Automated model selection in finance: General-to-specic modelling of the mean and volatility specications

General-to-Specific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multi-path GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multi-path GETS modelling in finance. Making use of a recent result on log-GARCH Models, we provide and study simple but general and flexible methods that automate financial multi-path GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential volatility specification can include log-ARCH terms, asymmetry terms, volatility proxies and other explanatory variables, the algorithm we propose returns parsimonious mean and volatility specifications. The finite sample properties of the methods are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods are very useful in practice.general-to-specific; specification search; model selection; finance; volatility

Knowledge spillovers in U.S. patents: a dynamic patent intensity model with secret common innovation factors

During the past two decades, innovations protected by patents have played a key role in business strategies. This fact enhanced studies of the determinants of patents and the impact of patents on innovation and competitive advantage. Sustaining competitive advantages is as important as creating them. Patents help sustaining competivite advantages by increasing the production cost of competitors, by signaling a better quality of products and by serving as barriers to entry. If patents are rewards for innovation, more R&D should be reflected in more patents applications but this is not the end of the story. There is empirical evidence showing that patents through time are becoming easier to get and more valuable to the firm due to increasing damage awards from infringers. These facts question the constant and static nature of the relationship between R&D and patents. Furthermore, innovation creates important knowledge spillovers due to its imperfect appropriability. Our paper investigates these dynamic effects using U.S. patent data from 1979 to 2000 with alternative model specifications for patent counts. We introduce a general dynamic count panel data model with dynamic observable and unobservable spillovers, which encompasses previous models, is able to control for the endogeneity of R&D and therefore can be consistently estimated by maximum likelihood. Apart from allowing for firm specific fixed and random effects, we introduce a common unobserved component, or secret stock of knowledge, that affects differently the propensity to patent of each firm across sectors due to their different absorptive capacity.Point process, Conditional intensity, Latent factor, R&D spillovers, Patents, Secret innovations

Catching up in total factor productivity through the business cycle : evidence from Spanish manufacturing surveys

Spain has recently experienced more than a decade of price stability and economic growth however now is showing one of the most significant slowdowns in economic activity of the EU economies. There is a general consensus that this slowdown in economic activity is particularly important in Spain due to the low level and low rates of growth experienced by total factor productivity (TFP) during more than a decade. Among the key policy elements that could enhance TFP of manufacturing firms in Spain we find those related to human capital, foreign direct investment, and process innovations. We evaluate the effect of recessions on the productivity growth of firms with different level of productivity. We present evidence on the dynamic of firm’s TFP through the business cycle allowing for a differentiated behavior for technological leaders and followers. We observe lower persistence and faster convergence in TFP during recessions and, higher persistence and non convergence in TFP during expansions. These empirical findings are consistent with the predictions obtained from the technological diffusion literature and from the fact that firm’s innovation is pro-cyclical. These conclusions are obtained from a microeconometric analysis of surveys of Spanish manufacturing firms (ESEE) from 1991 to year 2005.Productivity catching up, Technology diffusion, Pro-cyclical innovation, Technological leaders, Business cycle

Automated financial multi-path GETS modelling

General-to-Specific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multi-path GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multi-path GETS modelling in finance. We provide a result with associated methods that overcome many of the problems, and develop a simple but general and flexible algorithm that automates financial multi-path GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential variance specification can include log-ARCH terms, log-GARCH terms, asymmetry terms, Bernoulli jumps and other explanatory variables, the algorithm we propose returns parsimonious mean and variance specifications, and a fat-tailed distribution of the standardised error if normality is rejected. The finite sample properties of the methods and of the algorithm are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods and algorithm are very useful in practice.General-to-specfic Modelling, Finance, Volatility, Value-at-risk

Patents, secret innovations and firm's rate of return : differential effects of the innovation leader

This paper studies the dynamic interactions and the spillovers that exist among patent application intensity, secret innovation intensity and stock returns of a well-defined technological cluster of firms. We study the differential behavior when there is an Innovation Leader (IL) and the rest of the firms are Innovation Followers (IFs). The leader and the followers of the technological cluster are defined according to their patent innovation activity (stock of knowledge). We use data on stock returns and patent applications of a panel of technologically related firms of the United States (US) economy over the period 1979 to 2000. Most firms of the technological cluster are from the pharmaceutical-products industry. Interaction effects and spillovers are quantified by applying several Panel Vector Autoregressive (PVAR) market value models. Impulse Response Functions (IRFs) and dynamic interaction multipliers of the PVAR models are estimated. Secret patent innovations are estimated by using a recent Poisson-type patent count data model, which includes a set of dynamic latent variables. We show that firms’ stock returns, observable patent intensities and secret patent intensities have significant dynamic interaction effects for technologically related firms. The predictive absorptive capacity of the IL is the highest and this type of absorptive capacity is positively correlated with good firm performance measures. The innovation spillover effects that exist among firms, due to the imperfect appropriability of the returns of the investment in R&D, are specially important for secret innovations and less relevant for observed innovations. The flow of spillovers between followers and the leader is not symmetric being higher from the IL to the IFs.Patent count data model, Stock market value, Secret innovations, Absorptive capacity, Technological proximity, Panel Vector Autoregression (PVAR), Impulse Response Function (IRF), Efficient Importance Sampling (EIS)

COINTEGRATION TESTS BASED ON RECORD COUNTING STATISTICS

This paper presents of number of cointegration tests that exploit the statistical properties of the records from the original time series variables. We prove their consistency and obtain their asymptotic null distributions. Among the advantages of this novel methodology, the new tests are invariant with respect to the individual series’ variances and also with respect to monotonic transformations applied to these series. In addition, these tests are robust against the presence of level breaks as long as the number of these breaks increases slowly enough with the sample size. Finally, an alternative scheme is proposed to deal with additive outliers, which prevent them from causing size distortions.

EU Patent System: to be or not to be?

This paper introduce a list of desirable efficiency properties that any a patent system should have in order to enhance innovation, trade competitiveness, employment mobility and economic growth. We briefly overview the literature on patents and discuss the advantages and disadvantages of the present and recent proposals for the future of the European Union Patents System. In particular, we discuss the costinefficiencies observed in the current design of the EU Patent System based in a double structure layer divided in a central European Patent Office (EPO) and several nationalbased patent offices. This paper analyzes the likely backlashes of creating a third layer for a subâ€sample of EU countries. The paper suggests an alternative more efficient Patent System together with some policy implications.Innovation, Patents, Knowledge spillovers, Common European patent, Welfare losses, Patents’ languages, Cultural proximity, Competitive trade

Robust methodology for investment climate assessment on productivity: application to investment climate surveys from Central America

Developing countries are increasingly concerned about improving country competitiveness and productivity, as they face the increasing pressures of globalization and attempt to improve economic growth and reduce poverty. Among such countries, Investment Climate Assessments (ICA) surveys at the firm level, have become the standard way for the World Bank to identify key obstacles to country competitiveness, in order to prioritize policy reforms for enhancing competitiveness. Given the surveys objectives and the nature and limitations of the data collected, this paper discusses the advantages and disadvantages of using different productivity measures. The main objective is to develop a methodology to estimate, in a consistent manner, the productivity impact of the investment climate variables. The paper applies it to the data collected for ICAs in four countries: Costa Rica, Guatemala, Honduras and Nicaragua. Observations on logarithms (logs) of the variables are pooled across three countries (Guatemala, Honduras and Nicaragua). Endogeneity of the production function inputs and of the investment climate variables is addressed by using a variant of the control function approach, based on individual firm information, and by aggregating investment climate variables by industry and region. It is shown that it is possible to get robust results for 10 different productivity measures. The estimates for the four countries show how relevant the investment climate variables are to explain the average level of productivity. IC variables in several categories (red tape, corruption and crime, infrastructure and, quality and innovation) account for over 30 percent of average productivity. The policy implications are clear: investment climate matters and the relative impact of the various investment climate variables indicate where reform efforts should be directed in each country. It is argued that this methodology can be used as a benchmark to assess productivity effects in other ICA surveys. This is important because ICA surveys are available now for more than 65 developing countries.Total factor productivity, Investment climate, Competitiveness, Firm level determinants of productivity, Robust productivity impacts,

Assessing the impact of the investment climate on productivity using firm-level data : methodology and the cases of Guatemala, Honduras, and Nicaragua

Developing countries are increasingly concerned about improving country competitiveness and productivity as they face the increasing pressures of globalization and attempt to improve economic growth and reduce poverty. Among such countries, investment climate assessments (ICA) have become a standard instrument for identifying key obstacles to country competitiveness and imputing their impact on productivity, in order to prioritize policy reforms for enhancing competitiveness. Given the survey objectives and the nature and limitations of the data collected, the authors discuss the advantages and disadvantages of using different productivity measures based on data at the firm level. Their main objective is to develop a methodology to appropriately estimate, in a robust manner, the productivity impact of the investment climate variables. To illustrate the use of this methodology, the authors apply it to the data collected for ICAs in three countries-Guatemala, Honduras, and Nicaragua. Observations in logarithms (logs) of the variables, and not in rates of growth, are pooled from all three countries. The econometric analysis is done with variables in logs to reduce the impact of measurement errors and allow inclusion of as many observations as possible since the"panel"data set is very unbalanced. The authors address the endogeneity of the production function inputs and of the investment climate variables by using a variant of the control function approach based on individual firm information, and by aggregating investment climate variables by industry and region. The authors show that it is possible to get robust results for 10 differentproductivity measures, if one follows a consistent econometric methodology of specification and estimation. For policy analysis, they recommend using those results of investment climate variables on productivity that are robust for most of the productivity measures. The also analyze efficiency aspects of firms in each country. Finally, they decompose the results to obtain country-specific impacts and establish corresponding priorities for policy reform. The actual estimates for the three countries show the level of significance of the impact of investment climate variables on productivity. Variables in several categories, red tape and infrastructure in particular, appear to account for over 30 percent of productivity. The policy implications are clear: investment climate matters enormously and the relative impact of the various investment climate variables indicates where reform efforts should be directed. Given the robustness of the results, the authors argue that the econometric methodology of productivity analysis developed here ought to be used as a benchmark to assess productivity effects for other ICAs or surveys with firm-level data of similar characteristics.

Robust investment climate effects on alternative firm-level productivity measures

Developing countries are increasingly concerned about improving country competitiveness and productivity, as they face the increasing pressures of globalization and attempt to improve economic growth and reduce poverty. Among such countries, Investment Climate surveys (ICs) at the firm level, have become the standard way for the World Bank to identify key obstacles to country competitiveness, in order to prioritize policy reforms for enhancing competitiveness. Given the surveys objectives and the nature and limitations of the data collected, this paper discusses the advantages and disadvantages of using different total factor productivity (TFP) measures. The main objective is to develop a methodology to generate robust investment climate impacts (elasticities) on TFP under alternative measures. The paper applies it to the data collected for ICs in four developing countries: Costa Rica, Guatemala, Honduras and Nicaragua. Observations on logarithms of the production function variables are pooled across three countries (Guatemala, Honduras and Nicaragua). Endogeneity of the production function inputs and of the investment climate variables is addressed by using observable firm level information, a variant of the control function approach, considering IC variables as proxy and also by aggregating certain investment climate variables by industry and region. It is shown that by using this methodology it is possible to get robust IC “elasticities” on TFP for more than ten different TFP measures. The robust IC elasticity estimates for the five countries show how relevant the investment climate variables are to explain the average productivity of each country. IC variables in several categories (red tape, corruption and crime, infrastructure and, quality and innovation) account for over 30 percent of average productivity. The policy implications are clear: investment climate matters and the relative impact of the various investment climate variables helps indentifying where reform efforts should be directed in each country. It is argued that this robust methodology can be used as a benchmark to assess cross-country productivity effects in other IC surveys. This is important since similar firm-level IC surveys on several sectors (manufacturing, services, etc.) are now available at the World Bank for more than 65 developing countries.Total factor productivity measures, Investment climate, Observable fixed effects, Robust investment climate elasticities, Input-output elasticities