|
|
Modeling Uganda
Cincibuch, M. ; Kejak, Michal ; Vávra, D. ; Auda, O. ; Aslanyan, Gurgen ; Bečičková, H. ; Daniš, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kacer, R. ; Kameník, O. ; Konopecký, F. ; Lamazoshvili, Beka ; Lukáč, J. ; Menkyna, Robert ; Mirzoyan, Armen ; Motl, T. ; Musil, K. ; Plotnikov, S. ; Rasulova, Khanifakhon ; Remo, A. ; Vlček, J.
The report has three chapters. The chapter 1 summarizes main features of the Ugandan economy relevant for building the FPAS. The chapter 2 presents the structural macroeconomic model and its properties captured by decompositions of variances of the model’s variables in terms of the model shocks and by its impulse-response functions. The chapter also describes Bayesian vector autoregressions used for the near-term forecasting. The chapter 3 evaluates how the models perform empirically. The forecasting power is assessed both in the sample as well as by using an out-of-the-sample comparison with the standard random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison.
|
|
|
Modeling Rwanda
Auda, O. ; Bečičková, H. ; Cincibuch, M. ; Čižmár, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kameník, O. ; Katreniaková, D. ; Kejak, Michal ; Lamazoshvili, Beka ; Lukáč, J. ; Machala, J. ; Menkyna, Robert ; Musil, K. ; Rasulova, Khanifakhon ; Remo, A. ; Vávra, D. ; Vlček, J. ; Zemčík, Petr
The report consists of four chapters. Chapter 1 assesses the historical performance of forecasts for Rwanda. Historical forecasts since January 2010 are compared with the actual data as well as with projections of other institutions. Chapter 2 presents the structural macroeconomic model, its changes compared to the December 2009 version, and its properties captured by impulse-response functions and by variance decompositions of model’s variables in terms of the model shocks. Important is the part on the model-consistent interpretation of the recent economic Rwanda history. The section describing Bayesian vector autoregressions used for the near-term forecasting concludes. Chapter 3 evaluates how the models perform empirically. On the contrary to Chapter 1, the forecasting power is assessed both in the sample as well as by using an out-of-thesample comparison with the standard random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison. The last chapter gives an overview of the considerable country database that has been compiled.
|
|
|
Modeling Ethiopia
Auda, O. ; Bečičková, H. ; Cincibuch, M. ; Čižmár, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kameník, O. ; Katreniaková, D. ; Kejak, Michal ; Lamazoshvili, Beka ; Lukáč, J. ; Machala, J. ; Menkyna, Robert ; Musil, K. ; Rasulova, Khanifakhon ; Remo, A. ; Vávra, D. ; Vlček, J. ; Zemčík, Petr
The report has three chapters. Chapter 1 summarizes the main features of the Ethiopian economy relevant for building the forecasting and policy analysis system (FPAS). Chapter 2 presents the structural model, and near-term forecast models. Chapter 3 evaluates how the models perform empirically.
|
|
|
Modeling Haiti
Auda, O. ; Bečičková, H. ; Cincibuch, M. ; Čižmár, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kameník, O. ; Katreniaková, D. ; Kejak, Michal ; Lamazoshvili, Beka ; Lukáč, J. ; Machala, J. ; Menkyna, Robert ; Musil, K. ; Rasulova, Khanifakhon ; Remo, A. ; Vávra, D. ; Vlček, J. ; Zemčík, Petr
The report has four chapters. Chapter 2 summarizes the main features of the Haitian economy relevant for building the forecasting and policy analysis system (FPAS). Chapter 3 presents the structural macroeconomic model and its properties captured by the decompositions of variances of the model’s variables in terms of the model shocks and by its impulse-response functions. This chapter also describes Bayesian vector autoregressions used for the near-term forecasting. Chapter 4 evaluates how the models perform empirically. The forecasting power is assessed both in the sample and by using an out-of-the-sample comparison with the standard random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison.
|
|
|
Modeling Tanzania
Auda, O. ; Bečičková, H. ; Cincibuch, M. ; Čižmár, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kameník, O. ; Katreniaková, D. ; Kejak, Michal ; Lamazoshvili, Beka ; Lukáč, J. ; Machala, J. ; Menkyna, Robert ; Musil, K. ; Rasulova, Khanifakhon ; Remo, A. ; Vávra, D. ; Vlček, J. ; Zemčík, Petr
The report has three chapters. Chapter 1 summarizes the main features of the Tanzanian economy relevant for building the FPAS. Chapter 2 presents the structural macroeconomic model and its properties captured by the decompositions of variances of the model’s variables in terms of the model shocks and by its impulse-response functions. This chapter also describes Bayesian vector autoregressions used for the near-term forecasting. Chapter 3 evaluates how the models perform empirically. The forecasting power is assessed both in the sample and by using an out-of-the-sample comparison with the standard random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison.
|
|
|
Modeling Nigeria
Auda, O. ; Bečičková, H. ; Cincibuch, M. ; Čižmár, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kameník, O. ; Katreniaková, D. ; Kejak, Michal ; Lamazoshvili, Beka ; Lukáč, J. ; Machala, J. ; Menkyna, Robert ; Musil, K. ; Rasulova, Khanifakhon ; Remo, A. ; Vávra, D. ; Vlček, J. ; Zemčík, Petr
The report has three chapters. Chapter 1 summarizes the main features of the Nigerian economy relevant for building the FPAS. Chapter 2 presents the structural macroeconomic model and its properties captured by the decompositions of variances of the model’s variables in terms of the model shocks and by its impulse-response functions. This chapter also describes Bayesian vector autoregressions used for the near-term forecasting. Chapter 3 evaluates how the models perform empirically. The forecasting power is assessed both in the sample and by using an out-of-the-sample comparison with the standard random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison.
|
|
|
Modeling Cambodia
Cincibuch, M. ; Kejak, Michal ; Vávra, D. ; Auda, O. ; Aslanyan, Gurgen ; Bečičková, H. ; Daniš, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kacer, R. ; Kameník, O. ; Konopecký, F. ; Lamazoshvili, Beka ; Lukáč, J. ; Menkyna, Robert ; Mirzoyan, Armen ; Motl, T. ; Musil, K. ; Plotnikov, S. ; Rasulova, Khanifakhon ; Remo, A. ; Vlček, J.
The report consists of three chapters. Chapter 1 presents the structural macroeconomic model, its changes compared to the June 2010 version, and its properties captured by impulse-response functions and by variance decompositions of the model’s variables in terms of the model’s shocks. What is important is the part on the model-consistent interpretation of the recent economic history of Cambodia. The section describing Bayesian vector autoregressions used for the near-term forecasting concludes. Chapter 2 evaluates how the models perform empirically. The forecasting power is assessed using both within-sample and out-of-sample comparisons with the random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison. The last chapter provides an overview of the considerable country database that has been compiled.
|
|
|
Modeling Mozambique
Cincibuch, M. ; Kejak, Michal ; Vávra, D. ; Auda, O. ; Aslanyan, Gurgen ; Bečičková, H. ; Daniš, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kacer, R. ; Kameník, O. ; Konopecký, F. ; Lamazoshvili, Beka ; Lukáč, J. ; Menkyna, Robert ; Mirzoyan, Armen ; Motl, T. ; Musil, K. ; Plotnikov, S. ; Rasulova, Khanifakhon ; Remo, A. ; Vlček, J.
The report has four chapters. Chapter 1 summarizes the main features of the Mozambican economy relevant for building the FPAS. Chapter 2 presents the structural macroeconomic model and its properties, captured by the decompositions of the variances of the model’s variables in terms of the model shocks and by the model’s impulse-response functions. This chapter also describes the Bayesian vector autoregressions used for near-term forecasting. Chapter 3 evaluates how the models perform empirically. The forecasting power is assessed both in the sample and by using an out-of-the-sample comparison with the standard random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison. Chapter 4 summarizes the data used in the structural model and the issues identified during the data collection process.
|
|
|
Modeling Kenya
Auda, O. ; Bečičková, H. ; Cincibuch, M. ; Čižmár, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kameník, O. ; Katreniaková, D. ; Kejak, Michal ; Lamazoshvili, Beka ; Lukáč, J. ; Machala, J. ; Menkyna, Robert ; Musil, K. ; Rasulova, Khanifakhon ; Remo, A. ; Vávra, D. ; Vlček, J. ; Zemčík, Petr
The report has three chapters. Chapter 1 summarizes the main features of the Kenyan economy relevant for building the FPAS. Chapter 2 presents the structural macroeconomic model and its properties captured by the decompositions of variances of the model’s variables in terms of the model shocks and by its impulse-response functions. This chapter also describes Bayesian vector autoregressions used for the near-term forecasting. Chapter 3 evaluates how the models perform empirically. The forecasting power is assessed both in the sample and by using an out-of-the-sample comparison with the standard random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison.
|
|
|
Modeling Guatemala
Cincibuch, M. ; Kejak, Michal ; Vávra, D. ; Auda, O. ; Aslanyan, Gurgen ; Bečičková, H. ; Daniš, P. ; Hřebíček, H. ; Janjgava, Batlome ; Kacer, R. ; Kameník, O. ; Konopecký, F. ; Lamazoshvili, Beka ; Lukáč, J. ; Menkyna, Robert ; Mirzoyan, Armen ; Motl, T. ; Musil, K. ; Plotnikov, S. ; Rasulova, Khanifakhon ; Remo, A. ; Vlček, J.
The report consists of three chapters. Chapter 1 presents the structural macroeconomic model, its changes compared to the December 2009 version, and its properties captured by impulse-response functions and by variance decompositions of model’s variables in terms of the model shocks. Important is the part on the model-consistent interpretation of the recent economic history of Guatemala. The section describing Bayesian vector autoregressions used for the nearterm forecasting concludes. Chapter 2 evaluates how the models perform empirically. The forecasting power is assessed using both in-sample and out-of-sample comparisons with the random-walk benchmark. We conclude that the FPAS performs satisfactorily in this comparison. The last chapter provides an overview of the considerable country database that has been compiled.
|
host ::
RSS.