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The paper aims to present a design and numerical verification procedure of a composite casing of a microstrip antenna for an aerospace satellite.

The casing for the microstrip antenna was designed in a form of a laminate shell with variable number of layers of reinforcing fabric. The material properties, both static and dynamic, were determined experimentally and then exported to an environment of numerical analyses. The numerical modal analysis allows optimizing the geometry and lay-up of the casing in such a way that a number of modal shapes occurring in the operational frequency band was significantly reduced, several modal shapes with high displacement in flanges of the casing were eliminated and the values of natural frequencies were increased. A final model of the composite casing was subjected to two types of analyses which simulate typical operation conditions during spacecraft mission. These analyses contained thermomechanical quasi-static analyses with 12 loadcases and thermomechanical shock analyses with 9 loadcases, which simulate various mechanical and temperature conditions.

Results of the performed analyses were compared with safety margins determined by following requirements to spacecraft vehicles. The obtained results confirm the design feasibility, which allow considering the proposed design during manufacturing of a prototype in further studies.

Moreover, the presented results can be considered as a design methodology guideline, which can be helpful for engineers working in the aerospace industry.

The originality of the paper lies in the proposed design and verification procedure of composite elements subjected to operational loading during a spacecraft mission.

Seminar (Italian/English): ‘Trends in Materials and Manufacturing of PCBs’

The purpose of this paper is to develop robust metamodels, which allow propagating parametric uncertainties, in the presence of localized nonlinearities, with reduced cost and without significant loss of accuracy.

The proposed metamodels combine the generalized polynomial chaos expansion (gPCE) for the uncertainty propagation and reduced order models (ROMs). Based on the computation of deterministic responses, the gPCE requires prohibitive computational time for large-size finite element models, large number of uncertain parameters and presence of nonlinearities. To overcome this issue, a first metamodel is created by combining the gPCE and a ROM based on the enrichment of the truncated Ritz basis using static residuals taking into account the stochastic and nonlinear effects. The extension to the Craig–Bampton approach leads to a second metamodel.

Implementing the metamodels to approximate the time responses of a frame and a coupled micro-beams structure containing localized nonlinearities and stochastic parameters permits to significantly reduce computation cost with acceptable loss of accuracy, with respect to the reference Latin Hypercube Sampling method.

The proposed combination of the gPCE and the ROMs leads to a computationally efficient and accurate tool for robust design in the presence of parametric uncertainties and localized nonlinearities.

The chapter considers parameter identification, estimation, and model diagnostics in medium scale DSGE models from a frequency domain perspective using the framework developed in Qu and Tkachenko (2012). The analysis uses Smets and Wouters (2007) as an illustrative example, motivated by the fact that it has become a workhorse model in the DSGE literature. For identification, in addition to checking parameter identifiability, we derive the non-identification curve to depict parameter values that yield observational equivalence, revealing which and how many parameters need to be fixed to achieve local identification. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the nonparametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture. Overall, the results suggest that the frequency domain based approach, in part due to its ability to handle subsets of frequencies, constitutes a flexible framework for studying medium scale DSGE models.

The purpose of this paper is to examine the effects of exchange rate shock on the broad spectrum of the US economy using a factor-augmented VAR model (FAVAR).

The authors developed a two-factor FAVAR model and estimated it with the single-step Bayesian likelihood approach using the Gibbs sampling technique. The two factors represented, respectively, the economic activity and price pressures. The exchange rate shock was identified with the Choleski decomposition method for VARs. The authors used the data of 117 time series for the period from 1973:02 to 2007:12. Impulse responses and variance decompositions were computed as the main results.

The authors found that exchange rate shock has pervasive effects on the US economy as the following: depreciation does not appear to help reduce the US trade deficit as both import and export rise with the depreciation shock; in the short run, depreciation appears expansionary as industrial production, manufacturing and employment all increase within a year; in the medium run, depreciation appears inflationary, as consumer price, producer price, import price and export price all increase; and in the medium run, depreciation appears contractionary as personal consumption, consumer confidence, stock price and housing start tend to fall.

Some caveats remain: first, our simple model symmetrically estimates depreciation shock and appreciation shock and, hence, cannot draw inferences for how exchange rate appreciation and depreciation may affect the US economy asymmetrically. Second, the simple model used did not distinguish the different possible sources of exchange rate depreciation shock, the knowledge of which may lead to richer policy implications and is the direction of research for the future.

This research contributes to the literature of whether exchange rate is expansionary or contractionary to the US economy using the FAVAR model. This is the first comprehensive study in the literature studying the pervasive effects of the exchange rate on the broad spectrum of the US economy in one integrated model.

A vector autoregression model estimated on US data before and after 1980 documents systematic differences in the response of short- and long-term interest rates, corporate bond spreads and durable spending to news total factor productivity shocks. Interest rates across the maturity spectrum broadly increase in the pre-1980s and broadly decline in the post-1980s. Corporate bond spreads decline significantly, and durable spending rises significantly in the post-1980 period while the opposite short-run response is observed in the pre-1980 period. Measuring expectations of future monetary policy rates conditional on a news shock suggests that the Federal Reserve has adopted a restrictive stance before the 1980s with the goal of retaining control over inflation while adopting a neutral/accommodative stance in the post-1980 period.

This study undertakes a comprehensive investigation into the far-reaching repercussions of Covid-19 stimulus packages and containment policies on stock returns, meticulously examining a diverse array of 14 distinct markets.

This study employed the Panel SVAR model to analyze the relationships between various policies and stock market performance during the Covid-19 outbreak. The sample comprises 5432 daily observations spanning from December 2020 to January 2022 for the 14 selected markets, with missing data excluded.

The findings reveal three consistent impacts across all 14 markets. Firstly, stock returns immediately reversed and decreased within a day when Governments tightened containment policies. Secondly, economic stimulus packages led to a fall in stock returns. Thirdly, an increasing death rate caused the stock return to decrease in the following two days. These findings are supported by the uniform impulse responses in all three shocks, including common, composite and idiosyncratic shocks. Furthermore, all inverse root tests satisfy the stability conditions, indicating the stability and reliability of Panel SVAR estimations.

One vital implication is that all government decisions and measures taken against the shock of Covid-19 must consider economic impacts to avoid unnecessary financial losses and support the effective functioning of stock markets during similar shocks. Secondly, investors should view the decline in stock returns due to Covid-19 effects as temporary, resulting from anxiety about the outbreak. The study highlights the importance of monitoring the impact of policies on financial markets and the broader economy during crises. Overall, these insights can prove helpful for investment decisions and policymaking during future crises.

This study constitutes a noteworthy addition to the literature on behavioural finance and the efficient market hypothesis, offering a meticulous analysis of the multifaceted repercussions of Covid-19 on market interactions. In particular, it unveils the magnitude, duration and intricate patterns of market volatilities linked to significant shock events, encompassing a comprehensive dataset spanning 14 distinct markets.