Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)


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Christoph M. Schmidt for their careful guidance and advice on how to handle empirical problems and improve this work. I am also grateful for valuable comments from Prof. Jesus Mur and Prof. Jean Paelinck, whose work in spatial econometrics inspired me to do own research in the field.

Thanks so much, it was a pleasure to work with you and I hope we continue to do so. My colleagues at the RWI, Alfredo Paloyo and Arndt Reichert, were a great support as well—both in proof-reading earlier drafts and giving me an excellent preparation for my final dissertation defense. I also acknowledge financial support from the Evangelisches Studienwerk Villigst e. For sure, without their support I would not have made it. My wife Karla and my parents supported me so much throughout all the ups and downs over the last years.

I am incredibly happy about this. My kids Anna Luisa and Jonathan helped me to stay with both feet solid on the ground and were a source of joy and happiness, when I needed new inspiration for my work. I dedicate this book to them. Part I 2 3. Part II 5. This work starts from the empirical observation that all three dimensions, namely time, space, and structural functional forms, are important for an integrative framework of modern empirical analysis in regional science. While the notion of space is integral to research in regional science, adequate empirical tools such as modern methods in spatial statistics and spatial econometrics that allow for sound statistical inference in a regression framework have only been developed within the last years.

The research field of spatial econometrics initially evolved as a critical reflection of Paelinck about what contemporary regional econometrics and model building was neglecting at that time.

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Paelinck also hinted at two essential factors needed in order to correctly capture the spatial dynamics of the economy: i the relative location of the regions concerned and ii the intraregional location of activities. By now, spatial econometrics is becoming a mainstream tool in economics, geography, and regional science. Although considerable progress has been made, however, there are still many open challenges. The recent contributions of LeSage and Pace as well as Elhorst have clearly shown that the interpretation of re1A detailed historical tracking of the synthesis of spatial econometrics is given by Sarafoglu and Paelinck Next, regional scientists have become aware that a better understanding of causes and consequences of many regional economic phenomena requires a structural analysis, which ideally starts from a fully specified model, well-grounded in theory see Holmes This would allow properly addressing relevant issues of endogeneity, causality and simultaneity in regional modelling and policy analysis.

Having a long history in dealing with these concepts, modern macroeconomic theory and macroeconometric practice may therefore be a good source of inspiration for regional scientists. As Rickman points out in his contribution to the 50 year anniversary volume of the Journal of Regional Science, one way to go ahead is using the macroeconometric approach to construct structural models for regional policy analysis as an alternative to traditional, merely descriptive tools in regional science. Following the influential work of Sims , the use of vector autoregressive VAR models has become a widespread empirical tool complementary to dynamic single equation specifications.

The VAR approach starts from the general treatment of variables as being endogenous in a system of interdependent equations and grounds specification issues such as weak exogeneity of variables and the direction of causality on empirical testing. Only recently, VAR models have come to the focus of regional modelling with a first application by Carlino and DeFina For macroeconometric methods to be applied, tools are needed that are able to link time- and space-related analysis in a unified framework.

In this setting, econometric practice has greatly benefited from recent advances in the analysis of panel data, which enables researchers to track cross sectional units over time. The benefits from panel data are manifold. Most of them can be attributed to its greater capacity for capturing the complexity of human behavior compared to single cross-section or time-series data see Hsiao An important advantage is thus the ability to construct and test more complicated behavioral hypotheses including, e.

Tests for the poolability of the data and slope homogeneity are likely to increase the efficiency of estimation and allow identifying structural differences in the data. The latter would need either a structuralist- or experimentalist-modelling approach. By now, there is a huge literature on the latter, being able to spell out the importance of time-adjustment processes for economic variables, both in stationary as well as non-stationary data settings.

The Globalization Conundrum-Dark Clouds behind the Silver Lining

First contributions have already hinted at the potential power of such time—space combinations. Beenstock and Felsenstein , for instance, analyze, by means of panel data with a long time dimension, the importance of spatial interrelationships for the evolution of a system of long-run cointegrated variables over time. Additionally, Di Giacinto , among others, has demonstrated the potential use of spatial vector autoregressive SpVAR models, in particular for the computation of space—time impulse responses as a tool to summarize the information conveyed by regional dynamic multipliers to account for the simultaneity and two-way causality in modelling economic variables with an explicit role for spatial spillovers.

There are numerous further examples of the fruitful interaction between mainstream time series, panel and spatial econometrics. An illustrative one is Vaona , In his contributions, the author shows how to adapt familiar timeseries tools to the field of spatial econometrics, such as the Ramsey RESET statistic to disentangle model misspecifications of unknown form, which potentially lead to spurious spatial correlation in the model residuals.

Together with related work on spatial model testing strategies, such as Florax et al. Similar arguments hold for the concept of Granger causality, which has only recently been adapted to the field of spatial econometrics by Herrera et al. The other way around, panel time series econometrics has benefited from the consideration of spatial interdependence in the design of second generation panel unit root and cointegration tests see, e.

Although the above examples show that significant progress has been made, it still seems to be a long way until empirical models that fully account for the structural, temporal and spatial interrelatedness of regional economic systems are applicable. This, of course, is due to the inherent complexity of a global space—time— structural approach, which can approximately be described by Fig.

The best way to tackle these challenges is to start from a stylized framework for global analysis, which we will briefly discuss in the next section. Source: Own figure in extension to the single variable presentation in Haggett et al. The best way to map such time—space—structural relationships is to start from a graphical presentation. Figure 1. The figure is inherently multidimensional. In addition, we assume that the time interval between our data recording points is sufficiently long compared to the rate of operation of geographical processes, so that what appear as simultaneous effects between crosssections regions i and j may occur.

The simultaneous effects for each time plane represent a pure spatial autoregressive mechanism. Besides, different structural relationships through space and time have to be modelled between the three variables, either in a contemporaneous way as highlighted in Fig. Keeping the stylized model as simple as possible, we assume a linear or log-linearized relationship among the variables and rule out future information as determinant for the current value.

The asterisked variables in 1. There are different ways to specify the spatial weighting scheme W , including common borders, distances, or other forms of geographical and economic linkages. As Elhorst puts it, the choice of W may, in fact, be a delicate choice in empirical practice. Also, the inherent time—space simultaneity in 1. Research on panel econometrics has made considerable progress to give advice on most of these points. One general merit of the panel econometric approach, besides accounting for omitted variables through the inclusion of the unobservable 6 Additionally, one may start from a two-way specification and include time-fixed effects as well.

Otherwise, pooling the data leads to inconsistent estimates. Given the availability of panel data with increasing T , one possibility is then to start from an unrestricted individual coefficient model and test for the poolability for the whole set of cross-sections or different sub-groups. Another important feature of 1. In panel data settings, the estimation of dynamic specifications is not straightforward given the correlation of the lagged endogenous variable with the error term of the model. In the recent literature, different estimators have been proposed that typically start from first differencing the model to eliminate the unobservable individual effects from the model.

However, there still appears the problem that the transformed error term is correlated with the transformed lagged dependent variable and thus needs to be instrumented. In a seminal paper, Anderson and Hsiao recommend to use twice-lagged levels or first differences to serve as valid instruments. Subsequently, the estimation technique has been refined by the GMM approaches in Arellano and Bond as well as Blundell and Bond Besides estimating the transformed model in first differences, the latter estimator jointly estimates a stacked dataset in first differences as well as levels simultaneously.

For the latter, the Blundell—Bond estimator employs information in first differences to instrument the lagged endogenous variable. This likewise holds for the estimation of spatial econometric models as shown, e. In spatio-temporal data settings, Fingleton and Le Gallo have recently shown that GMM estimators developed for dynamic panel data are, for instance, also extremely useful in instrumenting further endogenous right-hand-side variables such as the spatial lag. This further indicates the advancing integration of both strands of the literature.

Right-hand-side endogeneity in turn is quite likely to occur in empirical applications, given the impact of measurement errors, omitted variables, or the existence of an unknown set of simultaneous structural equations. The latter argument is an important point. Here, the literature for panel data is still in its infancy to setup structural- or time-series-modelbased full-information solutions. The same holds for the joint inclusion of time and spatial lags of the endogenous variable in one unifying framework as shown in 1.

First experimental estimation approaches nevertheless point to the merits of this modelling direction. The latter opens up the modelling space from a spatial lag framework to a more general class of spatial Durbin type models, which may be seen as an adequate general starting point to test for a parsimonious version of the equation system. Only for the case of a stable comovement of X, Y and Z over time, the system can be estimated in its original form.

Otherwise, the risk of running spurious regressions is present. This calls for a global concept of cointegration analysis. Most of the topics dealt with start from a concrete empirical problem, while problem solving also aims at generating some new knowledge in a methodological way, e. The work is structured in three parts, addressing major issues in building up a stylized regional economic model.

All empirical applications used in this work use German regional data, mostly at the federal state level. The datasets can also be obtained from the author upon request. In the following, I present the outline and main results of my empirical research with three headings: i internal migration and the labor market, ii trade and FDI activity of German regions, and iii growth, factor and final demand modelling. The interplay between internal migration and regional labor market performance has for long been in the focus of economic policy making.

A central question to address is to what extent regional disparities in real wages, income, and unemployment can be balanced through labor migration as an equilibrating force. This work also looks at the feedback effects potentially arising from the migration response. Investigating such two-way interdependencies, Chap. Indeed we get evidence for such a puzzle throughout the mids, which is likely to be caused by huge West—East income transfers, a fast exogenously driven wage convergence, and the possibility of East—West commuting.

However, we also observe an inversion of this relationship for subsequent periods. That is, along with a second wave of East—West movements around , net flows out of East Germany were much higher than expected after controlling for its weak labor market and macroeconomic performance. Towards the sample end in , structural differences between the two macro regions turn out to be insignificant, indicating that migratory movements between East and West Germany react in a similar way to regional labor market signals.

This latter result may be taken as a first hint for the advancing labor market integration between the two macro regions. The analysis in Chap. Empirical support is found for the main transmission channels identified by the neoclassical framework, while the impact of labor market signals is tested to be of greatest magnitude for workforce relevant age-groups and especially young cohorts from 18 to 25 and 25 to 30 years. These results underline the prominent role played by labor market conditions in determining internal migration rates of the working population in Germany.

Chapter 4 analyzes the role of network interdependencies in a dynamic panel data model for German internal migration flows since re-unification. In the context of this chapter, network dependencies are associated with correlations of migration flows strictly attributable to proximate flows in geographic space. So far, a capacious account of spatial patterns in German migration data is still missing in the empirical literature. The analysis starts with the construction of spatial weighting matrices for the analyzed system of interregional flow data and applies spatial regression techniques to properly handle the underlying space—time interrelations.

Besides spatial extensions to commonly used dynamic panel data estimators based on the spatial lag and unconstrained spatial Durbin model, spatial filtering techniques are also applied. When combining both approaches to a mixed spatial-filtering-regression specification, the resulting model performs remarkably well in terms of capturing spatial dependence in the migration equation, and at the same time the combination of different techniques qualifies the model to pass essential IV diagnostic tests.

The basic message for future research is that space—time dynamics is highly relevant for modelling German internal migration flows. Chapter 5 specifies a four-equation system for exports, imports, inward and outward FDI of German states with EU27 countries between and in a gravity-type framework. The latter is a common empirical vehicle in the new trade literature and new economic geography, which accounts explicitly for the role of space in the specification of trading costs.

By using a simultaneous equation approach for panel data, the resulting empirical specification is also able to control for the underlying structural interrelation of these variables as either being substitutive 1. Starting from the aggregate perspective, the analysis supports earlier empirical evidence for Germany finding substitutive linkages between trade and outward FDI. The latter may be motivated with the alternative choice options that firms in a specific region face when serving foreign markets. Switching to the macro- regional perspective, we get further insights. For example, splitting the sample to isolate West German to EU27 trade-FDI linkages, the revealed variable correlations closely follow the predictions from new trade theory models, where export replacement effects of FDI are again operating.

However, at the same time, outward FDI are found to stimulate trade via reverse goods imports. This regional heterogeneity found in our estimation results thus emphasizes the need to explicitly take into account the regional dimension in the analysis of cross-variable linkages between trade and FDI. Chapter 6 backs up the empirical analysis in Chap. We compare the performance of IV and non-IV approaches in the presence of timefixed variables and right-hand-side endogeneity e. The HT model is the benchmark approach in estimating panel data sets with both timevarying and time-fixed regressors.

The simulation results show that the HT model with perfect knowledge about the underlying data structure instrument orthogonality has, on average, the smallest bias. However, compared to the empirically relevant specification with imperfect knowledge and instruments chosen by statistical criteria, simple non-IV rival estimators based on extensions of the fixed effects model FEM , such as the fixed effects vector decomposition FEVD as two-step estimator, perform equally well or even better.

We illustrate these findings by estimating gravity-type models for German regional export activity within the EU. The results show that the HT specification is likely to get upward biased results for the crucial trade costs variable proxied by geographical distances. Chapter 7 then adapts the global cointegration approach of Beenstock and Felsenstein to analyze the role of variables measuring the internationalization activity trade and FDI for output determination. The analysis shows that for German regions, neighboring effects are indeed important to track the long- and short-run evolution of output driven by trade and FDI for West German state level data during the period to For the long-run cointegration equation, the empirical results 12 1 Introduction and Outline support the hypothesis of export- and FDI-led growth.

We also show that export and outward FDI activities may exhibit positive cross-regional effects, giving rise to the notion of global cointegration. In the short run SpECM specification, direct and indirect spatial externalities are also highly present. As a sensitivity analysis, we use a spatial weighting matrix based on interregional goods transport flows rather than geographical distances. We account for the potential endogeneity problem of the latter approach by using historical data for intra-German transportation flows prior to the sample period.

Chapter 8 starts with an analysis of the finite sample properties of different estimators for dynamic panel data models in a simultaneous equation context. Since the notion of simultaneity arises for many economic relationships, it is important to analyze the finite sample performance of multiple equation estimators for panel data. Here, empirical guidance in the panel econometric literature is still missing.

In the context of this chapter, the most competitive estimators from the Monte Carlo simulation exercise are then applied to an analysis of the role of public and private capital accumulation on regional output growth among German states for the sample period — On the one hand the model is used to identify the likely two-way effects among the variables as, e.

For the latter purpose the baseline model is augmented by variables measuring interregional spillover effects from public capital as well as transfer payments from regional equalization schemes. We find positively directed but insignificant effects from interregional spillovers in transport infrastructure, while spillovers from science infrastructure even tend to be negative. The latter result is likely to originate from specific locational advantages of science infrastructure, which allows regions to poach production factors from its neighborhood.

For regional equalization transfers, we find mixed results, crucially depending on the specific policy program. Chapter 9 further looks at output growth driven by regional policy instruments. Using a neoclassical growth-model framework, we test for the policy impact on the speed of convergence to the long-run steady-state income. Our empirical specification is perfectly in line with the spirit of neoclassical growth theory, in which even a permanent increase in the physical investment rate may only exhibit a temporary effect on productivity growth, leaving the long-run growth rate unaffected.

The results reveal a significant positive direct effect of the regional policy instrument on labor productivity growth, with the speed of convergence being almost doubled for supported regions half way below their steady-state compared to the case of not being supported. In order to check for the robustness of the results, we also augment the standard regression approach by spatial econometric elements.

The Inclusion spatial lags of the regressand and right-hand-side regressors in the convergence equation shows that, besides the direct positive effect of the GRW investment support scheme, there is a negative spillover effect from the policy stimulus to neighboring regions. The latter effect may be explained by the increased attractiveness of the supported region, which is able to poach capital investments and other input factors from neighboring regions.

Though, on average, the indirect effect results in a slowdown in the speed of adjustment to the steady-state income, the net effect of GRW support to lagging regions is still positive for the analyzed sample period. Chapter 10 of this work then looks at the role played by income fluctuations in determining long- and short-run regional consumption functions for different samples of German states between and A particular focus is set on the analysis of homogeneity versus heterogeneity in the individual regional adjustment processes of consumption in consequence to current income changes.

Knowing more about the type of spatial response to policy changes may be seen as a further important field for future analysis in regional science. The latter may reflect liquidity constraints, myopic behavior or loss aversions. However, our results do not give strong empirical support for these phenomena. In the short-run approach past and current income changes turn out to be insignificant if we control for potentially omitted variables in particular, for a long West German sample between and , we get mixed results for a sample comprising all German states from onwards. Although we find income sensitivity in the specified Panel Error Correction Model ECM approach integrating the short- and long-run perspective, this share is found to be smaller than recently reported by other scholars based on German regional data.

By testing for slope homogeneity in the dynamic consumption model, we are able to identify regional asymmetries in the adjustment path due to income shocks. We finally also account for the likely role of spatial autocorrelation when dealing with regional data. The results for spatially filtered variables show that the estimated structural coefficients remain stable after filtering has been done and turn out to be even more in line with the predictions of neoclassical consumption theory.

That is, after controlling for the likely role of external habit formation in addition to internal 14 1 Introduction and Outline habit persistence, the share of excess sensitivity gets even smaller. This also raises doubts about whether current income changes are an effective measure for excess income sensitivity as typically used in the traditional empirical literature since they may simple capture the effects of omitted variables. Moreover, full poolability of the data is not rejected for the spatially filtered model. The share of excess sensitivity to income changes is rather small. References Ancot, J.

Five principles of spatial econometrics illustrated.

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Kuenne Eds. Anderson, T. Estimation of dynamic models with error components. Journal of the American Statistical Association, 76, — Anselin, L. Spatial econometrics: methods and models. Dordrecht: Kluwer Academic. Arellano, M. Panel data econometrics. Oxford: Oxford University Press. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, — Balestra, P. Pooling cross section and time series data in the estimation of dynamic model: the demand for natural gas.

Econometrica, 34 3 , — Baltagi, B. Panel unit root tests and spatial dependence. Journal of Applied Econometrics, 22 2 , — Beenstock, M. Spatial error correction and cointegration in non stationary panel data: regional house prices in Israel. Journal of Geographical Systems, 12 2 , — Blundell, R.

Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, — Bouayad-Agha, S. Estimation strategies for a spatial dynamic panel using GMM. A new approach to the convergence issue of European Regions. Spatial Economic Analysis, 5 2 , — Bradley, J. Burridge, P. Bootstrap inference in spatial econometrics: the J -test. Spatial Economic Analysis, 5 1 , 93— Capello, R. Annals of Regional Science, 41, — Modelling regional scenarios for the enlarged Europe. Berlin: Springer. Carlino, G. The differential regional effects of monetary policy: evidence from the U.

Journal of Regional Science, 39, — Cliff, A. Spatial processes. London: Pion. Di Giacinto, V. On vector autoregressive modeling in space and time. Elhorst, J. Applied spatial econometrics: raising the bar. Spatial Economic Analysis, 5 1 , 5— References 15 Engle, R. Co-integration and error-correction: representation, estimation and testing. Econometrica, 55, — Fingleton, B. Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: finite sample properties.

Papers in Regional Science, 87, — Florax, R. Regional Science and Urban Economics, 33, — Haggett, P. Locational analysis in human geography. London: Edward Arnold. Herrera, M. A non-parametric approach to spatial causality. Paper presented at the 4th world congress of the spatial econometrics association. Holmes, T. Structural, experimentalist, and descriptive approaches to empirical work in regional economics. Journal of Regional Science, 50 1 , 5— Hsiao, C.

Panel data analysis — advantages and challenges. Test, 16, 1— Kelejian, H. A spatial J -test for model specification against a single or a set of nonnested alternatives. Letters in Spatial and Resource Sciences, 1, 3— A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. Journal of Real State and Finance Economics, 17, 99— Kuh, E. The validity of cross-sectionally estimated bevaviour equations in time series applications.

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Econometrica, 27, — LeSage, J. Introduction to spatial econometrics. Mundlak, Y. Consequences of alternative specifications of Cobb—Douglas production functions. Econometrica, 33, — Neyman, J. Consistent estimation from partially consistent observations. Econometrica, 16 1 , 1— Paelinck, J. Hoe doelmatig kan regionaal en sectoraal beleid zijn? Leiden: Stenfert Kroese. Ramsey, J. Tests for specification errors in classical linear least squares regression analysis.

Rickman, D. Modern macroeconomics and regional economic modeling. Sarafoglu, N. On diffusion of ideas in the academic world: the case of spatial econometrics. Annals of Regional Science, 42 2 , — Sims, C. Macroeconomics and reality. Econometrica, 48 1 , 1— Vaona, A. Spatial autocorrelation or model misspecification?

Letters of Spatial and Resource Sciences, 2, 53— Journal of Geographical Systems, 12, 89— According to mainstream neoclassical theory the link between migration and regional labor market variables is assumed to work as follows: Regions with relatively high unemployment and low wage levels should experience net out-migration into regions with better employment opportunities. A rising number of available jobs in the target region as well as a decline in job opportunities in the home region then ensure that the regional labor market disparities will disappear over time.

In the long-run cross-regional labor market equilibrium unemployment differences can then only be explained by differences in regional wage levels as compensation for the higher unemployment risks, while otherwise factor prices are assumed to equalize across regions. A critical view of this concept of compensating differentials is given by Blanchflower and Oswald , , who introduce a wage-curve linking low wage levels and high unemployment rates for a particular region.

Recent empirical studies by Wagner , Baltagi and Blien and Baltagi et al. We kindly acknowledge the permission of Springer to reprint the article in this monograph. We put a particular emphasis on the analysis of the West and East German labor market integration since re-unification and investigate the likely two-way interdependences among migration and labor market variables. For empirical estimation we use internal migration flows between the German federal states NUTS1 level between and and apply dynamic panel data methods in a VAR context. The remainder of the chapter is organized as follows.

In the next section, we present a short literature review. Section 2. In Sect. First, from a partial equilibrium perspective we look at recent empirical contributions in specifying a stable long-run neoclassical migration equation. Second, using this long-run migration equation as an important building block for a more profound labor market analysis, we then augment the scope of the literature review to multiple equation approaches, which account more carefully for dynamic feedback effects among migration and labor market variables. Given the huge body of literature on the neoclassical migration model, it is not surprising that the empirical results for the long-run migration equation are somewhat mixed and country specific.

Focusing on empirical evidence for Germany, Decressin examines gross flows for West German states between and His results show that a wage increase in one region relative to others causes a disproportional rise in the gross migration flows in the first region, while a rise in the unemployment rate for a region relative to others disproportionally lowers the gross flows. However, the author does not find a significant link between bilateral gross migration and regional differences in wage level or unemployment when purely cross-sectional estimates are considered.

Difficulties in proving a significant influence of regional wage decreases on the migratory behavior within Germany are also found in earlier empirical studies based on micro-data to motivate individual migratory behavior in Germany. Subsequent micro studies mainly focused on qualifying the theoretically unsatisfactory result with respect to wage rates. Schwarze for example shows that by using the expected rather than actual wage rate the results turn significant.

Parikh and Van Leuvensteijn use the core neoclassical migration model with regional wage and unemployment differentials as driving forces for interregional migration augmented by additional indicators such as regional housing costs, geographical distance and inequality measures. For the short sample period —, the authors find a significant non-linear relationship between disaggregated regional wage rate differences and East—West migration, while unemployment differences are found to be insignificant. Hunt and Burda and Hunt analogously identify wage rate differentials and particularly the closing gap in regional differences driven by a fast East—West convergence as a powerful indicator in explaining observed state-to-state migration patterns.

Using data up to the late s, Burda and Hunt find that the decline in East—West migration starting from onwards can almost exclusively be explained by wage differentials and the fast East—West wage convergence, while unemployment differences do not seem to play an important part in explaining actual migration trends.

Building on this literature there is also a bulk of studies extending the scope of the analysis to a multiple equation setting in order to account more carefully for the likely feedback effects of migratory movements on labor market variables and their joint responses to shocks. Aiming to control for two-way effects has resulted in a variety of empirical specifications, either from a structural see e.


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Okun ; Muth ; Salvatore ; Bilger et al. The latter approach typically applies Vector Autoregression VAR models, which provide a valuable tool for analyzing the dynamics of economic processes. In particular the VAR approach is well suited to analyze regional adjustment processes in reaction to exogenous macroeconomic shocks. A general discussion of labor market analysis with VAR models is for instance given in Summers Using a VAR model for seven West German regions between and the author mainly finds the theoretically expected negative response of net in-migration to a one standard deviation shock in unemployment with a time-lag of about two to three years.

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Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)
Anatomy of Global Stock Market Crashes: An Empirical Analysis (SpringerBriefs in Economics)

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