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We estimate intergenerational income mobility in the USA and Sweden. To measure the degree to which income status is transmitted from one generation to another we propose a nonparametric estimator, which is particularly relevant for cross-country comparisons. Our approach allows intergenerational mobility to vary across observable family characteristics. Furthermore, it ts situations when data on fathers and sons come from di fferent samples. Finally, our estimator is consistent in the presence of measurement error in fathers' long-run economic status. We fi nd that family background captured by fathers' education matters for intergenerational income persistence in the USA more than in Sweden suggesting that the character of inequality in the two countries is rather di fferent.

We examine the quantitative predictions of heterogeneous firm models à la Melitz (2003) in the context of the Canada - US Free Trade Agreement (CUSFTA) of 1989. We compute predicted increases in trade flows and measured productivity across a range of standard models and compare them to the post-CUSFTA increases observed in the data. Our results point to a fundamental problem which most models we analyse face: predicted increases in measured productivity are too low by an order of magnitude relative to predicted increases in trade flows. Thus, most models are inherently incapable of simultaneously matching trade and productivity reactions to freer trade, raising doubts about the accuracy of the quantitative predictions of a large number of work-horse models in the literature. Using a multi-product firm extension of our baseline model as an example, we show that allowing for within-firm productivity increases has the potential to reconcile model predictions with the data.

This paper considers linear models with a spatial autoregressive error structure. Extending Arnold and Wied (2010), who develop an improved GMM estimator for the parameters of the disturbance process to reduce the bias of existing estimation approaches, we establish the asymptotic normality of a new weighted version of this improved estimator and derive the efficient weighting matrix. We also show that this efficiently weighted GMM estimator is feasible as long as the regression matrix of the underlying linear model is non-stochastic and illustrate the performance of the new estimator by a Monte Carlo simulation and an application to real data., Discussion Paper / SFB 823;22/2013

German metropolitan areas with more highly skilled workers became increasingly skilled between 1975 and 2003, and this has important implications for urban employment growth.Using for the first time German metropolitan areas instead of administrative regions we show that the share of college graduates affects growth by the same magnitude as it does in US MSAs. However, conventional estimators are biased upwards. Correcting for the endogeneity of initial employment and solving a common problem of under-identification shows that the effect is at least a third smaller and closer to 0.5% employment growth for a 10% increase in the concentration of skilled workers. The effect is robust to various controls across two data sets. We additionally question the view that aggregate productivity growth is solely due to college graduates. After distinguishing between six different skill levels we find positive growth effects of high school graduates with vocational training, especially if the local concentration of technical professionals is high. The concentration of non-technical university graduates becomes more important over time, but has less bearing on the marginal growth effects of other skill groups. City success may thus depend on the 'right' combination of skills as well as college graduates.Â

In this article we study the interaction between leading macroeconomic indicators (industrial production, stock prices, consumer sentiment and real interest rates) and financial sector leverage in major European countries. We base our analysis on monthly, country-aggregated panel VAR models for the pre-crisis period January 2003 to August 2008, and the crisis period September 2008 to June 2011. We find little evidence for a relationship between macroeconomic variables and leverage in the pre-crisis period, with only real interest rates having a negative short-term impact on leverage growth. We find positive feedback loops between sentiment and stock prices as well as MFI assets in the pre-crisis period, and a positive impact of real interest rate changes on equity and asset growth. Thus, balance sheet expansions were driven by sentiment and stock prices, while real interest changes allowed MFIs to profit from higher spreads. During the crisis period (starting in September 2008), we observe a countercyclical impact from leverage on sentiment and stock prices, while sentiment and stock prices bear a pro-cyclical impact on leverage. In contrast to this, MFI leverage in Luxembourg is negatively impacted by stock prices, suggesting significant impacts from marking-to-market. We conclude that leverage drives expectations of financial instability (via e.g. default expectations), while sentiment and stock prices drive financial institutions? investment decisions (via e.g. collateral value effects).

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran, and Tahmiscioglu (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem that arises, and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model. It is shown that the transformed maximum likelihood estimator continues to be consistent even in the presence of cross-sectional heteroskedasticity. We also obtain standard errors that are robust to cross-sectional heteroskedasticity of unknown form. By means of Monte Carlo simulation, we investigate the finite sample behavior of the transformed maximum likelihood estimator and compare it with various GMM estimators proposed in the literature. Simulation results reveal that, in terms of median absolute errors and accuracy of inference, the transformed likelihood estimator outperforms the GMM estimators in almost all cases.

We modify a previously suggested GMM estimator in a spatial panel regression model by taking into account the difference between disturbances and regression residuals and derive its asymptotic properties. Simulation results and an empirical application to Indonesian rice data illustrate the improvement in finite samples., Discussion Paper / SFB 823;29/2011

The paper suggests a CUSUM-type test for time-varying parameters in a recently proposed spatial autoregressive model for stock returns and derives its asymptotic null distribution as well as local power properties. As can be seen from Euro Stoxx 50 returns, a combination of spatial modelling and change point tests allows for superior risk forecasts in portfolio management., Discussion Paper / SFB 823;30/2011

We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a formalism, its parameters have been estimated mostly by fitting the numerical approximation of the associated non-Gaussian pdf to empirical data, cf. Castaing et al. [Physica D, 46, 177 (1990)]. More recently, an alternative estimator based upon qth order absolute moments has been introduced by Kiyono et al. [Phys. Rev. E 76 41113 (2007)]. In this paper, we pursue this moment-based approach further and develop a more rigorous Generalized Method of Moments (GMM) estimation procedure to cope with the documented difficulties of previous methodologies. We show that even under uncertainty about the actual number of cascade steps, our methodology yields very reliable results for the estimated intermittency parameter. Employing the Levinson-Durbin algorithm for best linear forecasts, we also show that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results from the GMM and Kiyono et al.'s procedure via Monte Carlo simulations. We finally test the applicability of our approach by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and foreign exchange markets.

This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts. Based on this model, we construct a dynamic collectability score (DCS) that estimates the account-specific probability of collecting a given portion of the outstanding debt over any given time horizon. The model integrates a variety of information sources, including historical repayment data, account-specific, and time-varying macroeconomic covariates, as well as scheduled account-treatment actions. Two model-identification methods are examined, based on maximum-likelihood estimation and the generalized method of moments. The latter allows for an operational-statistics approach, combining model estimation and performance optimization by tailoring the estimation error to business-relevant loss functions. The DCS framework is applied to a large set of account-level repayment data. The improvements in classification and prediction performance compared to standard bank-internal scoring methods are found to be significant. This paper was accepted by Noah Gans, stochastic models and simulation.

We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor et al. (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their consistency and asymptotic normality for a large number of spatial units N and a xed small number of time periods T. Monte Carlo simulations demonstrate that the root mean squared error of spatially corrected GMM estimates|which are based on a spatial lag and spatial error correction|is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is ignored. We show that the spatial Blundell-Bond estimators outperform the spatial Arellano-Bond estimators.

We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor et al. (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their consistency and asymptotic normality for a large number of spatial units N and a xed small number of time periods T. Monte Carlo simulations demonstrate that the root mean squared error of spatially corrected GMM estimates|which are based on a spatial lag and spatial error correction|is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is ignored. We show that the spatial Blundell-Bond estimators outperform the spatial Arellano-Bond estimators.

We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor et al. (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their consistency and asymptotic normality for a large number of spatial units N and a xed small number of time periods T. Monte Carlo simulations demonstrate that the root mean squared error of spatially corrected GMM estimates|which are based on a spatial lag and spatial error correction|is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is ignored. We show that the spatial Blundell-Bond estimators outperform the spatial Arellano-Bond estimators.

We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor et al. (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their consistency and asymptotic normality for a large number of spatial units N and a xed small number of time periods T. Monte Carlo simulations demonstrate that the root mean squared error of spatially corrected GMM estimates|which are based on a spatial lag and spatial error correction|is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is ignored. We show that the spatial Blundell-Bond estimators outperform the spatial Arellano-Bond estimators.

Microfinance institutions (MFIs) are constrained by double bottom-lines: meeting social obligations (the first bottom-line) and obtaining financial self-sufficiency (the second bottom-line). The proponents of the first bottom-line, however, are increasingly concerned that there is a trade-off between these two bottom-lines—i.e., getting hold of financial self-sufficiency may lead MFIs to drift away from their original social mission of serving the very poor, commonly known as mission drift in microfinance which is still a controversial issue. This study aims at addressing the concerns for mission drift in microfinance in a performance analysis framework. Chapter 1 deals with theoretical background, motivation and objectives of the topic. Then the study explores the validity of three major and related present-day concerns. Chapter 2 explores the impact of profitability on outreach-quality in MFIs, commonly known as mission drift, using a unique panel database that contains 4-9 years’ observations from 253 MFIs in 69 countries. Chapter 3 introduces factor analysis, a multivariate tool, in the process of analysing mission drift in microfinance and the exercise in this chapter demonstrates how the statistical tool of factor analysis can be utilised to examine this conjecture. In order to explore why some microfinance institutions (MFIs) perform better than others, Chapter 4 looks at factors which have an impact on several performance indicators of MFIs—profitability or sustainability, repayment status and cost indicators—based on quality-data on 353 institutions in 77 countries. The study also demonstrates whether such mission drift can be avoided while having self-sustainability. In Chapter 5 we examine the impact of capital and financing structure on the performance of microfinance institutions where estimations with instruments have been performed using a panel dataset of 782 MFIs in 92 countries for the period 2000-2007. Finally, Chapter 6 concludes the study by summarising the results from the previous chapters and suggesting some directions for future studies.

This paper suggests a two step estimation procedure for a spatial model with different kinds of spatial dependence and heteroscedastic innovations. Since maximum likelihood estimation is cumbersome due to the large number of parameters, we use a generalized method of moments approach to estimate the parameters of spatial correlation which does not need the large number of variance parameters to be known. For illustration purposes, we apply our estimation procedure to daily stock returns of the Euro Stoxx 50 members. JEL subject classifications: C13, C51, G12, Discussion Paper / SFB 823;08/2010

This paper provides an overview of the existing literature on panel data models with error cross-sectional dependence. We distinguish between spatial dependence and factor structure dependence and we analyse the implications of weak and strong cross-sectional dependence on the properties of the estimators. We consider estimation under strong and weak exogeneity of the regressors for both T fixed and T large cases. Available tests for error cross-sectional dependence and methods for determining the number of factors are discussed in detail. The finite-sample properties of some estimators and statistics are investigated using Monte Carlo experiments.

A puzzling piece of empirical evidence suggests that countries rich in natural resources tend to have dismal economic performance. This paradigm has come to be known as the “resource curse”. This paper deals with the role of institutional quality in explaining the transmission mechanism of the resource curse. I attempt to explain this phenomenon by using the index of economic freedom developed by the Fraser Institute as a proxy for the quality of institutions. The outcomes of the linear and non-linear interactions between resource abundance and institutional quality turn out to be the key elements that determine the intensity, if existent, or otherwise of the resource curse. Rather than look at cross country data like many others, I focus on the 10 provinces and 50 states in Canada and the US respectively over the 2000-2005 period.

Claims have been made that microfinance institutions (MFIs) experience mission drift as they increasingly cater to customers who are better off than their original customers. We investigate mission drift using average loan size as a main proxy and the MFI?s lending methodology, main market, and gender bias as further mission drift measures. We employ a large data set of rated, multi-country MFIs spanning 11 years, and perform panel data estimations with instruments. We find that the average loan size has not increased in the industry as a whole, nor is there a tendency towards more individual loans or a higher proportion of lending to urban costumers. Regressions show that an increase in average profit and average cost tends to increase average loan and the other drift measures. More focus should be given to cost efficiency in the MFI. Microfinance Mission Drift?

We study the problem of a firm that faces asymmetric information about the productivity of its potential workers. In our framework, a worker’s productivity is either assigned by nature at birth, or determined by an unobservable initial action of the worker that has persistent effects over time. We provide a characterization of the optimal dynamic compensation scheme that attracts only high productivity workers: consumption –regardless of time period– is ranked according to likelihood ratios of output histories, and the inverse of the marginal utility of consumption satisfies the martingale property derived in Rogerson (1985). However, in the case of i.i.d. output and square root utility we show that, contrary to the features of the optimal contract for a repeated moral hazard problem, the level and the variance of consumption are negatively correlated, due to the influence of early luck into future compensation. Moreover, in this example long-term inequality is lower under persistent private information

We study the problem of a firm that faces asymmetric information about the productivity of its potential workers. In our framework, a worker’s productivity is either assigned by nature at birth, or determined by an unobservable initial action of the worker that has persistent effects over time. We provide a characterization of the optimal dynamic compensation scheme that attracts only high productivity workers: consumption –regardless of time period– is ranked according to likelihood ratios of output histories, and the inverse of the marginal utility of consumption satisfies the martingale property derived in Rogerson (1985). However, in the case of i.i.d. output and square root utility we show that, contrary to the features of the optimal contract for a repeated moral hazard problem, the level and the variance of consumption are negatively correlated, due to the influence of early luck into future compensation. Moreover, in this example long-term inequality is lower under persistent private information

On an international post World War II dataset, we use an iterated GMM procedure to estimate and test the Campbell and Cochrane (1999, By force of habit: a consumption-based explanation of aggregate stock market behavior. Journal of Political Economy 107, 205–251.) habit formation model with a time-varying risk-free rate. In addition, we analyze the predictive power of the surplus consumption ratio for future stock and bond returns. We find that, although there are important cross-country differences and economically significant pricing errors, for the majority of countries in our sample the model gets empirical support in a variety of different dimensions, including reasonable estimates of risk-free rates. Further, for the majority of countries the surplus consumption ratio captures time-variation in expected returns. Together with the price-dividend ratio, the surplus consumption ratio contains significant information about future stock returns, also during the 1990s. In addition, in most countries the surplus consumption ratio is also a powerful predictor of future bond returns. Thus, the surplus consumption ratio captures time-varying expected returns in both stock and bond markets.

In this paper we evaluate the relative influence of external versus domestic inflation drivers in the 12 new European Union (EU) member countries. Our empirical analysis is based on the New Keynesian Phillips Curve (NKPC) derived in Galí and Monacelli (2005) for small open economies (SOE). Employing the Generalized Method of Moments (GMM), we find that the SOE NKPC is well supported in the new EU member states. We also find that the inflation process is dominated by domestic variables in the larger countries of our sample, whereas external variables are mostly relevant in the smaller countries.

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor, Kelejian, and Prucha (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and supplementing the dynamic instruments by lagged and weighted exogenous variables as suggested by Kelejian and Robinson (1993) yields new spatial dynamic panel data estimators. The performance of these spatial dynamic panel data estimators is in- vestigated by means of Monte Carlo simulations. We show that differences in bias as well as root mean squared error between spatial GMM estimates and corresponding GMM estimates in which spatial error correlation is ignored are small.

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor, Kelejian, and Prucha (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and supplementing the dynamic instruments by lagged and weighted exogenous variables as suggested by Kelejian and Robinson (1993) yields new spatial dynamic panel data estimators. The performance of these spatial dynamic panel data estimators is in- vestigated by means of Monte Carlo simulations. We show that differences in bias as well as root mean squared error between spatial GMM estimates and corresponding GMM estimates in which spatial error correlation is ignored are small.

30 pages, 1 figure, 5 boxes, Empirical studies assume that the macro Mincer return on schooling is con- stant across countries. Using a large sample of countries this paper shows that countries with a better quality of education have on average relatively higher macro Mincer coeficients. As rich countries have on average better educational quality, differences in human capital between countries are larger than has been typically assumed in the development accounting literature. Consequently, factor accumulation explains a considerably larger share of income differences across countries than what is usually found., The support from the Spanish Ministry of Education and Science under project SEJ2005-06357 is gratefully acknowledged. The author acknowledges the support of the Barcelona GSE Research Network and of the Government of Catalonia.

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor, Kelejian, and Prucha (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and supplementing the dynamic instruments by lagged and weighted exogenous variables as suggested by Kelejian and Robinson (1993) yields new spatial dynamic panel data estimators. The performance of these spatial dynamic panel data estimators is in- vestigated by means of Monte Carlo simulations. We show that differences in bias as well as root mean squared error between spatial GMM estimates and corresponding GMM estimates in which spatial error correlation is ignored are small.

This article investigates the impact of Objective 1 structural funds expenditure on EU regions by estimating an augmented conditional convergence econometric model. According to this model, growth convergence is influenced by the policy treatment, which affects the regional initial investment rate by interacting with other regional structural variables and eventually influencing its steady state level. The convergence model is specified in a dynamic panel-data form and estimated using a database of 206 EU15 regions observed from 1989 to 2000. A GMM estimation is applied to obtain consistent estimates of both parameter b and impact of the Objective 1 policies.

This paper applies GMMestimation to assess empirically the small open-economy New Keynesian Phillips Curve derived in Galí and Monacelli (2005). We obtain a testable specification where fluctuations in the terms of trade enter explicitly, thus allowing a comparison of the relevance of domestic versus external determinants of CPI inflation dynamics. For most countries in our sample the expected relative change in the terms of trade emerges as a more relevant inflation driver than the contemporaneous domestic output gap. Overall, our results indicate some, albeit moderate, support for the tested relationship based on data from ten OECD countries typically classified as open economies.

This paper suggests an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are different from observable regression residuals. Although this difference decreases in large samples, it is important in small samples. Monte Carlo simulations show that the bias can be reduced by 65 − 80% compared to a GMM estimator that neglects the difference between disturbances and residuals. The mean squared error is smaller, too.

This paper suggests an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are di.erent from observable regression residuals. Although this di.erence decreases in large samples, it is important in small samples. Monte Carlo simu­lations show that the bias can be reduced by 65 - 80% compared to a GMM estimator that neglects the difference between disturbances and residuals. The mean squared error is smaller, too.

Macroeconomic data suggest that the New Keynesian Phillips curve is quite flat - despite microeconomic evidence implying frequent price adjustments. While real rigidities may help to account for the conflicting evidence, we propose an alternative explanation: if price markup/cost-push shocks are persistent and negatively correlated with the labor share, the latter being a widely used measure for marginal costs, the estimated pass-through of measured marginal costs into inflation is limited, even if prices are fairly flexible. Using a standard New Keynesian model, we show that the GMM approach to the New Keynesian Phillips curve leads to inconsistent and upward biased estimates if cost-push shocks indeed are persistent. Monte Carlo experiments suggest that the bias is quite sizeable: we find average price durations estimated as high as 12 quarters, when the true value is about 2 quarters. Moreover, alternative estimators appear to be biased as well, while standard diagnostic tests fail to signal a misspecification of the model.

This paper uses the one-factor models proposed by Chan, Karolyi, Longstaff and Sanders (CKLS, 1992) to study the short-term interest rate in China. Nine stochastic models of the short-term interest rate were estimated with GMM. For the Chinese one-month inter bank loan rate, the research finds strong evidence for a mean-reverting feature in the short-term interest yield curve, but no evidence was found to indicate that the volatility is highly positively correlated with the level of interest rates. What is more, evidence was found that the CKLS model, the CIR SR model, and the Brennan-Schwartz model are correctly specified to model the Chinese short-term interest rate, so that these three models are able to adequately capture the dynamics of this interest rate. Finally, Theil’s U2 statistics and the Diebold and Mariano test were applied to explicitly evaluate the predictive power of the single factor models.

This paper discusses the impact of the international transfer of embodied technological change on the employment evolution of skills in a sample of low and middle income countries (LMICs). A large body of literature has already underlined the occurrence of widening wage and employment differentials between skilled and unskilled workers in high-income countries (HICs) (Katz and Autor, 1999). Such empirical evidence is consistent with both trade- and technology-based explanations while these competing theoretical frameworks predict opposite effects on within- country inequality in LMICs. Recent analytical advancements have found convergent elements between these two lines of research, especially in the prediction of the employment impact of technology transfer. However, a systematic lack of data in LMICs still hampers empirical research on the determinants of the witnessed increase in inequality in these economies. This paper provides a direct measure of technology transfer from HICs, that is from those economies which have already experienced the occurrence of skillbiased technological change, to LMICs. GMM techniques are applied to an original panel dataset comprising 28 manufacturing sectors for 23 countries over a decade. Econometric results provide direct robust evidence of the absolute skill-bias effect of technology import in LMICs which, therefore, represents an important determinant of the growing divide between skilled and unskilled workers in these countries.

The evolution of higher moments of the firm size distribution so far seems to be neglected in the empirical firm growth literature. Based on GMM estimates, this paper introduces simple Wald tests to investigate whether the firm size distribution converges in both the second and third central moment. Using a comprehensive sample of Austrian firms, the estimation results indicate a substantial reduction in both the second and third central moment for the younger age cohorts. This effect is much less pronounced for older firms. Across age cohorts one observes an increase in variance, while the third central moment tends to vanish.

This thesis deals with performance and growth in the Swedish banking sector, in an era following important changes such as the globalisation of financial markets, the harmonisation of legislation (e.g. the EU banking directives) and the implementation of new technology, such as Internet banking and other electronic delivery channels. The thesis consists of an introductory chapter and three self-contained papers. Paper 1: Market power and performance in Swedish Banking This paper analyses the degree of competition in the Swedish banking market, over the period 1996-2002. A structural simultaneous-equation model, which includes a conduct parameter, is estimated. The results indicate that the average bank’s conduct was more competitive than Cournot behaviour, although not perfectly competitive. The average Lerner index obtained equals 22%. Furthermore, the results indicate that competition among commercial banks was significantly more intense than among savings banks, despite the formers’ much larger size. This finding may suggest that commercial banks operate in a more contestable environment. Finally, the results show that banks operating more than one office experienced significantly better performance, in terms of higher price-cost margins, than one-office banks (unit banks). Paper 2: Competition in Swedish Local Banking Markets In contrast to urban and metropolitan bank customers, rural bank customers still rely on the physical network (branches) as the prime access channel. This means that high customer loyalty and entry barriers can be expected to prevail in rural banking. Against this background, the paper analyses the degree of competition in rural banking markets, using a variation of the Bresnahan and Reiss entry model. According to the results, entry thresholds increase more than proportionately with each additional entry, suggesting that profit margins shrink as a result of new entry. The resulting pro-competitive effect is most pronounced in markets with a relatively few number of competitors. Finally, the results suggest that a greater share of “multi-market banks” in a given market promotes local competition – a result which parallels a number of international studies. Paper 3: A Dynamic Analysis of Firm Growth in Swedish Banking This paper examines firm growth dynamics in the new banking environment, by testing the validity of Gibrat’s Law of Proportionate Effect on Swedish data. The point of departure in the paper is the expectation that large banks should be able to more fully exploit scale and scope economies associated with technological innovations such as internet banking, than smaller banks, and therefore grow faster. Using a panel of 79 Swedish banks over the period 1995-2002, I find no empirical evidence that large banks grew faster, nor any significant evidence that firm sizes were mean-reverting. Hence the Law was not rejected. However, growth was not entirely random, as banks with a more diversified revenue mix experienced significantly higher growth rates than their less diversified counterparts.

Empirical studies find that changes in schooling are not correlated with changes in per capita income. Similarly, the estimation in levels also produces minor coefficients for years of schooling. Low social returns and measurement error in educational variables have been invoked as possible explanations for such findings. This paper shows that collinearity between physical and human capital stocks seriously undermines the ability of educational indicators to display significance in panel data estimates. On top of that, failure to cope with endogeneity has produced biased estimates. As opposed to the earlier empirical literature, the social return on schooling is positive and significant, but no Lucas-type externalities are observed. Finally, the quality of education emerges as a significant determinant of heterogeneity in social returns across countries.

We examine the ability of the New Keynesian Phillips curve to explain U.S. inflation dynamics when inflation forecasts (from the Federal Reserve’s Greenbook and the Survey of Professional Forecasters) are used as a proxy for inflation expectations. The New Keynesian Phillips curve is estimated against the alternative of the hybrid Phillips curve, which allows for a backward-looking component in the price-setting behavior in the economy. The results are compared with those obtained using actual data on future inflation as conventionally employed in empirical work under the assumption of rational expectations. The empirical evidence provides, in contrast to most of the relevant literature, considerable support for the standard forward-looking New Keynesian Phillips curve when inflation expectations are measured using inflation forecasts that are observable in real time. In this case, lagged-inflation terms become insignificant in the hybrid specification. The evidence in favor of the New Keynesian Phillips curve becomes even stronger when real-time data on lagged inflation are used instead of the final inflation data used in standard specifications. Our work is closely related to the work of Roberts (1997), who used survey measures of inflation expectations in an empirical inflation model and found evidence that it is less-than-perfectly rational expectations and not the underlying structure of the economy that account for the presence of lagged inflation in empirical estimates of the New Keynesian model.

Multifractal processes have recently been proposed as a new formalism for modelling the time series of returns in insurance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns - a feature that has been found in virtually all financial data. Initial difficulties stemming from non-stationarity and the combinatorial nature of the original model have been overcome by the introduction of an iterative Markov-switching multifractal model in Calvet and Fisher (2001) which allows for estimation of its parameters via maximum likelihood and Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility components. From a practical point of view, ML also becomes computationally unfeasible for large numbers of components even if they are drawn from a discrete distribution. Here we propose an alternative GMM estimator together with linear forecasts which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo studies show that GMM performs reasonably well for the popular Binomial and Lognormal models and that the loss incurred with linear compared to optimal forecasts is small. Extending the number of volatility components beyond what is feasible with MLE leads to gains in forecasting accuracy for some time series.

International audience; L'objectif de cet article est d'évaluer la tarification des services d'eau potable français délégués à des opérateurs privés. Les auteurs spécifient un modèle économétrique décrivant à la fois l'offre et la demande en eau potable. La structure de production des services d'eau municipaux est représentée au moyen d'une fonction de coût translog. Une demande en eau potable est spécifiée en prenant en compte les usagers domestiques et industriels. La méthode des moments généralisés (GMM) est utilisée sur des données de panel de services d'eau potable de la région de Bordeaux pour estimer conjointement les équations d'offre et de demande en eau. A partir des estimations des paramètres technologiques et de demande, sont simulées une tarification au coût marginal (tarification de premier rang) et les variations de surplus social. Il y a une différence significative entre les prix marginaux observés et les coûts marginaux. La tarification optimale est d'abord caractérisée par des prix marginaux plus élevés mais aussi par un abonnement plus bas. Toutefois un changement de tarification n'entraîne pas d'importants effets sur le bien-être.

"The author derives and estimates a model for profit maximization of German magazines. Quarterly data on German women's magazines observed between 1998 and 2001 are used in the econometrics. Main empirical results are that magazines with particularly circulation-sensitive advertising prices set cover prices below marginal cost and there are large and highly significant returns to scale and scope in production." (author's abstract) "Diese Arbeit entwickelt und schätzt ein Modell für die Gewinnmaximierung deutscher Zeitschriften. In der ökonometrischen Analyse werden vierteljährliche Daten des Zeitraums 1998 bis 2001 verwendet. Es zeigt sich, dass die Preise von Zeitschriften, deren Anzeigenpreis besonders stark auf Veränderungen in der verkauften Auflage reagieren, deutlich unter den Produktionskosten liegen. Zudem finden sich grosse und hochsignifikante Skalenerträge." (Autorenreferat)

In this paper we investigate GMM-based unit root inference in an autoregressive panel data model with individual-specific levels. We consider tests based on GMM estimators of the AR parameter and moment condition tests. The limiting distributions of the corresponding test statistics are derived when the AR parameter is unity and local-to-unity. This provides information about which statistics lead to valid test procedures. The performance of the valid tests in terms of their local power can then be compared. The results show that the GMM estimator of the AR parameter based on the Arellano-Bover type moment conditions, expressing that lagged differences are used as instruments for the equations in levels, can be used to detect a unit root. On the other hand, the widely used GMM estimator of the AR parameter based on the Arellano-Bond type moment conditions, expressing that lagged levels are used as instruments for the equations in first-differences, can not be used for this purpose. Instead a moment condition test of the hypothesis that the Arellano-Bond type moment conditions do not identify the AR parameter is valid as a unit root test. Finally, a simulation study demonstrates that the local power of the tests provides good approximations of their actual power in finite samples.

A model is considered in which optimal search intensity is a result of a tradeoff between short-run losses due to higher search costs (more interviews, commuting?) and long-run gains due to a higher chance of finding a job. We show that this optimal search intensity is higher in areas characterized by larger cost of living and/or higher labor market tightness. We then test this model using county-level data in England for the period 1991-2000. As predicted by the theoretical model, both the (county) cost of living and the (county) labor market tightness are found to have a positive and significant effect on the (county) search intensity. We also find positive spatial correlation between counties (i.e. clustering of counties with similar level of search intensity) and strong spatial spillover effects.

Basing our empirical work on the British New Earnings Survey Panel Data between 1990 and 1996, we show that overtime hours of male workers contain significant individual effects. We also show that using suitable techniques to deal with the lagged overtime variable serves to alter radically the estimated speed of adjustment of overtime to its desired level. Our results are consistent with firms either guaranteeing the length of weekly overtime or following institutionalised custom and practice in their overtime arrangements. They are far less supportive of traditional demand-side analyses of overtime working.

I derive and estimate a model for cover price setting in print media markets where actors are faced by two interrelated demand curves: the demand for the print medium and the demand for advertising space. Publicly available data on German women?s magazines observed between 1998 and 2001 are used in the GMM estimation. Main findings are that my estimated marginals cost coincide well with those of industry sources, magazines with a high circulation elasticity of advertising demand price markedly below marginal cost and website provision neither has a significant effect on magazine nor on advertising demand. Keywords: magazines, cost estimation, GMM estimation, website provision

Using a panel of 260 Swedish municipalities over the period 1987-1996, this paper investigates the direct displacement effects of active labour market programmes (ALMPs). Compared to earlier studies on this topic, we have more and better data. From our GMM estimations, we find that (i) there are direct displacement effects from those ALMPs that generate subsidised labour (in the order of approximately 65 percent), but there seems to be no (significant) displacement effects from training, (ii) most ALMPs seem to increase labour force participation, and (iii) the adjustment to the optimal level of employment seems to be sluggish. A consequence of (ii) is that the earlier studies have overstated the displacement effects (since they normalised with the labour force).