Can a habit formation model really explain the Forward Premium Anomaly?

Verdelhan (2009) mostra que desejando-se explicar o comporta- mento do prêmio de risco nos mercados de títulos estrangeiros usando- se o modelo de formação externa de hábitos proposto por Campbell e Cochrane (1999) será necessário especi car o retorno livre de risco de equilíbrio de maneira pró-cíclica. Mostramos que esta especi cação só é possível sobre parâmetros de calibração implausíveis. Ainda no processo de calibração, para a maioria dos parâmetros razoáveis, a razão preço-consumo diverge. Entretanto, adotando a sugestão pro- posta por Verdelhan (2009) - de xar a função sensibilidade (st) no seu valor de steady-state durante a calibração e liberá-la apenas du- rante a simulação dos dados para se garantir taxas livre de risco pró- cíclicas - conseguimos encontrar um valor nito e bem comportado para a razão preço-consumo de equilíbrio e replicar o foward premium anom- aly. Desconsiderando possíveis inconsistências deste procedimento, so- bre retornos livres de risco pró-cíclicos, conforme sugerido por Wachter (2006), o modelo utilizado gera curvas de yields reais decrescentes na maturidade, independentemente do estado da economia - resultado que se opõe à literatura subjacente e aos dados reais sobre yields.

Can a habit formation model really explain the forward premium anomaly?

Verdelhan (2009) shows that if one is to explain the foreign exchange forward premium behavior using Campbell and Cochrane (1999)’s habit formation model one must specify it in such a way to generate pro-cyclical short term risk free rates. At the calibration procedure, we show that this is only possible in Campbell and Cochrane’s framework under implausible parameters specifications given that the price-consumption ratio diverges in almost all parameters sets. We, then, adopt Verdelhan’s shortcut of fixing the sensivity function λ(st) at its steady state level to attain a finite value for the price-consumption ratio and release it in the simulation stage to ensure pro-cyclical risk free rates. Beyond the potential inconsistencies that such procedure may generate, as suggested by Wachter (2006), with procyclical risk free rates the model generates a downward sloped real yield curve, which is at odds with the data.

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.

The life-cycle effects of social security in a model with housing and endogenous benefits

This article develops a life-cycle general equilibrium model with heterogeneous agents who make choices of nondurables consumption, investment in homeowned housing and labour supply. Agents retire from an specific age and receive Social Security benefits which are dependant on average past earnings. The model is calibrated, numerically solved and is able to match stylized U.S. aggregate statistics and to generate average life-cycle profiles of its decision variables consistent with data and literature. We also conduct an exercise of complete elimination of the Social Security system and compare its results with the benchmark economy. The results enable us to emphasize the importance of endogenous labour supply and benefits for agents' consumption-smoothing behaviour.

A conditional likelihood ratio test for structural models

This paper develops a general method for constructing similar tests based on the conditional distribution of nonpivotal statistics in a simultaneous equations model with normal errors and known reducedform covariance matrix. The test based on the likelihood ratio statistic is particularly simple and has good power properties. When identification is strong, the power curve of this conditional likelihood ratio test is essentially equal to the power envelope for similar tests. Monte Carlo simulations also suggest that this test dominates the Anderson- Rubin test and the score test. Dropping the restrictive assumption of disturbances normally distributed with known covariance matrix, approximate conditional tests are found that behave well in small samples even when identification is weak.

Debt and default in a growth model

This paper presents a small open economy model with capital accumulation and without commitment to repay debt. The optimal debt contract specifies debt relief following bad shocks and debt increase following good shocks and brings first order benefits if the country's borrowing constraint is binding. Countries with less capital (with higher marginal productivity of capital) have a higher debt-GDP ratio, are more likely to default on uncontingent bonds, require higher debt relief after bad shocks and pay a higher spread over treasury. Debt relief prescribed by the optimal contract following the interest rate hikes of 1980-81 is more than half of the debt forgiveness obtained by the main Latin American countries through the Brady agreements.

Consumption-wealth ratio and expected stock returns: evidence from panel data

This paper investigates the role of consumption-wealth ratio on predicting future stock returns through a panel approach. We follow the theoretical framework proposed by Lettau and Ludvigson (2001), in which a model derived from a nonlinear consumer’s budget constraint is used to settle the link between consumption-wealth ratio and stock returns. Using G7’s quarterly aggregate and financial data ranging from the first quarter of 1981 to the first quarter of 2014, we set an unbalanced panel that we use for both estimating the parameters of the cointegrating residual from the shared trend among consumption, asset wealth and labor income, cay, and performing in and out-of-sample forecasting regressions. Due to the panel structure, we propose different methodologies of estimating cay and making forecasts from the one applied by Lettau and Ludvigson (2001). The results indicate that cay is in fact a strong and robust predictor of future stock return at intermediate and long horizons, but presents a poor performance on predicting one or two-quarter-ahead stock returns.