Volume 23, Issue 5

This paper discusses Nadaraya-Watson estimators for the unknown coefficients in second-order diffusion model with jumps constructed with Gamma asymmetric kernels. Compared with existing nonparametric estimators constructed with Gaussian symmetric kernels, local constant smoothing using Gamma asymmetric kernels possesses some extra advantages such as boundary bias correction, variance reduction and resistance to sparse design points, which is validated through theoretical details and finite sample simulation study. Under the regular conditions, the weak consistency and the asymptotic normality of these estimators are presented. Finally, the statistical advantages of the nonparametric estimators are depicted through 5-minute high-frequency data from Shenzhen Stock Exchange in China.

The literature of time series models with threshold effects makes the assumption of a constant threshold value over different periods. However, this time-homogeneity assumption tends to be too restrictive owing to the fact that the threshold value that triggers regime switching could possibly be time-varying. This study herein proposes a threshold model in which the threshold value is assumed to be a latent variable following an autoregressive (AR) process. The newly proposed model was estimated using a Markov Chain Monte Carlo (MCMC) algorithm under a Bayesian framework. The Monte Carlo simulations are presented to assess the effectiveness of the Bayesian approaches. An illustration of the model was made through an application to a regime-sensitive Taylor rule employing U.S. data.

We investigate the dynamics of a cobweb model with heterogeneous expectations, generalizing the famous model of Brock and Hommes (Brock, W. A., and C. H. Hommes. 1997. “A Rational Route to Randomness.” Econometrica 65 (5): 1059–1095) by adding a new predictor – periodical expectation – which predicts that the price will revert to its level two periods previously. We show that under the periodical expectation, when the agents are homogeneous, they will always make wrong predictions, whereas when the agents are heterogeneous, they may be either smart or stupid depending on their counterparts’ types. Moreover, in the cobweb framework, the periodical predictor may be a destabilizer in some cases, but a stabilizer in other cases. We also find that in the cobweb model with rational, naive and periodical expectations, under certain refined conditions, the rational predictor may be endogenously dominated by naive and periodical predictors.

By applying the Sherman–Morrison–Woodbury (SMW) formula and a discrete cosine transformation matrix, De Jong and Sakarya [De Jong, R. M., and N. Sakarya. 2016. “The Econometrics of the Hodrick–Prescott Filter.” Review of Economics and Statistics 98 (2): 310–317] recently derived an explicit formula for the smoother weights of the Hodrick–Prescott filter. More recently, by applying the SMW formula and the spectral decomposition of a symmetric tridiagonal Toeplitz matrix, Cornea-Madeira [Cornea-Madeira, A. 2017. “The Explicit Formula for the Hodrick–Prescott Filter in Finite Sample.” Review of Economics and Statistics 99: 314–318] provided a simpler formula. This paper provides an alternative simpler formula for it and explains the reason why our approach leads to a simpler formula.

I propose a simple skewness-based test of symmetry suitable for a stationary time series. The test is based on the difference between the squared deviation of a process above its median with that below it. The test has many attractive features: it is applicable to weakly dependent processes, it has a familiar form, it can be implemented using regression, and it has a standard Gaussian limiting distribution under the null hypothesis of symmetry. The finite sample properties of the test statistic are examined via Monte Carlo simulation and suggest that it has better size-adjusted power compared to competing tests in the literature when examining moderately persistence processes. I apply the test to a range of US economic and financial data and find stronger support for asymmetry in financial series compared to economic series.

Drawing on Harrod, Kalecki and Kaldor, this paper seeks to revive the view that ceteris paribus firms reduce investment if they have already built up high capacities relative to their assessment of the normal market potential. This reaction establishes a fundamental stabilizing mechanism for the economy. The paper adapts the idea to the growth context of a neo-Kaleckian baseline model, where its destabilizing Harrodian sentiment adjustments are specified in a canonical and microfounded way that introduces a natural nonlinearity into the global dynamics. Supposing local instability, the resulting two-dimensional system is shown to generate persistent self-sustaining cyclical behaviour. In addition, the model is numerically calibrated to period and amplitude of the US business cycle. On the whole, based on an old and almost forgotten conception, the paper advances a most elementary approach to business cycle modelling.