Infrastructure and productivity in Latin America: is there a relationship in the long run?

This article analyses the relationship between infrastructure and total factor productivity (TFP) in the four major Latin American economies: Argentina, Brazil, Chile and Mexico. We hypothesise that an increase in infrastructure has an indirect effect on long-term economic growth by raising productivity. To assess this theory, we use the traditional Johansen methodology for testing the cointegration between TFP and physical measures of infrastructure stock, such as energy, roads, and telephones. We then apply the Lütkepohl, Saikkonen and Trenkler Test, which considers a possible level shift in the series and has better small sample properties, to the same data set and compare the two tests. The results do not support a robust long-term relationship between the series; we do not find strong evidence that cuts in infrastructure investment in some Latin American countries were the main reason for the fall in TFP during the 1970s and 1980s.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Convergence in Brazil: recent trends and long run prospects

This paper applies to the analysis of the interstate income distribution in BraziI a set of techniques that have been widely used in the current empirical literature on growth and convergence. Usual measures of dispersion in the interstate income distribution (the coefficient of variation and Theil' s index) suggest that cr-convergence was an unequivoca1 feature of the regional growth experience in BraziI, between 1970 and 1986. After 1986, the process of convergence seems, however, to have sIowed down almost to a halt. A standard growth modeI is shown to fit the regional data well and to expIain a substantial amount of the variation in growth rates, providing estimates of the speed of (conditional) J3-convergence of approximateIy 3% p.a .. Different estimates of the long run distribution implied by the recent growth trends point towards further reductions in the interstate income inequality, but also suggest that the relative per capita incomes of a significant number of states and the number of ''very poor" and "poor" states were, in 1995, already quite c10se to their steady-state values.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

The long-term effects of conditional cash transfers on child labor and school enrollment

This paper investigates the long-term e ects of conditional cash transfers on school attainment and child labor. To this end, we construct a dynamic heterogeneous agent model, calibrate it with Brazilian data, and introduce a policy similar to the Brazilian Bolsa Fam lia. Our results suggest that this type of policy has a very strong impact on educational outcomes, sharply increasing primary school completion. The conditional transfer is also able to reduce the share of working children from 22% to 17%. We then compute the transition to the new steady state and show that the program actually increases child labor over the short run, because the transfer is not enough to completely cover the schooling costs, so children have to work to be able to comply with the program's schooling eligibility requirement. We also evaluate the impacts on poverty, inequality, and welfare.

International migration, capital flows and the global economy: a long run view

Includes bibliography

Long run economic development in Latin America in a comparative perspective: proximate and ultimate causes

Includes bibliography

Clinical predictors of long-term outcome in obsessive-compulsive disorder

Background: The purpose of this study was to investigate demographic and clinical factors associated with the long-term outcome of obsessive-compulsive disorder (OCD). Methods: A hundred ninety-six previously untreated patients with DSM-IV criteria OCD completed a 12-week randomized open trial of group cognitive-behavioral therapy (GCBT) or fluoxetine, followed by 21 months of individualized, uncontrolled treatment, according to international guidelines for OCD treatment. OCD severity was assessed using the Yale–Brown Obsessive-Compulsive Scale (Y-BOCS) at different times over the follow-up period. Demographics and several clinical variables were assessed at baseline. Results: Fifty percent of subjects improved at least 35% from baseline, and 21.3% responded fully (final Y-BOCS score < or = 8). Worse prognosis was associated with earlier age at onset of OCD (P = 0.045), longer duration of illness (P = 0.001) presence of at least one comorbid psychiatric disorder (P = 0.001), comorbidity with a mood disorder (P = 0.002), higher baseline Beck-Depression scores (P = 0.011), positive family history of tics (P = 0.008), and positive family history of anxiety disorders (P = 0.008). Type of initial treatment was not associated with long-term outcome. After correction for multiple testing, the presence of at least one comorbid disorder, the presence of a depressive disorder, and duration of OCD remained significant. Conclusions: Patients under cognitive-behavioral or pharmacological treatment improved continuously in the long run, regardless of initial treatment modality or degree of early response, suggesting that OCD patients benefit from continuous treatment. Psychiatric comorbidity, especially depressive disorders, may impair the long-term outcome of OCD patients.

Memory of childbirth in the second year: the long-term effect of a negative birth experience and its modulation by the perceived intranatal relationship with caregivers

OBJECTIVE: To assess the memory of various subdimensions of the birth experience in the second year postpartum, and to identify women in the first weeks postpartum at risk of developing a long-term negative memory. DESIGN, METHOD, OUTCOME MEASURES: New mothers' birth experience (BE) was assessed 48-96 hours postpartum (T1) by means of the SIL-Ger and the BBCI (perception of intranatal relationships); early postnatal adjustment (week 3 pp: T1(bis)) was also assessed. Then, four subgroups of women were defined by means of a cluster-analysis, integrating the T1/T1(bis) variables. To evaluate the memory of the BE, the SIL-Ger was again applied in the second year after childbirth (T2). First, the ratings of the SIL-Ger dimensions of T1 were compared to those at T2 in the whole sample. Then, the four subgroups were compared with respect to their ratings of the birth experience at T2 (correlations, ANOVAs and t-tests). RESULTS: In general, fulfillment, emotional adaptation, physical discomfort, and anxiety improve spontaneously over the first year postpartum, whereas in negative emotional experience, control, and time-going-slowly no shift over time is observed. However, women with a negative overall birth experience and a low level of perceived intranatal relationship at T1 run a high risk of retaining a negative memory in all of the seven subdimensions of the birth experience. CONCLUSIONS: Women at risk of developing a negative long-term memory of the BE can be identified at the time of early postpartum, when the overall birth experience and the perceived intranatal relationship are taken into account.

Do avoidance goals always impair performance? How neuroticism moderates the short- and long-term effects of avoidance versus approach goals on performance

Previous research agrees that approach goals have positive effects whereas avoidance goals have negative effects on performance. By contrast, the present chapter looks at the conditions under which even avoidance goals may have positive effects on performance. We will first review the previous research that supports the positive consequences of avoidance goals. Then we will argue that the positive and negative consequences of approach and avoidance goals on performance depend on an individual‘s neuroticism level and the time frame of their goal striving. Because neuroticism is positively related to avoidance goals, we assume that individuals with high levels of neuroticism may derive some benefits from avoidance goals. We have specified this assumption by hypothesizing that the fit between an individual‘s level of neuroticism and their avoidance goals leads to favorable consequences in the short term – but to negative outcomes in the long run. A short-term, experimental study with employees and a long-term correlative field study with undergraduate students were conducted to test whether neuroticism moderates the short- and long-term effects of avoidance versus approach goals on performance. Experimental study 1 showed that individuals with a high level of neuroticism performed best in the short term when they were assigned to avoidance goals, whereas individuals with a low level of neuroticism performed best when pursuing approach goals. However, study 2 indicated that in the long run individuals with a high level of neuroticism performed worse when striving for avoidance goals, whereas individuals with a low level of neuroticism were not impaired at all by avoidance goals. In summary, the pattern of results supports the hypothesis that a fit between a high level of neuroticism and avoidance goals has positive consequences in the short term, but leads to negative outcomes in the long run. We strongly encourage further research to investigate short- and long-term effects of approach and avoidance goals on performance in conjunction with an individual‘s personality, which may moderate these effects.