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Seasonality in economic time series can "obscure" movements of other components in a series that are operationally more important for economic and econometric analyses. In practice, one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. This book presents a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to extract the seasonal and the non-seasonal component from an observed series. Once this process is carried out, there will be no need to revise these components at a later stage when new observations become available. The authors describe the main features of CAMPLET, evaluate the outcomes of CAMPLET and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating processes, and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: US non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands. Furthermore they show how CAMPLET performs under the COVID-19 crisis, and its attractiveness in dealing with daily data. This book appeals to scholars and students of econometrics and statistics, interested in the application of statistical methods for empirical economic modeling.

We empirically determine whether a Ricardian or a non-Ricardian regime is more plausible for the euro area, following the research strategy of Canzoneri et al. (Am Econ Rev 91:1221-1238, 2001). A Vector AutoRegressive model for the primary government balance and the government debt is estimated for the period 1980q2-2013q4. Our model uses dummy interaction terms to account for the breaks due to the introduction of the Euro Convergence Criteria (ECC) and the start of the global financial crisis, respectively. No evidence is found in favour of either regime for the pre-ECC period. In the post-ECC period, a Ricardian regime is more plausible. Some evidence points in the direction of a non-Ricardian regime for the period after the start of the financial crisis.

The paper contributes to a small but growing literature that estimates tax reaction functions of governments competing with other governments. We analyze consumption tax competition between US states, employing a panel of state-level data for 1977-2003. More specifically, we study the impact of a state's spatial characteristics (i.e., its size, geographic position, and border length) on the strategic interaction with its neighbors. For this purpose, we calculate for each state an average effective consumption tax rate, which covers both sales and excise taxes. In addition, we pay attention to dynamics by including lagged dependent variables in the tax reaction function. We find overwhelming evidence for strategic interaction among state governments, but only partial support for the effect of spatial characteristics on tax setting. Tax competition seems to have lessened in the 1990s compared to the early 1980s.

AbstractCattel's (Multivar Behav Res 1:245–276, 1966) heuristic determines the number of factors as the elbow point between 'steep' and 'not steep' in the scree plot. In contrast, an elbow is by definition absent in points on a hyberbole with corresponding equisized surfaces. We formalize this heuristic and propose a criterion to determine the number of factors by comparing surfaces under the scree plot. Monte Carlo simulations shows that the finite-sample properties of our proposed criterion outperform benchmarks in the dynamic factor model literature.