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    • As to the degree of interest rate smoothing it

    2018-10-30

    As to the degree of interest rate smoothing (), it is believed to follow a beta distribution with a mean of 0.6 and a standard deviation of 0.15, as pointed out by Castro (2011). The other Taylor rule parameters (, and ) are assumed to follow a gamma distribution with a mean of 0.5 and a standard deviation of 0.09 (Palma and Portugal, 2011). In line with Liu and Mumtaz (2011), it is assumed that the elasticity of debt relative to the interest rate premium follows a gamma distribution with a mean of 0.01 and a standard deviation of 0.02. All of the autoregressive parameters of the exogenous disturbances were assumed to follow the beta distribution with a mean of 0.5 and a standard deviation of 0.25. This assumption was also employed by Castro (2011) in the stochastic analytical model with a Bayesian approach (SAMBA). The parameters of the standard deviations of the shocks follow an inverse gamma distribution with a mean of 0.5 and a standard deviation of 10. The probabilities of the transition matrices were based on Liu and Mumtaz (2011) and these parameters were assumed to have a Dirichelet distribution with a mean of 18 and a unit standard deviation. According to these authors, this mean and this standard deviation indicate that the probability of remaining in the same regime is equal to 0.95.
    Results
    Conclusion Unlike other studies that addressed time-invariant parameters, the present paper analyzed the behavior of the major Brazilian economic variables using open-economy DSGE models and allowing for Markov switching in certain parameters estimated by Bayesian methods. The method proposed by Farmer et al. (2008) was used to solve the MS-DSGE model. The method consists in rewriting the model so that it includes fixed parameters with extended states, whose MSV solution written as an MS-VAR solves the original model. The open-economy DSGE model used, developed by Justiniano and Preston (2010), contemplates the interaction of households, domestic and import firms, and the central bank. The model also incorporates characteristics such as habit formation, indexation to past inflation (e.g., sources of rigidity), in addition to a monopolistic melanin with sticky prices for both types of firms. Using a two-step estimation process with Metropolis Hastings algorithm, four models were estimated for the Brazilian economy with quarterly data for 1996–2012: Model 0 – no regime shifts; Model 1 – two regime shifts in the volatility of exogenous shocks; Model 2 – two regime shifts in the volatility of exogenous shocks and in parameters (indexation rate) and (fraction of firms that do not adjust their prices) of the Phillips curve; and Model 3 – two regime shifts in the volatility of exogenous shocks and in the Taylor rule equation parameters. The analysis made in this paper focused on the model with shifts in the monetary policy rule parameters, in the Phillips curve, including independent regime shifts in the volatilities of exogenous shocks, and on a model with Markov switching only in volatilities. However, other regime shifts in other Brazilian economic parameters could be investigated. The extension of the analysis to other parameters of the model used herein or even to other DSGE models is a suggestion for future studies. The same applies to solution methods; despite the fact that the MSV solution used by Farmer et al. (2008) is large, chiasma is not exhaustive.
    Introduction Large informal sectors are a distinctive feature of developing economies. Firms operate informally as a means of avoiding regulations and taxation. However, there are costs to this action, such as lack of access to formal credit markets and to the legal system. Moreover, there is evidence suggesting that firms in the informal sector are also less productive (La Porta and Shleifer, 2008). In the present paper, we explore some elements of this tradeoff by embedding a standard entrepreneurship model (Evans and Jovanovic, 1989) with an informal sector. Specifically, there are two sectors: the formal sector and the informal sector. In the formal sector, entrepreneurs have imperfect access to credit markets and have to pay taxes, while in the informal sector they can evade the payment of taxes but are barred from credit markets. Furthermore, technology is less efficient and more labor intensive in the informal sector.