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Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt–Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000–December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018–2020. The forecast of unemployment rate relies on the next two years, 2021–2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt–Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold–Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.

Over the last three decades the supply of economic statistics has vastly improved. Unfortunately, statistics on regional price levels (sub-national purchasing power parities) have been exempt from this positive trend, even though they are indispensable for meaningful spatial comparisons of regional output, income, wages, productivity, standards of living, and poverty. To improve the situation, our paper demonstrates that a highly disaggregated and reliable regional price index can be compiled from data that already exist. We use the micro price data that have been collected for Germany's Consumer Price Index in May 2016. For the computation we introduce a multi-stage version of the Country- Product-Dummy method. The unique quality of our price data set allows us to depart from previous spatial price comparisons and to compare only exactly identical products. We find that the price levels of the 402 counties and cities of Germany are largely driven by the cost of housing and to a much lesser degree by the prices of goods and services. The overall price level in the most expensive region, Munich, is about 27 percent higher than in the cheapest region. Our results also reveal strong spatial autocorrelation.

Analyzing the poverty and distributional impact of macro events requires understanding how shocks or policy changes on the macro level affect household income and consumption. It is clear that this poses a formidable task, which of course raises the question of the appropriate methodology to address such questions. This paper presents one possible approach: A sequential methodology that combines a macroeconomic model with a behavioral micro-simulation. We discuss the merits and shortcomings of this approach with a focus on developing country applications with a short to medium run time horizon. - This chapter is a re-print of: Lay, J. (2010). Sequential macro-micro modelling with behavioural microsimulations. International Journal of Microsimulation, 3(1), 24-34.

Although the causal effect of social performance on financial performance is a critical issue for companies and their stakeholders, there has been no consistent econometric ap­proach in the relevant literature to examine this relationship yet. From this point of view, the main motivation of this study is twofold: first, it aims to reveal the differential results of static and dynamic panel data methods used to estimate the impact of corporate social performance (CSP) on corporate financial performance (CFP). Second, in order to take the initiative for a consistent and reliable estimation method of the causal relationship between CSP and CFP, this study aims at drawing attention to the challenges of system generalized method of moments, which is suggested as an efficient method to solve the endogeneity problem in dynamic models. To this end, the impact of CSP on CFP for a sample of BRICS countries was analyzed through both static and dynamic panel data specifications. The main results reveal that static panel data models estimated with pooled OLS, random and fixed effects result in inconsistent and biased parameter estimates. This study discusses that although the two-step system GMM is suggested as a reliable method to deal with the endo­geneity issue, some critical specifications should be considered while utilizing this method to achieve robust and efficient results., methods, data, analyses, Online First

"Econometric modelling of the property market has been exercised for several decades. Despite advancements in the field, there is still an element of uncertainty in property market modelling and forecasting. This uncertainty arises due to prevailing modelling practices. On one hand, researchers select the best performing model and disregard alternatives. On the other hand, researchers face a dilemma in deciding which model to choose when competing specifications produce different results. A possible solution is to use the principle of combination forecasting to reduce uncertainty and improve accuracy. Certainly, combination forecasting has its limitations. One criticism is that combination forecasting has predominantly focused on national property markets analysis. To enhance the application of combination forecasting, it would be useful to use it in research on regional markets analysis." (author's abstract)

This paper develops a two-dimensional structural framework for valuing credit default swaps and corporate bonds in the presence of default contagion. Modelling the values of related firms as correlated geometric Brownian motions with exponential default barriers, analytical formulae are obtained for both credit default swap spreads and corporate bond yields. The credit dependence structure is influenced by both a longer-term correlation structure as well as by the possibility of default contagion. In this way, the model is able to generate a diverse range of shapes for the term structure of credit spreads using realistic values for input parameters. © 2008 Taylor and Francis.

Using nominal and real exchange rates for Ireland relative to Germany and the UK from 1975 to 2003, this paper explores likely sources of nonlinearity in purchasing power parity (PPP) relationships and difficulties in employing an I(1)/I(0) econometric framework. Tests for fractional integration and nonlinearity, including random field regression-based procedures, are applied. Results reveal shortcomings in the standard cointegration and smooth transition autoregression approaches to modelling, and point to multiple structural changes models. Such a model for the case of Ireland and Germany suggests that PPP holds not only in the long run but also in the medium to short term.

In this article it is advocated to select a model only if it significantly contributes to the accuracy of a combined forecast. Using hold- out-data forecasts of individual models and of the combined forecast, a useful test for equal forecast accuracy can be designed. An illustration for real-time forecasts for Gross Domestic Profit (GDP) in the Netherlands shows its ease of use.

In this article, we investigate the performance of a panel data stationarity test when cross-sectional correlation is modelled by a time-specific factor. Size distortions, that occur especially when the number of cross sections is small, are documented. To eliminate these distortions, a new set of critical values is supplied. When investigating the rejection frequency under the alternative hypothesis, it is found that the panel data stationarity test that uses the supplied critical values maintain good power characteristics even when only a subset of the cross-sectional units have a unit root.

Based on a theoretical monetary exchange-rate model in continuous time, this article establishes a sequential estimation framework which is capable of indicating central bank intervention in the run-up to a currency union. Using daily pre-European Monetary Union (pre-EMU) exchange-rate data for the countries of the current Euro zone, we find mixed evidence of active pre-EMU intervention policies (so-called institutional frontloading strategies). Our estimation framework is highly relevant to economic and political agents operating in financial markets of the upcoming EMU accession countries.

In this article, we aim at modelling the long-run behaviour of the Real Effective Exchange Rates (REER) for a pool of African countries. Not much attention has been paid to this group of countries, in particular, to the existence of nonlinearities in the long-run path of such a variable. Controlling for two sources of nonlinearities, i.e. asymmetric adjustment to equilibrium and nonlinear deterministic trends allows us to gain some insight about the behaviour of the African REER. We find that these sources of nonlinearities help us to explain the apparent unit root behaviour found applying linear unit root tests for most of the countries.

This article examines business cycle features of UK Gross Domestic Product (GDP) sectors with regard to trends, cycles and growth. The empirical study adopts the Kalman filter to decompose these GDP sectors into trend and cycle components. The general model of this study encompasses a number of alternative specifications about trend growth, therefore accommodating diverse views on growth. There is reasonable support in the results for a mean-reverting stochastic growth model for the UK economy. The characteristics in trends and cycles of UK GDP sectors are discussed, focusing on their similarities and differences around business cycles.

In this paper, we propose a quantitative measure for inflation expectations based on consumer survey data. Thereafter, we proceed to testing the rationality assumption. This issue is of noteworthy interest in its own as it is commonly assumed in the theoretical modelling literature that the rational expectations hypothesis holds. This analysis is conducted for the euro area as a whole, as well as for several member countries, using a sample covering the last two decades. Moreover, we also assess if the conclusions hold when one focuses on the post-euro introduction period.

International audience; In this study, we systemically apply nine recent panel unit root tests to the same fourteen macroeconomic and financial series as those considered in the seminal paper by Nelson and Plosser (1982). The data cover OECD countries from 1950 to 2003. Our results clearly point out the difficulty that applied econometricians would face when they want to get a simple and clear-cut diagnosis with panel unit root tests. We confirm the fact that panel methods must be very carefully used for testing unit roots in macroeconomic or financial panels. More precisely, we find mitigated results under the cross-sectional independence assumption, since the unit root hypothesis is rejected for many macroeconomic variables. When international cross-correlations are taken into account, conclusions depend on the specification of these cross-sectional dependencies. Two groups of tests can be distinguished. The first group tests are based on a dynamic factor structure or an error component model. In this case, the non stationarity of common factors (international business cycles or growth trends) is not rejected, but the results are less clear with respect to idiosyncratic components. The second group tests are based on more general specifications. Their results are globally more favourable to the unit root assumption.

In this article, a decomposition method for Tobit models is derived, which allows the differences in observed outcome variables between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part attributable to differences in the estimated coefficients. Monte Carlo simulations demonstrate that in the case of censored dependent variables this decomposition method produces more reliable results than the conventional Blinder–Oaxaca decomposition for linear regression models. Finally, our method is applied to a decomposition of the gender wage gap using German data.

This study reexamines the sustainability hypothesis by testing whether government revenues and expenditures for eight rich OECD countries between 1977Q1 and 2005Q4 are cointegrated. For this purpose, a non-stationary panel data approach is adopted, which is general enough to permit for cross-country dependence as well as structural breaks representing major shifts in fiscal policy. In contrast to many earlier studies, the results reported in this study suggest that the sustainability hypothesis cannot be rejected.

This article proposes productivity analysis for evaluating the relative efficiency in corporate real estate usage across decision-making units. Using data from the Greek Telecommunications Organization (GTO), we measure the productivity of 127 branches using the number of employees and the total area covered per building as inputs and the number of telephony access lines as outputs. We apply three nonparametric Data Envelopment Analysis (DEA) models assuming: Constant Returns to Scale (CRS), Variable Returns to Scale (VRS) and Slacks-Based Measures (SBM), respectively. We discuss how the proposed approach can provide real estate managers and analysts a multi-informational tool that allows the quantification of targets and may serve as a guide tool for the efficient employment of real estate assets.

Here we derive the Lévy characteristic triplet for the GNIG probability law. This characterizes the corresponding Lévy process. In addition we derive equivalent martingale measures with which to price simple put and call options. This is done under two different equivalent martingale measures. We also present a multivariate Lévy process where the marginal probability distribution follows a GNIG Lévy process. The main contribution is, however, a stochastic process which is characterized by autocorrelation in moments equal and higher than two, here a multivariate specification is provided as well. The main tool for achieving this is to add an integrated Feller square root process to the dynamics of the second moment in a time-deformed Browninan motion. Applications to option pricing are also considered, and a brief discussion is held on the topic of estimation of the suggested process.

State space models are a widely used tool in time series analysis to deal with processes which gradually change over time. Model specification however is a diffcult task as one has to decide which components to include in the model and to specify whether these are fixed or stochastic. In the Bayesian approach, model selection relies on the posterior probabilities of a model given the data. These can be determined for each model separately by using Bayes' rule, which requires estimation of the marginal likelihoods by some numerical methods. The modern approach to Bayesian model selection is to apply model space MCMC methods by sampling jointly model indicators and parameters, as e.g. in the stochastic variable selection approach (George and McCulloch, 1997), which is usually applied to model selection for regression models. In this talk we show that a stochastic model search MCMC method is feasible for Gaussian as well as non-Gaussian time series data (binary data, multinomial data, count data) that chooses appropriate components in a structural time series model and decides, if these components are deterministic or stochastic. For non-Gaussian state space models the stochastic model search MCMC methods makes use of auxiliary mixture sampling developed in Frühwirth-Schnatter and Wagner (2006) for count data and in Frühwirth- Schnatter and Frühwirth (2007) for binary and multinomial data.

We analyse the asymptotic properties of mean–variance efficiency tests based on generalised methods of moments, and parametric and semiparametric likelihood procedures that assume elliptical innovations. We study the trade-off between efficiency and robustness, and prove that the parametric estimators provide asymptotically valid inferences when the conditional distribution of the innovations is elliptical but possibly misspecified and heteroskedastic. We compare the small sample performance of the alternative tests in a Monte Carlo study, and find some discrepancies with their asymptotic properties. Finally, we present an empirical application to US stock returns, which rejects the mean–variance efficiency of the market portfolio.

Most electricity markets exhibit high volatilities and occasional distinctive price spikes, which result in demand for derivative products which protect the holder against high prices. In this paper we examine a simple spot price model that is the exponential of the sum of an Ornstein-Uhlenbeck and an independent mean reverting pure jump process. We derive the moment generating function as well as various approximations to the probability density function of the logarithm of the spot price process at maturity $T$. Hence we are able to calibrate the model to the observed forward curve and present semi-analytic formulae for premia of path-independent options as well as approximations to call and put options on forward contracts with and without a delivery period. In order to price path-dependent options with multiple exercise rights like swing contracts a grid method is utilised which in turn uses approximations to the conditional density of the spot process.

Time-series estimation of gasoline demand elasticities often does not take into account the possibility of non-stationarity in the underlying data, which may render the parameter estimates spurious. Studies have shown that the time trending variables used to explain gasoline demand could be difference stationary and therefore may require cointegration analysis to assess the relationship among the trending variables. In this work we use the cointegration technique to derive long run and short run demand elasticities of non-commercial gasoline consumption using time-series data for the USA from 1949 to 2004. We also attempt to incorporate the presence of a structural break in the data generation process of the time trending variables. Our results show that the consumption of gasoline and lifetime income have a long term stable relationship after the second oil shock of 1978. Prior to the first oil shock of 1973, no such long run relationship could be established through cointegration.

The collective rationality hypothesis initiated by Chiappori (1988a) and applied by Seaton (1997, 2001) for a two-person household is used to distinguish the organizational behaviour of firms. Firms produce satisfaction to groups as traditional managerial and early behavioural theories of the firm of Williamson, Baumol and Marris suggest, as well as more modern principle agent models. Under certain conditions, intra-firm bargaining leads to a Pareto optimal outcome. What makes this work an important contribution is that it identifies a set of nonvacuous testable restrictions to empirically detect if firm-level data satisfy Pareto optimal behaviour for the main decision makers in the organization.

Understanding the process of economic growth involves comparing competing theoretical models and evaluating their empirical relevance. Our approach is to take the neoclassical stochastic growth model directly to the data and make inferences about the model parameters of interest. In this paper, output follows a jump-diffusion process. By imposing parameter restrictions we derive two solutions in explicit form. Based on them, we obtain transition densities in closed form and employ maximum likelihood techniques to estimate the model parameters. In extensive Monte Carlo simulations we demonstrate that population parameters of the underlying data generating process can be recovered. We find empirical evidence for jumps in monthly and quarterly data on industrial production for the UK, the US, Germany, and the euro area (Euro12).

In this article, we analyse the relationship between productive physical investment and economic growth from a panel perspective for a sample of 61 countries spanning the period 1950 to 1992. The analysis can be thought of as two-fold. First, we test the empirical validity of AK models following the logic by Jones (1995). For that purpose, we determine the degree of persistence of physical investment rates and growth by employing recently developed panel unit-root tests that enable us to make more reliable inferences about the existence of stochastic trends in the series. Second, we estimate the long-run effect of physical investment on growth by using panel data techniques rather than cross-section regressions. Overall, our findings cast doubts on the rejection of the empirical validity of the AK model, as suggested by Jones’ analysis.

We propose an estimator of the conditional distribution of X t | X t − 1 , X t − 2 , … , and the corresponding regression function E ( X t | X t − 1 , X t − 2 , … ) , where the conditioning set is of infinite order. We establish consistency of our estimator under stationarity and ergodicity conditions plus a mild smoothness condition.

In this paper we examine the following two hypotheses, which traditional theories of capital structure are relatively silent about: (i) the determinants of financial leverage decisions are different for micro, small, medium and large firms; and (ii) the factors that determine whether or not a firm issues debt are different from those that determine how much debt it issues. Using a binary choice model to explain the probability of a firm raising debt and a fractional regression model to explain the relative amount of debt issued, we find strong support for both hypotheses. Confirming recent empirical evidence, we find also that, although larger firms are more likely to use debt, conditional on their having some debt, firm size is negatively related to the proportion of debt used by firms.

The paper discusses tests for the correct specification of a model when data is observed in a d -dimensional lattice, extending previous work when the data is collected in the real line. As it happens with the latter type of data, the asymptotic distributions of the tests are functionals of a Gaussian sheet process, say B ( ν ) , ν ∈ [ 0 , π ] d . Since it is not easy to find a time transformation h ( ν ) such that B ( h ( ν ) ) becomes the standard Brownian sheet, a consequence is that the critical values are difficult, if at all possible, to obtain. So, to overcome the problem of its implementation, we propose employing a bootstrap approach, showing its validity in our context.

The pricing accuracy and pricing performance of local volatility models crucially depends on absence of arbitrage in the implied volatility surface: an input implied volatility surface that is not arbitrage-free invariably results in negative transition probabilities and/ or negative local volatilities, and ultimately, into mispricings. The common smoothing algorithms of the implied volatility surface cannot guarantee the absence arbitrage. Here, we propose an approach for smoothing the implied volatility smile in an arbitrage-free way. Our methodology is simple to implement, computationally cheap and builds on the well-founded theory of natural smoothing splines under suitable shape constraints. Unlike other methods, our approach also works when input data are scarce and not arbitrage-free. Thus, it can easily be integrated into standard local volatility pricers.

We study a simple, solvable model that allows us to investigate effects of credit contagion on the default probability of individual firms, in both portfolios of firms and on an economy wide scale. While the effect of interactions may be small in typical (most probable) scenarios they are magnified, due to feedback, by situations of economic stress, which in turn leads to fatter tails in loss distributions of large loan portfolios., 11 pages, 3 figures