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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.

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.

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.

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.

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 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.

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.

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.

in the prediction of quantiles of daily Standard&Poor’s 500 (S&P 500) returns we consider how to use high-frequency 5-minute data. We examine methods that incorporate the high frequency information either indirectly, through combining forecasts (using forecasts generated from returns sampled at different intraday interval), or directly, through combining high frequency information into one model. We consider subsample averaging, bootstrap averaging, forecast averaging methods for the indirect case, and factor models with principal component approach, for both direct and indirect cases. We show that in forecasting the daily S&P 500 index return quantile (Value-at-Risk or VaR is simply the negative of it), using high-frequency information is beneficial, often substantially and particularly so, in forecasting downside risk. Our empirical results show that the averaging methods (subsample averaging, bootstrap averaging, forecast averaging), which serve as different ways of forming the ensemble average from using high-frequency intraday information, provide an excellent forecasting performance compared to using just low-frequency daily information.

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.

This paper studies the efficiency of an econometric model where the volatility is modeled by a GARCH (1,1) process, and the innovations follow a standardized form of the Pearson type-IV distribution. The performance of the model is examined by in sample and out of sample testing, and the accuracy is explored by a variety of Value-at-Risk methods, the success/failure ratio, the Kupiec-LR test, the independence and conditional coverage tests of Christoffersen, the expected shortfall measures, and the dynamic quantile test of Engle and Manganelli. Overall, the proposed model is a valid and accurate model performing better than the skewed Student-t distribution, providing the financial analyst with a good candidate as an alternative distributional scheme.

The paper investigates demand characteristics of beverage consumption with a focus on the role of income in Nigeria. It uses double hurdle model to analyze the probability of purchasing beverages and the demand for beverages based on the 2003/2004 Nigeria Living Standards Survey (NLSS) data. Within this context, the empirical results show that household income increases the probability of purchasing and the demand for beverages in the study. Specifically, our results reveal that 10 percent rises in household income increases the consumption of beverages by about 17 percent. Meaning that an average household in the sample considered the demand for beverages to be luxury. But a closer look at the result across income groups show that households in the low and high-income groups considered the demand for beverages to be necessity and luxury, respectively in the study.

The National Electricity Market was established in 1998 as a response to the overall liberalization and restructuring of the Australian electricity sector. The wholesale market integration effects of this establishment, however, remain to be systematically examined. We use econometric techniques based on pairwise unit root tests, cointegration analysis and a time varying coefficient model to study the extent of market integration in the physically interconnected regional markets based on daily electricity spot prices. The results from the pairwise unit root tests provide mixed evidence of price convergence while cointegration analysis does not reject the absence of persistent price differences across the physically interconnected regions. The results from the time-varying filtered coefficient model suggest that full market integration has not been achieved yet. We empirically show the presence of significant network losses and constraints across the interregional interconnectors in the NEM. Our findings suggest that convergence in generation and network ownership, coupled with harmonisation of network regulation and regulatory institutional framework, will be increasingly important factors in improving wholesale market integration across all energy-only markets as they experience an increase in the share of renewable energy.

This paper investigates the factors responsible for predicting 2012 U.S. Presidential election. Though contemporary discussions on Presidential election mention that unemployment rate will be a deciding factor in this election, it is found that unemployment rate is not significant for predicting the forthcoming Presidential election. Except GDP growth rate, various other economic factors like interest rate, inflation, public debt, change in oil and gold prices, budget deficit/surplus and exchange rate are also not significant for predicting the U.S. Presidential election outcome. Lewis-Beck and Rice (1982) proposed Gallup rating, obtained in June of the election year, as a significant indicator for forecasting the Presidential election. However, the present study finds that even though there exists a relationship between June Gallup rating and incumbent vote share in the Presidential election, the Gallup rating cannot be used as the sole indicator of the Presidential elections. Various other non-economic factors like scandals linked to the incumbent President and the performance of the two parties in the midterm elections are found to be significant. We study the influence of the above economic and non-economic variables on voting behavior in U.S. Presidential elections and develop a suitable regression model for predicting the 2012 U.S. Presidential election. The emergence of new non-economic factors reflects the changing dynamics of U.S. Presidential election outcomes. The proposed model forecasts that the Democrat candidate Mr. Barack Obama is likely to get a vote percentage between 51.818 % - 54.239 %, with 95% confidence interval.

We estimate three different models of speculative behaviour using oil price data. There are two major results: (i) The three-regime model of Brooks and Katsaris (2005) and a three-regime variant of van Norden and Schaller (2002) fit the oil price data reasonably well; and (ii) Both models show that the probabilities of being in a bubble collapsing state and a bubble expansion state spike in late-2008/early-2009. This provides some support for the claim by Phillips and Yu (2010) and Gilbert (2010) that a bubble in oil prices existed for short period in 2008.

We introduce a two-country no-arbitrage term-structure model to analyse the joint dynamics of bond yields, macroeconomic variables, and the exchange rate. The model allows to understand how exogenous shocks to the exchange rate affect the yield curves, how bond yields co-move in different countries, and how the exchange rate is influenced by the interactions between macroeconomic variables and time-varying bond risk premia. Estimating the model with US and German data, we obtain an excellent fit of the yield curves and we are able to account for up to 75 per cent of the variability of the exchange rate. We find that time-varying risk premia play a non-negligible role in exchange rate fluctuations due to the fact that a currency tends to appreciate when risk premia on long-term bonds denominated in that currency rise. A number of other novel empirical findings emerge.

More than 10 years ago O’Donoghue (2001a) surveyed the dynamic microsimulation models that had been developed up to that point. However many of the barriers mentioned have been gradually overcome in the last decade . This paper surveys the development and practices in dynamic microsimulation over the past decade, and discusses the methodological challenges today. It provides an overview of the methodological choices made in more than 60 known dynamic microsimulation models and examines the advantages and disadvantages of different practices. In addition, this paper reviews the main progress made in the field and explores how future microsimulation models could evolve potentially.

Sweden is one of the countries with the largest public fiscal intervention and narrowest income inequality in the world. This article investigates to what extent these two features are interconnected and whether economic growth affects and is affected by this relationship. Empirical results from vector auto-regression models reveal the existence of important long-run non-Keynesian effects (i.e., lessening fiscal expansions and, conversely, expansionary fiscal contractions) and significant downward effects of government expenditures on income inequality. The existence of a negative trade-off between growth and inequality is an important stylized fact which deserves close attention by policy makers.

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.

We propose an asset pricing model where preferences display generalized disappointment aversion (Routledge and Zin, 2009) and the endowment process involves long-run volatility risk. These preferences, which are embedded in the Epstein and Zin (1989) recursive utility framework, overweight disappointing results as compared to expected utility, and display relatively larger risk aversion for small gambles. With a Markov switching model for the endowment process, we derive closed-form solutions for all returns moments and predictability regressions. The model produces first and second moments of price-dividend ratios and asset returns and return predictability patterns in line with the data. Compared to Bansal and Yaron (2004), we generate: i) more predictability of excess returns by price-dividend ratios; ii) less predictability of consumption growth rates by price-dividend ratios. Differently from the Bansal and Yaron model, our results do not depend on a value of the elasticity of intertemporal substitution greater than one.

This article looks at the gains from improved market efficiency in long-distance grain trade in the second half of the nineteenth century, when violations of the law of one price were reduced due to improved information transmission. Two markets, a major export centre, Chicago, and a major importer, Liverpool, are analysed. We show that the law of one price equilibrium was an ‘attractor equilibrium’. The implication is that prices converged to that equilibrium in a tâtonnement process. Because of asymmetrically timed information between markets separated by long distances there were periods of excess demand as well as excess supply, which triggered off the tâtonnement process. Over time, adjustments to equilibrium, as measured by the half-life of a shock, became faster and violations of the law of one price become smaller. There were significant gains from improved market efficiency, which took place after the information ‘regime’ shifted from pre-telegraphic communication to a regime with swift transmission of information in an era that saw the development of a sophisticated commercial press and telegraphic communication. This article is the first attempt to actually measure the gains from improved market efficiency and it demonstrates that improved market efficiency probably stimulated trade more than falling transatlantic transport costs. Deadweight losses decline significantly as markets became more efficient. The conventional view that Harberger triangles are almost always insignificant is challenged.

The aim of this article is to analyze the relationships between common shocks affecting the real economy and those underlying co-fluctuations in U.S. financial markets. In order to do this, we test for links between these common factors and also use the econometric theory of non-stationary panel data to estimate the relationships. The estimates prove the existence of significant relationships between financial and macroeconomic factors. It is also shown that there are forces pulling U.S. financial markets to move with the real economy, as seen through nearly instantaneous adjustment to a new equilibrium.

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.

Dollarization brought economic stability to Ecuador and higher economic growth. The labor market has not reacted accordingly and unemployment rates remain stubbornly around 10%. I use a simple econometric model of the labor market to disentangle the impact on employment of GDP growth, real wages, the cost of capital, and the real exchange rate. I found two opposing effects at work. On one hand, vigorous economic growth has led to a substantial expansion of labor demand (scale effect). On the other hand, changes in relative factor prices brought about by the dollarization process have played against employment creation (substitution effects): real minimum wages have increased while at the same time the real price of imported intermediate goods and the cost of capital have declined steadily. Together, these price changes indicate that labor is becoming a more expensive factor of production and, thus, signal for substituting labor away.

Redistributing incomes has always been one of the main goals of Iranian policy makers, although political regimes have changed frequently between 1991 and 2004. We have applied a microsimulation using the Oaxaca-Blinder decomposition and a Heckman correction for sample selection bias to compare simulation results for a hypothetical unchanged situation with the actual policy shift observed. While we are able to identify the years in which policy shifts occurred, our results suggest that the intended redistribution goals were at most partially achieved, affecting only some occupations and being offset by changes to the level of family incomes.

In this paper, we use longitudinal data on Self-Reported Health Status from the Panel Study of Income Dynamics to estimate a model of the evolution of health over the life-cycle. The model allows for two sources of persistence in health: unobserved heterogeneity, which models an individual’s (unobserved) ability to cope with health shocks, and state dependence, which models the extent to which the ability to cope with health shocks depends on health status. We allow for flexibility in both sources of persistence. Estimation indicates that heterogeneity is an important determinant of health suggesting that a person’s health today has important antecedents earlier on in life. We also find evidence of state dependence. However, its magnitude depends crucially on the individual’s age and unobserved heterogeneity. The relative contributions of heterogeneity and state dependence that we uncover have different implications for how health policy should be conducted.

The relationship between government revenue and government expenditure is important, given its relevance for policy especially with respect to the budget deficit. The purpose of this paper is to investigate the relationship between government revenue and government expenditure in Namibia. It investigates the causal relationship between government revenue and government expenditure using the Granger causality test through cointegrated vector autoregression (VAR) methods for the period the period 1977 to 2007. The paper tests whether government revenue causes government expenditure or whether the causality runs from government expenditure to government revenue, and if there is bi-directional causality. The results show that there is unidirectional causality from government revenue to government expenditure. This suggests that unsustainable fiscal imbalances can be mitigated by policies that stimulate government revenue.

This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. We find that the regressions are reasonably well specified for conditional betas, even in settings where simple predictive regressions are severely biased. However, there are biases in estimates of the conditional alphas. When time-varying alphas are suppressed and only time-varying betas are considered, the betas become biased. Previous studies overstate the significance of time-varying alphas.

The movement of sovereign yields is important for both investment and risk management. This paper applies a method that was first developed by Diebold et al. (2006) to model the sovereign bond yield curves of the US, Japan and Germany. By including observable macroeconomic variables as well as the latent factors of the yield curve, we find evidence of a strong interaction between the yield curve and macro-variables in the US and Germany but not in Japan. We also estimate the dynamic conditional correlations of the latent factors to reveal the cross-country correlations of the bond markets. Copyright © 2007 John Wiley & Sons, Ltd.

Standard econometric tests for whether money causes output will be meaningless if monetary policy is chosen optimally to smooth fluctuations in output. If U.S. monetary policy were chosen to smooth U.S. output, we show that U.S. money will not Granger-cause U.S. output. Indeed, as shown by Rowe and Yetman [2002. Identifying policy-makers objectives: an application to the bank of Canada. Canadian Journal of Economics 35 (2), 239–256], if there is a (say) 6 quarter lag in the effect of money on output, then U.S. output will be unforecastable from any information set available to the Fed lagged 6 quarters. But if other countries, for example Hong Kong, have currencies that are fixed to the U.S. dollar, Hong Kong monetary policy will then be chosen in Washington D.C., with no concern for smoothing Hong Kong output. Econometric causality tests of U.S. money on Hong Kong output will then show evidence of causality. We test this empirically. Our empirical analysis also provides a measure of the degree to which macroeconomic stabilisation is sacrificed by adopting a fixed exchange rate rather than an independent monetary policy.

Spatial econometrics is a fast-growing field in the series of quantitative disciplines, auxiliaries of economics and related social sciences. Space, friction, interdependence, spatio-temporal components, externalities and many other aspects interact and should be treated adequately in this field. The publication of the Paelinck and Klaassen book in the late 1970s generated virtually the field spatial econometrics. This article studies the diffusion of spatial econometrics, through experienced history on the one hand, on the other through bibliometric methods. Although this field was an “Invisible College” up to 2006 (absence of any organization in form of association, conference, journal, etc.), the databases depict a fast diffusion in the past and strong prospects for the future.

This paper introduces econometric modeling with discrete (categorical) dependent variables. Such models, commonly referred to as qualitative response (QR) models, have become a standard tool of microeconometric analysis. Microeconometric research represents empirical analysis of microdata, i.e. economic information about individuals, households and firms. Microeconometrics has been most widely adopted in various fields, such as labour economics, consumer behavior, or economy of transport. The latest research shows that this methodology can also be successfully transferred to macroeconomic context and applied to time series and panel data analysis in a wider scope. .

This paper studies the relationship between a set of commodity prices in a small open economy like Uruguay and the corresponding international and regional prices. The empirical methodology used is the multivariate cointegration procedure based on maximum likelihood methods introduced by Johansen (1988) as well as estimations of half-life persistence indicators. In the case of cereals, the evidence suggests strong market integration between domestic and regional markets and, to some extent, also to international markets. Therefore, directly or indirectly, domestic prices are connected with the efficient price signal. Results for beef indicate strong market integration between the domestic market and the regional market, which is not so well connected with international markets. Thus, domestic price appears to be linked to a regional price that is not linked to the efficient price signal.

The research article presents the highly innovative theoretical research results: 1) the new quantum microeconomics theory in the quantum econophysics science is formulated; the idea on the existence of the discrete-time induced quantum transitions of firm’s earnings (the firm’s value) in the quantum microeconomics theory in the quantum econophysics science is proposed; 2) the formulas (1, 2) to compute the firm’s discrete-time EBITDA (the firm’s value) changes at the different time moments in the quantum microeconomics theory in the quantum econophysics science is derived; 3) the formulas (3, 4) to calculate the distribution of a number of the firms’ excited business processes of certain value at the selected firm’s state in the economy of scale and scope in terms of the quantum microeconomics theory in the quantum econophysics science is presented; 4) the notion on the wave function in the quantum microeconomics theories in the quantum econophysics science is introduced; 5) the formulas (5, 6) to predict the firm’s discrete-time EBITDA (the firm’s value) state changes in the national/global economies at the certain time moment, using the wave functions in the quantum econophysical time-dependent/time independent wave equations in the quantum microeconomic theory in the quantum econophysics science, are derived; 6) the evolutionary shifts from the classic economic theories to the quantum economic theories, from the analogue economic signal processing to the digital economic signal processing, from the continuous-time signal filtering economic prediction techniques to the wave functions computing economic prediction techniques in application to the quantum econophysics science are described; 7) the perspectives of application of the quantum microeconomics theory in the quantum econophysics science with the aim to solve the various economic problems in the real- and speculative- sectors of economic markets are discussed.

This paper studies the transmission of crime shocks to the economy in a sample of 32 Mexican states over the period from 1993 to 2012. The paper uses a panel structural VAR approach which accounts for the heterogeneity of the dynamic state level responses in GDP, FDI and international migration flows, and measures the transmission via the impulse response of homicide rates. The approach also allows the study of the pattern of economic responses among states. In particular, the percentage of GDP devoted to new construction and the perception of public security are characteristics that are shown to be associated with the sign and magnitude of the responses of economic variables to crime shocks.

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 contributes in economic literature by investigating the impact of defense spending on income inequality in case of Iran using time series data over the period of 1971–2011. For this purpose, we have applied the ARDL bounds testing approach to cointegration for long run relationship in the presence of structural breaks arising in the series. The stationarity properties of the variables are tested using structural break unit root tests. The causal relationship between defense spending and income inequality is examined by employing the VECM Granger causality approach. Our findings validate the long run relationship between the series. The results indicate that defense spending improves income distribution in Iran. An inverted-U shaped relationship exists between defense spending and income inequality while economic growth reduces income inequality. The causality analysis reveals that defense spending Granger causes income inequality and feedback effect exists between income inequality and economic growth.

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.

We present a three-stage, nonparametric estimation procedure to recover willingness to pay for housing attributes. In the first stage we estimate a nonparametric hedonic home price function. In the second stage we recover each consumer's taste parameters for product characteristics using first-order conditions for utility maximization. Finally, in the third stage we estimate the distribution of household tastes as a function of household demographics. As an application of our methods, we compare alternative explanations for why blacks choose to live in center cities while whites suburbanize.

Inflation targeting makes the Central Bank’s conditional inflation forecast the operational target for monetary policy. Successful inflation targeting requires knowing the transmission mechanisms to infl ation from shocks as well as instruments. The econometric implications are that the exogeneity assumptions of a conditonal forecasting model of inflation are crucial to the quality of the forecasts. We advise that econometric inflation forecasting should be based on ac ore wage-price model, grafted into aw ider set of equations that capture the important transmission mechanisms between inflation and policy instruments (interest rate, exchange rate) as well as between inflation and shocks to the economy. We develop am odel of the inflation process in Norway by estimating a dynamic model of wages and prices, supplemented with marginal models of the transmission mechanisms of shocks and instruments. The exogeneity assumptions are tested and accepted. Finally, we demonstrate the model responses to shocks and corresponding changes in monetary instruments and examine the suitability of the full system for inflation forecasting.

We discuss statistical inference problems associated with identification and testability in econometrics, and we emphasize the common nature of the two issues. After reviewing the relevant statistical notions, we consider in turn inference in nonparametric models and recent developments on weakly identified models (or weak instruments). We point out that many hypotheses, for which test procedures are commonly proposed, are not testable at all, while some frequently used econometric methods are fundamentally inappropriate for the models considered. Such situations lead to ill-defined statistical problems and are often associated with a misguided use of asymptotic distributional results. Concerning nonparametric hypotheses, we discuss three basic problems for which such difficulties occur: (1) testing a mean (or a moment) under (too) weak distributional assumptions; (2) inference under heteroskedasticity of unknown form; (3) inference in dynamic models with an unlimited number of parameters. Concerning weakly identified models, we stress that valid inference should be based on proper pivotal functions —a condition not satisfied by standard Wald-type methods based on standard errors — and we discuss recent developments in this field, mainly from the viewpoint of building valid tests and confidence sets. The techniques discussed include alternative proposed statistics, bounds, projection, split-sampling, conditioning, Monte Carlo tests. The possibility of deriving a finite-sample distributional theory, robustness to the presence of weak instruments, and robustness to the specification of a model for endogenous explanatory variables are stressed as important criteria assessing alternative procedures., Nous analysons les problèmes d’inférence associés à l’identification et à la testabilité en économétrie, en soulignant la similarité entre les deux questions. Après une courte revue des notions statistiques requises, nous étudions tour à tour l’inférence dans les modèles non-paramétriques ainsi que les résultats récents sur les modèles structurels faiblement identifiés (ou les instruments faibles). Nous remarquons que beaucoup d’hypothèses, pour lesquelles des tests sont régulièrement proposés, ne sont pas en fait testables, tandis que plusieurs méthodes économétriques fréquemment utilisées sont fondamentalement inappropriées pour les modèles considérés. De telles situations conduisent à des problèmes statistiques mal posés et sont souvent associées à un emploi mal avisé de résultats distributionnels asymptotiques. Concernant les hypothèses non-paramétriques, nous analysons trois problèmes de base pour lesquels de telles difficultés apparaissent: (1) tester une hypothèse sur un moment avec des restrictions trop faibles sur la forme de la distribution; (2) l’inférence avec hétéroscédasticité de forme non spécifiée; (3) l’inférence dans les modèles dynamiques avec un nombre illimité de paramètres. Concernant les modèles faiblement identifiés, nous insistons sur l’importance d’utiliser des fonctions pivotales —une condition qui n’est pas satisfaite par les méthodes usuelles de type Wald basées sur l’emploi d’écart-types — et nous passons en revue les développements récents dans ce domaine, en mettant l’accent sur la construction de test et régions de confiance valides. Les techniques considérées comprennent les différentes statistiques proposées, l’emploi de bornes, la subdivision d’échantillon, les techniques de projection, le conditionnement et les tests de Monte Carlo. Parmi les critères utilisés pour évaluer les procédures, nous insistons sur la possibilité de fournir une théorie distributionnelle à distance finie, sur la robustesse par rapport à la présence d’instruments faibles ainsi que sur la robustesse par rapport la spécification d’un modèle pour les variables explicatives endogènes du modèle.

We examine time-series characteristics of China’s capital flows during 1998-2014. More specifically, we employ Kalman filtering state-space models to gauge relative importance of permanent and transitory components in China’s overall FDI, equity, bond, other investment, and bank credit flows. Our results show that only in the case of FDI are both gross inflow and net flow dominated by a permanent stochastic level, suggesting that this source of capital is largely permanent. Incorporating covariates into the state-space models, we find that larger difference between RMB onshore and offshore interest rates encourages capital inflows that are dominated by a transitory component. Greater global risk perception, proxied by S&P 500’s volatility index, on the other hand, discourages them. These covariates imply that capital control may not be effective in stemming volatile and speculative flows. Our results on bilateral capital flows between China and US also suggest that these flows are less persistent and more volatile during 1998-2014 than previously found based on 1988-1997 data. Our results bear important policy implications as China engages in further reforms in its domestic financial system and greater integration with the world financial system.

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.