Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).

Accessibility as a determinant of opportunities : A case study from Peruvian Amazonia

Accessibility is a crucial factor for interaction between areas in economic, cultural, political and environmental terms. Therefore, information concerning accessibility is relevant for informed decision making, planning and research. The Loreto region in the Peruvian Amazonia provides an interesting scene for an accessibility study. Loreto is sparsely populated and because there are few roads in the region, in practice all movement and transportation happens along the river network. Due to the proximity of the Andes, river dynamics are strong and annual changes in water level combined with these dynamic processes constantly reshape accessibility patterns of the region. Selling non-timber forest products (NTFP) and agricultural products (AP) in regional centres is an important income source for local rain forest dwellers. Thus, accessibility to the centres is crucial for the livelihood of local population. -- In this thesis I studied how accessible the regional centre Iquitos is from other parts of Loreto. In addition, I studied the regional NTFP/AP trade patterns and compared them with patterns of accessibility. Based on GPS-measurements, using GIS, I created a time-distance surface covering Loreto. This surface describes the time-distance to Iquitos, along the river network. Based on interview material, I assessed annual changes to accessibility patterns in the region. The most common regional NTFP/AP were classified according to the amount of time they can be preserved, and based on the accessibility surface, I modelled a catchment area for each of these product classes. -- According to my results, navigation speeds vary considerably in different parts of the river network, depending on river types, vessels, flow direction and season. Navigating downstream is, generally, faster than upstream navigation. Thus, Iquitos is better accessible from areas situated south and south west of the city, like along the rivers Ucayali and Marañon. Differences in accessibility between different seasons are also substantial: during the dry season navigation is slower due to lower water levels and emerging sand bars. Regularly operating boats follow routes only along certain rivers and close to Iquitos transport facilities are more abundant than in more distant areas. Most of the products present in Iquitos market places are agricultural products, and the share of NTFP is significantly smaller. Most of the products were classified in product class 2, and the catchment area for these products is rather small. Many products also belonged to class 5, and the catchment area for these products reaches up to the edges of my study area, following the patterns of the river network. -- The accessibility model created in this study predicts travel times relatively well, although in some cases the modelled time-distances are substantially shorter than observed time-distances. This is partly caused by the fact that real-life navigation routes are more complicated than the modelled routes. Rain forest dwellers having easier access to Iquitos have more opportunities in terms of the products they decide to market. Thus, they can better take advantage of other factors affecting the market potential of different products. -- In all, understanding spatial variation in accessibility is important. In the Amazonian context it is difficult to combine the accessibility-related needs of the local dwellers with conservation purposes and the future challenge lies in finding solution that satisfy both of these needs.