Volume 26, Issue 4

In this paper multivariate State Space (SS) models are used to evaluate and forecast household loans in Brazil, taking into account two Google search terms in order to identify credit demand: financiamento (type of loan used to finance goods) and empréstimo (a more general type of loan). Our framework is coupled with nonlinear features, such as Markov-switching and threshold point. We explore these nonlinearities to build identification strategies to disentangle the supply and demand forces which drive the credit market to equilibrium over time. We also show that the underlying nonlinearities significantly improves the performance of SS models on forecasting the household loans in Brazil, particularly in short-term horizons.

Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; the Poisson, the linear and quadratic negative binomial, the double Poisson and the generalized Poisson. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by calculating point and density forecasts.

This paper analyzes the reaction function of monetary authority in India from 1997 Q 1 to 2019 Q 4 using nonlinear Taylor rule. It has been found that monetary policy reaction function (MPRF) in India is asymmetric and is influenced by the state of the economy, determined by the lagged interest rate. To capture such asymmetry, we have used a set of nonlinear models including smooth transition regression (STR) model, threshold regression (TR) model and Markov-switching regression (MSR) model along with the instrumental variable estimation technique. The analysis discloses that the behaviour of the Reserve Bank of India (RBI) is asymmetric, reacts aggressively to output gap in general and particularly during periods of high interest rate. Furthermore, the RBI reacts more to inflation and output gap during low volatile regimes in MSR models compared to high volatile regimes. We also found that there is a high degree of inertia in the policy rates of the RBI. The study concludes that nonlinear models may not only help in understanding the behaviour of the RBI but also prevent from making incorrect and misleading conclusions in Indian context.

We introduce a new mixture autoregressive model which combines Gaussian and Student’s t mixture components. The model has very attractive properties analogous to the Gaussian and Student’s t mixture autoregressive models, but it is more flexible as it enables to model series which consist of both conditionally homoscedastic Gaussian regimes and conditionally heteroscedastic Student’s t regimes. The usefulness of our model is demonstrated in an empirical application to the monthly U.S. interest rate spread between the 3-month Treasury bill rate and the effective federal funds rate.

This paper introduces an unbiased estimator based on least squares involving time-specific cross-sectional averages for a first-order panel autoregression with a strictly exogenous covariate. The proposed estimator is straightforward to implement as long as the variables of interest have sufficient time variation. The number of cross-sections ( N ) and the number of time periods ( T ) can be large, and there is no restriction on the growth rate of N relative to T . It is demonstrated via both theory and a simulation study that the estimator is asymptotically unbiased, and it can provide correct empirical coverage probabilities for the ‘true’ coefficients of the model for various combinations of N and T . An empirical application is also provided to confirm the feasibility of the proposed approach.

This paper introduces rescaled variance [V/S] tests for seasonal stationarity. The V/S statistic is designed by Giraitis, L., P. Kokoszka, R. Leipus, and G. Teyssière. 2003. “Rescaled Variance and Related Tests for Long Memory in Volatility and Levels.” Journal of Econometrics 112: 265–94 to be the mean corrected versions of the KPSS statistic. In the seasonal context, Canova, F., and B. E. Hansen. 1995. “Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability.” Journal of Business & Economic Statistics 13: 237–52 present the seasonal generalization of the KPSS statistic. In this regard, I aim to strengthen the work of Canova, F., and B. E. Hansen. 1995. “Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability.” Journal of Business & Economic Statistics 13: 237–52 [CH] by mean correction in the seasonal framework. I obtain the asymptotic distributions of the seasonal V/S tests. The V/S tests enjoy better power performance than the CH tests while exhibiting similiar size performance. Furthermore, by data pre-filtering, I propose robustified versions of the V/S statistics to eliminate the unattended unit root problem observed in the CH tests.