properties of Bayes factors and Bayesian information criterion\ud (BIC) are investigated. When model uncertainty is substantial, we recommend the use\ud of Bayesian Model Averaging to obtain point estimators with lower root mean squared\ud errors (RMSE). We also study the implications of different levels of inclusion probabilities\ud by simulations.

Wind energy is the fastest growing renewable energy technology. Wind turbines do not produce any form of pollution and when strategically placed, it naturally blends with the natural landscape. In the long run, the cost of electricity using wind turbines is cheaper than conventional power plants since it doesn’t consume fossil fuel. Wind speed modelling and forecasting are important in the wind energy industry starting from the feasibility stage to actual operation. Forecasting wind speed is vital in the decision-making process related to wind turbine sizes, revenues, maintenance scheduling and actual operational control systems. This paper models and forecasts wind speeds of turbines in the Northwind Bangui Bay wind farm using the Vector Auto Regressive (VAR) model. The explanatory variables used are local wind speed (Laoag), humidity, temperature and pressure generated from the meteorological station in Laoag City. Wind speeds of turbines and other weather factors were found to be stationary using Augmented Dickey-Fuller (ADF) test. The use of VAR model, from daily time series data, reveals that wind speeds of the turbines can be explained by the past wind speed, the wind speed in Laoag, humidity, temperature and pressure. Results of the analysis, using the forecast error variance decomposition, show that wind speed in Laoag, temperature and humidity are important determinants of the wind speeds of the turbines.

Thailand, as the largest electricity market for Laos, has imported significant amounts of electricity from Laos since the operation of first hydropower plant in Laos. However, currently there have been a number of new power particularly nuclear power plants in Thailand being studied implying the possibility of reduction in Thailand’s electricity import from Laos. Since Thailand is the largest market of Laos’ electricity, the change in demand for electricity from Thailand has substantial impact on the Lao economy. The first simulation conducted in this paper shows that Thailand reduces the import of electricity from Laos shows that it statistically has a large impact on Laos’ electricity export sector, due to the reduction of income from electricity export. However, the feasibility of nuclear power projects in Thailand became unclear since the incident of damages of nuclear power plants in Japan on 11 March 2011. Therefore, there may be a possibility that Thailand may abolish the nuclear power projects. Consequently, Thailand may increase the import of electricity from Laos. The second case of simulation shows that the increase of Thailand’s electricity import from Laos has positive effect on Lao economy through the significant increase in income from electricity export. The increase in Thailand’s electricity import from Laos, not only has positive effect on Thai economy in terms of increasing consumption of electricity stimulating economic activities in Thailand, but it also has positive effect on Lao economy in terms of increasing income from electricity export.

The nexus between trade openness and energy demand is hot topic of discussion among academicians and researchers, and numerous studies are available in existing literature while investigating the nexus between trade openness and energy demand. This paper explores the relationship between energy consumption, trade openness and economic growth in case of Thailand. In doing so, we have applied Bayer and Hanck cointegration approach to test whether the long run relationship exists between the variables. Our results confirm the presence of cointegration between the variables. Energy consumption stimulates economic growth. Trade openness adds in economic growth. The causality analysis reveals that energy consumption Granger causes economic growth and in resulting, economic growth Granger causes energy consumption. Trade openness and energy consumption are interdependent i.e. trade openness Granger causes energy consumption and in return, energy consumption Granger causes trade openness. This paper openness up new directions for policy making authorities in Thailand to design a comprehensive energy and trade policies to sustain economic growth for long run.

Examinations of the dynamics of daily returns and volatility in stock markets of the U.S., Hong Kong and mainland China (Shanghai and Shenzhen) over 2 January 2001 to 8 February 2013 suggest: (1) evidence of unidirectional return spillovers from the U.S. to the other three markets, but no spillover between Hong Kong and either of the two mainland China markets, (2) evidence of unidirectional ARCH and GARCH effects from the U.S. to the other three markets, (3) correlations of returns vary across markets, with the highest correlation of 93.5% between the two Chinese markets, medium correlation of 30% between mainland China and Hong Kong markets and low correlations of 6.4% and 7.2% between the U.S. and China’s two markets, thus, international investors may benefit by allocating their assets in China’s markets, (4) the patterns of dynamic conditional correlations from the DCC model suggest an increase in correlation between China and other stock markets since the most recent financial crisis of 2007.

The present study investigates the determinants of life expectancy in the presence of economic misery using Pakistan’s time series data over the period of 1972-2012. The stationary properties of the variables are examined by applying unit root test accommodating structural breaks. The ARDL bounds testing approach to cointegration is applied to examine the long run relationship between the variables. Our findings show that cointegration between the variables is confirmed. Moreover, health spending improves life expectancy. Food supply contributes to life expectancy. A rise in economic misery deteriorates life expectancy. Urbanization enhances life expectancy while illiteracy declines it. The causality analysis reveals that life expectancy is Granger cause of health spending, food supply, economic misery, urbanization and illiteracy. This paper opens up new insights for policy making authorities to consider the role of economic misery while formulating comprehensive economic policy to improve life expectancy in Pakistan.

The classical Chow test for structural instability requires strictly exogenous regressors and a break-point specified in advance. In this paper, we consider two generalisations, the one-step recursive Chow test (based on the sequence of studentised recursive residuals) and its supremum counterpart, which relaxes these requirements. We use results on the strong consistency of regression estimators to show that the one-step test is appropriate for stationary, unit root or explosive processes modelled in the autoregressive distributed lags (ADL) framework. We then use the results in extreme value theory to develop a new supremum version of the test, suitable for formal testing of structural instability with an unknown break-point. The test assumes the normality of errors and is intended to be used in situations where this can be either assumed nor established empirically. Simulations show that the supremum test has desirable power properties, in particular against level shifts late in the sample and against outliers. An application to U.K. GDP data is given.

This paper investigates the weak form efficiency in the BRIC countries and Turkey with use of autocorrelation analysis, unit root tests, Johansen cointegration and Granger causality test. Monthly data covers the period from July 1997 to December 2013. Our findings indicate the efficiency among the stock markets in the weak form. The empirical findings indicate monthly closing prices of indices follow the random walk procedure. According to Granger causality and Johansen cointegration tests we found the long-run relationship between China and India, also China and Turkey.

Liquidity is an important variable for the bank and the banking system components. So it is interesting to show its determinants. Thus, we used a sample of 18 banks in Tunisia in for 2000-2010period. We estimated two measures of liquidity (liquid assets / total assets; total loans / total deposits). Through the method of static panel and method of panel dynamic, we found that (financial performance, capital / total assets, operating costs/ total assets, growth rate of GDP, inflation rate, delayed liquidity) have significant impact on bank liquidity while (size, total loans / total assets, financial costs/ total credits, total deposits / total assets) does not have a significant impact on bank liquidity.

As a basis for information recovery in open dynamic microeconomic systems, we emphasize the connection between adaptive intelligent behavior, causal entropy maximization and self-organized equilibrium seeking behavior. This entropy-based causal adaptive behavior framework permits the use of information-theoretic methods as a solution basis for the resulting pure and stochastic inverse economic-econometric problems. We cast the information recovery problem in the form of a binary network and suggest information-theoretic methods to recover estimates of the unknown binary behavioral parameters without explicitly sampling the configuration-arrangement of the sample space.

http://www.jstatsoft.org/v63/i18 Recent advances in the implementation of spatial econometrics model estimation tech- niques have made it desirable to compare results, which should correspond between im- plementations across software applications for the same data. These model estimation techniques are associated with methods for estimating impacts (emanating effects), which are also presented and compared. This review constitutes an up-to-date comparison of generalized method of moments and maximum likelihood implementations now available. The comparison uses the cross-sectional US county data set provided by Drukker, Prucha, and Raciborski ( 2013d ). The comparisons will be cast in the context of alternatives us- ing the MATLAB Spatial Econometrics toolbox, Stata 's user-written sppack commands, Python with PySAL and R packages including spdep , sphet and McSpatial .

I study 46 vintages of FRB/US, the principal macro model used by Federal Reserve Board staff for forecasting and policy analysis, as measures of real-time model uncertainty. I also study the implications of model uncertainty for the robustness of commonly applied, simple monetary policy rules. I first document that model uncertainty poses substantial challenges for policymakers in that key model properties differ in important ways across model vintages. Then I show that the parameterization of optimized simple policy rule--rules that are intended to be robust with respect to model uncertainty--also differ substantially across model vintages. Included in the set of rules are rules that eschew feedback on the output gap, rules that target nominal income growth, and rules that allow for time variation in the equilibrium real interest rate. I find that many rules, which previous research has shown to be robust in artificial economies, would have failed to provide adequate stabilization in the real-time, real-world environment seen by the Fed staff. However, I do identify certain policy rules that would have performed relatively well, and I characterize the key features of those rules to draw more general lessons about the design of monetary policy under model uncertainty.

During the last years the monetary policy initiatives of the main central banks have been profoundly influenced by quantitative easing (QE). Blessing, curse, effective instruments or a simple fad, these unconventional measures have occupied the center stage of academic and public attention. In this context, this paper focuses on a wide set of public speeches delivered by officials belonging to the most relevant central banks. These statements cover a large pallet of topics including areas such as QE, tapering, financial stability, unemployment or interest rates. The aim of this study is to investigate the impact that these speeches have on the volatility of exchange rates. For this purpose, the methodology relies on an econometric event study that incorporates three volatility models and intraday five-minute frequencies. The results indicate the fact that public statements have a clear, evident, significant and robust impact on the observed assets.

This paper presents several econometric models that explain how the performance of companies listed on the Bucharest Stock Exchange is influenced by corporate governance practices of listed companies comply how the principles and recommendations of the Corporate Governance Code of the Bucharest Stock Exchange. They raised issues related to the composition and size of the Board of Directors. The research proposes a brief description of the characteristics governing bodies of companies listed on the Bucharest Stock Exchange in 2010-2013, and their impact on corporate performance. The research methodology consists of estimating regression coefficients of the regression models of panel data, using E-views program and identifying the relationships established between corporate performance and some board characteristics (there is a balance between executive and non-executive members, Board size, existence of independent members, existence of the committees / advisory boards – Nomination Committee, Remuneration, Audit) or the relationships between the existence of Board Committees and composition of Board of Directors (the existence of a balance between executive and non-executive members and the existence of independent members). The analysis results for companies listed on the Bucharest Stock Exchange is observed that the variable which shows a balance between executive and non-executive directors has a positive impact on the existence of the Audit Committee, Remuneration Committee, which confirms Recommendation 29 and Recommendation 22 of the Code of Governance BSE. The relationship established between the variable that shows the existence of the independent and performance of the company is negative, and the existence of a balance between executive and non-executive members and positive net margin rate.

The present paper introduces a VAR model with exogenous variables for testing one-sided (non-)causality by extending the works of Dufour and Renault (1998) and Dufour et al. (2006). In this context, it derives a test statistic for formally investigating one sided (non-)causality, while providing a simple algorithm for implementing the one sided (non-)causality test in a system framework and not equation-by-equation extending, thus, Dufour et al. (2006). We illustrate our approach by using a monthly dataset including dummy variables on Total Car Sales in the area of Athens over the period 2003-2012. According to our findings all variables cause the evolution of Total Sales cycles immediately and for almost eight (8) quarters when most of the causality effects die out completely. Clearly, future research on extending the methodology to a panel set-up would be of great interest.

This paper discusses nonparametric kernel regression with the regressor being a \(d\)-dimensional \(\beta\)-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \(\sqrt{n(T)h^{d}}\), where \(n(T)\) is the number of regenerations for a \(\beta\)-null recurrent process and the limiting distribution (with proper normalization) is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.

According to World Public Opinion poll, no country leader enjoyed worldwide trust in 2008. Only the leaders of China, Iran, and Russia received consistently higher trust ratings domestically than abroad. Not incidentally, these countries also score low in political and press freedoms. We build a parsimonious regression model that seeks to explain differences in trust ratings using country and leader characteristics. We find that just nine variables can explain over 60% of the variation in leader trust ratings. One of the strongest associative determinants of leader trust is press freedom.

This paper focuses on finding starting-values for the estimation of Vector STAR models. Based on a Monte Carlo study, different procedures are evaluated. Their performance is assessed with respect to model fit and computational effort. I employ (i) grid search algorithms and (ii) heuristic optimization procedures, namely differential evolution, threshold accepting, and simulated annealing. In the equation-by-equation starting-value search approach the procedures achieve equally good results. Unless the errors are cross-correlated, equation-by-equation search followed by a derivative-based algorithm can handle such an optimization problem sufficiently well. This result holds also for higher-dimensional Vector STAR models with a slight edge for heuristic methods. For more complex Vector STAR models which require a multivariate search approach, simulated annealing and differential evolution outperform threshold accepting and the grid search.

In this paper, we develop a new model of a static game of incomplete information with a large number of players. The model has two key distinguishing features. First, the strategies are subject to threshold effects, and can be interpreted as dependent censored random variables. Second, in contrast to most of the existing literature, our inferential theory relies on a large number of players, rather than a large number of independent repetitions of the same game. We establish existence and uniqueness of the pure strategy equilibrium, and prove that the censored equilibrium strategies satisfy a near-epoch dependence property. We then show that the normal maximum likelihood and least squares estimators of this censored model are consistent and asymptotically normal. Our model can be useful in a wide variety of settings, including investment, R&amp, D, labor supply, and social interaction applications.

Many econometric analyses have attempted to model medal winnings as dependent on per capita GDP and population size. This approach ignores the size and composition of the team of athletes, especially the role of female participation and the role of sports culture, and also provides an inadequate explanation of the variability between the outcomes of countries with similar features. This paper proposes a model that offers two substantive advancements, both of which shed light on previously hidden aspects of Olympic success. First, we propose a selection model that treats the process of fielding any winner and the subsequent level of total winnings as two separate, but related, processes. Second, our model takes a more structural angle, in that we view GDP and population size as inputs into the "production" of athletes. After that production process, those athletes then compete to win medals. We use country-level panel data for the seven Summer Olympiads from 1988 to 2012. The size and composition of the country's Olympic team are shown to be highly significant factors, as is also the past performance, which generates a persistence effect.

In the context of international financial crisis, this paper aims to analyze the impact of the liquidity level on the monetary policy transmission effectiveness of the Moroccan Central Bank (Bank Al Maghrib, BAM). After a long period of liquidity excess, the Moroccan banking system through, since 2007, a liquidity shortage that forces BAM to inject a regular and massive quantity of liquidity. Thus, to evaluate the influence of liquidity level on the monetary policy transmission effectiveness of Bank Al Maghrib, we apply a VAR modeling over the period 1998-2012, by distinguishing the period of liquidity excess and liquidity shortage and using two types of liquidity indicators. Our results show that in an excess liquidity period the monetary policy transmission would be less efficient, especially in the longer term. Instead, a liquidity shortage situation makes it more effective.

This study attempts to explore the causal relationship between renewable and non-renewable electricity consumption, output and carbon dioxide (CO 2 ) emissions for 10 Middle East and North Africa (MENA) countries over the period of 1980–2009. The results from panel Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) show that renewable and non-renewable electricity consumption add in CO 2 emissions while output (real gross domestic product (GDP) per capita) exhibits an inverted U-shaped relationship with CO 2 emissions i.e. the environment Kuznets curve (EKC) hypothesis is validated. The short-run dynamics indicate the unidirectional causality running from renewable and non-renewable electricity consumption and output to CO 2 emissions. In the long-run, there appears to be a bidirectional causality between electricity consumption (renewable and non-renewable) and CO 2 emissions. The findings suggest that future reductions in CO 2 emissions might be achieved at the cost of economic growth.

The Heckman sample selection model relies on the assumption of normal and homoskedastic disturbances. However, before considering more general, alternative semiparametric models that do not need the normality assumption, it seems useful to test this assumption. Following Meijer and Wansbeek (2007), the present contribution derives a GMM-based pseudo-score LM test on whether the third and fourth moments of the disturbances of the outcome equation of the Heckman model conform to those implied by the truncated normal distribution. The test is easy to calculate and in Monte Carlo simulations it shows good performance for sample sizes of 1000 or larger.

A well-documented empirical result is that market expectations extracted from futures contracts on the federal funds rate are among the best predictors for the future course of monetary policy. We show how this information can be exploited to produce accurate forecasts of bond excess returns and to construct profitable investment strategies in bond markets. We use an exponential tilting method for incorporating market expectations into forecasts from a standard term-structure model and then derive the implied forecasts for bond excess returns. We find that the method delivers substantial improvements in out-of-sample accuracy relative to a number of benchmarks. The accuracy improvements are both statistically and economically significant for bond maturities of up to two years and forecast horizons less than one year, and would have allowed an investor to obtain positive cumulative excess returns from simple "riding the yield curve" investment strategies over the past ten years. For long forecast horizons and bond maturities of four or five years, the preferred forecast is instead one implied by a simple autoregressive model.

This note analyzes the empirical size of the augmented Dickey and Fuller (ADF) statistic proposed by Perron and Rodríguez (2003) when the errors are fractional. This ADF is based on a searching procedure for additive outliers based on first-differences of the data named td. Simulations show that empirical size of the ADF is not affected by fractional errors confirming the claim of Perron and Rodríguez (2003) that the procedure td is robust to departures of the unit root framework. In particular the results show low sensitivity of the size of the ADF statistic respect to the fractional parameter (d). However, as expected, when there is strong negative moving average autocorrelation or negative autoregressive autocorrelation, the ADF statistic is oversized. These difficulties are fixed when sample increases (from T = 100 to T = 200). Empirical application to eight quarterly Latin- American inflation series is also provided showing the importance of taking into account dummy variables for the detected additive outliers.

In a recent paper, Fajardo et al (2009) propose an alternative semiparametric estimator of the fractional parameter in ARFIMA models which is robust to the presence of additive outliers. The results are very interesting, however, they use samples of 300 or 800 observations which are rarely found in macroeconomics or economics. In order to perform a comparison, I use the procedure to detect for additive outliers based on the estimator Tau- d suggested by Perron and Rodríguez (2003). Further, I use dummy variables associated to the location of the selected outliers to estimate the fractional parameter. I found better results for the mean and bias of this parameter when T = 100 and the results in terms of the standard deviation and the MSE are very similar. However, for higher sample sizes as 300 or 800, the robust procedure performs better, specially based on the standard deviation and MSE measures. Empirical applications for seven Latin American ination series with very small sample sizes contaminated by additive outliers is discussed. What we nd is that when no correction for additive outliers is performed, the fractional parameter is underestimated.

Testing causality-in-mean and causality-in-variance has been largely studied. However, none of the tests can detect causality-in-mean and causality-in-variance simultaneously. In this paper, we introduce a factor double autoregressive (FDAR) model. Based on this model, a score test is proposed to detect causality-in-mean and causality-in-variance simultaneously. Furthermore, strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for the FDAR model are established. A small simulation study shows good performances of the QMLE and the score test in finite samples. A real data example on the causal relationship between Hong Kong stock market and US stock market is given.

In transition countries affected by uncertainty, the educational system usually suffers from lack of funds from the government and it is affected by various reforms. It is important to see how education influences economic growth and how this growth can be improved by investing in education. In this article, after a literature and econometric models review, the influence of primary, secondary and tertiary education over the GDP growth will be analyzed for Bulgaria, Czech Republic and the Netherlands, using regressions models, with the aid of computer software tool EViews. The models will be tested in order to obtain a good and reliable model.

This paper considers the Granger-causality in conditional quantile and examines the potential of improving conditional quantile forecasting by accounting for such a causal relationship between financial markets. We consider Granger-causality in distributions by testing whether the copula function of a pair of two financial markets is the independent copula. Among returns on stock markets in the US, Japan and U.K., we find significant Granger-causality in distribution. For a pair of the financial markets where the dependent (conditional) copula is found, we invert the conditional copula to obtain the conditional quantiles. Dependence between returns of two financial markets is modeled using a parametric copula. Different copula functions are compared to test for Granger-causality in distribution and in quantiles. We find significant Granger-causality in the different quantiles of the conditional distributions between foreign stock markets and the US stock market. Granger-causality from foreign stock markets to the US stock market is more significant from UK than from Japan, while causality from the US stock market to UK and Japan stock markets is almost equally significant.

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an “in-sample” period – purportedly utilized only for model specification/estimation – and a “post-sample” period, purportedly utilized (only at the end of the analysis) for model validation/testing purposes. This partitioning is usually infeasible, however, with samples of modest length – e.g., T ≤ 150 – as is common in both quarterly data sets and/or in monthly data sets where institutional arrangements vary over time, simply because there is in such cases insufficient data available to credibly accomplish both purposes separately. A cross-sample validation (CSV) testing procedure is proposed below which both eliminates the aforementioned a priori partitioning and which also substantially ameliorates this power versus credibility predicament – preserving most of the power of in-sample testing (by utilizing all of the sample data in the test), while also retaining most of the credibility of post-sample testing (by always basing model forecasts on data not utilized in estimating that particular model’s coefficients). Simulations show that the price paid, in terms of power relative to the in-sample Granger-causality F test, is manageable. An illustrative application is given, to a re-analysis of the Engel andWest [1] study of the causal relationship between macroeconomic fundamentals and the exchange rate; several of their conclusions are changed by our analysis. Published version

The recent push for a federal energy policy that could substantially change electricity prices in the U.S. highlights the need to obtain accurate residential electricity demand estimates. Many electricity demand estimates have been obtained based on the assumption that consumers optimize with respect to known marginal prices, but increasing empirical evidence suggests that consumers are more likely to respond to average prices. Under this assumption, this paper develops a new strategy based on GMM to estimate household electricity demand. Our approach allows a national-level demand estimation from publicly available expenditure data and utility-level consumption data, complementing studies that use individual billing data which are richer yet often proprietary. We estimate the price elasticity near -1, which is at the upper end (in magnitude) among the estimates from previous studies. We apply our elasticity estimates in a U.S. climate policy simulation to determine how these elasticity estimates alter consumption and price outcomes compared to the more conservative elasticity estimates commonly used in policy analysis.

Since the 1980s, the Chinese economy has developed rapidly, with an average annual growth rate around 10%. Energy consumption in China has greatly increased as well. This paper investigates the relationship between energy consumption, economic development and temperature in China by adopting provincial panel data from 1990 to 2011. Different from existing studies, in this paper, we use a panel smooth transition regression (PSTR) model to estimate the non-linearity relationship. Four different threshold variables including two lagged endogenous variables and two important exogenous variables have been considered. We find that energy intensity and the ratio of gross capital formation are suitable for the non-linearity model. The estimated elasticities of time dynamic indicate that energy consumption is income inelastic and temperature inelastic. Elasticities of real income at first increase and then decrease, however, elasticities of temperature gradually increase after the year 1993. Last of all, we propose some policy implications.

The main objective of this paper is to test the hypothesis that emerging markets are more sensitive to negative shocks than positive ones, and also that developed ones do not exhibit this same pattern. Using the family of ARCH models, the conditional variances of exchange rates in Brazil, Mexico and Singapore, representing the emerging countries, and the Euro Zone, UK and Japan, representing the developed ones, are estimated and forecasted. The results indicate that there is no relationship between the country being either developed or emerging, and its best fit is given by a model symmetrical or asymmetrical.

This paper discusses pitfalls in the application of the rolling trace test. This procedure is based on the iterative calculation of Johansen's (1988) trace test for the rank of a cointegration system in windows of equal length that roll over the sample. Pitfalls lie in the selection of the window length and of the lag order for short-run coefficients as well as in the presence of stationary variables in some sub-periods. We give practical recommendations to solve these issues and demonstrate their implications when assessing the integration of four major European stock markets.

In this study, we attempt to estimate the long term determinants of international reserves in the emerging Asian for the period 1980-2011. In this study, we attempt to estimate the long term determinants of international reserves in the emerging Asia for the period 1980-2011. Utilizing a well-established panel data estimation technique fully modified OLS (FMOLS), we find that trade openness, country size, export volatility and opportunity cost of reserve accumulation are significant factors explaining the determinants of international reserves in the emerging Asia. Under the environment of high dependency on inelastic imports and capital mobility with frequent and costly financial crises, it seems that these countries self-insurance themselves against the possible currency crisis.

The study examines the causal relationship between inflation uncertainty and output growth uncertainty for selected African countries. Asymmetric BEKK GARCH-M model is used to derive measures of uncertainty for inflation and output growth, and bootstrap causality testing approach is used to examine the causal links between them. The findings suggest that Logue and Sweeney (1981) hypothesis and Devereux (1989) hypothesis are supported for Algeria and South Africa; a trade-off hypothesis of Taylor (1981) and Fuhrer (1997) is supported for Gabon, Libya and Tunisia; no causality whatsoever is found for Congo Republic and Nigeria, while for Libya and Tunisia, inflation uncertainty does not affect output growth uncertainty. Further studies are needed to shed more light on the relationship between these important macroeconomic variables for African countries which are still under-researched.

This study extends prior research on referee bias and close bias in professional soccer by examining whether Major League Soccer (MLS) referees’ discretion over stoppage time (i.e., extra play beyond regulation) is influenced by end-of-regulation match scores and/or home field advantage. To do so, we employ a grouped-data regression model and a partially adaptive model. Both account for the imprecise measurement in reported stoppage time. For the 2011 season we find no home field advantage. In fact, stoppage time is the same with a one or two goal deficit at the end of regulation, regardless of which team is ahead. However, the 2011 results do point to an increase in stoppage time of 12 to 20 seconds for nationally televised matches. For the 2012 season, the nationally televised effect disappears due to an increase in stoppage time for those matches not nationally televised. However, a home field advantage is present. Facing a one-goal deficit at the end of regulation, the home team receives about 33 seconds more stoppage time than a visiting team facing the same deficit.

This article analyzes determinants of exports and imports by French overseas departments and reveals insights into the market access of these ultra-peripheral regions. Special attention focuses on Mayotte Island, which will take on the status of the "Outermost Region of Europe" in 2014. The estimates, based on gravity models, show that (a) economic crises in a partner country hinders imports from French overseas departments; (b) immigration, a serious concern in Mayotte, has a significant, negative impact on the trade deficit, by both raising imports and reducing exports; (c) historical colonial links can still explain trade; and (d) a common language with the partner country boosts exports from Mayotte more than exports from other overseas departments.

This paper explores the effectiveness of boosting, often regarded as the state of the art classification tool, in giving warning signals of recessions 3, 6, and 12 months ahead. Boosting is used to screen as many as 1,500 potentially relevant predictors consisting of 132 real and financial time series and their lags. Estimation over the full sample 1961:1–2011:12 finds that there are fewer than 10 important predictors and the identity of these variables changes with the forecast horizon. There is a distinct difference in the size and composition of the relevant predictor set before and after mid-1980. Rolling window estimation reveals that the importance of the term and default spreads are recession specific. The Aaa spread is the most robust predictor of recessions three and 6 months ahead, while the risky bond and 5-year spreads are important for 12 months ahead predictions. Certain employment variables have predictive power for the two most recent recessions when the interest rate spreads were uninformative. Warning signals for the post-1990 recessions have been sporadic and easy to miss. The results underscore the challenge that changing characteristics of business cycles pose for predicting recessions. Prevoir les recessions. Ce texte explore l'efficacite de la methode dite du ‘boosting’, qu'on considere souvent comme un instrument de classification qui est a la fine pointe de l'art de prevoir les recessions 3, 6 et 12 mois a l'avance. Cette methode est utilisee pour passer au crible quelques 1500 predicteurs potentiellement pertinents construits a partir de 132 series chronologiques de variables reelles et financieres plus ou moins decalees. Des estimations de l'echantillon complet pour la periode du debut de 1961 a la fin de 2011 revelent qu'aussi peu que dix predicteurs sont importants, et que l'identite de ces variables change selon l'horizon de prevision considere. Il y a aussi une difference marquee dans la taille et la composition de cet ensemble de predicteurs avant et apres le milieu des annees 1980. Il appert que l'importance des ecarts de credit (ecarts des taux d'interet et des risques de defaut de paiement) est specifique a la recession particuliere. L'ecart Aaa est le predicteur le plus robuste des recessions dans trois et six mois, alors que la debenture risquee et l'ecart des taux d'interet pour la fenetre de 5 ans sont les predicteurs importants pour un horizon temporel de 12 mois. Certaines variables reliees a l'emploi ont eu un certain pouvoir de prediction pour les deux dernieres recessions quand les ecarts de taux d'interet n'ont pas ete eclairants. Les signaux des clignotants pour les recessions d'apres 1990 ont ete sporadiques et faciles a manquer. Les resultats soulignent le defi que posent a ceux qui font des previsions de recessions les caracteristiques changeantes des cycles economiques.

The increased in internet and telecommunication usage has become the reason for the adopting of e-filling systems for annual tax report or SPT (based on Indonesian Taxation Authority Decree Number Kep.88/PJ/2004 on 14th May 2004 about electronic based annual tax report). On the contrary, this development does not show in the usage of the e-filling systems. This condition is the reason for this study to investigate the implementation of e-filling systems taxation policy by using decomposition theory of planned behavior. This theory is used to understand and test the influenced factors in the usage of electronic based annual tax report (e-filling) and the persistent of e-filling system in Indonesia. Population in this study is the Indonesian citizen’s tax payers who have got tax registration number (NPWP) both who have or have not used e-filling to report the annual tax return in Surabaya area, East Java-Indonesia. Sample is taken using a non-probability sampling, with convienient sampling method. Analytical technique used in this observation is Structural Equation Method (SEM). The results showed that e-filling user’s attitude, subjective norms, and perceived behavior control influence the users’ intention to use e-filling systems. These results indicated that Indonesian citizens’ intention to use the e-filling facilities to report their annual tax income is depend upon their attitudes and controls on the systems. Even if Indonesian tax payers realized that with e-filling systems they will be able to increase the effectiveness, more optimize services, hasten and improved efficiency in annual tax reporting, the taxpayer does not completely understand about how to operate the e-filling system. Therefore, it is imperative for the government to socialize and educate them on the importance of e-filling systems.