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In this chapter, using a combination of long-run and sign restrictions to identify aggregate monetary and productivity factors, I find that the monetary factor is responsible for long swings in nominal variables but has little effect on fluctuations in output, real wage, or labor input growth. The productivity factor in addition to increasing output growth and real wage growth in the short and long run, also results in increases in labor input and decreases in prices, but the quantitative effect of the productivity factor on labor input is relatively small. These results are robust to the number of factors included in the model and to alternative priors about the short-run effects of the monetary factor, and to the inclusion of oil prices. Oil prices, in fact, appear to be largely driven by the other aggregate factors.

This paper aims to examine the effects of fiscal policy associated with increases in government expenditures, tax revenue and budget deficit on the South African economy.

Structural VARs based on the Blanchard‐Quard decomposition identification scheme were used in the empirical analysis. With the aid of quarterly data covering the period 1990:1 to 2008:4, the identified true models are used to estimate various impulse‐response functions. The impulse‐response functions represent the responses of real output and interest rates to shocks from tax revenue, budget deficit and government consumption and investment expenditures.

The results suggest that the fiscal policy instruments have varied effects on output and interest rates. The effect of the fiscal policy on output appears to be quite modest but persistent; however, the response from interest rate is temporary and substantial most cases.

The debate on the efficacy of fiscal policy in stimulating growth seems to have assumed new prominence in the wake of the recent global financial crisis. This paper contributes to the discourse from a South African focused empirical effort. Other fiscal policy authorities may find the paper valuable.

What impact did privatization have on employment in Russian industry? Utilizes data collected from a panel of 6,205 civilian manufacturing firms in the Central, Volga, North Caucasus, Northern and Western Siberian regions of Russia to explore in more detail the relationship between changes in ownership and employment in Russian industry between 1992 and 1995. In particular investigates whether change in ownership structure is relatively more important than industry, region, or the competitive position of the firm in explaining variation in the employment response to changing output conditions during the initial stage of Russia’s transition from plan to market.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

Scatter in the outcome of repeated experiments is unavoidable due to measurement errors in addition to the non-linear nature of the output responses with unknown influential input parameters. It is a standard practice to select an orthogonal array in the Taguchi approach for tracing optimum input parameters by conducting a few number of experiments and confirm them through additional experimentation (if necessary). The purpose of this paper is to present a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

The modified Taguchi approach is proposed to find the optimum process parameters and the expected range of the output response.

This paper presents a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

Adequacy of this methodology should be examined by considering the test data on different materials and structures.

The introduction of Chauvenet’s criterion and opposing the signal-to-noise ratio transformation on repeated experiments of each test run will provide fruitful results and less computation burden.

Provides an evaluation of the reality of the German economy after unification, also answers to some of the questions that the post‐unification era has raised, analyzes aggregate and sectoral data of the former GDR and the Federal Republic of Germany over the period 1970‐1989. The results characterize the former GDR with a steeper supply curve. While the central plan assumed a steady growth of real output over time, it eliminated producers’ incentives to vary capacity utilization in response to demand pressures. Demand pressures proved inflationary without determining conditions in the labor market. In contrast, the market‐oriented plan in West Germany tied output expansion and contraction with demand fluctuations. Consequently, inflationary effects of demand fluctuations appeared moderate in West Germany and real output growth was not sustained at a high level over time. Demand fluctuations determined employment changes in West Germany. Implications of these differences are analyzed in light of the reality of the post‐unification in Germany.

Metamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters.

In this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.

This new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.

This paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

The study investigates the impact of external shocks on output composition (consumption and investment) in Nigeria for the period 1981:Q1– 2018:Q4. Trade-weighted variables from the country's five major trading partners are constructed to capture the impact.

The study employs a block exogeneity open economy structural vector autoregressive (SVAR) analysis in studying the stated relationship.

The study reveals that external shocks significantly affect consumption and investment in Nigeria. Results from the structural impulse response function suggest that foreign output, real effective exchange rate and foreign interest rate have significant negative effects on consumption and investment. Specifically, results from error variance decomposition show that foreign inflation and real effective exchange rate shocks are major drivers of fluctuations in consumption and investment in Nigeria. Interestingly, the study finds that oil price shock accounts for minor variations in consumption and investment in Nigeria.

The findings suggest that consumption and investment in Nigeria are substantially and largely driven by external shocks.

There is need for the monetary authority and the Nigerian government to design appropriate policies to stabilise the naira and salvage the country's exchange rate from unexpected large swings so as to reduce the vulnerability of the economy to external shocks.

Previous studies on external shocks have concentrated on the impact of external shocks on aggregate variables such as output and inflation, while few studies on external shocks in Nigeria capture external shocks through single-country data. This study differs from previous similar studies in Nigeria in two ways. First, the study examines the impact of external shocks on output composition such as consumption and investment. Second, the study captures the impact of external shocks on the two components of gross domestic product (GDP) by constructing trade-weighted variables from Nigeria's five major trading partners.

To provide a good insight into solving a multi‐response optimization problem using neuro‐fuzzy model and Taguchi method of experimental design.

Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision‐making process. In this paper, a four‐step procedure is proposed to resolve the parameter design problem involving multiple responses. This approach employs the advantage of both artificial intelligence tool (neuro‐fuzzy model) and Taguchi method of experimental design to tackle problems involving multiple responses optimization.

The proposed methodology is validated by revisiting a case study to optimize the three responses for a double‐sided surface mount technology of an electronic assembly. Multiple signal‐to‐noise ratios are mapped into a single performance statistic through neuro‐fuzzy based model, to identify the optimal level settings for each parameter. Analysis of variance is finally performed to identify parameters significant to the process.

The proposed model will be validated in future by conducting a real life case study, where multiple responses need to be optimized simultaneously.

It is believed that the proposed procedure in this study can resolve a complex parameter design problem with multiple responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready‐made neural and statistical software like Neuro Work II professional and Minitab.

This study adds to the literature of multi‐optimization problem, where a combination of the neuro‐fuzzy model and Taguchi method is utilized hand‐in‐hand.