Fiscal Policy and Economic Stability: Does PIGS stand for Procyclicality In Government Spending?

The Financial Crisis has hit particularly hard countries like Ireland or Spain. Procyclical fiscal policy has contributed to a boom-bust cycle that undermined fiscal positions and deepened current account deficits during the boom. We set up an RBC model of a small open economy, following Mendoza (1991), and introduce the effect of fiscal policy decisions that change over the cycle. We calibrate the model on data for Ireland, and simulate the effect of different spending policies in response to supply shocks. Procyclical fiscal policy distorts intertemporal allocation decisions. Temporary spending boosts in booms spur investment, and hence the need for external finance, and so generates very volatile cycles in investment and the current account. This economic instability is also harmful for the steady state level of output. Our model is able to replicate the relation between the degree of cyclicality of fiscal policy, and the volatility of consumption, investment and the current account observed in OECD countries.

Stability and Limit Oscillations of a Control Event-Based Sampling Criterion

This paper investigates the presence of limit oscillations in an adaptive sampling system. The basic sampling criterion operates in the sense that each next sampling occurs when the absolute difference of the signal amplitude with respect to its currently sampled signal equalizes a prescribed threshold amplitude. The sampling criterion is extended involving a prescribed set of amplitudes. The limit oscillations might be interpreted through the equivalence of the adaptive sampling and hold device with a nonlinear one consisting of a relay with multiple hysteresis whose parameterization is, in general, dependent on the initial conditions of the dynamic system. The performed study is performed on the time domain.

Seasonal Stability Tests in gretl. An Application to International Tourism Data

The seasonal stability tests of Canova & Hansen (1995) (CH) provide a method complementary to that of Hylleberg et al. (1990) for testing for seasonal unit roots. But the distribution of the CH tests are unknown in small samples. We present a method to numerically compute critical values and P-values for the CH tests for any sample size and any seasonal periodicity. In fact this method is applicable to the types of seasonality which are commonly in use, but also to any other.

Efficiency vs. Stability in a Mixed Network Formation Model

Documentos de Trabajo

Stability analysis of RF amplifiers based on MIMO pole-zero identification

Spurious oscillations are one of the principal issues faced by microwave and RF circuit designers. The rigorous detection of instabilities or the characterization of measured spurious oscillations is still an ongoing challenge. This project aims to create a new stability analysis CAD program that tackles this chal- lenge. Multiple Input Multiple Output (MIMO) pole-zero identification analysis is introduced on the program as a way to create new methods to automate the stability analysis process and to help designers comprehend the obtained results and prevent incorrect interpretations. The MIMO nature of the analysis contributes to eliminate possible controllability and observability losses and helps differentiate mathematical and physical quasi-cancellations, products of overmodeling. The created program reads Single Input Single Output (SISO) or MIMO frequency response data, and determines the corresponding continuous transfer functions with Vector Fitting. Once the transfer function is calculated, the corresponding pole/zero diagram is mapped enabling the designers to analyze the stability of an amplifier. Three data processing methods are introduced, two of which consist of pole/zero elimina- tions and the latter one on determining the critical nodes of an amplifier. The first pole/zero elimination method is based on eliminating non resonant poles, whilst the second method eliminates the poles with small residue by assuming that their effect on the dynamics of a system is small or non-existent. The critical node detection is also based on the residues; the node at which the effect of a pole on the dynamics is highest is defined as the critical node. In order to evaluate and check the efficiency of the created program, it is compared via examples with another existing commercial stability analysis tool (STAN tool). In this report, the newly created tool is proved to be as rigorous as STAN for detecting instabilities. Additionally, it is determined that the MIMO analysis is a very profitable addition to stability analysis, since it helps to eliminate possible problems of loss of controllability, observability and overmodeling.