## wp18160

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### I. Introduction and research questions
- Credit growth in Lithuania began to grow in mid-2015 and since then "credit has grown annually by 5.2 percent on average."
- Acceleration of credit coincided with strong wage growth, rapid increases in housing prices, and rapid expansion of the housing stock.
- Banking system dominated by Nordic banks; many Nordic countries experienced rapid credit growth and sharp housing price increases in recent years.
- Main research questions:
  - How closely are credit, housing price, and output cycles synchronized within Lithuania and across other Baltic and Nordic countries?
  - What are the main determinants of a credit recovery?
  - How do shocks in credit, housing prices, and output in Lithuania and other Baltic and Nordic countries impact Lithuania’s credit?
- Paper contributions:
  - Focus on credit, housing price, and output cycles in Baltic and Nordic countries, with emphasis on Lithuania.
  - Assess how shocks in other Baltic and Nordic countries spill over to Lithuania.
- Structure:
  - Part II: compares Lithuania’s current credit cycle to the pre-crisis credit boom and assesses financial stability risks using stylized facts.
  - Part III: describes features of cycles and comovements within and across countries.
  - Part IV: panel regressions to assess determinants of a credit upturn.
  - Part V: assesses spillovers from other Baltic and Nordic countries to Lithuania.
  - Part VI: conclusions and implications for financial stability.

### II. Stylized Facts (current credit cycle vs. pre-crisis boom)
- SF1: High and rapid credit growth characterizes financial crises.
  - Post-crisis credit growth peaked at "8.4 percent in August 2016", compared with "64 percent prior to the global financial crisis (April 2006)."
  - Households: "9.4 percent (August 2016)" vs "53.4 percent (April 2006)."
  - Nonfinancial corporate (NFC) sector: "7.8 percent (September 2017)" vs "128.1 percent (May 2004)."
  - Outstanding stock of credit at end 2017 was "41.1 percent of GDP", well below the 2009 peak ("65.9 percent of GDP").
- SF2: High loan-to-deposit ratio is associated with crises; current funding differs from pre-crisis.
  - Banking system now relies exclusively on deposits (loan-to-deposit ratio below 100) whereas prior to the global financial crisis the loan-to-deposit ratio had reached "188 percent."
  - Since "2009Q4" deposits are growing faster than loans, unlike pre-crisis.
- SF3: Extensive parent funding declines.
  - Parent funding as percent of deposits declined from a peak of "42.8 percent in 2008Q4" to "15 percent of total deposits at end-2016."
- SF4: Private sector leveraging has improved after deleveraging.
  - Household indebtedness in loans declined from a pre-crisis peak of "34.0 percent of GDP in March 2009" to "22.3 percent of GDP in 2017Q3."
  - Corporate indebtedness in loans declined from "63.6 percent of GDP in March 2009" to "41.9 percent of GDP in 2017Q3."
- SF5: Easy credit conditions persist with low interest rates.
  - Interest rates for corporate loans and mortgages near historic lows—"2.29 percent for NFCs and 3.66 for households at end-2017."
  - Long-term interest rates (in euros) are volatile and have climbed near post-crisis peaks; recent steepening of the yield curve may reflect expected future interest hikes.
- SF6: Housing market growth notable but below pre-crisis peaks.
  - Number of housing transactions has increased steadily since 2010 and is nearing the pre-crisis peak, though pace has declined recently.
  - Housing prices rising steadily especially after 2014, but at markedly slower pace than pre-crisis years.
  - Housing prices remain about "20 percent below the pre-crisis peak."
  - Housing completions and building permits are near or have exceeded pre-crisis levels.
- SF7: High share of real estate loans.
  - Housing loans and credit for construction and real estate activities are well above the levels at the peak of the pre-crisis years (April 2006).
  - High concentration of construction and real estate loans is partially compensated by fewer loans in professional, scientific, and technical activities and other loans.
- Overall assessment:
  - Indicators such as rate of credit growth, stock of outstanding credit, loan-to-deposit ratio, and limited parent funding point to a benign credit cycle.
  - Continued vigilance warranted because of continued low interest rates, rising housing prices, and skewed lending composition toward housing loans, construction and real estate activities.

### III. Cycle features, synchronization, and comovement
- Data and methodology:
  - Quarterly data for seven Baltic and Nordic countries over the period "1995–2017Q3."
  - Credit measured as loans by monetary financial institutions to household and NFC sectors; output measured by real GDP (chained volume series); housing price by OECD house price index. Data deflated using the CPI and seasonally adjusted.
  - Turning points identified using Harding and Pagan (2002) algorithm on log-levels with censoring rules:
    - Minimum duration of a complete cycle: "five quarters."
    - Minimum duration of each phase: "at least two quarters."
    - Peak condition: {[(yt - yt-2) > 0, (yt - yt-1) > 0] and [(yt+2 - yt) < 0, (yt+1 - yt) < 0]}
    - Trough condition: {[(yt - yt-2) < 0, (yt - yt-1) < 0] and [(yt+2 - yt) > 0, (yt+1 - yt) > 0]}
  - Cycle metrics: duration (Du, Dd), amplitude (Au, Ad), slope (Au/Du, Ad/Dd).
- Credit cycles (Lithuania):
  - Lithuania has undergone two complete credit cycles since 1995 and is in the upturn phase of a third cycle.
  - Credit cycles are asymmetric: upturns longer and larger than downturns.
  - Average duration of a credit upturn in Lithuania: 19 quarters; downturn: 10 quarters.
  - Average amplitude of upturns is much bigger than of downturns.
  - Normalized by duration, upturn and downturn slopes in Lithuania are the lowest among the Baltics; relative to the Nordics, Lithuania has the highest upturn speed but less than average downturn speed.
- Housing price cycles (Lithuania):
  - Housing price cycle is more volatile (more upturns and downturns) than most peers.
  - Housing prices in an upturn for a little more than half of the time (one of the lowest shares among peers).
  - Average duration of housing price upturn in Lithuania: 7.7 quarters.
  - During upturns, Lithuania’s housing prices increase more rapidly (higher slope) and decline more slowly during downturns (downward rigidity).
- Output cycles (Lithuania):
  - Output has been in an upturn 87 percent of the time.
  - Average duration of output upturn: 26 quarters.
  - Downturns are shorter than in other countries.
  - Both upturn and downturn speeds are faster than the Baltic and Nordic average.
- Three-cycle comparison (credit, housing prices, output):
  - Housing price cycles are most frequent.
  - Output upturns last the longest; housing price upturns last the shortest.
  - On the down phase: credit downturns last the longest; output downturns last the shortest.
  - Credit changes at the fastest pace (slope) during both upturns and downturns.
  - Housing prices adjust the least during downturns (downward rigidity).
- Concordance and comovement:
  - Concordance index (Harding and Pagan, 2002) used to measure fraction of time two series are in the same phase.
  - Lithuania’s within-country concordances:
    - Credit–housing price concordance: 0.77.
    - Credit–output concordance: 0.60.
    - Output–housing price concordance higher than credit–output.
  - Lithuania’s synchronization with peers (averages across Baltic and Nordic countries):
    - Output cycle: 0.84 (most synchronized across countries).
    - Credit cycle: 0.67.
    - Housing price cycle: 0.60 (least synchronized, strong local influences).
  - Pairwise concordance extremes:
    - High: 0.91 (Lithuania–Estonia output cycles).
    - Low: 0.45 (Lithuania credit cycle and Denmark output cycle).

### IV. Determinants of credit upturns (panel regressions)
- Dependent variable:
  - CCUP = Presence of credit upturn (binary: 1 when credit is in an upturn phase, 0 otherwise).
- Key regressors:
  - Presence of housing price upturn (binary).
  - Presence of output upturn (binary).
  - Presence of a credit downturn during the global financial crisis (2007–08 dummy).
  - Interest rate: 3-month money market rate.
  - Private sector indebtedness: sum of household and NFC indebtedness, percent of GDP.
  - Consumer inflation: harmonized index of consumer prices.
  - Deposit growth: household and NFC deposits.
  - Banks’ return on assets.
  - Country fixed effects (γi).
- Main regression findings (Table 6 summary):
  - Housing price upturn: coefficients positive and statistically significant under all specifications (examples: 1.218*, 1.261*, 1.374*, 1.194*, 1.435*, 1.385*, 1.359*, 1.301*).
  - Output upturn: not significant when controlling for other variables (examples: -0.180 (0.72), 0.149 (0.304), 0.041 (0.322)).
  - Credit supply factors:
    - Return on assets: positive and significant (examples: 0.424*, 0.397*, 0.422*, 0.426*, 0.327*).
    - Deposit growth: positive and significant (examples: 0.181*, 0.156*, 0.168*).
    - Inflation: negative and significant (examples: -0.075*, -0.051*).
  - Credit demand factors:
    - Interest rate: significant in all specifications but sign varies; in full specification (equation 8) higher interest rate reduces likelihood of credit upturn (captures credit demand).
    - Private sector indebtedness: negative correlation (example coefficient -0.020* (0.008)).
  - Number of observations: 637 (all regressions include country fixed effects).

### V. VAR analysis: domestic and external shocks to credit
- VAR specification:
  - G(L)Yt = c + εt, with G(L) a 5x5 polynomial, Yt a 5x1 vector: GDP = D(LN_GDP), HPI = D(LN_HPI), CR = D(LN_CR), R = change in 3-month money market rate, CAR = change in capital adequacy.
  - VAR estimated in differences; lag selection supports one lag; differenced series are I(0); VAR satisfies stability condition.
  - Impulse response functions used to trace one-time temporary shocks.
- Main VAR results (region vs. Lithuania; Tables 7–8):
  - Lithuania’s elasticities are higher than the region’s: Lithuania is more prone to volatility.
  - Vulnerability of Lithuania’s credit to shocks (ranking):
    1. Credit shocks (largest vulnerability)
    2. Output shocks
    3. Housing price shocks
  - In both region and Lithuania, credit shocks do not have a large impact on real GDP; instead, real GDP shocks have a strong impact on real credit.
  - Speed of adjustment (percent of total adjustment within four quarters, Table 8):
    - Region: Housing price shock 92; Real credit shock 75; Real GDP shock 81.
    - Lithuania: Housing price shock 90; Real credit shock 79; Real GDP shock 78.
  - Credit’s response to shocks is slower in Lithuania than in the region, especially to real GDP shocks.
  - Credit shocks have the most protracted impact on Lithuania’s real GDP and housing prices.
  - The most rapidly transmitted shock in Lithuania is a housing price shock, which translates to rapid gains in real GDP.
- Selected elasticities (Table 7 examples):
  - Region: Housing price shock 2.011; Real credit shock 0.103; Real GDP shock 0.78.
  - Lithuania: Housing price shock 0.05; Real credit shock 3.00; Real GDP shock 0.30.

### VI. Cross-country transmission and cross-variable shocks
- Cross-country interconnectedness:
  - Credit developments across countries are most strongly interconnected.
  - On average, a one percent credit shock in other Baltic and Nordic countries leads to a 1.9 percent increase in Lithuania’s credit, with 68 percent of the adjustment taking place within four quarters.
  - Country heterogeneity:
    - Biggest positive impacts on Lithuania’s credit from credit shocks in Denmark, followed by Finland, and Latvia.
    - Credit shocks in Estonia, Norway, and Sweden have a negative impact on Lithuania’s credit (possible channels: parent banks reallocate resources, preemptive regulatory responses, confidence effects reducing demand).
- Housing prices:
  - On average, a one percent housing price shock in Baltic and Nordic countries leads to a 0.7 percent increase in housing prices in Lithuania, with 80 percent of the adjustment taking place within four quarters.
  - Biggest positive impacts on Lithuania’s housing prices from Denmark, followed by Estonia and Finland.
  - Housing price shocks in Norway and Sweden have negative impacts on Lithuania’s housing prices; largest decline in Lithuania’s housing prices is from shocks in Sweden.
- Real GDP:
  - On average, a one percent real GDP shock in Baltic and Nordic countries leads to a 1.1 percent increase in Lithuania’s real GDP, with 87 percent of the adjustment taking place within four quarters.
  - Biggest impacts on Lithuania’s real GDP from real GDP shocks in Finland, followed by Sweden; shocks in other countries do not exert a significant impact.
- Cross-variable shocks on Lithuania:
  - Real GDP shock in Baltic and Nordic countries → Lithuania’s credit: On average, a one percent real GDP shock leads to a 2.4 percent increase in Lithuania’s credit.
  - Housing price shock in Baltic and Nordic countries → Lithuania’s credit: A one percent housing price shock leads to a 2.0 percent increase in Lithuania’s credit.
  - Other cross-variable shocks: the only additional significant one is the effect of a real GDP shock in Baltic and Nordic countries on Lithuania’s housing prices.
  - Least pronounced: effect of a credit shock in Baltic and Nordic countries on Lithuania’s real GDP (may indicate reverse causality: real economy affects credit).

### VII. Key empirical findings and financial-stability implications
- Empirical findings:
  - Strong comovement between Lithuania’s credit and housing price cycles; panel regressions confirm housing price upturns strongly predict credit upturn presence.
  - Lithuania’s credit is more sensitive (higher elasticities) to shocks than the Baltic and Nordic region.
  - Shocks have a more protracted impact on Lithuania’s credit than in the region, especially credit shocks.
  - Housing prices are more responsive to local conditions; external shocks have limited impact on Lithuania’s housing prices.
  - Domestic credit shocks do not significantly impact Lithuania’s economy; external economic shocks do.
  - Strong response of real GDP to domestic housing price shocks may indicate a strong wealth effect.
- Financial-stability implications and risks:
  - High elasticity of credit to domestic shocks suggests greater proneness to financial volatility.
  - Lower speed of adjustment in Lithuania’s credit suggests credit volatility may last longer.
  - High elasticity of credit to credit shocks combined with relatively slow transmission poses risk of protracted credit downturns.
  - Housing price developments domestically and in peers have a strong impact on Lithuania’s credit and could pose significant risks to financial stability.

### VIII. Policy recommendations and operational guidance
- Proactively respond to shocks to prevent credit, housing, and economic imbalances from building up.
- Facilitate credit’s adjustment to shocks; review and, where necessary, streamline existing regulations to facilitate credit’s adjustment to domestic shocks.
- Closely monitor housing price, credit, and economic developments in other Baltic and Nordic countries; deepen cooperation in the Nordic-Baltic Stability Group for early response.
- Monitor housing prices and broader housing market developments in Lithuania and peers; regulators may develop close working relationships with real estate market participants to assess underlying trends.
- Take into account strong macro-financial links when preserving financial stability; reinforce supervision and regulation to address credit procyclicality.
- Recognize policies to ease credit to boost growth are unlikely to be effective given the low elasticity of real GDP to credit shocks.
- Periodically review macroprudential instruments to ensure continued effectiveness; support with strong supervision, enforcement, and appropriate macroeconomic and structural policies.

### IX. Caveats, data scope, and data definitions
- Analysis sample: "1995Q1–2017Q3."
- Coverage: Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden.
- Data definitions and sources (selected):
  - Real GDP: Chained volume series, seasonally and working day adjusted in millions of euros. Source: Haver.
  - Credit: Nominal credit to household and nonfinancial corporate sectors, seasonally adjusted, converted to euros, deflated using the HICP (or national CPI). Sources: Haver, National statistical offices, IFS, Eurostat, staff estimates.
  - Housing prices: Real house price index, seasonally adjusted (OECD and staff estimates).
  - Interest rate: 3-month money market interest rate.
  - Consumer inflation: Harmonized consumer price index.
  - Deposits: Household and NFC deposits at credit institutions.
  - Private sector indebtedness: Liabilities of non-financial corporations and households (Eurostat and staff estimates).
  - Capital Adequacy Ratio, Return on assets, EMBI, World growth, Productivity growth: sources as listed in Annex I.
- Caveats:
  - Lithuania implemented strong post-crisis measures; current vulnerability may be lower than historical patterns imply.
  - Differences in financial development across countries may not be fully captured.

### X. Selected empirical tables and key statistics (selected figures preserved exactly)
- Table 1. Credit Cycle Characteristics (Lithuania summary)
  - Number of upturns: 3.0
  - Number of downturns: 3.0
  - Number of quarters in upturn: 55.0
  - Number of quarters in downturn: 31.0
  - Time in upturn (percent): 60.4
  - Time in downturn (percent): 34.1
  - Duration of upturn (average): 19.0
  - Duration of downturn (average): 10.0
  - Amplitude of upturn (average), percent: 358.1
  - Amplitude of downturn (average), percent: -28.0
  - Slope of upturn 1/: 12.8
  - Slope of downturn 1/: -3.3
- Table 2. Housing Price Cycle Characteristics (selected averages)
  - Number of upturns (average): 4.3
  - Number of downturns (average): 4.3
  - Number of quarters in upturn (median): 53.0
  - Time in upturn (percent) (average): 65.2
  - Time in downturn (percent) (average): 10.8
  - Amplitude of upturn (average), percent (median): 42.1
  - Amplitude of downturn (average), percent (median): -12.9
- Table 3. Output Cycle Characteristics (selected averages)
  - Number of upturns (average): 3.0
  - Time in upturn (percent): 86.8
  - Time in downturn (percent): 8.8
  - Duration of upturn (average): 26.3
  - Amplitude of upturn (average), percent: 48.2
  - Amplitude of downturn (average), percent: -9.8
- Table 4. Lithuania: Credit, Housing Price, and Output Cycles (Lithuania summary)
  - Credit Cycle: Number of upturns 3.0; Number of downturns 3.0; Number of quarters in upturn 55.0; Number of quarters in downturn 31.0; Time in upturn (percent) 60.4; Time in downturn (percent) 34.1; Duration of upturn (average) 19.0; Duration of downturn (average) 10.0; Amplitude of upturn (average), percent 358.1; Amplitude of downturn (average), percent -28.0; Slope of upturn 1/ 12.8; Slope of downturn 1/ -3.3
  - Housing Price Cycle: Number of upturns 6.0; Number of downturns 5.0; Number of quarters in upturn 46.0; Number of quarters in downturn 32.0; Time in upturn (percent) 51.7; Time in downturn (percent) 36.0; Duration of upturn (average) 7.7; Duration of downturn (average) 5.5; Amplitude of upturn (average), percent 35.4; Amplitude of downturn (average), percent -11.9; Slope of upturn 1/ 5.8; Slope of downturn 1/ -1.2
  - Output Cycle: Number of upturns 3.0; Number of downturns 2.0; Number of quarters in upturn 79.0; Number of quarters in downturn 8.0; Time in upturn (percent) 86.8; Time in downturn (percent) 8.8; Duration of upturn (average) 26.3; Duration of downturn (average) 4.0; Amplitude of upturn (average), percent 48.2; Amplitude of downturn (average), percent -9.8; Slope of upturn 1/ 1.8; Slope of downturn 1/ -2.2
- Table 5. Synchronization (selected concordance)
  - Lithuania–Baltic and Nordic average concordances: Credit cycle 0.67; Housing price cycle 0.64; Output cycle 0.59
- Table 6. Determinants (selected coefficients)
  - Presence of upturn in housing price cycle: coefficients include 1.218*, 1.261*, 1.374*, 1.194*, 1.435*, 1.385*, 1.359*, 1.301*.
  - Return on assets: coefficients 0.424*, 0.397*, 0.422*, 0.426*, 0.327*.
  - Interest rate: coefficients 0.200*, 0.119*,-0.369*,-0.450*.
  - Deposit growth rate: coefficients 0.181*, 0.156*, 0.168*.
  - Inflation rate: coefficients -0.075*, -0.051*.
  - Private sector indebtedness (percent of GDP): coefficient -0.020* (0.008).
  - Number of observations: 637.
- Table 7 (selected entries): Elasticities to one percent shocks
  - Region: Housing price shock 2.011; Real credit shock 0.103; Real GDP shock 0.78.
  - Lithuania: Housing price shock 0.05; Real credit shock 3.00; Real GDP shock 0.30.
- Table 8 (selected entries): Speed of adjustment (percent within four quarters)
  - Region: Housing price shock 92; Real credit shock 75; Real GDP shock 81.
  - Lithuania: Housing price shock 90; Real credit shock 79; Real GDP shock 78.

*Source: IMF staff work as presented in the provided content unit (wp18160).*

### References..............................................................................................................

### wp18160 - References..............................................................................................................

### I. Introduction and research questions
- Credit growth in Lithuania began to grow in mid-2015 and since then "credit has grown annually by 5.2 percent on average."
- Acceleration of credit coincided with strong wage growth, rapid increases in housing prices, and rapid expansion of the housing stock.
- Banking system dominated by Nordic banks; many Nordic countries experienced rapid credit growth and sharp housing price increases in recent years.
- Main research questions:
  - How closely are credit, housing price, and output cycles synchronized within Lithuania and across other Baltic and Nordic countries?
  - What are the main determinants of a credit recovery?
  - How do shocks in credit, housing prices, and output in Lithuania and other Baltic and Nordic countries impact Lithuania’s credit?
- Paper contributions:
  - Focus on credit, housing price, and output cycles in Baltic and Nordic countries, with emphasis on Lithuania.
  - Assess how shocks in other Baltic and Nordic countries spill over to Lithuania.
- Structure of the paper:
  - Part II: compares Lithuania’s current credit cycle to the pre-crisis credit boom and assesses financial stability risks using stylized facts.
  - Part III: describes features of cycles and comovements within and across countries.
  - Part IV: panel regressions to assess determinants of a credit upturn.
  - Part V: assesses spillovers from other Baltic and Nordic countries to Lithuania.
  - Part VI: conclusions and implications for financial stability.

### II. The current credit cycle vs. pre-crisis credit boom — Stylized Facts (SF)
- SF1: High and rapid credit growth characterizes financial crises.
  - Current growth and stock of credit well below pre-crisis years.
  - Post-crisis credit growth peaked at "8.4 percent in August 2016", compared with "64 percent prior to the global financial crisis (April 2006)."
  - Households: "9.4 percent (August 2016)" vs "53.4 percent (April 2006)."
  - Nonfinancial corporate (NFC) sector: "7.8 percent (September 2017)" vs "128.1 percent (May 2004)."
  - Outstanding stock of credit at end 2017 was "41.1 percent of GDP", well below the 2009 peak ("65.9 percent of GDP").
- SF2: High loan-to-deposit ratio is associated with crises; current funding differs from pre-crisis.
  - Banking system now relies exclusively on deposits (loan-to-deposit ratio below 100) whereas prior to the global financial crisis the loan-to-deposit ratio had reached "188 percent."
  - Since "2009Q4" deposits are growing faster than loans, unlike pre-crisis.
- SF3: Extensive parent funding declines.
  - Parent funding as percent of deposits declined from a peak of "42.8 percent in 2008Q4" to "15 percent of total deposits at end-2016."
- SF4: Private sector leveraging has improved after deleveraging.
  - Household indebtedness in loans declined from a pre-crisis peak of "34.0 percent of GDP in March 2009" to "22.3 percent of GDP in 2017Q3."
  - Corporate indebtedness in loans declined from "63.6 percent of GDP in March 2009" to "41.9 percent in 2017Q3."
- SF5: Easy credit conditions persist with low interest rates.
  - Interest rates for corporate loans and mortgages near historic lows—"2.29 percent for NFCs and 3.66 for households at end-2017."
  - Long-term interest rates (in euros) are volatile and have climbed near post-crisis peaks; recent steepening of the yield curve may reflect expected future interest hikes.
- SF6: Housing market growth notable but below pre-crisis peaks.
  - Number of housing transactions has increased steadily since 2010 and is nearing the pre-crisis peak, though pace has declined recently.
  - Housing prices rising steadily especially after 2014, but at markedly slower pace than pre-crisis years.
  - Housing prices remain about "20 percent below the pre-crisis peak."
  - Housing completions and building permits are near or have exceeded pre-crisis levels.
- SF7: High share of real estate loans.
  - Housing loans and credit for construction and real estate activities are well above the levels at the peak of the pre-crisis years (April 2006).
  - High concentration of construction and real estate loans is partially compensated by fewer loans in professional, scientific, and technical activities and other loans.
- Overall assessment:
  - Indicators such as rate of credit growth, stock of outstanding credit, loan-to-deposit ratio, and limited parent funding point to a benign credit cycle.
  - Continued vigilance warranted because of continued low interest rates, rising housing prices, and skewed lending composition toward housing loans, construction and real estate activities.

### III. Main features and synchronization of credit, housing price, and output cycles — data and methodology
- Data:
  - Quarterly data for seven Baltic and Nordic countries over the period "1995–2017Q3."
  - Credit cycle measured using loans by monetary financial institutions to household and NFC sectors.
  - Output cycle measured using real GDP (chained volume series).
  - Housing price cycle measured using house price index by the OECD.
  - All data deflated using the CPI and seasonally adjusted.
  - Annex I provides more details on data coverage and sources.
- Turning point identification:
  - Uses the "classical" definition of the output cycle to create a chronology of cycles; advantages include robustness of turning points to new data.
  - Algorithm: Harding and Pagan (2002) applied to the log-level of quarterly series.
  - Censoring rules and minimal duration requirements:
    - Minimum duration of a complete cycle: "five quarters."
    - Minimum duration of each phase of the cycle: "at least two quarters."
  - Peak identification condition for quarterly series yt:
    - {[(yt - yt-2) > 0, (yt - yt-1) > 0] and [(yt+2 - yt) < 0, (yt+1 - yt) < 0]}
  - Trough identification condition:
    - {[(yt - yt-2) < 0, (yt - yt-1) < 0] and [(yt+2 - yt) > 0, (yt+1 - yt) > 0]}
- Cycle features definitions:
  - Duration of an upturn, Du: number of quarters, k, it takes for a variable to reach its peak after a trough.
  - Duration of a downturn, Dd: number of quarters, k, it takes for a variable to reach a trough after its peak.
  - Amplitude of an upturn, Au: change in yt from a trough, ym, to the next peak, yp, i.e., Au = yp – ym.
  - Amplitude of a downturn, Ad: change in yt from its peak, yp, to the next trough, ym, i.e., Ad = ym – yp.
  - Slope of a cycle:
    - Upturn slope = Au / Du.
    - Downturn slope = Ad / Dd.
- Analytical focus:
  - Examine frequency, duration, amplitude, and slope of credit, housing price, and output cycles in Lithuania and other Baltic and Nordic countries.
  - Assess degree of synchronization within and across countries and links among credit, housing prices, and economic activity.

*Source: IMF staff work as presented in the provided content unit.*

### 20.      Lithuania has undergone two complete credit cycles since 1995 and is currently in the

### 20.      Lithuania has undergone two complete credit cycles since 1995 and is currently in the upturn phase of a third cycle

### Credit cycles: duration, amplitude, and slope
- Lithuania has undergone two complete credit cycles since 1995 and is in the upturn phase of a third cycle.
- Credit cycles in Lithuania and other Baltic and Nordic countries are not symmetric: downturns are much shallower and short lived (Table 1).
- The average duration of a credit upturn in Lithuania has been almost double that of a downturn: 19 quarters against 10 quarters.
- In other countries, credit upturns last even longer; the average duration of credit upturns in Baltic and Nordic countries is almost three times that of downturns.
- The average amplitude of credit upturns is much bigger than of downturns, reflecting the long duration of upturns.
- Normalizing amplitude by duration shows that the speed (slope) of upturns and downturns in Lithuania is the lowest among the Baltics.
- Relative to the Nordics, Lithuania has the highest upturn speed, but less than average downturn speed.

### Housing price cycles: volatility and dynamics
- Lithuania’s housing price cycle is more volatile (more upturns and more downturns) than that of most Baltic and Nordic countries (Table 2).
- Housing prices in Lithuania are in an upturn for a little more than half of the time, one of the lowest among Baltic and Nordic countries.
- The average duration of a housing price upturn in Lithuania is 7.7 quarters, the second lowest among peers.
- During an upturn, Lithuania’s housing prices increase more rapidly than peers (higher slope) and decline more slowly during a downturn (downward rigidity).

### Output (real GDP) cycles: prevalence and speed
- Lithuania’s output has been in an upturn 87 percent of the time.
- The average duration of an output upturn in Lithuania was 26 quarters.
- Lithuania’s downturns have been shorter than those in other Baltic and Nordic countries.
- Both the speed of upturns and downturns in Lithuania has been faster than the average in Baltic and Nordic countries.

### Comparison of credit, housing price, and output cycles (three-cycle comparison)
- Housing price cycles are the most frequent among the three cycles (Table 4).
- Of the three cycles:
  - Output upturns last the longest.
  - Housing price upturns last the shortest.
- On the down phase:
  - Credit downturns last the longest.
  - Output downturns last the shortest.
- Credit changes at the fastest pace (slope) both during upturns and downturns.
- Housing prices adjust the least during downturns, indicating downward rigidity.

### Concordance index and comovement methodology
- The concordance index (Harding and Pagan, 2002) measures the fraction of time two series are in the same phase of their cycles.
- Ctx and Cty are binary indicators: 0 if in downturn phase at time t; 1 if in upturn phase at time t.
- T = the number of time periods in the sample.
- Two series are perfectly procyclical if the concordance index equals unity, and perfectly countercyclical if zero.

### Comovement among Lithuania’s credit, housing price, and output cycles
- Credit-housing price concordance: 0.77 (strongest comovement among Lithuania’s three cycles).
- Credit-output concordance: 0.60 (weakest synchronization among Lithuania’s three cycles).
- Lithuania’s output cycle has a higher synchronization with the housing price cycle than with the credit cycle.

### Comovement of Lithuania’s cycles with other Baltic and Nordic countries
- Lithuania’s credit cycle is most closely synchronized with Estonia’s and Denmark’s credit cycles.
- Lithuania’s housing price cycle is more synchronized with Denmark’s and Estonia’s housing price cycles.
- Lithuania’s output cycle is most synchronized with Estonia’s and Norway’s output cycles.
- Average concordances across peers:
  - Lithuania’s output cycle: 0.84 (most synchronized with other countries’ output cycles).
  - Lithuania’s credit cycle: 0.67.
  - Lithuania’s housing price cycle: 0.60 (least synchronized, suggesting strong local influences).
- Across all cycles, synchronization ranges from a high of 0.91 (Lithuania’s and Estonia’s output cycles) to a low of 0.45 (Lithuania’s credit cycle and Denmark’s output cycle).
- Key highest concordances involve Lithuania’s output cycle.

### Summary conclusion on cycle linkages
- Lithuania’s credit cycle is most closely linked to housing price developments in Lithuania.
- Credit and housing price developments in other countries have, on average, a considerable impact on Lithuania’s credit.
- Economic developments in the region have a somewhat lesser effect on Lithuania’s credit.
- Results are based on cycle comovements and ignore other factors that may influence credit.

### Determinants of credit upturns — econometric approach
- Analysis focuses on determinants of the presence of a credit upturn using panel regressions with country fixed effects and logit estimation.
- Dependent variable:
  - CCUP = Presence of credit upturn (binary: 1 when credit is in an upturn phase, 0 otherwise).
- Key regressors included:
  - Presence of a housing price upturn (binary).
  - Presence of an economic output upturn (binary).
  - Presence of a credit downturn during the global financial crisis (2007–08 dummy).
  - Interest rate, measured by the 3-month money market rate.
  - Private sector indebtedness, defined as the sum of household and NFC sector indebtedness, as a percent of GDP.
  - Consumer inflation, measured by the harmonized index of consumer prices.
  - Deposit growth, measured by household and NFC deposits.
  - Banks’ return on assets.
- Country fixed effects (γi) were included to correct for omitted variable bias.

### Regression results and interpretation (Table 6)
- Housing price upturn: coefficient positive and statistically significant under all specifications — strong role for housing price upturns in presence of a credit upturn.
- Output upturn: does not have a significant impact on the presence of a credit upturn when controlling for other variables.
- Credit supply factors supported:
  - Bank profitability (return on assets) is positively correlated with the presence of a credit upturn and robust across specifications.
  - Deposit growth positively contributes to the presence of a credit upturn.
  - Inflation negatively influences the presence of a credit upturn.
- Credit demand factors:
  - Interest rate is significant in all specifications but signs vary across specifications; in the full specification (equation 8), a higher interest rate reduces the likelihood of a credit upturn (captures credit demand).
  - Private sector indebtedness is negatively correlated with the presence of credit upturns, suggesting high debt levels dampen credit demand by reducing loan eligibility.
- Overall conclusion: regression analysis provides further support for the strong link between credit and housing prices; no evidence that an economic upturn independently contributes to a credit upturn when controlling for credit demand and supply variables.

### Assessing impact of domestic and external shocks on credit — VAR framework
- Method: panel vector autoregression (VAR) to assess transmission of credit, housing price, and output shocks in Lithuania and other Baltic and Nordic countries.
- VAR specification:
  - G(L)Yt = c + εt, where G(L) is a 5x5 polynomial in the lag operator, Yt is a 5x1 vector of endogenous variables, c is a 5x1 vector of constants, and εt is a 5x1 vector of innovations.
- Endogenous variables vector Yt:
  - GDP = log difference in real GDP
  - HPI = log difference in the real housing price index
  - CR = log difference in real credit
  - R = change in 3-month money market rate
  - CAR = change in capital adequacy
- VAR estimated in differences to ensure stationarity.
- Diagnostic tests:
  - Differenced series are I(0).
  - Lag selection supports inclusion of one lag.
  - VAR satisfies stability condition (no roots outside the unit circle).
- Impulse response functions trace the effect of a one-time temporary shock to a variable on current and future values of all endogenous variables.

### Main VAR results: Lithuania vs. region (Figures 1–6; Tables 7–8)
- Lithuania’s elasticities (sensitivity to shocks) are higher than the region’s (Table 7): Lithuania is more prone to volatility.
- Lithuania’s credit is highly vulnerable to all shocks, with vulnerability ranking:
  1. Credit shocks (largest vulnerability)
  2. Output shocks
  3. Housing price shocks
- In both the region and Lithuania, credit shocks do not have a large impact on real GDP; instead, real GDP shocks have a strong impact on real credit.
- Speed of adjustment to shocks is considerably lower in Lithuania than in the Baltic and Nordic region (Table 8):
  - Credit’s response to shocks is slower in Lithuania than in the region, especially its response to real GDP shocks.
  - Credit shocks have the most protracted impact on Lithuania’s real GDP and housing prices (smallest speed of adjustment among shocks).
  - The most rapidly transmitted shock in Lithuania is a housing price shock, which translates to rapid gains in real GDP.

### Cross-country shock transmission (Table 9; Figures 7–9)
- Interconnectedness summary:
  - Credit developments across countries are the most strongly interconnected.
  - On average, a one percent credit shock in other Baltic and Nordic countries leads to a 1.9 percent increase in Lithuania’s credit, with 68 percent of the adjustment taking place within four quarters.
  - There is large variability by country:
    - Biggest positive impacts on Lithuania’s credit from credit shocks in Denmark, followed by Finland, and Latvia.
    - Credit shocks in Estonia, Norway, and Sweden have a negative impact on Lithuania’s credit.
      - Potential channels: parent banks reallocate resources to domestic markets, preemptive regulatory responses in Lithuania, or confidence effects reducing demand.
  - Housing prices are the least interconnected:
    - On average, a one percent housing price shock in Baltic and Nordic countries leads to a 0.7 percent increase in housing prices in Lithuania, with 80 percent of the adjustment taking place within four quarters.
    - Biggest positive impacts on Lithuania’s housing prices from Denmark, followed by Estonia and Finland.
    - Housing price shocks in Norway and Sweden have negative impacts on Lithuania’s housing prices; the largest decline in Lithuania’s housing prices is from shocks in Sweden.
  - Real GDP interconnection:
    - On average, a one percent real GDP shock in Baltic and Nordic countries leads to a 1.1 percent increase in Lithuania’s real GDP, with 87 percent of the adjustment taking place within four quarters.
    - The speed of adjustment for real GDP shocks is the highest among all shocks.
    - Biggest impacts on Lithuania’s real GDP from real GDP shocks in Finland, followed by Sweden; shocks in other countries do not exert a significant impact.

### Cross-variable shocks and their impacts on Lithuania
- Cross-variable shocks have sizable impacts on Lithuania:
  - The most powerful is the effect of a real GDP shock in Baltic and Nordic countries on Lithuania’s credit:
    - On average, a one percent real GDP shock in Baltic and Nordic countries leads to a 2.4 percent increase in credit in Lithuania.
  - Housing price shocks in Baltic and Nordic countries also have a sizable impact on Lithuania’s credit:
    - A one percent housing price shock in Baltic and Nordic countries leads to a 2.0 percent increase in Lithuania’s credit.
  - Among other cross-variable shocks, the only significant one is the effect of a real GDP shock in Baltic and Nordic countries on Lithuania’s housing prices.
  - The least pronounced cross-variable shock is the effect of a credit shock in Baltic and Nordic countries on Lithuania’s real GDP.
    - This may indicate reverse causality: real economic developments affect credit rather than the other way around.

*Source: wp18160 - 20.      Lithuania has undergone two complete credit cycles since 1995 and is currently in the upturn phase of a third cycle (PDF).*

### 47.      Shocks in other Baltic and Nordic countries are generally transmitted quickly except for

### wp18160 - 47.      Shocks in other Baltic and Nordic countries are generally transmitted quickly except for

### Transmission of shocks to Lithuania
- Credit shocks in other Baltic and Nordic countries take considerably longer to be transmitted to Lithuania; Lithuania’s credit, housing price, and real GDP response to credit shocks is among the weakest.
- Housing price and real GDP shocks transmit rapidly to Lithuania: "In both cases, 87 percent of the adjustment is completed within four quarters."
- Housing price and real GDP shocks also have a rapid transmission to Lithuania’s housing prices.
- Shocks originating in Lithuania or other Baltic and Nordic countries have a strong impact on Lithuania’s credit; shocks in other countries also have a significant impact on Lithuania’s credit.

### Relative vulnerabilities and bilateral impacts
- Of all the Baltic and Nordic countries, Lithuania is most vulnerable to shocks from Finland: aggregate shocks from Finland (average of housing price, credit, and real GDP shocks) have the largest impact on Lithuania, with housing price, credit, and real GDP elasticities well above those for other countries.
- Shocks from Denmark and Sweden have a strong impact on Lithuania’s credit.
- Shocks from Norway and Latvia seem to have the least impact.

### Key empirical findings
- Strong comovement between Lithuania’s credit and housing price cycles; panel regressions confirm the relationship between the presence of a credit upturn and the presence of a housing price upturn.
- Lithuania’s credit is more sensitive (higher elasticities) to shocks than the Baltic and Nordic region.
- Shocks have a more protracted impact on Lithuania’s credit than in the Baltic and Nordic region, especially credit shocks.
- Housing prices are more responsive to local conditions because the impact of shocks in other Baltic and Nordic countries on Lithuania’s housing prices is rather limited.
- Domestic credit shocks do not significantly impact Lithuania’s economy.
- Economic shocks in other Baltic and Nordic countries have a significant impact on Lithuania’s housing prices and economy.
- Lithuania’s strong response of real GDP to domestic housing price shocks may indicate a strong wealth effect.

### Financial-stability implications and specific risks
- Lithuania’s high elasticity of credit to domestic shocks relative to the Baltic and Nordic region suggests greater proneness to financial volatility (sensitivity to shocks).
- Lower speed of adjustment in Lithuania’s credit relative to the Baltic and Nordic region suggests credit volatility may last longer in Lithuania.
- High elasticity of credit to credit shocks in Lithuania and other countries, combined with relatively slow transmission, poses significant risks for protracted credit downturns.
- Housing prices both in Lithuania and other Baltic and Nordic countries have a strong impact on Lithuania’s credit and could pose significant risks to financial stability.

### Policy recommendations and operational guidance
- Authorities should proactively respond to shocks by preventing credit, housing, and economic imbalances from building up to minimize their impact on credit.
- Authorities should take measures to facilitate credit’s adjustment to shocks; regulators may want to review and, where necessary, streamline existing regulations to facilitate credit’s adjustment to domestic shocks.
- Closely monitor housing price, credit, and economic developments in other Baltic and Nordic countries; deepen cooperation in the context of the Nordic-Baltic Stability Group to facilitate early response to shocks.
- Monitor closely housing prices and broader housing market developments in Lithuania and other Baltic and Nordic countries; regulators may consider developing close working relationships with real estate market participants to assess underlying trends.
- Take into account strong macro-financial links when preserving financial stability; reinforced supervision and regulation are called for to address credit procyclicality.
- Recognize that policies to ease credit to boost economic growth are unlikely to be effective given the low elasticity of real GDP to credit shocks.
- Periodically review macroprudential instruments to ensure continued effectiveness; effective macroprudential policy should be supported by strong supervision and enforcement and complemented by appropriate macroeconomic and structural policies.

### Caveats and data scope
- The analysis is based on a sample of data covering the period 1995–2017Q3.
- Following the global financial crisis, Lithuania has implemented strong measures to reduce its vulnerability to shocks; it is possible Lithuania now operates in a more benign environment than implied by the analysis.
- Differences in financial development in Baltic and Nordic countries may not have been fully captured in the empirical analysis, which could undermine the accuracy of the results.
- Despite caveats, the main conclusions and policy recommendations remain valid.

*Source: wp18160 (PDF chapter/section).*

### References

### References (wp18160)

### Key cited works
- Adrian, Tobias and Hyun Song Shin, 2010, “Liquidity and Leverage,” Journal of Financial Intermediation, 19 (3), 418-437.
- Bernanke, B., and M. Gertler, 1989, "Agency Costs, Net Worth, and Business Fluctuations," American Economic Review, 79 (1), 14–31.
- Bernanke, B., M. Gertler, and S. Gilchrist, 1999, “The Financial Accelerator in a Quantitative Business Cycle Framework,” in Handbook of Macroeconomics Volume 1, ed. by J. B. Taylor and M. Woodford (New York: Elsevier).
- Burns, Arthur F., and Wesley C. Mitchell, 1946, Measuring Business Cycles (New York: NBER).
- Carrol, Christopher D., Misuzu Otsuka, and Jirka Slacalek, 2006, “How Large Is the Housing Wealth Effect? A New Approach,” JHU Economics Working Paper Archive 535.
- Cecchetti, Stephen G, 2006, “Measuring the Macroeconomic Risks Posed by Asset Price Booms,” in J.Y. Campbell (ed.) Asset Prices and Monetary Policy, U of Chicago Press.
- Cerutti, Eugenio, Jihad Dagher, and Giovanni Dell’Ariccia, 2015a, “Housing Finance and Real-estate Booms: A Cross-Country Perspective,” IMF Staff Discussion Note 15/12 (Washington: International Monetary Fund).
- Che, Natasha Xingyuan, and Yoko Shinagawa, 2014, “Financial Soundness Indicators and the Characteristics of Financial Cycles,” IMF Working Paper, 14.
- Claessens, Stijn, M. Ayhan Kose, and Marco E. Terrones, 2011, “Financial Cycles: What? How? When?”, IMF Working Paper, 76.
- Claessens, Stijn, M. Ayhan Kose, and Marco E. Terrones, 2011, “How do business and financial cycles interact?”, IMF Working Paper, 88.
- Cochrane, John H., 2005, “Financial Markets and the Real Economy,” NBER Working Paper, 11193.
- Constantinescu, Mihnea and Povilas Lastauskas, 2017, “The Knotty Interplay Between Credit and Housing,” Bank of Lithuania Working Paper, 45.
- Croux, C., M. Forni, and L. Reichlin, 2001, “A Measure of Comovement for Economic Variables, Theory and Empirics,” The Review of Economics and Statistics, 83(2), 232-241.
- Dell’Ariccia, Giovanni, Deniz Igan, Luc Laeven, and Hui Tong, with Bas Bakker and Jerome Vandenbussche, 2012, “Policies for Macrofinancial Stability: How to Deal with Credit Booms,” IMF Staff Discussion Note 12/06 (Washington: International Monetary Fund).
- Feldstein, M., 2007, “Housing, Credit Markets and the Business Cycle,” NBER Working Paper, 13471.
- Harding, Don and Adrian Pagan, 2002, “Dissecting the Cycle: A Methodological Investigation,” Journal of Monetary Economics Vol. 49, pp. 365-81.
- Haroon Mumtaz and Paolo Surico, 2009, “The Transmission of International Shocks: A Factor-Augmented VAR Approach,” Journal of Money, Credit and Banking, 41, Supplement 1, 71-100.
- Helbling, Thomas, and Raju Huidrom, M. Ayhan Kose, and Christopher Otrok, 2010, “Do Credit Shocks Matter? A Global Perspective,” IMF Working Paper, 261.
- Igan, Deniz, Alain Kabundi, Francisco Nadal De Simone, Marcelo Pinheiro, and Natalia Tamarisa, 2009, “Three Cycles: Housing, Credit, and Real Activity,” IMF Working Paper, 231.
- International Monetary Fund, 2014, “Finland: 2014 Article IV Consultation—Staff Report” (Washington: International Monetary Fund).
- International Monetary Fund, 2014, “Sweden: 2014 Article IV Consultation—Staff Report” (Washington: International Monetary Fund).
- International Monetary Fund, 2014, “Norway: 2014 Article IV Consultation—Staff Report” (Washington: International Monetary Fund).
- International Monetary Fund, 2014, “Baltic Cluster Report—Selected Issues paper” (Washington: International Monetary Fund).
- Kiyotaki, N. and J. Moore, 1997, “Credit Cycles,” Journal of Political Economy, 105(2), 211-248.
- Mendoza, Enrique, and Marco E. Terrones, 2008, “An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro Data,” NBER Working Paper, 14049.

### Data coverage and variable definitions (Annex I)
- Sample Period: 1995Q1–2017Q3
- Coverage: Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden

- Real GDP: Chained volume series, seasonally and working day adjusted in millions of euros. Source: Haver.
- Credit: Nominal credit to household and nonfinancial corporate sectors, seasonally adjusted, converted to euros, and deflated using the HICP (or national CPI if not available). Sources: Haver, National statistical offices, IFS, Eurostat, and staff estimates.
- Housing prices: Real house price index, seasonally adjusted. If not available, proxied by rent prices, seasonally adjusted. Source: OECD and staff estimates.
- Interest rate: 3-month money market interest rate. Sources: Eurostat, IFS, and national authorities.
- Consumer inflation: Harmonized consumer price index. Source: Haver.
- Deposits: Household and NFC deposits at credit institutions. Sources: National authorities and staff estimates including annualized quarterly growth rates.
- Private sector indebtedness: Liabilities (loans, securities other than shares) held by non-financial corporations and households. Sources: Eurostat and staff estimates, including annualized quarterly growth rates.
- Capital Adequacy Ratio: Regulatory capital as a percent of risk-weighted assets. Sources: FSI database, World Bank Global Financial Development Database and staff estimates including annualized quarterly growth rates.
- Return on assets: Return on assists for depository institutions. Sources: FSI database, World Bank Global Financial Development Database and staff estimates, including annualized quarterly growth rates.
- EMBI: J.P. Morgan EMBI Global Total Return Index. Source: Bloomberg.
- World growth: GDP of OECD countries, volume estimates, seasonally adjusted. Source: OECD database.
- Productivity growth: Real labor productivity per hour worked (NSA, 2010=100), seasonally Adjusted. Sources: Haver and staff estimates.

### Selected empirical tables and key statistics
- Table 1. Credit Cycle Characteristics (selected Lithuania summary)
  - Number of upturns: 3.0
  - Number of downturns: 3.0
  - Number of quarters in upturn: 55.0
  - Number of quarters in downturn: 31.0
  - Time in upturn (percent): 60.4
  - Time in downturn (percent): 34.1
  - Duration of upturn (average): 19.0
  - Duration of downturn (average): 10.0
  - Amplitude of upturn (average), percent: 358.1
  - Amplitude of downturn (average), percent: -28.0
  - Slope of upturn 1/: 12.8
  - Slope of downturn 1/: -3.3

- Table 2. Housing Price Cycle Characteristics (selected averages)
  - Number of upturns (average): 4.3
  - Number of downturns (average): 4.3
  - Number of quarters in upturn (median): 53.0
  - Time in upturn (percent) (average): 65.2
  - Time in downturn (percent) (average): 10.8
  - Amplitude of upturn (average), percent (median): 42.1
  - Amplitude of downturn (average), percent (median): -12.9

- Table 3. Output Cycle Characteristics (selected averages)
  - Number of upturns (average): 3.0
  - Time in upturn (percent): 86.8
  - Time in downturn (percent): 8.8
  - Duration of upturn (average): 26.3
  - Amplitude of upturn (average), percent: 48.2
  - Amplitude of downturn (average), percent: -9.8

- Table 4. Lithuania: Credit, Housing Price, and Output Cycles (Lithuania summary)
  - Credit Cycle: Number of upturns 3.0; Number of downturns 3.0; Number of quarters in upturn 55.0; Number of quarters in downturn 31.0; Time in upturn (percent) 60.4; Time in downturn (percent) 34.1; Duration of upturn (average) 19.0; Duration of downturn (average) 10.0; Amplitude of upturn (average), percent 358.1; Amplitude of downturn (average), percent -28.0; Slope of upturn 1/ 12.8; Slope of downturn 1/ -3.3
  - Housing Price Cycle: Number of upturns 6.0; Number of downturns 5.0; Number of quarters in upturn 46.0; Number of quarters in downturn 32.0; Time in upturn (percent) 51.7; Time in downturn (percent) 36.0; Duration of upturn (average) 7.7; Duration of downturn (average) 5.5; Amplitude of upturn (average), percent 35.4; Amplitude of downturn (average), percent -11.9; Slope of upturn 1/ 5.8; Slope of downturn 1/ -1.2
  - Output Cycle: Number of upturns 3.0; Number of downturns 2.0; Number of quarters in upturn 79.0; Number of quarters in downturn 8.0; Time in upturn (percent) 86.8; Time in downturn (percent) 8.8; Duration of upturn (average) 26.3; Duration of downturn (average) 4.0; Amplitude of upturn (average), percent 48.2; Amplitude of downturn (average), percent -9.8; Slope of upturn 1/ 1.8; Slope of downturn 1/ -2.2

- Table 5. Lithuania: Synchronization of Cycles (selected concordance)
  - Lithuania's concordance index with Baltic and Nordic countries (average of all countries) — Credit cycle: 0.67; Housing price cycle: 0.64; Output cycle: 0.59
  - Pairwise examples shown (selected): Lithuania–Estonia concordance 0.85 (credit cycle); Lithuania–Finland concordance 0.67 (credit cycle)

- Table 6. Determinants of the Presence of a Credit Upturn (regression summary)
  - Presence of upturn in housing price cycle: coefficients range (examples) 1.218*, 1.261*, 1.374*, 1.194*, 1.435*, 1.385*, 1.359*, 1.301* (standard errors shown in parentheses; * denotes statistical significance at the 5 percent level)
  - Presence of upturn in output cycle: coefficients shown (examples) -0.180 (0.72), 0.149 (0.304), 0.041 (0.322)
  - Presence of global financial crisis (dummy variable): coefficients shown (examples) 1.233* (0.423), 0.767 (0.436), 0.465 (0.450), 0.821 (0.493), 2.290* (0.598), 2.769* (0.635)
  - Return on assets: coefficients 0.424*, 0.397*, 0.422*, 0.426*, 0.327* (standard errors in parentheses)
  - Interest rate: coefficients 0.200*, 0.119*,-0.369*,-0.450* (standard errors in parentheses)
  - Deposit growth rate: coefficients 0.181*, 0.156*, 0.168* (standard errors in parentheses)
  - Inflation rate: coefficients -0.075*, -0.051* (standard errors in parentheses)
  - Private sector indebtedness (percent of GDP): coefficient -0.020* (0.008)
  - Number of observations: 637 (all regressions include country fixed effects; dependent variable is the presence of credit upturn)

- Table 7. Elasticities of Housing Prices, Real Credit, and Real GDP to One Percent Shocks (selected entries)
  - Region: Housing price shock 2.011; Real credit shock 0.103; Real GDP shock 0.78
  - Lithuania: Housing price shock 0.05; Real credit shock 3.00; Real GDP shock 0.30
  - (Table reports elasticities to one percent shocks for region and Lithuania across Housing Prices, Real Credit, Real GDP.)

- Table 8. Speed of Adjustment of Housing Prices, Real Credit, and Real GDP to One Percent Shocks (percent of total adjustment within four quarters) (selected)
  - Region: Housing price shock 92; Real credit shock 75; Real GDP shock 81
  - Lithuania: Housing price shock 90; Real credit shock 79; Real GDP shock 78
  - (Table entries given as percent of total adjustment within four quarters for region and Lithuania across variables.)

- Table 9a–9c and Table 10: Detailed elasticities and speeds of adjustment for Lithuania’s response to one percent shocks in Baltic and Nordic countries (multiple panels with country- and region-specific elasticities and percent adjustments within four quarters — see tables for full numeric detail).

### Figures and annexes
- Figures 1–9 and Figures A1–A6 present accumulated impulse responses (Cholesky one s.d. innovations ± 2 S.E.) for regional and country-specific shocks to D(LN_HPI), D(LN_CR), and D(LN_GDP) across 12 quarters for Baltic and Nordic countries and Lithuania. (Figures show graphical responses for credit shocks, housing price shocks, and real GDP shocks; numerical values are presented in the figures and accompanying tables.)

*Source: wp18160 - References (IMF staff estimates, sample period 1995Q1–2017Q3).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2018/wp18160.pdf_