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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.

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.

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 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 .

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 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 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.

The recent volatile behaviour of U.K. inflation has been officially attributed to a sequence of "unusual" price changes, prompting renewed interest in the construction of measures of "core inflation", from which such unusual price changes may be down- weighted or even excluded. This paper proposes a new approach to constructing core inflation based on detailed analysis of the temporal stochastic structure of the individual prices underlying a particular index. This approach is illustrated using the section structure of the U.K. retail price index (RPI), providing a number of measures of core inflation that can be automatically calculated and updated to provide both a current assessment and forecasts of the underlying inflation rate in the U.K.

We decompose the textual data in a daily Norwegian business newspaper into news topics and investigate their predictive and causal role for asset prices. Our three main findings are: (1) a one unit innovation in the news topics predict roughly a 1 percentage point increase in close-to-open returns and signifcant continuation patterns peaking at 4 percentage points after 15 business days, with little sign of reversal; (2) simple zero-cost news-based investment strategies yield signifcant annualized risk-adjusted returns of up to 20 percent; and (3) during a media shortage, due to an exogenous strike, returns for firms particularly exposed to our news measure experience a substantial fall. Our estimates suggest that between 20 to 40 percent of the news topics' predictive power is due to the causal media effect. Together these findings lend strong support for a rational attention view where the media alleviate information frictions and disseminate fundamental information to a large population of investors.

The dynamic behaviour of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only for very short maturities. We argue that this is partly due to the ability of survey participants to incorporate information about the current state of the economy as well as forward-looking information such as that contained in monetary policy announcements. We show how the informational advantage of survey expectations about short yields can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible projection method that anchors the model forecasts to the survey expectations in segments of the yield curve where the informational advantage exists and transmits the superior forecasting ability to all remaining yields. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to, without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy for the whole yield curve, with improvements of up to 52% over the years 2000-2012 relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates. JEL Classification: G1, E4, C5

Se realiza un análisis empírico sobre el nivel óptimo de desagregación sectorial y la mejor estrategia de modelización econométrica para la predicción de la inflación en México. Se comparan diferentes estrategias de modelización desagregada basadas en: 1) modelos ARIMA univariantes, 2) metodologías de datos de panel, 3) modelos de corrección del equilibrio y 4) modelos de factores dinámicos. Se encuentra que la consideración de desagregación sectorial es útil a la hora de predecir la tasa de inflación agregada en México. Es más, la predicción de la inflación basada en modelos con datos de panel, modelos de corrección del equilibrio y factores dinámicos superan a simples estrategias extrapolativas basadas en modelos ARIMA univariantes.

The paper investigates risk preferences among different types of individuals. We use several different measures of risk preferences, including questions on choices between uncertain income streams suggested by Barsky, Juster, Kimball, and Shapiro (1997) and a number of ad hoc measures. As in Barsky et al., 1997 , Arrondel and Calvo-Pardo, 2002 , we first analyze individual variation in the risk aversion measures and explain them by background characteristics (both “objective” characteristics and other subjective measures of risk preference). Next we incorporate the measured risk preferences into a household portfolio allocation model, which explains portfolio shares, while accounting for incomplete portfolios and fixed costs. Our results show that a measure based on factor analysis of answers to a number of simple risk preference questions has the most explanatory power. The Barsky et al. (1997) measure has less explanatory power than this “a-theoretical” measure, suggesting that sophisticated measures based on economic theory may exceed the financial capability of respondents. Fixed costs turn out to provide an economically and statistically highly significant explanation for incomplete portfolios.

Applied econometric work takes a superficial approach to causality. Understanding economic affairs, making good policy decisions, and progress in the economic discipline depend on our ability to infer causal relations from data. We review the dominant approaches to causality in econometrics, and suggest why they fail to give good results. We feel the problem cannot be solved by traditional tools, and requires some out-of-the-box thinking. Potentially promising approaches to solutions are discussed.

We provide a comprehensive analysis of the determinants of trading in the sovereign credit default swaps (CDS) market, using weekly data for single-name sovereign CDS from October 2008 to September 2015. We describe the anatomy of the sovereign CDS market, derive a law of motion for gross positions and their components, and identify the key factors that drive the cross-sectional and time-series properties of trading volume and net notional amounts outstanding. While a single principal component accounts for 54 percent of the variation in sovereign CDS spreads, the largest common factor explains only 7 percent of the variation in sovereign CDS net notional amounts outstanding. Moreover, unlike for CDS spreads, common global factors explain very little of the variation in sovereign CDS trading and net notional amounts outstanding, suggesting that it is driven primarily by idiosyncratic country risk. We analyze several local and regional channels that may explain the trading in sovereign CDS: (a) country-specific credit risk shocks, including changes in a country's credit rating and related outlook changes, (b) the announcement and issuance of domestic and international debt, (c) macroeconomic sentiment derived from conventional and unconventional monetary policy, macro-economic news and shocks, and (d) regulatory channels, such as changes in bank capital adequacy requirements. All our findings suggest that sovereign CDS are more likely used for hedging than for speculative purposes.

This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the performance of the proposed method.

In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model, little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as it leads potentially to much improved inferences for the regression coefficients. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe, and the proposed bias correction procedure is very effective.

The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random effects (SRE) model, and in this article, we calibrate it to the SAR model of interest using a generalisation of the Moran operator that allows for heteroskedasticity and an asymmetric SAR spatial dependence matrix. In general, spatial data have a measurement-error component, which we model, and we use restricted maximum likelihood to estimate the SRE model covariance parameters, its required computational time is only the order of the size of the dataset. Our implementation is demonstrated using mean usual weekly income data from the 2011 Australian Census.

A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and loss functions can lead to misspecification, BIC sometimes indicates the wrong regime switching framework. Depending on the Data Generating Process used in the experiments, great care is needed when choosing a criterion.

This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that plug-in bandwidths are relatively small. Several empirical examples show that the plug-in bandwidths are typically similar in magnitude to their cross-validated counterparts.

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of parameters of exogenous variables is inconsistent and determine its asymptotic bias. I provide simulation results to evaluate the performance of the MLE. The simulation results indicate that the MLE imposes a substantial amount of bias on both autoregressive and moving average parameters.

The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We argue that, in general, such instruments (weak or strong) cannot exist.

We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard &amp, Poor’s 500 (S&amp, P 500) and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility.

We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corresponding eigenvector of the alpha correlation matrix., Comment: 15 pages; a trivial typo corrected in Eq. (43), no other changes

This paper investigates the performance of a jackknife correction to a test for cointegration rank in a vector autoregressive system. The limiting distributions of the jackknife-corrected statistics are derived and the critical values of these distributions are tabulated. Based on these critical values the finite sample size and power properties of the jackknife-corrected tests are compared with the usual rank test statistic as well as statistics involving a small sample correction and a Bartlett correction, in addition to a bootstrap method. The simulations reveal that all of the corrected tests can provide finite sample size improvements, while maintaining power, although the bootstrap procedure is the most robust across the simulation designs considered.

The empirical literature studying the sector bias of technical change has only focused on skill-biased technical change. In this paper, I analyse the sector bias of both factor-neutral and factor-biased technical change. In Norwegian data from 1972 to 2007 the empirical evidence is not clear on the impact of a sector bias of skill-biased technical change, but it points to a sector bias of factor-neutral technical change from the 1970s to the 1990s. That said, the impact of the sector bias seems to have reduced towards the latter part of the sample period. I also evaluate the cross-section model used in the literature and show the strong restrictions that must be placed on a vector equilibrium correction model to end up with the standard model. If these restrictions do not hold, the results reported in the literature may be biased. I show that the restrictions are strongly rejected, and that erroneously imposing them significantly changes the estimates of skill-biased technical change in many sectors. These results can, to some extent, be traced back to how the cross-section model ignores initial disequilibrium and imposes factors of production to be either complements or substitutes

To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a ‘split-half’ analysis, the simplest specialization of a multiple-path block-search algorithm. Monte Carlo simulations, extended to sequential reduction, confirm the accuracy of nominal significance levels under the null and show retentions when location shifts occur, improving the non-null retention frequency compared to the corresponding impulse-indicator saturation (IIS)-based method and the lasso.

We investigate the effects of the establishment of the euro on the markups of French manufacturing firms. Merging firm-level census data with customs data, we estimate time-varying firm-specific markups and distinguish between eurozone exporters from other firms between 1995 and 2007. We find that the establishment of the euro has had a pronounced pro-competitive impact by reducing firm markups by 14 percentage points. By reducing export costs, the euro represented an opportunity for eurozone exporters to increase their margins relative to other firms. Quantile regressions show that the euro has led to a reduction in the variance of markups.

The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial dimension is potentially large. The identifying assumption is approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two-stage least squares (2SLS) to account for endogeneity of the spatial lag. The developed two-step estimator is of more general interest. It may be used in applications where the number of endogenous regressors and the number of instrumental variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo simulation results show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable sample size, T .

We investigate the effects of the establishment of the euro on the markups of French manufacturing firms. Merging firm-level census data with customs data, we estimate time-varying firm-specific markups and distinguish between eurozone exporters from other firms between 1995 and 2007. We find that the establishment of the euro has had a pronounced pro-competitive impact by reducing firm markups by 14 percentage points. By reducing export costs, the euro represented an opportunity for eurozone exporters to increase their margins relative to other firms. Quantile regressions show that the euro has led to a reduction in the variance of markups.

In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian’s index of global real economic activity and the index of OECD World industrial production. We develop an econometric approach based on desirable econometric properties in relation to the quarterly measure of World or global gross domestic product to evaluate and to choose across different alternatives. The method is designed to evaluate short-term, long-term and predictability properties of the indicators. World steel production is proven to be the best monthly indicator of global economic activity in terms of our econometric properties. Kilian’s index of global real economic activity also accurately predicts World GDP growth rates. When extending the analysis to an out-ofsample exercise, both Kilian’s index of global real economic activity and the World steel production produce accurate forecasts for World GDP, confirming evidence provided by the econometric properties. Specifically, a forecast combination of the three indices produces statistically significant gains up to 40% at nowcast and more than 10% at longer horizons relative to an autoregressive benchmark.

Econometric methods are applied in the analysis of many phenomena in the economic reality, such as production, costs, and supply search. Valuable results are found in certain limits and under certain conditions by using functions. Synergy of statistical data and the application of mathematical methods will be used to analyze the demand function.

This paper models the firm's production process as a system of simultaneous technologies for desirable and undesirable outputs. Desirable outputs are produced by transforming inputs via the conventional transformation function, whereas (consistent with the material balance condition) undesirable outputs are by-produced via the so-called "residual generation technology". By separating the production of undesirable outputs from that of desirable outputs, not only do we ensure that undesirable outputs are not modeled as inputs and thus satisfy costly disposability, but we are also able to differentiate between the traditional (desirable-output-oriented) technical productivity and the undesirable-output-oriented environmental, or so-called "green", productivity. To measure the latter, we derive a Solow-type Divisia environmental productivity index which, unlike conventional productivity indices, allows crediting the ceteris paribus reduction in undesirable outputs. Our index also provides a meaningful way to decompose environmental productivity into environmental technological and efficiency changes.

It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that if this assumption is violated, then the characterization of Granger non-causality in a VAR model fails to hold. In these situations Granger non-causality test results must be interpreted with caution.

This paper shows that a qualitative analysis can always be used in evaluating a model's validity both in general and compared to other hypothesized models. The analysis relates the sign patterns and possibly other information of hypothesized structural arrays to the sign pattern of the estimated reduced form. It is demonstrated that such an analysis can always potentially falsify the hypothesized structural sign patterns or support an analysis of the relative likelihoods of alternative structural hypotheses, if neither are falsified. It is also noted that a partially specified structural hypothesis can be sometimes falsified by estimating as few as one reduced form equation. Additionally, zero restrictions in the structure can themselves be falsified; and, when so, current practice proposes estimated structural arrays that are impossible. It is further shown how the information content of the hypothesized structural sign patterns can be measured using Shannon's (1948) concept of entropy. In general, the lower the hypothesized structural sign pattern's entropy, the more a priori information it proposes about the sign pattern of the estimated reduced form. As an hypothesized structural sign pattern has a lower entropy, it is more subject to type 1 error and less subject to type 2 error.

We employ a stochastic dominance (SD) approach to analyze the components that contribute to environmental degradation over time. The variables that are considered include countries’ greenhouse gas (GHG) emissions and water pollution. Our approach is based on pair-wise SD tests. First, we study the dynamic progress of each separate variable over time, from 1990 to 2005, within 5-year horizons. Then, pair-wise SD tests are used to study the major industry contributors to the overall GHG emissions and water pollution at any given time, to uncover the industry which contributes the most to total emissions and water pollution. We find that CO2 emissions not only contribute the most to the GHG emissions over time, but also increased within 15 year in the first-order SD sense. On the other hand, water pollution increased in a second-order SD sense. Pair-wise industry comparisons suggest that the major industry contributors to the CO2 emissions have always been the electricity and heat production sectors, while the transport sector has been the second contributor between 1990 and 2005. Finally, the food industry gradually became the major contributing industry for water pollution over time.

The goal of this study is to investigate the causal relationship between financial development and economic growth in Gulf Cooperation Council (GCC) countries, i.e. Bahrain, Oman, Kuwait, Qatar, United Arab Emirates and Saudi Arabia, over the period 1980-2012. We employ panel unit root tests, and Error Correction Model and cointegration techniques to detect long-run and short-run causalities between the variables used in our study. The overall empirical results reveal that the financial sector development contributes significantly to economic growth in the GCC countries. Our results could be of great interest for policymakers since the financial sector could play a crucial role in lowering the dependency of the governments to oil revenues and could contribute significantly to spur economic growth.

We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also investigate the properties of an iterative scheme when applying the analytical bias formula, and we find that this can imply slightly better finite-sample properties for very small sample sizes while for larger sample sizes there is no gain by iterating. Finally, we also pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias.

This paper explores the relationship between electricity consumption, foreign direct investment, capital and economic growth in the case of the Kingdom of Bahrain. The Cobb–Douglas production is used over the period of 1980Q1–2010Q4. We have applied the ARDL bounds testing approach and found that cointegration exists among the series. Electricity consumption, foreign direct investment and capital add in economic growth. The VECM Granger causality analysis has exposed the feedback effect between electricity consumption and economic growth and the same is true for foreign direct investment and electricity consumption. This study suggests government authorities to explore new sources of energy to achieve sustainable economic development for the long run.

Spillovers resulting from fiscal and monetary policy are compared and analysed in small static, small dynamic and large dynamic multi-country models. To compare the size of the spillovers, we consider simulations in which GDP for a certain number of years is held one percent above base in the country where the shock originates. The results indicate that spillovers are large in size. An important transmission mechanism in the contribution to foreign GDP is found to be the foreign real interest rate, contributions to foreign GDP generated through trade are found to be small. In empirical models with endogenous exchange and interest rates, it was found that under floating exchange rate regimes spillovers are much smaller than under pegged exchange rate regimes. Furthermore, we note . that under floating exchange rate regimes, spillovers seem to be larger in small dynamic models than in large empirical models. Q 1998 Elsevier Science B.V.

In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M 0 and M 1 , introduced by Piccolo in 1990. In particular, we show that this set of linear restrictions is equivalent to a null distance d(M 0 , M 1 ) between two given ARMA models. This result provides the logical basis for using d(M 0 , M 1 ) = 0 as a null hypothesis in our test. Some Monte Carlo evidence about the finite sample behavior of our testing procedure is provided and two empirical examples are presented.

Egypt, Morocco and Tunisia face challenges competing on the global markets, as shown by their relatively low and stagnant export shares. The limited export competitiveness has hampered external demand, growth and employment. Applying, for the first time to North Africa, the stock-flow approach to the real equilibrium exchange rate, this paper evaluates the countries??? real exchange rate misalignments during the past three decades. While Egypt experienced periods of substantial misalignment, including in recent years, the exchange rates in Morocco and Tunisia have broadly reflected the underlying fundamentals. In all three countries structural factors are key to boosting exports, alongside of avoiding sizeable future misalignments. Intra-regional trade ??? both with North Africa and the rest of the continent ??? together with greater orientation to fast growing emerging markets could also raise countries??? external competitiveness.

A fast method is developed for value-at-risk and expected shortfall prediction for univariate asset return time series exhibiting leptokurtosis, asymmetry and conditional heteroskedasticity. It is based on a GARCH-type process driven by noncentral t innovations. While the method involves the use of several shortcuts for speed, it performs admirably in terms of accuracy and actually outperforms highly competitive models. Most remarkably, this is the case also for sample sizes as small as 250.

The aim of this article is to analyse the major sources of transaction costs in financial markets, in particular to find the amounts of such costs on the Warsaw Stock Exchange (WSE). Sources of transaction costs are considered: commissions, bid-ask spread and market impact. The commissions are only briefly described since they are explicitly stated and easily measured. More attention is paid to the bid-ask spread which is one of the main causes of trading costs. It is shown that the investor who wants to outperform the Polish market should usually expect a much higher bid-ask spread than it follows from the officially used calculations. Then it is demonstrated how historical spreads can be used in predicting their future values. This seems to be important from the practical point of view, since forecasting trading costs is a compelling task for financial managers. Next, market impact and market impact costs are considered. The practical method of measuring these is applied and discussed.

This paper discusses two alternative two-part models for fractional response variables that are defined as ratios of integers. The first two-part model assumes a Binomial distribution and known group size. It nests the one-part fractional response model proposed by Papke and Wooldridge (1996) and, thus, allows one to apply Wald, LM and/or LR tests in order to discriminate between the two models. The second model extends the first one by allowing for overdispersion in the data. We demonstrate the usefulness of the proposed two-part models for data on the 401(k) pension plan participation rates used in Papke and Wooldridge (1996).

Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips' results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311-340.) by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.