We model illegal immigration across the US-Mexico border into Arizona, California, and Texas as an unobservable variable applying a Multiple Indicators Multiple Causes model. Using state-level data from 1985 to 2004, we test the incentives and deterrents influencing illegal immigration. Better labor market conditions in a US state and worse in Mexico encourage illegal immigration while more intense border enforcement deters it. Estimating the state-specific inflow of illegal Mexican immigrants we find that the 1994/95 peso crisis in Mexico led to significant increases in illegal immigration. US border enforcement policies in the 1990s provided temporary relief while post-9/11 re-enforcement has reduced illegal immigration.

We investigate whether and how financial constraints of private firms depend on bank lending behavior. Bank lending behavior, especially its scale, scope and timing, is largely driven by bank business models which differ between privately owned and state-owned banks. Using a unique dataset on private small and medium-sized enterprises (SMEs) we find that an increase in relative borrowings from local state-owned banks significantly reduces firms’ financial constraints, while there is no such effect for privately owned banks. Improved credit availability and private information production are the main channels that explain our result. We also show that the lending behavior of local state-owned banks can be sustainable because it is less cyclical and neither leads to more risk taking nor underperformance.

In response to the financial crisis of 2008/2009, numerous new taxes on financial institutions have been discussed or implemented around the world. This paper discusses the connection between the incidence of the taxes, their incentive effects, and policy makers’ objectives. Combining basic insights from banking theory with standard models of tax incidence shows that the incidence of such taxes will disproportionately fall on small and medium size enterprises. The arguments presented suggest it is unlikely that the taxes will have a beneficial impact on financial stability or raise significant amounts of revenue without increasing the cost of capital to bank dependent firms significantly.

We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data which allows us to simultaneously address some important methodological issues, such as spatial clustering, nonlinearities and overdispersion. We apply this model to study location determinants of inward greenfield investments occurred over the 2003-2007 period in 249 European regions. The inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity which induces spatial clustering. Allowing for nonlinearities reveals, in line with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some limit value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs would never overcome positive agglomeration externalities.

The interplay between banks and the macroeconomy is of key importance for financial and economic stability. We analyze this link using a factor-augmented vector autoregressive model (FAVAR) which extends a standard VAR for the U.S. macroeconomy. The model includes GDP growth, inflation, the Federal Funds rate, house price inflation, and a set of factors summarizing conditions in the banking sector. We use data of more than 1,500 commercial banks from the U.S. call reports to address the following questions. How are macroeconomic shocks transmitted to bank risk and other banking variables? What are the sources of bank heterogeneity, and what explains differences in individual banks’ responses to macroeconomic shocks? Our paper has two main findings: (i) Average bank risk declines, and average bank lending increases following expansionary shocks. (ii) The heterogeneity of banks is characterized by idiosyncratic shocks and the asymmetric transmission of common shocks. Risk of about 1/3 of all banks rises in response to a monetary loosening. The lending response of small, illiquid, and domestic banks is relatively large, and risk of banks with a low degree of capitalization and a high exposure to real estate loans decreases relatively strongly after expansionary monetary policy shocks. Also, lending of larger banks increases less while risk of riskier and domestic banks reacts more in response to house price shocks.

We exploit a unique sample to analyze how homophily (affinity for similar others) and social ties affect career outcomes in banking. We test if these factors increase the probability that the appointee to an executive board is an outsider without previous employment at the bank compared to being an insider. Homophily based on age and gender increase the chances of the outsider appointments. Similar educational backgrounds, in contrast, reduce the chance that the appointee is an outsider. Greater social ties also increase the probability of an outside appointment. Results from a duration model show that larger age differences shorten tenure significantly, whereas gender similarities barely affect tenure. Differences in educational backgrounds affect tenure differently across the banking sectors. Maintaining more contacts to the executive board reduces tenure. We also find weak evidence that social ties are associated with reduced profitability, consistent with cronyism in banking.

In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameters estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.

We use a multivariate state space framework to analyze the short run impact of money on prices in the United States. The key contribution of this approach is that it allows to identify the impact of money growth on inflation without having to model money demand explicitly.

Using our results, that provide evidence for a substantial impact of money on prices in the US, we analyze the consequences of the Fed's response to the financial crisis. Our results indicate a raise of US inflation above 5% for more than a decade. Alternative exit strategies that we simulate cannot fully compensate for the monetary pressure without risking serious repercussions on the real economy. Further simulations of a double dip in the United States indicate that a repetition of the unusually expansive monetary policy – in addition to increased inflation – might cause growth losses exceeding the contemporary easing of the crisis.

This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.

The important role of credit has long been identified as a key factor for economic development (see e.g. Wicksell (1898), Keynes (1931), Fisher (1933) and Minsky (1957, 1964)). Even before the financial crisis most researchers and policy makers agreed that financial frictions play an important role for business cycles and that financial turmoils can result in severe economic downturns (see e.g. Mishkin (1978), Bernanke (1981, 1983), Diamond (1984), Calomiris (1993) and Bernanke and Gertler (1995)). However, in practice researchers and policy makers mostly used simplified models for forecasting and simulation purposes. They often neglected the impact of financial frictions and emphasized other non financial market frictions when analyzing business cycle fluctuations (prominent exceptions include Kiyotaki and Moore (1997), Bernanke, Gertler, and Gilchrist (1999) and Christiano, Motto, and Rostagno (2010)). This has been due to the fact that most economic downturns did not seem to be closely related to financial market failures (see Eichenbaum (2011)). The outbreak of the subprime crises ― which caused panic in financial markets and led to the default of Lehman Brothers in September 2008 ― then led to a reconsideration of such macroeconomic frameworks (see Caballero (2010) and Trichet (2011)). To address the economic debate from a new perspective, it is therefore necessary to integrate the relevant frictions which help to explain what we have experienced during recent years.

In this thesis, I analyze different ways to incorporate relevant frictions and financial variables in macroeconometric models. I discuss the potential consequences for standard statistical inference and macroeconomic policy. I cover three different aspects in this work. Each aspect presents an idea in a self-contained unit. The following paragraphs present more detail on the main topics covered.